Publications

Predicting resistance to chemotherapy using chromosomal instability signatures

Thompson, J. S., et al, Nature Genetics, June 2025

Abstract

Chemotherapies are often given without precision biomarkers, exposing patients to toxic side effects without guaranteed benefit. Here we present chromosomal instability signature biomarkers that identify resistance to platinum-, taxane- and anthracycline-based treatments using a single genomic test. In retrospectively emulated randomized-control biomarker clinical trials using real-world cohorts (n = 840), predicted resistant patients had elevated treatment failure risk for taxane (hazard ratio (HR) of 7.44) and anthracycline (HR of 1.88) in ovarian, taxane (HR of 3.98) and anthracycline (HR of 3.69) in metastatic breast and taxane (HR of 5.46) in metastatic prostate. Nonrandomized emulations showed predictive capacity for platinum resistance in ovarian (HR of 1.46) and anthracycline in sarcoma (HR of 3.59). We demonstrate feasibility using whole-genome sequencing, capture-panel sequencing and cell-free DNA. Our findings highlight the clinical value of chromosomal instability signatures in predicting resistance to chemotherapies across multiple cancer types, with the potential to transform the one-size-fits-all chemotherapy approach into precise, tailored treatment.

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Imaging-based assessment of response to olaparib in platinum-sensitive relapsed ovarian cancer patients

Delgardo-Ortet, M., et all, Frontiers in Oncology, June 2025

Background: High-grade serous carcinoma is a highly metastatic disease with a limited longterm disease control from systemic anti-cancer treatment, for which the radiological treatment response assessment metrics are imprecise. In this work, we developed noninvasive imagingbased measurements of spatial and longitudinal heterogeneity in a retrospective analysis of a phase 2 non-randomized study of germline BRCA1/BRCA2 mutated (gBRCAm) ovarian cancer patients treated with combination of PARP inhibitors (PARPi) and immune checkpoint inhibitors (ICIs).

Methods: Lesions identified in CT images at baseline, week 4 (after PARPi only) and week 12 (after 8 weeks of PARPi + ICIs) were manually segmented. Anatomical networks of the metastatic sites were constructed to represent patterns of disease distribution. Volume and first-order radiomic features were computed and compared to different assessments of treatment response.

Results: The average number of edges per patient in the anatomical networks and total volumetric burden decreased with treatment were measured, differentiating between responders and nonresponders. Changes in volume at week 4 provided better indication of long-term response than the default RECIST assessment at the same time-point. Significant differences were also found between responders and non-responders in the first-order radiomic feature Energy.

Conclusions: In this feasibility study, we have demonstrated that noninvasive image-based analysis can identify quantitative imaging features associated with the response to the combination of PARPi and ICIs. These can be used to identify markers of response to ICIs from negative trials of a disease with limited response to ICIs.

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Neoadjuvant PARP inhibitor scheduling in BRCA1 and BRCA2 related breast cancer: PARTNER, a randomized phase II/III trial

Abraham, J., et al., Nature Communications, May 2025

Abstract

Poly (ADP-ribose) polymerase inhibitors (PARPi) exploit DNA repair deficiency in germline BRCA1 and BRCA2 pathogenic variant (gBRCAm) cancers. Haematological toxicity limits chemotherapy-PARPi treatment combinations. In preclinical models we identified a schedule combining olaparib and carboplatin that avoids enhanced toxicity but maintains anti-tumour activity. We investigated this schedule in a neoadjuvant, phase II-III, randomised controlled trial for gBRCAm breast cancers (ClinicalTrials.gov ID:NCT03150576; PARTNER). The research arm included carboplatin (Area Under the Curve 5, 3-weekly); paclitaxel (80 mg/m², weekly) day 1, plus olaparib (150 mg twice daily) day 3-14 (4 cycles), followed by anthracycline-containing chemotherapy (3 cycles); control arm gave chemotherapy alone. The primary endpoint, pathological complete response rate, showed no statistical difference between research 64.1% (25/39); control 69.8% (30/43) (p = 0.59). However, estimated survival outcomes at 36-months demonstrated improved event-free survival: research 96.4%, control 80.1% (p = 0.04); overall survival: research 100%, control 88.2% (p = 0.04) and breast cancer specific survival: research 100%, control 88.2% (p = 0.04). There were no statistical differences in relapse-free survival and distant disease-free survival, both were: research 96.4%, control 87.9% (p = 0.20). Similarly, local recurrence-free survival and time to second cancer were both: research 96.4%, control 87.8% (p = 0.20). The PARTNER trial identified a safe, tolerable schedule combining neoadjuvant chemotherapy with olaparib. This combination demonstrated schedule-dependent overall survival benefit in early-stage gBRCAm breast cancer. This result needs confirmation in larger trials.

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Assessment of cancer biomarkers in the Grenfell firefighter cohort study

Feary J., et al, Scientific Reports from Nature Press, May 2025

Abstract

Firefighters are exposed to a diverse range of harmful substances, including polycyclic aromatic hydrocarbons, benzene, and other carcinogens. These toxic compounds induce DNA damage, often causing the formation of DNA adducts and other lesions that can contribute to the development of various diseases, including cancer. Recent advancements in molecular diagnostics have shown that circulating cell-free DNA (cfDNA) in plasma is a valuable biomarker for detecting DNA damage and disease states. In this study, we explored whether changes in the quantity and quality of plasma cfDNA might reveal DNA lesions or serve as early markers for diseases such as cancer in UK firefighters. Whilst there are few published epidemiological studies of risk of cancer in UK firefighters, there are none on molecular markers in this population. All the 685 firefighters who participated in the study were employed by the London Fire Brigade in 2017; many of them also attended the Grenfell Tower fire, the most devastating fire to occur in the UK in modern history. In this exploratory analysis, we sought to gain insights into the potential long-term health impacts of toxic smoke exposure on these first responders by analysing both the concentration of cfDNA present and specific genetic alterations in cfDNA. Using next-generation sequencing and a panel that detects pathogenic DNA variants linked to various cancers, we analysed a subset of 261 firefighters. Our findings revealed that 11 firefighters carried pathogenic DNA variants associated with cancer, but we found no association between fire smoke exposure and the presence of these variants.

