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SUMMARY:Cambridge MedAI Seminar - October 2025 - Dr Shangqi Gao and Dr Shu
 ncong Wang
DTSTART:20251030T114500Z
DTEND:20251030T130000Z
UID:TALK238912@talks.cam.ac.uk
CONTACT:Hannah Clayton
DESCRIPTION:Sign up on Eventbrite: https://medai_october2025.eventbrite.co
 .uk\n\nJoin us for the *Cambridge AI in Medicine Seminar Series*\, hosted 
 by the *Cancer Research UK Cambridge Centre* and the *Department of Radiol
 ogy at Addenbrooke’s*. This series brings together leading experts to ex
 plore cutting-edge AI applications in healthcare—from disease diagnosis 
 to drug discovery. It’s a unique opportunity for researchers\, practitio
 ners\, and students to stay at the forefront of AI innovations and engage 
 in discussions shaping the future of AI in healthcare.\n\nThis month’s s
 eminar will be held on *Thursday 30 October 2025\, 12-1pm at the Jeffrey C
 heah Biomedical Centre (Main Lecture Theatre)\, University of Cambridge* a
 nd *streamed online via Zoom*. A light lunch from Aromi will be served fro
 m 11:45. The event will feature the following talks:\n\n*_Explainable Inte
 gration of Kidney Cancer Radiology and Pathology_ - Dr Shangqi Gao\, Resea
 rch Associate\, Early Cancer Institute\, Department of Oncology\, Universi
 ty of Cambridge*\n\nDr Shangqi Gao is a Research Associate at the Universi
 ty of Cambridge\, working with Dr Mireia Crispin. Prior to this\, he was a
  Postdoctoral Research Assistant at the University of Oxford\, collaborati
 ng with Prof. Clare Verrill and Prof. Jens Rittscher. Shangqi Gao earned a
  Ph.D. in Statistics from Fudan University\, an M.Sc. in Applied Mathemati
 cs from Wuhan University\, and a B.Sc. in Mathematics and Applied Mathemat
 ics from Northwestern Polytechnical University. Shangqi is the recipient o
 f the Shanghai Natural Science Award (2023)\, the Elsevier–MedIA 1st Pri
 ze & Medical Image Analysis MICCAI Best Paper Award (2023) and the MICCAI 
 AMAI Best Paper Award (2025). He currently serves as President of the MICC
 AI Special Interest Group on Explainable AI for Medical Image Analysis.\n\
 n*Abstract*: We present an explainable AI framework for kidney cancer anal
 ysis 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 featur
 es from whole-slide images via foundation models. Pathological and radiolo
 gical graphs are then built to capture spatial correlations\, and graph ne
 ural networks with sparsity-informed probabilistic integration identify ke
 y biomarkers and risk patterns. This approach ensures explainability and f
 airness in distinguishing low- and high-risk patients\, addressing the int
 rinsic heterogeneity of kidney cancer.\n\n*_Development of an AI tool for 
 Quality Assessment in Prostate MRI Using PI-QUAL v2: A Multicenter\, Multi
 reader Study_ - Dr Shuncong Wang\, Research Associate\, Department of Radi
 ology\, University of Cambridge*\n\nShuncong is a research associate in th
 e Department of Radiology at the University of Cambridge\, His research fo
 cuses on using artificial intelligence in radiology to enhance disease cha
 racterization and prognostication.\n\n*Abstract*:\nBackground: This study 
 aims to develop an AI-based tool for automated quality assessment of prost
 ate MRI in accordance with the PI-QUAL v2 criteria.\n\nMethod: A total of 
 767 retrospectively collected prostate mpMRI exams from five centers were 
 included. Six experienced radiologists independently assessed image qualit
 y\, and their aggregated ratings served as the reference standard. Inter-r
 ater agreement and agreement between AI predictions and individual radiolo
 gists were evaluated using weighted Cohen’s kappa coefficients. The diff
 erence between inter-rater variability and average AI–radiologist agreem
 ent was tested against zero.\n\nResult: Inter-radiologist agreement for PI
 -QUAL v2 scores was moderate\, with weighted Cohen’s kappa values rangin
 g from 0.22 to 0.70. Agreement was higher for DWI and DCE sequences compar
 ed with T2WI. The difference between AI–radiologist and inter-radiologis
 t agreement was not statistically significant in most settings (p < 0.001)
 \, except when comparing radiologists from the same institution (p > 0.001
 ).\n\nConclusion: The AI model can serve as a reliable standalone tool for
  automated prostate MRI quality assessment.\n\n\nThis is a hybrid event so
  you can also join via Zoom:\n\nhttps://zoom.us/j/99050467573?pwd=UE5OdFdT
 SFdZeUtIcU1DbXpmdlNGZz09\n\nMeeting ID: 990 5046 7573 and Passcode: 617729
 \n\nWe look forward to your participation! If you are interested in gettin
 g involved and presenting your work\, please email Ines Machado at im549@c
 am.ac.uk\n\nFor more information about this seminar series\, see: https://
 www.integratedcancermedicine.org/research/cambridge-medai-seminar-series/
LOCATION:Jeffrey Cheah Biomedical Centre (Main Lecture Theatre)\, Universi
 ty of Cambridge
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