[2410.21256] Multi-modal AI for comprehensive breast cancer prognostication

<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"> <head> <title>[2410.21256] Multi-modal AI for comprehensive breast cancer prognostication</title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20240822" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/browse_search.css" /> <script language="javascript" src="/static/browse/0.3.4/js/accordion.js" /></script> <link rel="canonical" href="https://arxiv.org/abs/2410.21256"/> <meta name="description" content="Abstract page for arXiv paper 2410.21256: Multi-modal AI for comprehensive breast cancer prognostication"><meta property="og:type" content="website" /> <meta property="og:site_name" content="arXiv.org" /> <meta property="og:title" content="Multi-modal AI for comprehensive breast cancer prognostication" /> <meta property="og:url" content="https://arxiv.org/abs/2410.21256v1" /> <meta property="og:image" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:secure_url" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:width" content="1200" /> <meta property="og:image:height" content="700" /> <meta property="og:image:alt" content="arXiv logo"/> <meta property="og:description" content="Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for many patients. We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. Specifically, we utilized a vision transformer-based pan-cancer foundation model trained with self-supervised learning to extract features from digitized H&E-stained slides. These features were integrated with clinical data to form a multi-modal AI test predicting cancer recurrence and death. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts originating from seven countries. Of these, 3,502 patients from five cohorts were used exclusively for evaluation, while the remaining patients were used for training. Our test accurately predicted our primary endpoint, disease-free interval, in the five external cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.01]). In a direct comparison (N=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, with a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.01)]). The test demonstrated robust accuracy across all major breast cancer subtypes, including TNBC (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no diagnostic tools are currently recommended by clinical guidelines. These results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Multi-modal AI for comprehensive breast cancer prognostication"/> <meta name="twitter:description" content="Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods,..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="Multi-modal AI for comprehensive breast cancer prognostication" /><meta name="citation_author" content="Witowski, Jan" /><meta name="citation_author" content="Zeng, Ken" /><meta name="citation_author" content="Cappadona, Joseph" /><meta name="citation_author" content="Elayoubi, Jailan" /><meta name="citation_author" content="Chiru, Elena Diana" /><meta name="citation_author" content="Chan, Nancy" /><meta name="citation_author" content="Kang, Young-Joon" /><meta name="citation_author" content="Howard, Frederick" /><meta name="citation_author" content="Ostrovnaya, Irina" /><meta name="citation_author" content="Fernandez-Granda, Carlos" /><meta name="citation_author" content="Schnabel, Freya" /><meta name="citation_author" content="Ozerdem, Ugur" /><meta name="citation_author" content="Liu, Kangning" /><meta name="citation_author" content="Steinsnyder, Zoe" /><meta name="citation_author" content="Thakore, Nitya" /><meta name="citation_author" content="Sadic, Mohammad" /><meta name="citation_author" content="Yeung, Frank" /><meta name="citation_author" content="Liu, Elisa" /><meta name="citation_author" content="Hill, Theodore" /><meta name="citation_author" content="Swett, Benjamin" /><meta name="citation_author" content="Rigau, Danielle" /><meta name="citation_author" content="Clayburn, Andrew" /><meta name="citation_author" content="Speirs, Valerie" /><meta name="citation_author" content="Vetter, Marcus" /><meta name="citation_author" content="Sojak, Lina" /><meta name="citation_author" content="Soysal, Simone Muenst" /><meta name="citation_author" content="Baumhoer, Daniel" /><meta name="citation_author" content="Choucair, Khalil" /><meta name="citation_author" content="Zong, Yu" /><meta name="citation_author" content="Daoud, Lina" /><meta name="citation_author" content="Saad, Anas" /><meta name="citation_author" content="Abdulsattar, Waleed" /><meta name="citation_author" content="Beydoun, Rafic" /><meta name="citation_author" content="Pan, Jia-Wern" /><meta name="citation_author" content="Makmur, Haslina" /><meta name="citation_author" content="Teo, Soo-Hwang" /><meta name="citation_author" content="Pak, Linda Ma" /><meta name="citation_author" content="Angel, Victor" /><meta name="citation_author" content="Zilenaite-Petrulaitiene, Dovile" /><meta name="citation_author" content="Laurinavicius, Arvydas" /><meta name="citation_author" content="Klar, Natalie" /><meta name="citation_author" content="Piening, Brian D." /><meta name="citation_author" content="Bifulco, Carlo" /><meta name="citation_author" content="Jun, Sun-Young" /><meta name="citation_author" content="Yi, Jae Pak" /><meta name="citation_author" content="Lim, Su Hyun" /><meta name="citation_author" content="Brufsky, Adam" /><meta name="citation_author" content="Esteva, Francisco J." /><meta name="citation_author" content="Pusztai, Lajos" /><meta name="citation_author" content="LeCun, Yann" /><meta name="citation_author" content="Geras, Krzysztof J." /><meta name="citation_date" content="2024/10/28" /><meta name="citation_online_date" content="2024/10/28" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2410.21256" /><meta name="citation_arxiv_id" content="2410.21256" /><meta name="citation_abstract" content="Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for many patients. We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. Specifically, we utilized a vision transformer-based pan-cancer foundation model trained with self-supervised learning to extract features from digitized H&E-stained slides. These features were integrated with clinical data to form a multi-modal AI test predicting cancer recurrence and death. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts originating from seven countries. Of these, 3,502 patients from five cohorts were used exclusively for evaluation, while the remaining patients were used for training. Our test accurately predicted our primary endpoint, disease-free interval, in the five external cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.01]). In a direct comparison (N=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, with a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.01)]). The test demonstrated robust accuracy across all major breast cancer subtypes, including TNBC (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no diagnostic tools are currently recommended by clinical guidelines. These results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a>  <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" tabindex="-1" 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href="https://arxiv.org/search/cs?searchtype=author&query=Witowski,+J" rel="nofollow">Jan Witowski</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zeng,+K" rel="nofollow">Ken Zeng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cappadona,+J" rel="nofollow">Joseph