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Angiogenic and immune predictors of neoadjuvant axitinib response in renal cell carcinoma with venous tumour thrombus

Wray, R. et al., Nature Communications, Apr 2025

Abstract

Venous tumour thrombus (VTT), where the primary tumour invades the renal vein and inferior vena cava, affects 10–15% of renal cell carcinoma (RCC) patients. Curative surgery for VTT is high-risk, but neoadjuvant therapy may improve outcomes. The NAXIVA trial demonstrated a 35% VTT response rate after 8 weeks of neoadjuvant axitinib, a VEGFR-directed therapy. However, understanding non-response is critical for better treatment. Here we show that response to axitinib in this setting is characterised by a distinct and predictable set of features. We conduct a multiparametric investigation of samples collected during NAXIVA using digital pathology, flow cytometry, plasma cytokine profiling and RNA sequencing. Responders have higher baseline microvessel density and increased induction of VEGF-A and PlGF during treatment. A multi-modal machine learning model integrating features predict response with an AUC of 0.868, improving to 0.945 when using features from week 3. Key predictive features include plasma CCL17 and IL-12. These findings may guide future treatment strategies for VTT, improving the clinical management of this challenging scenario.

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Large-scale analysis of whole genome sequencing data from formalin-fixed paraffin-embedded cancer specimens demonstrates preservation of clinical utility

Basyuni, S., et al., Nature Communications, Sep 2024

Abstract

Whole genome sequencing (WGS) provides comprehensive, individualised cancer genomic information. However, routine tumour biopsies are formalin-fixed and paraffin-embedded (FFPE), damaging DNA, historically limiting their use in WGS. Here we analyse FFPE cancer WGS datasets from England’s 100,000 Genomes Project, comparing 578 FFPE samples with 11,014 fresh frozen (FF) samples across multiple tumour types. We use an approach that characterises rather than discards artefacts. We identify three artefactual signatures, including one known (SBS57) and two previously uncharacterised (SBS FFPE, ID FFPE), and develop an “FFPEImpact” score that quantifies sample artefacts. Despite inferior sequencing quality, FFPE-derived data identifies clinically-actionable variants, mutational signatures and permits algorithmic stratification. Matched FF/FFPE validation cohorts shows good concordance while acknowledging SBS, ID and copy-number artefacts. While FF-derived WGS data remains the gold standard, FFPE-samples can be used for WGS if required, using analytical advancements developed here, potentially democratising whole cancer genomics to many.

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The PARTNER trial of neoadjuvant olaparib with chemotherapy in triple-negative breast cancer

Abraham, J.E., et al., Nature, April 2024

Abstract

PARTNER is a prospective, phase II–III, randomized controlled clinical trial that recruited patients with triple-negative breast cancer^1,2, who were germline BRCA1 and BRCA2 wild type³. Here we report the results of the trial. Patients (n = 559) were randomized on a 1:1 basis to receive neoadjuvant carboplatin–paclitaxel with or without 150 mg olaparib twice daily, on days 3 to 14, of each of four cycles (gap schedule olaparib, research arm) followed by three cycles of anthracycline-based chemotherapy before surgery. The primary end point was pathologic complete response (pCR)⁴, and secondary end points included event-free survival (EFS) and overall survival (OS)⁵. pCR was achieved in 51% of patients in the research arm and 52% in the control arm (P = 0.753). Estimated EFS at 36 months in the research and control arms was 80% and 79% (log-rank P > 0.9), respectively; OS was 90% and 87.2% (log-rank P = 0.8), respectively. In patients with pCR, estimated EFS at 36 months was 90%, and in those with non-pCR it was 70% (log-rank P < 0.001), and OS was 96% and 83% (log-rank P < 0.001), respectively. Neoadjuvant olaparib did not improve pCR rates, EFS or OS when added to carboplatin–paclitaxel and anthracycline-based chemotherapy in patients with triple-negative breast cancer who were germline BRCA1 and BRCA2 wild type. ClinicalTrials.gov ID: NCT03150576.

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Automated Small Kidney Cancer Detection in Non-Contrast Computed Tomography

McGough, W., et al., IEE, August 2024

Abstract:

This study introduces an automated pipeline for renal cancer (RC) detection in non-contrast computed tomography (NCCT). In the development of our pipeline, we test three detections models: a shape model, a 2D-, and a 3D axial-sample model. Training (n=1348) and testing (n=64) data were gathered from open sources (KiTS23, Abdomen1k, CTORG) and Cambridge University Hospital (CUH). Results from cross-validation and testing revealed that the 2D axial sample model had the highest small (≤40mm diameter) RC detection area under the curve (AUC) of 0.804. Our pipeline achieves 61.9% sensitivity and 92.7% specificity for small kidney cancers on unseen test data. Our results are much more accurate than previous attempts to automatically detect small renal cancers in NCCT, the most likely imaging modality for RC screening. This pipeline offers a promising advance that may enable screening for kidney cancers.

