Cambridge MedAI Seminar Series - October 2025

Join us for the Cambridge AI in Medicine Seminar Series, hosted by the Cancer Research UK Cambridge Centre and the Department of Radiology at Addenbrooke's. This series brings together leading experts to explore cutting-edge AI applications in healthcare - from disease diagnosis to drug discovery. It's a unique opportunity for researchers, practitioners, and students to stay at the forefront of AI innovations and engage in discussions shaping the future of AI in healthcare.

This month's seminar will be held on Thursday 30 October 2025, 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 11:45. The event will feature the following talks:

Explainable Integration of Kidney Cancer Radiology and Pathology – Dr Shangqi Gao, Research Associate, Early Cancer Institute, Department of Oncology, University of Cambridge

Dr Shangqi Gao is a Research Associate at the University of Cambridge, working with Dr Mireia Crispin. Prior to this, he was a Postdoctoral Research Assistant at the University of Oxford, collaborating with Prof. Clare Verrill and Prof. Jens Rittscher. Shangqi Gao earned a Ph.D. in Statistics from Fudan University, an M.Sc. in Applied Mathematics from Wuhan University, and a B.Sc. in Mathematics and Applied Mathematics from Northwestern Polytechnical University. Shangqi is the recipient of the Shanghai Natural Science Award (2023), the Elsevier–MedIA 1st Prize & Medical Image Analysis MICCAI Best Paper Award (2023) and the MICCAI AMAI Best Paper Award (2025). He currently serves as President of the MICCAI Special Interest Group on Explainable AI for Medical Image Analysis.

Abstract: We present an explainable AI framework for kidney cancer analysis that integrates pathological and radiological information to enhance prognostic assessment. Using TNM staging guidelines and pathology reports, we construct interpretable pathological concepts and extract deep features from whole-slide images via foundation models. Pathological and radiological graphs are then built to capture spatial correlations, and graph neural networks with sparsity-informed probabilistic integration identify key biomarkers and risk patterns. This approach ensures explainability and fairness in distinguishing low- and high-risk patients, addressing the intrinsic heterogeneity of kidney cancer.

Development of an AI tool for Quality Assessment in Prostate MRI Using PI-QUAL v2: A Multicenter, Multireader Study - Dr Shuncong Wang, Research Associate, Department of Radiology, University of Cambridge

Shuncong is a Research Associate in the Department of Radiology at the University of Cambridge. His research focuses on using artificial intelligence in radiology to enhance disease characterization and prognostication.

Abstract:

Background: This study aims to develop an AI-based tool for automated quality assessment of prostate MRI in accordance with the PI-QUAL v2 criteria.

Method: A total of 767 retrospectively collected prostate mpMRI exams from five centers were included. Six experienced radiologists independently assessed image quality, and their aggregated ratings served as the reference standard. Inter-rater agreement and agreement between AI predictions and individual radiologists were evaluated using weighted Cohen’s kappa coefficients. The difference between inter-rater variability and average AI–radiologist agreement was tested against zero.

Result: Inter-radiologist agreement for PI-QUAL v2 scores was moderate, with weighted Cohen’s kappa values ranging from 0.22 to 0.70. Agreement was higher for DWI and DCE sequences compared with T2WI. The difference between AI–radiologist and inter-radiologist agreement was not statistically significant in most settings (p < 0.001), except when comparing radiologists from the same institution (p > 0.001).

Conclusion: The AI model can serve as a reliable standalone tool for automated prostate MRI quality assessment.

This is a hybrid event so you can also join via Zoom:

https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09

Meeting ID: 990 5046 7573 and Passcode: 617729

We look forward to your participation! If you are interested in getting involved and presenting your work, please email Ines Machado at [email protected]

For more information about this seminar series, see: https://www.integratedcancermedicine.org/research/cambridge-medai-seminar-series/

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Host or Publisher Cancer Research UK Cambridge Centre