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MedAI 1 год назад

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MedAI

Title: HECTOR, a multimodal deep learning model predicting recurrence risk in endometrial cancer Speaker: Sarah Volinsky Abstract: Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal (n = 353) and two external (n = 160 and n = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan–Meier analysis). HECTOR helps delivery of personalized treatment in EC. Speaker Bio: Sarah Volinsky is a deep learning engineer with specific expertise in computational pathology. She is completing her 4-year PhD research in The Leiden University Medical Center in the Netherlands where she is the principal junior researcher of the AIRMEC team, a joint collaboration with Dr. Tjalling Bosse and Dr. Nanda Horeweg at the Leiden University Medical Center and Prof. Viktor Koelzer at the University Hospital of Basel. As part of her PhD research work, she has been developing deep learning models using histology images, outcomes, genomic data, specifically in the domain of endometrial cancer for predicting molecular alterations and outcomes. This has led to two first author publications in the Lancet digital health 2023, Nature Medicine 2024 and platform presentations in several international conferences including USCAP and AACR. Prior to this, she was working in the UK as a machine learning engineer after completing her second Msc in Data Science in London. She has also graduated from a Msc in financial engineering and a bachelor in mathematics in Paris. ------ The MedAI Group Exchange Sessions are a platform where we can critically examine key topics in AI and medicine, generate fresh ideas and discussion around their intersection and most importantly, learn from each other. We will be having weekly sessions where invited speakers will give a talk presenting their work followed by an interactive discussion and Q&A. Our sessions are held every Monday from 1pm-2pm PST. To get notifications about upcoming sessions, please join our mailing list: https://mailman.stanford.edu/mailman/... For more details about MedAI, check out our website: https://medai.stanford.edu. You can follow us on Twitter @MedaiStanford Organized by members of the Rubin Lab (http://rubinlab.stanford.edu) and Machine Intelligence in Medicine and Imaging (MI-2) Lab: Nandita Bhaskhar (https://www.stanford.edu/~nanbhas) Amara Tariq (  / amara-tariq-475815158  ) Avisha Das (https://dasavisha.github.io/)

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