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Radiology and multi-scale data integration for precision oncology

Paverd, H., et al., NJP Precision Oncology, Jul 2024

Abstract

In this Perspective paper we explore the potential of integrating radiological imaging with other data types, a critical yet underdeveloped area in comparison to the fusion of other multi-omic data. Radiological images provide a comprehensive, three-dimensional view of cancer, capturing features that would be missed by biopsies or other data modalities. This paper explores the complexities and challenges of incorporating medical imaging into data integration models, in the context of precision oncology. We present the different categories of imaging-omics integration and discuss recent progress, highlighting the opportunities that arise from bringing together spatial data on different scales.

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Acceptability of adding a non-contrast abdominal CT scan to screen for kidney cancer and other abdominal pathology within a community-based CT screening programme for lung cancer: A qualitative study

Usher-Smith, J. A., et al., PLOS ONE, Jul 2024

Abstract

Objectives

The Yorkshire Kidney Screening Trial (YKST) is a feasibility study of adding non-contrast abdominal CT scanning to screen for kidney cancer and other abdominal malignancies to community-based CT screening for lung cancer within the Yorkshire Lung Screening Trial (YLST). This study explored the acceptability of the combined screening approach to participants and healthcare professionals (HCPs) involved in the trial.

Methods

We conducted semi-structured interviews with eight HCPs and 25 participants returning for the second round of scanning within YLST, 20 who had taken up the offer of the additional abdominal CT scan and five who had declined. Transcripts were analysed using thematic analysis, guided by the Theoretical Framework of Acceptability.

Results

Overall, combining the offer of a non-contrast abdominal CT scan alongside the low-dose thoracic CT was considered acceptable to participants, including those who had declined the abdominal scan. The offer of the additional scan made sense and fitted well within the process, and participants could see benefits in terms of efficiency, cost and convenience both for themselves as individuals and also more widely for the NHS. Almost all participants made an instant decision at the point of initial invitation based more on trust and emotions than the information provided. Despite this, there was a clear desire for more time to decide whether to accept the scan or not. HCPs also raised concerns about the burden on the study team and wider healthcare system arising from additional workload both within the screening process and downstream following findings on the abdominal CT scan.

Conclusions

Adding a non-contrast abdominal CT scan to community-based CT screening for lung cancer is acceptable to both participants and healthcare professionals. Giving potential participants prior notice and having clear pathways for downstream management of findings will be important if it is to be offered more widely.

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High-resolution and highly accelerated MRI T2 mapping as a tool to characterise renal tumour subtypes and grades

Horvat- Menih, I., et al., European Radiology Experimental, Jul 2024

Abstract

Background

Clinical imaging tools to probe aggressiveness of renal masses are lacking, and T2-weighted imaging as an integral part of magnetic resonance imaging protocol only provides qualitative information. We developed high-resolution and accelerated T2 mapping methods based on echo merging and using k-t undersampling and reduced flip angles (TEMPURA) and tested their potential to quantify differences between renal tumour subtypes and grades.

Methods

Twenty-four patients with treatment-naïve renal tumours were imaged: seven renal oncocytomas (RO); one eosinophilic/oncocytic renal cell carcinoma; two chromophobe RCCs (chRCC); three papillary RCCs (pRCC); and twelve clear cell RCCs (ccRCC). Median, kurtosis, and skewness of T2 were quantified in tumours and in the normal-adjacent kidney cortex and were compared across renal tumour subtypes and between ccRCC grades.

Results

High-resolution TEMPURA depicted the tumour structure at improved resolution compared to conventional T2-weighted imaging. The lowest median T2 values were present in pRCC (high-resolution, 51 ms; accelerated, 45 ms), which was significantly lower than RO (high-resolution; accelerated, p = 0.012) and ccRCC (high-resolution, p = 0.019; accelerated, p = 0.008). ROs showed the lowest kurtosis (high-resolution, 3.4; accelerated, 4.0), suggestive of low intratumoural heterogeneity. Lower T2 values were observed in higher compared to lower grade ccRCCs (grades 2, 3 and 4 on high-resolution, 209 ms, 151 ms, and 106 ms; on accelerated, 172 ms, 160 ms, and 102 ms, respectively), with accelerated TEMPURA showing statistical significance in comparison (p = 0.037).

Conclusions

Both high-resolution and accelerated TEMPURA showed marked potential to quantify differences across renal tumour subtypes and between ccRCC grades.

Trial registration

ClinicalTrials.gov, NCT03741426. Registered on 13 November 2018.

Relevance statement

The newly developed T₂ mapping methods have improved resolution, shorter acquisition times, and promising quantifiable readouts to characterise incidental renal masses.

Graphical Abstract

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Fast and High-Resolution T2 Mapping Based on Echo Merging Plus k-t Undersampling with Reduced Refocusing Flip Angles (TEMPURA) as Methods for Human Renal MRI

Li, H., et al., Magnetic resonance in Medicine, May 2024

Abstract

Purpose

To develop a highly accelerated multi-echo spin-echo method, TEMPURA, for reducing the acquisition time and/or increasing spatial resolution for kidney T₂ mapping.

Methods

TEMPURA merges several adjacent echoes into one k-space by either combining independent echoes or sharing one echo between k-spaces. The combined k-space is reconstructed based on compressed sensing theory. Reduced flip angles are used for the refocusing pulses, and the extended phase graph algorithm is used to correct the effects of indirect echoes. Two sequences were developed: a fast breath-hold sequence; and a high-resolution sequence. The performance was evaluated prospectively on a phantom, 16 healthy subjects, and two patients with different types of renal tumors.

