The next seminar will be held on 17 September 2024, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom.
A light lunch from Aromi will be served from 11:50.
This month will feature the following two talks:
Cascaded Transformer plus U-net in Medical Image Segmentation – Dr Xin Du, Postdoctoral Research Associate, Department of Physics, University of Cambridge
Xin Du is a postdoctoral researcher in the RadNet data science team at the Cavendish Laboratory. She was a Ph.D. student at the University of Southampton, with research interests in information theory, Cascade Learning, and transfer learning with applications to problems in computer vision, biology, and human activity monitoring from wearable sensors. Xin’s work is aimed at developing new learning algorithms and architectures, and deeper understanding of them in the context of these applied problems. Currently, she is focusing on auto-segmentation of 3D medical images with deep learning and trying to develop a way to combine the information from both text descriptions and medical image contexts. Outside of research, she enjoys baking, travelling, meeting new people, and exploring new activities.
Abstract: Radiotherapy plays a crucial role in modern medicine but requires considerable time for manually contouring radio-sensitive organs at risk, which can delay treatment processing. With the significant success of deep convolutional neural networks, auto-segmentation in medical image analysis has shown substantial improvements in saving time and reducing inter-operator variability. While convolutional neural networks utilise the locality of convolution operations, they lose global and long-range semantic information. To address this, we propose a cascaded transformer U-net for medical image segmentation that compensates for long-range dependencies and mitigates computational requirements without compromising performance.
Machine learning for treatment stratification in kidney cancer – Rebecca Wray, PhD Student, Early Cancer Institute, University of Cambridge & Dr Hania Paverd, Clinical Research Training Fellow, Early Cancer Institute, University of Cambridge
Rebecca completed her undergraduate degree in Biosciences from Durham University, where she specialised in Biochemistry and Molecular Biology, before moving to Cambridge to join CS Genetics, a biotechnology start-up investigating novel single-cell RNA-sequencing methods. She then joined Dr Annie Speak’s group at the Cambridge Institute for Therapeutic Immunology and Infectious Disease (CITIID). Currently, Rebecca is in her second year of the prestigious Cancer Research UK (CRUK) Cambridge Centre MRes + PhD programme. Under the mentorship of Dr Mireia Crispin-Ortuzar and Dr James Jones, she is employing data-driven approaches to uncover novel biomarkers and mechanisms related to treatment failure and resistance in kidney cancer.
Hania is a medical doctor specialising in Radiology, with a research interest in machine learning for medical image analysis. She studied Medicine at Newnham College, University of Cambridge, before moving onto Specialty Training in Radiology at Addenbrooke’s Hospital. She is currently in her first year of PhD as a Clinical Research Training Fellow at the Early Cancer Institute in Cambridge, working under the supervision of Dr Mireia Crispin-Ortuzar and Dr Matthew Hoare. Her PhD research focuses on computational analysis of CT and MRI scans, integrated with other data modalities such as genomic data, to enhance risk stratification for patients with liver disease and improve early detection of liver cancer.
Abstract: Clear cell renal cell carcinoma (ccRCC) is the most lethal urological malignancy. The cancer is highly heterogeneous, and therapy response varies between patients. In a subset of cases, the tumour extends into the renal vein and inferior vena cava, termed venous tumour thrombus (VTT), which complicates surgical intervention. While response signatures have been developed for metastatic RCC, there’s a notable gap for patients with VTT. Here we present molecular analysis of data from NAXIVA, a single-arm Phase II study, where 35% of patients showed a reduction in VTT length in response to Axitinib, a tyrosine kinase inhibitor.
We develop a machine learning model which uses baseline and dynamic data taken from blood samples early in treatment, and demonstrates good patient stratification. We report novel biological markers for positive response to anti-angiogenic agents, including CCL17, IL-12p70, PlGF and Tie-2. This research paves the way for better patient stratification and response prediction, offering promising avenues for personalised therapy in ccRCC.
