Cambridge MedAI Seminar Series

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• Jack Dixon, Gift Mungmeeprued, Bevis Drury
• Tuesday 11 June 2024, 12:00-13:00
• Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge.

If you have a question about this talk, please contact Ines Machado.

The next seminar will be held on 11 June 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 talks:

How Low Can We Go? – Investigating the interaction between cancer-detecting AI and low-dose quantum noise in CT images – Jack Dixon, Master’s student, Department of Physics, University of Cambridge

Jack is an undergraduate student at the University of Cambridge currently studying for a Master’s Degree in Natural Sciences. He specialises in physics, and is particularly interested in statistical and computational physics. As part of his degree, he undertook a research project within the Early Cancer Institute under the supervision of William McGough, Dr Mireia Crispin-Ortuzar and Dr Ander Biguri, focused on low-dose CT scan simulation and deep-learning based segmentation.

Abstract: Renal cancers (RC) are associated with more than 140,000 deaths annually. Mortality rates for RC could be reduced if a suitable screening program to allow for early diagnosis was constructed. Trials into screening, such as the Yorkshire Kidney Screening Trial, use non-contrast enhanced CT scans and ideally seek to lower the dose of ionising radiation as much as is feasible. In order for a screening program to be effective, it must be both cost-effective and (relatively) safe. To this end, my Master’s project focused on assessing the performance of automatic renal segmentation models as the incident radiation dose of the input CT scans is decreased. This involved first attempting to construct and validate a low-dose CT scan simulation technique that can be applied retroactively, and then assessing segmentation performance as the dose is decreased. The renal and cancer segmentation models produced both displayed strong positive ranked correlation between Dice similarity coefficient and incident dose to a significance level of 2.5%. We conclude that renal segmentation performance in non-contrast enhanced CT scans is correlated with the incident dose.

Deformable registration to assess tumour progression in ovarian cancer patients – Gift Mungmeeprued – Master’s student, Department of Physics, University of Cambridge

Gift Mungmeeprued is a master’s student in the Department of Physics at the University of Cambridge. She is interested in machine learning to make healthcare more accessible and affordable.

Abstract: High-grade serous ovarian carcinoma (HGSOC) is the most common and deadliest subtype of ovarian cancer, often characterised by multi-site and heterogeneous tumours. The standard line of treatment for HGSOC in the UK is neoadjuvant chemotherapy (NACT) followed by delayed primary surgery. Response Evaluation Criteria in Solid Tumours (RECIST 1.1) is the current standardised criteria to assess the tumour response to NACT based on measurements of tumour diameters in pre- and post-NACT CT scans. While RECIST is designed to be relatively quick for radiologists to evaluate, it only captures 1-dimensional global change in tumour size. In this talk, we explored the use of deformable image registration as an automated tool to assess tumour response to NACT . Registration between pre- and post-NACT CT scans reveals spatial heterogeneity of changes within the tumour and across multiple disease sites.

Automating Segmentation and Chemotherapy Response Measurement in Ovarian Cancer with Multitask Deep Learning – Bevis Drury, Master’s student, Department of Physics, University of Cambridge

Bevis is a Part III student studying physics at the University of Cambridge. He is interested in applying machine learning to all areas of research, from physics to medicine.

Abstract: High Grade Serous Ovarian Cancer (HGSOC) is the most common type of ovarian cancer. Often diagnosed at advanced stages, HGSOC presents significant challenges due to its heterogeneity and metastatic nature. Treatment of HGSOC begins with either immediate primary surgery, or neoadjuvant chemotherapy prior to delayed primary surgery. To track disease progression, radiologists routinely use abdominopelvic CT imaging. The patient’s radiological response to treatment can be measured using the Response Evaluation Criteria in Solid Tumours (RECIST), which compares CT scans taken before and after treatment. Manual calculation of RECIST is time-consuming and often inconsistent between radiologists, impacting the accuracy and reliability of treatment assessments. This paper develops a multitask deep learning architecture for automating the segmentation and chemotherapy response prediction of HGSOC patients. The model combines features from two identical U-Net architectures, which are then used to predict binarised RECIST labels. We use a training cohort of 99 HGSOC cases with pre- and post-treatment CT scans, and an external validation cohort of 49 cases. For the validation cohort, we predict binarised RECIST labels with an AUC of 0.78. We are the first to predict RECIST labels for HGSOC patients using multitask deep learning, establishing this research as a benchmark for future work. RECIST measurements are not currently used in clinical practice, so this framework aims to provide radiologists with real-time segmentations and RECIST labels leading to more informed decisions.

This talk is part of the Cambridge MedAI Seminar Series series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• CCC talks for website
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge MedAI Seminar Series
• Cambridge talks
• Chris Davis' list
• Interested Talks
• Interested Talks
• Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587

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