Results

The fast TEMPURA method reduced the acquisition time from 3–5 min to one breath-hold (18 s). Phantom measurements showed that fast TEMPURA had a mean absolute percentage error (MAPE) of 8.2%, which was comparable to a standardized respiratory-triggered sequence (7.4%), but much lower than a sequence accelerated by purely k-t undersampling (21.8%). High-resolution TEMPURA reduced the in-plane voxel size from 3 × 3 to 1 × 1 mm², resulting in improved visualization of the detailed anatomical structure. In vivo T₂ measurements demonstrated good agreement (fast: MAPE = 1.3%–2.5%; high-resolution: MAPE = 2.8%–3.3%) and high correlation coefficients (fast: R = 0.85–0.98; high-resolution: 0.82–0.96) with the standardized method, outperforming k-t undersampling alone (MAPE = 3.3–4.5%, R = 0.57–0.59).

Conclusion

TEMPURA provides fast and high-resolution renal T₂ measurements. It has the potential to improve clinical throughput and delineate intratumoral heterogeneity and tissue habitats at unprecedented spatial resolution.

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Risk-stratified screening for the early detection of kidney cancer

Rossi, S. H., et al., The Surgeon, Feb 2024

Abstract

Earlier detection and screening for kidney cancer has been identified as a key research priority, however the low prevalence of the disease in unselected populations limits the cost-effectiveness of screening. Risk-stratified screening for kidney cancer may improve early detection by targeting high-risk individuals whilst limiting harms in low-risk individuals, potentially increasing the cost-effectiveness of screening. A number of models have been identified which estimate kidney cancer risk based on both phenotypic and genetic data, and while several of the former have been shown to identify individuals at high-risk of developing kidney cancer with reasonable accuracy, current evidence does not support including a genetic component. Combined screening for lung cancer and kidney cancer has been proposed, as the two malignancies share some common risk factors. A modelling study estimated that using lung cancer risk models (currently used for risk-stratified lung cancer screening) could capture 25% of patients with kidney cancer, which is only slightly lower than using the best performing kidney cancer-specific risk models based on phenotypic data (27%–33%). Additionally, risk-stratified screening for kidney cancer has been shown to be acceptable to the public. The following review summarises existing evidence regarding risk-stratified screening for kidney cancer, highlighting the risks and benefits, as well as exploring the management of potential harms and further research needs.

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Deep learning-based segmentation of multisite disease in ovarian cancer

Buddenkotte, T., et al., European Radiology Experimental, Dec 2023

Abstract

Purpose

To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.

Methods

A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established “no-new-Net” framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test.

Results

Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10^–7, 3 × 10^–4, 4 × 10^–2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10^–3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions.

Conclusion

Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions.

Relevance statement

Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines.

Graphical Abstract

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Lithographically defined encoded magnetic heterostructures for the targeted screening of kidney cancer

Pichot, S. l., et al., Nanoscale Advances, Dec 2023

Abstract

Renal cell carcinoma (RCC) is the 7th commonest cancer in the UK and the most lethal urological malignancy; 50% of all RCC patients will die from the condition. However, if identified early enough, small RCCs are usually cured by surgery or percutaneous procedures, with 95% 10 year survival. This study describes a newly developed non-invasive urine-based assay for the early detection of RCC. Our approach uses encoded magnetically controllable heterostructures as a substrate for immunoassays. These heterostructures have molecular recognition abilities and embedded patterned codes for a rapid identification of RCC biomarkers. The magnetic heterostructures developed for this study have a magnetic configuration designed for a remote multi axial control of their orientation by external magnetic fields, this control facilitates the code readout when the heterostructures are in liquid. Furthermore, the optical encoding of each set of heterostructures provides a multiplexed analyte capture platform, as different sets of heterostructures, specific to different biomarkers can be mixed together in a patient sample. Our results show a precise magnetic control of the heterostructures with an efficient code readout during liquid immunoassays. The use of functionalised magnetic heterostructures as a substrate for immunoassay is validated for urine specimen spiked with recombinant RCC biomarkers. Initial results of the newly proposed screening method on urine samples from RCC patients, and controls with no renal disorders are presented in this study. Comprehensive optimisation cycles are in progress to validate the robustness of this technology as a novel, non-invasive screening method for RCC.

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A Mathematical Model of Blood Loss during Renal Resection

Cowley, J., et al., Fluids, Dec 2023

Abstract

In 2021, approximately 51% of patients diagnosed with kidney tumors underwent surgical resections. One possible way to reduce complications from surgery is to minimise the associated blood loss, which, in the case of partial nephrectomy, is caused by the inadequate repair of branching arteries within the kidney cut during the tumor resection. The kidney vasculature is particularly complicated in nature, consisting of various interconnecting blood vessels and numerous bifurcation, trifurcation, tetrafurcation, and pentafurcation points. In this study, we present a mathematical lumped-parameter model of a whole kidney, assuming a non-Newtonian Carreau fluid, as a first approximation of estimating the blood loss arising from the cutting of single or multiple vessels. It shows that severing one or more blood vessels from the kidney vasculature results in a redistribution of the blood flow rates and pressures to the unaltered section of the kidney. The model can account for the change in the total impedance of the vascular network and considers a variety of multiple cuts. Calculating the blood loss for numerous combinations of arterial cuts allows us to identify the appropriate surgical protocols required to minimise blood loss during partial nephrectomy as well as enhance our understanding of perfusion and account for the possibility of cellular necrosis. This model may help renal surgeons during partial organ resection in assessing whether the remaining vascularisation is sufficient to support organ viability.

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Short-term psychosocial outcomes of adding a non-contrast abdominal computed tomography (CT) scan to the thoracic CT within lung cancer screening

Usher-Smith. J.A., et al., BJUI, Dec 2023

Abstract

Objectives

To evaluate psychological, social, and financial outcomes amongst individuals undergoing a non-contrast abdominal computed tomography (CT) scan to screen for kidney cancer and other abdominal malignancies alongside the thoracic CT within lung cancer screening.

