Multi-modal AI for comprehensive breast cancer prognostication
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| author | Witowski, Jan Zeng, Ken G. Cappadona, Joseph Elayoubi, Jailan Choucair, Khalil Chiru, Elena Diana Chan, Nancy Kang, Young-Joon Howard, Frederick Ostrovnaya, Irina Fernandez-Granda, Carlos Schnabel, Freya Steinsnyder, Zoe Ozerdem, Ugur Liu, Kangning Abdulsattar, Waleed Zong, Yu Daoud, Lina Beydoun, Rafic Saad, Anas Thakore, Nitya Sadic, Mohammad Yeung, Frank Liu, Elisa Hill, Theodore Swett, Benjamin Rigau, Danielle Clayburn, Andrew Speirs, Valerie Vetter, Marcus Sojak, Lina Soysal, Simone Baumhoer, Daniel Pan, Jia-Wern Makmur, Haslina Teo, Soo-Hwang Pak, Linda Ma Angel, Victor Zilenaite-Petrulaitiene, Dovile Laurinavicius, Arvydas Klar, Natalie Piening, Brian D. Bifulco, Carlo Jun, Sun-Young Yi, Jae Pak Lim, Su Hyun Brufsky, Adam Esteva, Francisco J. Pusztai, Lajos LeCun, Yann Geras, Krzysztof J. |
| author_facet | Witowski, Jan Zeng, Ken G. Cappadona, Joseph Elayoubi, Jailan Choucair, Khalil Chiru, Elena Diana Chan, Nancy Kang, Young-Joon Howard, Frederick Ostrovnaya, Irina Fernandez-Granda, Carlos Schnabel, Freya Steinsnyder, Zoe Ozerdem, Ugur Liu, Kangning Abdulsattar, Waleed Zong, Yu Daoud, Lina Beydoun, Rafic Saad, Anas Thakore, Nitya Sadic, Mohammad Yeung, Frank Liu, Elisa Hill, Theodore Swett, Benjamin Rigau, Danielle Clayburn, Andrew Speirs, Valerie Vetter, Marcus Sojak, Lina Soysal, Simone Baumhoer, Daniel Pan, Jia-Wern Makmur, Haslina Teo, Soo-Hwang Pak, Linda Ma Angel, Victor Zilenaite-Petrulaitiene, Dovile Laurinavicius, Arvydas Klar, Natalie Piening, Brian D. Bifulco, Carlo Jun, Sun-Young Yi, Jae Pak Lim, Su Hyun Brufsky, Adam Esteva, Francisco J. Pusztai, Lajos LeCun, Yann Geras, Krzysztof J. |
| contents | Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. However, current tools including genomic assays lack the accuracy required for optimal clinical decision-making. We developed a novel artificial intelligence (AI)-based approach that integrates digital pathology images with clinical data, providing a more robust and effective method for predicting the risk of cancer recurrence in breast cancer patients. Specifically, we utilized a vision transformer 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 female 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 evaluation cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.001]). In a direct comparison (n=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, achieving a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent prognostic information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.001)]). The test demonstrated robust accuracy across major molecular 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 improves upon the accuracy of existing prognostic tests, while being applicable to a wider range of patients. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21256 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-modal AI for comprehensive breast cancer prognostication Witowski, Jan Zeng, Ken G. Cappadona, Joseph Elayoubi, Jailan Choucair, Khalil Chiru, Elena Diana Chan, Nancy Kang, Young-Joon Howard, Frederick Ostrovnaya, Irina Fernandez-Granda, Carlos Schnabel, Freya Steinsnyder, Zoe Ozerdem, Ugur Liu, Kangning Abdulsattar, Waleed Zong, Yu Daoud, Lina Beydoun, Rafic Saad, Anas Thakore, Nitya Sadic, Mohammad Yeung, Frank Liu, Elisa Hill, Theodore Swett, Benjamin Rigau, Danielle Clayburn, Andrew Speirs, Valerie Vetter, Marcus Sojak, Lina Soysal, Simone Baumhoer, Daniel Pan, Jia-Wern Makmur, Haslina Teo, Soo-Hwang Pak, Linda Ma Angel, Victor Zilenaite-Petrulaitiene, Dovile Laurinavicius, Arvydas Klar, Natalie Piening, Brian D. Bifulco, Carlo Jun, Sun-Young Yi, Jae Pak Lim, Su Hyun Brufsky, Adam Esteva, Francisco J. Pusztai, Lajos LeCun, Yann Geras, Krzysztof J. Artificial Intelligence Computer Vision and Pattern Recognition Image and Video Processing Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. However, current tools including genomic assays lack the accuracy required for optimal clinical decision-making. We developed a novel artificial intelligence (AI)-based approach that integrates digital pathology images with clinical data, providing a more robust and effective method for predicting the risk of cancer recurrence in breast cancer patients. Specifically, we utilized a vision transformer 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 female 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 evaluation cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.001]). In a direct comparison (n=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, achieving a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent prognostic information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.001)]). The test demonstrated robust accuracy across major molecular 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 improves upon the accuracy of existing prognostic tests, while being applicable to a wider range of patients. |
| title | Multi-modal AI for comprehensive breast cancer prognostication |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2410.21256 |