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<p>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, <i>p</i>&#xa0;&lt;&#xa0;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, <i>p</i>=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.</p>

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Multi-modal AI for comprehensive breast cancer prognostication

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

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  • 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 M. Saad,
  • Nitya Thakore,
  • Mohammad Sadic,
  • Frank Yeung,
  • Elisa Liu,
  • Theodore Hill,
  • Benjamin Swett,
  • Danielle Rigau,
  • Andrew J. Clayburn,
  • Valerie Speirs,
  • Marcus Vetter,
  • Lina Sojak,
  • Simone Muenst,
  • Daniel Baumhoer,
  • Jia-Wern Pan,
  • Haslina Makmur,
  • Soo-Hwang Teo,
  • Linda M. 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

摘要

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.