Subjects and Methods

The Yorkshire Kidney Screening Trial (YKST) is a feasibility study of adding a non-contrast abdominal CT scan to the thoracic CT within lung cancer screening. A total of 500 participants within the YKST, comprising all who had an abnormal CT scan and a random sample of one-third of those with a normal scan between 14/03/2022 and 24/08/2022 were sent a questionnaire at 3 and 6 months. Outcomes included the Psychological Consequences Questionnaire (PCQ), the short-form of the Spielberger State–Trait Anxiety Inventory, and the EuroQoL five Dimensions five Levels scale (EQ-5D-5L). Data were analysed using regression adjusting for participant age, sex, socioeconomic status, education, baseline quality of life (EQ-5D-5L), and ethnicity.

Results

A total of 380 (76%) participants returned questionnaires at 3 months and 328 (66%) at 6 months. There was no difference in any outcomes between participants with a normal scan and those with abnormal scans requiring no further action. Individuals requiring initial further investigations or referral had higher scores on the negative PCQ than those with normal scans at 3 months (standardised mean difference 0.28 sd, 95% confidence interval 0.01–0.54; P = 0.044). The difference was greater in those with anxiety or depression at baseline. No differences were seen at 6 months.

Conclusion

Screening for kidney cancer and other abdominal malignancies using abdominal CT alongside the thoracic CT within lung cancer screening is unlikely to cause significant lasting psychosocial or financial harm to participants with incidental findings.

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The impact of imputation quality on machine learning classifiers for datasets with missing values

Shadbahr, T., Communications Medicine, Oct 2023

Background

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier’s performance.

Methods

We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data.

Results

The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised.

Conclusions

It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable.

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Position statement on clinical evaluation of imaging AI

McCague, C., et al., The Lancet Digital Health, Jul 2023

Governments and medical associations across the world, including the US Food and Drug Administration, the UK Medicines and Healthcare products Regulatory Agency, the Royal College of Radiologists, and the European Society of Radiology, believe the advent of health technologies associated with artificial intelligence (AI) will be the most radical change in how medical care is delivered in our lifetime. At a time of unprecedented demand for medical imaging, when hospitals struggle with staffing shortages, AI tools could provide a solution.

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Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Delgardo-Ortet, M., et al, Frontiers in Oncology, Feb 2023

Background: High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours.

Methods: In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process.

Results: Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm³) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments.

Conclusions: We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.

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Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation

Buddenkotte, T., et al., Computers in Biology and Medicine, Sept 2023

Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we present the following contributions. First, we show that the classical approaches fail to approximate the classification probability. Second, we propose a scalable and intuitive framework for uncertainty quantification in medical image segmentation that yields measurements that approximate the classification probability. Third, we suggest the usage of k-fold cross-validation to overcome the need for held out calibration data. Lastly, we motivate the adoption of our method in active learning, creating pseudo-labels to learn from unlabeled images and human–machine collaboration.

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Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Crispin-Ortuzar, M., et al Nature Communications, Oct 2023

High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.

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Comparison of tumor-informed and tumor-naïve sequencing assays for ctDNA detection in breast cancer

Santonja, A., et al, EMBO Mol Med, May 2023

Problem

Circulating tumor DNA (ctDNA) can be used as a non-invasive liquid biopsy in cancer patients to track disease burden in blood. Different strategies have been used to quantify ctDNA, but few studies have compared the performance of different tumor-informed and tumor-naïve assays to detect ctDNA in the same patient samples.

Results

Our results demonstrate that ctDNA dynamics and tumor allele fractions were highly concordant when targeting different mutation types in serial blood samples collected from breast cancer patients undergoing treatment. Tumor-informed assays showed the highest sensitivity for detection of ctDNA at low concentrations. SNV-hybrid capture, targeting thousands of single nucleotide variants, and SV-multiplex PCR, targeting tens of structural variants, were able to detect ctDNA down to a few parts per million.

Impact

Choice of assay for ctDNA quantification depends on many factors including the required sensitivity for its intended use, the mutation type being assayed, turnaround time, and cost. This study demonstrates that personalized assays targeting patient-specific mutations identified in the tumor were the most sensitive assays to detect low levels of ctDNA in blood, and SV-multiplex PCR has potential to be used as a clinical diagnostic assay.

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Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression

Sciacovelli, M., Nature Communications, Dec 2022

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we integrate multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies.

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Multiparameter single-cell proteomic technologies give new insights into the biology of ovarian tumors

Funingana, I. G., et al, Seminars in Immunopathology, Jan 23

High-grade serous ovarian cancer (HGSOC) is the most lethal gynecological malignancy. Its diagnosis at advanced stage compounded with its excessive genomic and cellular heterogeneity make curative treatment challenging. Two critical therapeutic challenges to overcome are carboplatin resistance and lack of response to immunotherapy. Carboplatin resistance results from diverse cell autonomous mechanisms which operate in different combinations within and across tumors. The lack of response to immunotherapy is highly likely to be related to an immunosuppressive HGSOC tumor microenvironment which overrides any clinical benefit. Results from a number of studies, mainly using transcriptomics, indicate that the immune tumor microenvironment (iTME) plays a role in carboplatin response. However, in patients receiving treatment, the exact mechanistic details are unclear. During the past decade, multiplex single-cell proteomic technologies have come to the forefront of biomedical research. Mass cytometry or cytometry by time-of-flight, measures up to 60 parameters in single cells that are in suspension. Multiplex cellular imaging technologies allow simultaneous measurement of up to 60 proteins in single cells with spatial resolution and interrogation of cell–cell interactions. This review suggests that functional interplay between cell autonomous responses to carboplatin and the HGSOC immune tumor microenvironment could be clarified through the application of multiplex single-cell proteomic technologies.

