Renal Cancer

Automated Small Kidney Cancer Detection in Non-Contrast Computed Tomography

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

Abstract:

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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

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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

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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

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Risk-stratified screening for the early detection of kidney cancer

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

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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

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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

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A Mathematical Model of Blood Loss during Renal Resection

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

Abstract

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

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

Abstract

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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

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Automatic whole tumour burden segmentation and tissue-specific sub segmentation

Tissue-specific sub-segmentation of CT images is especially interesting in cancer with high degrees of molecular heterogeneity like ovarian and renal cancer, but manual segmentation of tumours is very time consuming. We are in the process of developing automated methods that will help with the sub-segmentation and will speed up the process. Automated segmentation of tumours for multisite disease has not been done for abdominal images up to a level of clinical relevance so far and we are working towards an automated segmentation tool for multisite disease which will be a great achievement and a very useful tool in the clinic. The segmentations are needed for a radiomics based pipeline that uses statistics based on computed tomography (CT) images to predict the lesions responses to chemo therapy.

We finished the work on automated subsegmentation of different tumour tissues in ovarian cancer and renal cancer and published an article in Computers in Biology and Medicine.

Further information on the All-in-one Cancer Imaging project funded by the Wellcome Trust Innovator Award.

Further information on the Tissue-Specific Sub Segmentation project.

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Computational Biomedicine - 3D tumour mould

Molecular stratification of patients based on integration of data from heterogeneous habitats derived from imaging and digital pathology:

To address intra-tumoural heterogeneity, we have developed a novel method to link imaging (MRI) data to digital pathology data by developing an image guided 3D-printed tumour mould which allows us to establish and validate the relationship between imaging habitats and tissue-based measurements.

Patients potentially benefit in a number of ways:

• Improved representation of heterogeneous tumour profile based on multiple targeted samples
• Improved stratification and informed decision for precision therapeutics based on actionable targets detected through targeted sampling – before and after chemotherapy treatment
  • Combination of immunotherapy and genomically targeted therapies

Further information on the 3D Tumour Mould project.

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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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Integration of ctDNA and imaging

Identifying minimal residual disease using ctDNA in combination with imaging:

Our machine-learning based model to predict detection is being developed to triage samples for personalised analysis in diseases, e.g. renal clear cell carcinoma (RCC), where ctDNA levels are low. In addition, limited data from RCC patients shows that plasma, and for the first time, urine ctDNA may better represent tumour heterogeneity than tissue biopsy. In addition, we demonstrated that longitudinal ctDNA sampling can be utilised to track disease course. These methods will allow very early identification of development of recurrence and maximise opportunity for cure.

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Radiomics of standard of care imaging in renal cell carcinoma

In this project we investigate the ability of radiomics of standard of care imaging in RCC to support clinical decision making. Radiomics models may help address clinical questions which are difficult to assess with subjective image interpretation. Questions, which are of particular interest to our group, are the imaging-based prediction of tumour subtype, aggressiveness and early response to therapy.

Imaging plays an important role in oncological treatment response assessment for many cancer types. With the availability of an increasing number of targeted and non-targeted anti-cancer drugs for renal cancer, the rapid detection of treatment response and relapse become of increasing clinical relevance. In collaboration with colleagues from the departments of urology and oncology, we investigate how multiparametric MRI can be utilised for treatment stratification in current and future clinical trials.

The radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma study gives a deeper dive into this research; 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.

Further information on the Radiomics of Standard of Care Imaging in Renal Cell Carcinoma project.

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Renal Hyperpolarised Carbon-13 MRI

The Renal 13C project has shown a very strong correlation between tumour grade and lactate. The last patient awaits surgery before this can be confirmed. Prior to COVID-19, the samples were due to be sequenced in the Sanger Institute, but this has now been greatly delayed.

Hyperpolarised [1-13C]-pyruvate MRI is a non-ionising, quantitative imaging technique which allows our group to investigate the core of these metabolic changes. Our interest covers both clinical and translational aspects of this technique.

While hyperpolarised [1-13C]-pyruvate MRI enables unknown insights into patient tumour metabolism, it is currently reserved for a small number of centres worldwide which have the relevant infrastructure and expertise. The group is also investigating the ability of radiomics of standard of care imaging in RCC to support clinical decision making. Radiomics models may help address clinical questions which are difficult to assess with subjective image interpretation. Questions, which are of particular interest to the group, are the imaging-based prediction of tumour subtype, aggressiveness and early response to therapy.

Imaging plays an important role in oncological treatment response assessment for many cancer types. With the availability of an increasing number of targeted and non-targeted anti-cancer drugs for renal cancer, the rapid detection of treatment response and relapse become of increasing clinical relevance. In collaboration with colleagues from the departments of urology and oncology, the group investigates how multiparametric MRI can be utilised for treatment stratification in current and future clinical trials.

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