Summary of Multi-modal Ai For Comprehensive Breast Cancer Prognostication, by Jan Witowski et al.

by Jan Witowski, Ken G. Zeng, Joseph Cappadona, Jailan Elayoubi, Khalil Choucair, Elena Diana Chiru, Nancy Chan, Young-Joon Kang, Frederick Howard, Irina Ostrovnaya, Carlos Fernandez-Granda, Freya Schnabel, Zoe Steinsnyder, Ugur Ozerdem, Kangning Liu, Waleed Abdulsattar, Yu Zong, Lina Daoud, Rafic Beydoun, Anas Saad, Nitya Thakore, Mohammad Sadic, Frank Yeung, Elisa Liu, Theodore Hill, Benjamin Swett, Danielle Rigau, Andrew Clayburn, Valerie Speirs, Marcus Vetter, Lina Sojak, Simone Soysal, Daniel Baumhoer, Jia-Wern Pan, Haslina Makmur, Soo-Hwang Teo, Linda Ma Pak, Victor Angel, Dovile Zilenaite-Petrulaitiene, Arvydas Laurinavicius, Natalie Klar, Brian D. Piening, Carlo Bifulco, Sun-Young Jun, Jae Pak Yi, Su Hyun Lim, Adam Brufsky, Francisco J. Esteva, Lajos Pusztai, Yann LeCun, Krzysztof J. Geras

First submitted to arxiv on: 28 Oct 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The novel AI-based approach integrates digital pathology images with clinical data to predict cancer recurrence risk in breast cancer patients. A vision transformer model extracts features from digitized slides and combines them with clinical data to form a multi-modal test. The test was developed and evaluated using data from 8,161 female breast cancer patients across 15 cohorts. It accurately predicted disease-free interval (C-index: 0.71, HR: 3.63, p<0.001) and outperformed Oncotype DX (HR: 3.11, p<0.001). The test demonstrated robust accuracy across major molecular breast cancer subtypes, including TNBC.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The paper uses AI to help doctors decide the best treatment for women with breast cancer. They take pictures of the cancer cells and combine them with information about each patient’s health. This helps create a better way to predict how likely it is that the cancer will come back. The new method was tested on thousands of patients from around the world and worked well. It was even more accurate than a commonly used test called Oncotype DX.