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Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker–driven learning framework

Rossi, S. H., et al, Science Advances, Sep 2022

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.

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Hyperpolarized 13C-Pyruvate Metabolism as a Surrogate for Tumor Grade and Poor Outcome in Renal Cell Carcinoma—A Proof of Principle Study

Ursprung. S., et al, Cancers, Jan 2022

We evaluated renal cancer with varying aggressive appearances on histology, using an emerging form of non-invasive metabolic MRI. This imaging technique assesses the uptake and metabolism of a breakdown product of glucose (pyruvate) labelled with hyperpolarized carbon-13. We show that pyruvate metabolism is dependent on the aggressiveness of an individual tumor and we provide a mechanism for this finding from tissue analysis of molecules influencing pyruvate metabolism, suggesting a role for its membrane transporter.

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Multi-omic machine learning predictor of breast cancer therapy response

Sammut, S. J., et al, Nature, Dec 2021

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment¹. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy². Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery³ were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

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Hyperpolarized carbon-13 MRI for very early response assessment of neoadjuvant chemotherapy in breast cancer patients

Woitek, R., et al, Cancer Research, Sept 2021.

Hyperpolarized ¹³C-MRI is an emerging tool for probing tissue metabolism by measuring ¹³C-label exchange between intravenously injected hyperpolarized [1–¹³C]pyruvate and endogenous tissue lactate. Here, we demonstrate that hyperpolarized ¹³C-MRI can be used to detect early response to neoadjuvant therapy in breast cancer.

Media coverage for this publication in the Sunday Times.

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Reproducibility standards for machine learning in the life sciences

Heil, B.J. et al, Nature Methods, 30 Aug 2021.

To make machine-learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model and code publication, programming best practices and workflow automation. By meeting these standards, the community of researchers applying machine-learning methods in the life sciences can ensure that their analyses are worthy of trust.

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The use of hyperpolarised 13C-MRI in clinical body imaging to probe cancer metabolism

Woitek, R and Gallagher, F. A, British Journal of Cancer, 28 Jan 2021.

Metabolic reprogramming is one of the hallmarks of cancer and includes the Warburg effect, which is exhibited by many tumours. This can be exploited by positron emission tomography (PET) as part of routine clinical cancer imaging. However, an emerging and alternative method to detect altered metabolism is carbon-13 magnetic resonance imaging (MRI) following injection of hyperpolarised [1-¹³C]pyruvate. The technique increases the signal-to-noise ratio for the detection of hyperpolarised ¹³C-labelled metabolites by several orders of magnitude and facilitates the dynamic, noninvasive imaging of the exchange of ¹³C-pyruvate to ¹³C-lactate over time. The method has produced promising preclinical results in the area of oncology and is currently being explored in human imaging studies. The first translational studies have demonstrated the safety and feasibility of the technique in patients with prostate, renal, breast and pancreatic cancer, as well as revealing a successful response to treatment in breast and prostate cancer patients at an earlier stage than multiparametric MRI.

This review focuses on the strengths of the technique and its applications in the area of oncological body MRI including noninvasive characterisation of disease aggressiveness, mapping of tumour heterogeneity, and early response assessment.

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18F-C2Am: a targeted imaging agent for detecting tumor cell death in vivo using positron emission tomography

Bulat, F., et al, EJNMMI Research, 9 Dec 2020.

Trialing novel cancer therapies in the clinic would benefit from imaging agents that can detect early evidence of treatment response. The timing, extent and distribution of cell death in tumors following treatment can give an indication of outcome. We describe here an ¹⁸F-labeled derivative of a phosphatidylserine-binding protein, the C2A domain of Synaptotagmin-I (C2Am), for imaging tumor cell death in vivo using PET.

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Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer

Beer, L., Martin-Gonzalez, P., et al, European Radiology, 14 Dec 2020.

We developed a precision tissue sampling technique that uses radiomic habitats to guide in vivo biopsies using CT/US fusion and that can be seamlessly integrated in the clinical routine for patients with HGSOC.

Six patients with suspected HGSOC scheduled for US-guided biopsy before starting neoadjuvant chemotherapy were included in this prospective study from September 2019 to February 2020. The tumour segmentation was performed manually on the pre-biopsy contrast-enhanced CT scan. Spatial radiomic maps were used to identify tumour areas with similar or distinct radiomic patterns, and tumour habitats were identified using the Gaussian mixture modelling. CT images with superimposed habitat maps were co-registered with US images by means of a landmark-based rigid registration method for US-guided targeted biopsies. The dice similarity coefficient (DSC) was used to assess the tumour-specific CT/US fusion accuracy.

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Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer.

Martin-Gonzalez, P. et al., Insights Into Imaging, 17 Aug 2020.

In this review, we describe how these challenges might be overcome by integrating quantitative features extracted from medical imaging with the analysis of paired genomic profiles, a combined approach called radiogenomics, to generate virtual biopsies. Radiomic studies have been used to model different imaging phenotypes, and some radiomic signatures have been associated with paired molecular profiles to monitor spatiotemporal changes in the heterogeneity of tumours. We describe different strategies to integrate radiogenomic information in a global and local manner, the latter by targeted sampling of tumour habitats, defined as regions with distinct radiomic phenotypes.