Cappadona</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Elayoubi,+J" rel="nofollow">Jailan Elayoubi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chiru,+E+D" rel="nofollow">Elena Diana Chiru</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chan,+N" rel="nofollow">Nancy Chan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kang,+Y" rel="nofollow">Young-Joon Kang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Howard,+F" rel="nofollow">Frederick Howard</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ostrovnaya,+I" rel="nofollow">Irina Ostrovnaya</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fernandez-Granda,+C" rel="nofollow">Carlos Fernandez-Granda</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schnabel,+F" rel="nofollow">Freya Schnabel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ozerdem,+U" rel="nofollow">Ugur Ozerdem</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+K" rel="nofollow">Kangning Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Steinsnyder,+Z" rel="nofollow">Zoe Steinsnyder</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Thakore,+N" rel="nofollow">Nitya Thakore</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sadic,+M" rel="nofollow">Mohammad Sadic</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yeung,+F" rel="nofollow">Frank Yeung</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+E" rel="nofollow">Elisa Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hill,+T" rel="nofollow">Theodore Hill</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Swett,+B" rel="nofollow">Benjamin Swett</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rigau,+D" rel="nofollow">Danielle Rigau</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Clayburn,+A" rel="nofollow">Andrew Clayburn</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Speirs,+V" rel="nofollow">Valerie Speirs</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vetter,+M" rel="nofollow">Marcus Vetter</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sojak,+L" rel="nofollow">Lina Sojak</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Soysal,+S+M" rel="nofollow">Simone Muenst Soysal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Baumhoer,+D" rel="nofollow">Daniel Baumhoer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Choucair,+K" rel="nofollow">Khalil Choucair</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zong,+Y" rel="nofollow">Yu Zong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Daoud,+L" rel="nofollow">Lina Daoud</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Saad,+A" rel="nofollow">Anas Saad</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Abdulsattar,+W" rel="nofollow">Waleed Abdulsattar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Beydoun,+R" rel="nofollow">Rafic Beydoun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pan,+J" rel="nofollow">Jia-Wern Pan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Makmur,+H" rel="nofollow">Haslina Makmur</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Teo,+S" rel="nofollow">Soo-Hwang Teo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pak,+L+M" rel="nofollow">Linda Ma Pak</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Angel,+V" rel="nofollow">Victor Angel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zilenaite-Petrulaitiene,+D" rel="nofollow">Dovile Zilenaite-Petrulaitiene</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Laurinavicius,+A" rel="nofollow">Arvydas Laurinavicius</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Klar,+N" rel="nofollow">Natalie Klar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Piening,+B+D" rel="nofollow">Brian D. Piening</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bifulco,+C" rel="nofollow">Carlo Bifulco</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jun,+S" rel="nofollow">Sun-Young Jun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yi,+J+P" rel="nofollow">Jae Pak Yi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lim,+S+H" rel="nofollow">Su Hyun Lim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Brufsky,+A" rel="nofollow">Adam Brufsky</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Esteva,+F+J" rel="nofollow">Francisco J. Esteva</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pusztai,+L" rel="nofollow">Lajos Pusztai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=LeCun,+Y" rel="nofollow">Yann LeCun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Geras,+K+J" rel="nofollow">Krzysztof J. Geras</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Multi-modal AI for comprehensive breast cancer prognostication, by Jan Witowski and 50 other authors</div> <a class="mobile-submission-download" href="/pdf/2410.21256">View PDF</a> <a class="mobile-submission-download" href="https://arxiv.org/html/2410.21256v1">HTML (experimental)</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for many patients. We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. Specifically, we utilized a vision transformer-based pan-cancer foundation model trained with self-supervised learning to extract features from digitized H&E-stained slides. These features were integrated with clinical data to form a multi-modal AI test predicting cancer recurrence and death. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts originating from seven countries. Of these, 3,502 patients from five cohorts were used exclusively for evaluation, while the remaining patients were used for training. Our test accurately predicted our primary endpoint, disease-free interval, in the five external cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.01]). In a direct comparison (N=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, with a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.01)]). The test demonstrated robust accuracy across all major breast cancer subtypes, including TNBC (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no diagnostic tools are currently recommended by clinical guidelines. These results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools. </blockquote>  <div class="metatable"> <table summary="Additional metadata"><tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Artificial Intelligence (cs.AI)</span>; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)</td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2410.21256">arXiv:2410.21256</a> [cs.AI]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2410.21256v1">arXiv:2410.21256v1</a> [cs.AI]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2410.21256" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2410.21256</a><div class="button-and-tooltip"> <button class="more-info" aria-describedby="more-info-desc-1"> <svg height="15" role="presentation" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 110c23.196 0 42 18.804 42 42s-18.804 42-42 42-42-18.804-42-42 18.804-42 42-42zm56 254c0 6.627-5.373 12-12 12h-88c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h12v-64h-12c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h64c6.627 0 12 5.373 12 12v100h12c6.627 0 12 5.373 12 12v24z" class=""></path></svg> <span class="visually-hidden">Focus to learn more</span> </button>  <div role="tooltip" id="more-info-desc-1"> <span class="left-corner"></span> arXiv-issued DOI via DataCite</div> </div> </td> </tr></table> </div> </div> </div> <div class="submission-history"> <h2>Submission history</h2> From: Krzysztof J. 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[2410.21256] Multi-modal AI for comprehensive breast cancer prognostication