| Authors |
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 M ; Thakore, Nitya ; Sadic, Mohammad ; Yeung, Frank ; Liu, Elisa ; Hill, Theodore ; Swett, Benjamin ; Rigau, Danielle ; Clayburn, Andrew J ; Speirs, Valerie ; Vetter, Marcus ; Sojak, Lina ; Muenst, Simone ; Baumhoer, Daniel ; Pan, Jia-Wern ; Makmur, Haslina ; Teo, Soo-Hwang ; Pak, Linda M ; Angel, Victor ; Žilėnaitė-Petrulaitienė, Dovilė ; Laurinavičius, 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 |
| Abstract [eng] |
Treatment selection in breast cancer is guided by risk assessment using molecular subtypes and clinicopathological characteristics. However, current approaches lack the precision required for optimal clinical decision-making. To address this, we use data from 8161 patients to develop and evaluate an AI test integrating digital pathology with clinical data. The AI test provides a robust method for predicting disease-free interval (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p < 0.001]). In a direct comparison, the AI test displays numerically higher discrimination (C-index: 0.67 [0.61–0.74]) than the standard-of-care 21-gene assay (C-index: 0.61 [0.49–0.73]). Across molecular subtypes, the AI test demonstrates robust prognostic performance, including in triple negative breast cancer (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no guideline-recommended assays currently exist. These findings highlight the potential of AI-based pathology tests as a promising tool for improved risk stratification across all major subtypes, with implications for clinical decision-making. |