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Hyperpolarized 13C MRI of Tumor Metabolism Demonstrates Early Metabolic Response to Neoadjuvant Chemotherapy in Breast Cancer

Woitek R. et al, Radiology: Imaging Cancer, July 2020

The purpose of this research was to compare hyperpolarized carbon 13 (13C) MRI with dynamic contrast material–enhanced (DCE) MRI in the detection of early treatment response in breast cancer. In this institutional review board–approved prospective study, a woman with triple-negative breast cancer (age, 49 years) underwent 13C MRI after injection of hyperpolarized [1–carbon 13 {13C}]-pyruvate and DCE MRI at 3 T at baseline and after one cycle of neoadjuvant therapy. The 13C-labeled lactate-to-pyruvate ratio derived from hyperpolarized 13C MRI and the pharmacokinetic parameters transfer constant (Ktrans) and washout parameter (kep) derived from DCE MRI were compared before and after treatment.

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3D-printed moulds of renal tumours for image-guided tissue sampling in the clinical setting

Mireia C.-O., et al., JCO Clinical Cancer Informatics, 2020

Spatial heterogeneity of tumours is a major challenge in precision oncology. We have developed an open source computational framework to automatically produce patient-specific 3D-printed moulds that can be used in the clinical setting and applied it to patients with renal cancer undergoing radical nephrectomy. Our work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumour heterogeneity on multiple spatial scales.

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ctDNA monitoring using patient-specific sequencing and integration of variant reads

Wan, J.C.M., et al., Sci Transl Med, 17 Jun 2020

Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci.

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Unraveling tumor–immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy

Jiménez-Sánchez, A. et al, Nature Genetics, 2020

In this study we demonstrate that the tumour–immune microenvironment (TME) of advanced high-grade serous ovarian cancer (HGSOC) is intrinsically heterogeneous and that chemotherapy induces local immune activation. Therefore, exploring new combination therapies and therapeutic targets based on a greater understanding of the TME has the potential to change the current treatment paradigm and improve clinical outcomes in patients with HGSOC.

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Tissue-specific and Interpretable Sub-segmentation of Whole Tumour Burden on CT Images by Unsupervised Fuzzy Clustering

Rundo L. et al, Artificial Intelligence in Medicine, 2020

This is the first method for Computed Tomography tissue-specific image segmentation of whole tumours. In particular, our computational framework based on unsupervised fuzzy clustering techniques sub-segments tumour lesions into hypo-dense (cystic/necrotic), hyper-dense (calcified), and intermediately dense (soft tissue) tumour components. The results obtained on ovarian and renal cancer are accurate and reliable.

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Executable cancer models: successes and challenges

Fisher, J. Nature Reviews Cancer, 2020

This perspective discusses how executable computational models, integrating various data sets derived from preclinical models and cancer patients, can be used to represent the dynamic biological behaviours inherent in cancer. The article argues that these models might be used as patient avatars to improve personalised treatments.

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Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

Beer, L., et al, European Radiology, April 2020

This study investigated the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC). It provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra- and inter-site heterogeneity, and the abundance of several proteins.

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Comprehensive characterization of cell-free tumor DNA in plasma and urine of patients with renal tumors

Smith, C.G. et al, Genome Med, 2020

New research detecting tumour DNA in blood and urine of kidney cancer patients opens up potential for clinical management.

The detection of ctDNA in blood or urine – known as ‘liquid biopsies’ – has been established as a method for non-invasive monitoring for many cancers, such as breast, lung and colorectal. This study is the most extensive analysis to date in patients with kidney cancers.

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Imaging mass cytometry and multi-platform genomics define the phenogenomic landscape of breast cancer

Ali, R. et al, Nature Cancer, 2020

Zooming in on breast cancer reveals how mutations shape the tumour landscape. Scientists have created one of the most detailed maps of breast cancer ever achieved, revealing how genetic changes shape the physical tumour landscape.

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Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma - a systematic review and meta-analysis

Ursprung, S. et al, European Radiology, 2020

Radiomics algorithms show promise for answering clinical questions where subjective interpretation is challenging or not established. However, the generalizability of findings to prospective cohorts needs to be demonstrated in future trials for progression towards clinical translation. Improved sharing of methods including code and images could facilitate independent validation of radiomics signatures.

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Imaging breast cancer using hyperpolarized carbon-13 MRI

Gallagher F., Woitek R. et al, PNAS, 2020

A new type of scan that involves magnetising molecules allows doctors to see in real-time which regions of a breast tumour are active. This is the first time that researchers have demonstrated that a scanning technique, called carbon-13 hyperpolarised imaging, can be used to monitor breast cancer.

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Variational Autoencoders for Cancer Data Integration Design Principles and Computational Practice

Simidjievski N. et al., Frontiers in Genetics, 2019

The rapid technological developments in cancer research yield large amounts of complex heterogeneous data on different scales—from molecular to clinical and image data. To capitalize on the inter-dependencies and relations across heterogeneous types of data about each patient, integrating multiple types and sources of data is essential. We provide a clear methodological and computational framework for designing and systematically analysing several deep-learning approaches for data integration based on Variational Autoencoders (VAEs), that enable clinicians to investigate cancer traits and translate the results into clinical applications. We demonstrate how these deep-learning approaches can be designed, built, and, in particular, applied to tasks of integrative analyses of heterogeneous breast cancer data.

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Rethinking drug design in the artificial intelligence era

Schneider P. et al., Nature Reviews Drug Discovery, 2019

Artificial Intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the ‘grand challenges’ in small-molecule drug discovery with AI and the approaches to address them.

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Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modelling

Kreuzaler P. et al., PNAS, 2019

Cells with higher levels of Myc proliferate more rapidly and supercompetitively eliminate neighbouring cells. Nonetheless, tumour cells in aggressive breast cancers typically exhibit significant and stable heterogeneity in their Myc levels, which correlates with refractoriness to therapy and poor prognosis. This suggests that Myc heterogeneity confers some selective advantage on breast tumour growth and progression. To investigate this, we created a traceable MMTV-Wnt1–driven in vivo chimeric mammary tumour model comprising an admixture of low-Myc– and reversibly switchable high-Myc–expressing clones. Our study illustrates the power of executable models in elucidating mechanisms driving tumour heterogeneity and offers an innovative strategy for identifying combination therapies tailored to the oligoclonal landscape of heterogeneous tumours.

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Computed Tomography-Derived Radiomic Metrics Can Identify Responders to Immunotherapy in Ovarian Cancer

Himoto Y. et al., JCO Precision Oncology, 2019

We set out to determine if radiomic measures of tumour heterogeneity derived from baseline contrast-enhanced computed tomography (CE-CT) are associated with durable clinical benefit and time to off-treatment in patients with recurrent ovarian cancer (OC) enrolled in prospective immunotherapeutic trials. We found that fewer disease sites and lower intra- and intertumour heterogeneity modelled from the baseline CE-CT may indicate better response of OC to immunotherapy.

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Modeling breast cancer progression to bone: how driver mutation order and metabolism matter

Ascolani G. and Lio P., BMC Medical Genomics, 2019

We introduce a quantitative model in the framework of Cellular Automata to investigate the effects of metabolic mutations and mutation order on cancer stemness and tumour cell migration from breast, blood to bone metastasised sites. Our work provides a quantitative basis of how the order of driver mutations and the number of mutations altering metabolic processes matter for different cancer clones through their progression in breast, blood and bone compartments. This work is innovative because of multi compartment analysis and could impact proliferation of therapy-resistant clonal populations and patient survival.

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Quantifying normal human brain metabolism using hyperpolarized [1-13C]pyruvate and magnetic resonance imaging

Grist. J. T. et al, Neuroimage, 2019

Hyperpolarized 13C Magnetic Resonance Imaging (13C-MRI) provides a highly sensitive tool to probe tissue metabolism in vivo and has recently been translated into clinical studies. We report the cerebral metabolism of intravenously injected hyperpolarized [1-13C]pyruvate in the brain of healthy human volunteers for the first time. Imaging normal brain metabolism with hyperpolarized [1-13C]pyruvate and subsequent quantification, have important implications for interpreting pathological cerebral metabolism in future studies.

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Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups

Oscar, R.M. et al, Nature, 2019

The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. We present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.

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MFICM Publications

Predicting resistance to chemotherapy using chromosomal instability signatures

Abstract

Imaging-based assessment of response to olaparib in platinum-sensitive relapsed ovarian cancer patients

Neoadjuvant PARP inhibitor scheduling in BRCA1 and BRCA2 related breast cancer: PARTNER, a randomized phase II/III trial

Abstract

Assessment of cancer biomarkers in the Grenfell firefighter cohort study

Abstract

Angiogenic and immune predictors of neoadjuvant axitinib response in renal cell carcinoma with venous tumour thrombus

Abstract

Large-scale analysis of whole genome sequencing data from formalin-fixed paraffin-embedded cancer specimens demonstrates preservation of clinical utility

Abstract

The PARTNER trial of neoadjuvant olaparib with chemotherapy in triple-negative breast cancer

Abstract

Automated Small Kidney Cancer Detection in Non-Contrast Computed Tomography

Abstract:

Radiology and multi-scale data integration for precision oncology

Abstract

Acceptability of adding a non-contrast abdominal CT scan to screen for kidney cancer and other abdominal pathology within a community-based CT screening programme for lung cancer: A qualitative study

Abstract

Objectives

Methods

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Conclusions

High-resolution and highly accelerated MRI T2 mapping as a tool to characterise renal tumour subtypes and grades

Abstract

Background

Methods

Results

Conclusions

Trial registration

Relevance statement

Graphical Abstract

Fast and High-Resolution T2 Mapping Based on Echo Merging Plus k-t Undersampling with Reduced Refocusing Flip Angles (TEMPURA) as Methods for Human Renal MRI

Abstract

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Methods

Results

Conclusion

Risk-stratified screening for the early detection of kidney cancer

Abstract

Deep learning-based segmentation of multisite disease in ovarian cancer

Abstract

Purpose

Methods

Results

Conclusion

Relevance statement

Graphical Abstract

Lithographically defined encoded magnetic heterostructures for the targeted screening of kidney cancer

Abstract

A Mathematical Model of Blood Loss during Renal Resection

Abstract

Short-term psychosocial outcomes of adding a non-contrast abdominal computed tomography (CT) scan to the thoracic CT within lung cancer screening

Abstract

Objectives

Subjects and Methods

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The impact of imputation quality on machine learning classifiers for datasets with missing values

Background

Methods

Results

Conclusions

Position statement on clinical evaluation of imaging AI

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Comparison of tumor-informed and tumor-naïve sequencing assays for ctDNA detection in breast cancer

Problem

Results

Impact

Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression

Multiparameter single-cell proteomic technologies give new insights into the biology of ovarian tumors

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker–driven learning framework

Hyperpolarized 13C-Pyruvate Metabolism as a Surrogate for Tumor Grade and Poor Outcome in Renal Cell Carcinoma—A Proof of Principle Study

Multi-omic machine learning predictor of breast cancer therapy response

Hyperpolarized carbon-13 MRI for very early response assessment of neoadjuvant chemotherapy in breast cancer patients

Reproducibility standards for machine learning in the life sciences