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<p>We developed and validated IICM+, a multimodal model integrating clinicopathologic variables, transcriptomic features, and multiscale histopathology-derived image representations to predict distant recurrence in hormone receptor-positive, HER2-negative, node-negative early breast cancer. Image features included tile-level embeddings and slide-level representations generated by a custom multimodal foundation model pretrained on paired histopathology images, RNA sequencing, and pathology reports. Using TAILORx specimens with long-term follow-up, models were trained in a development cohort with five-fold cross-validation (<i>n</i> = 2808) and evaluated in an independent institutional holdout validation set (<i>n</i> = 1621). In holdout validation, IICM+ showed strong prognostic discrimination for overall distant recurrence (C-index 0.735, 95% CI 0.681–0.782), early distant recurrence (0.791, 95% CI 0.715–0.858), and late distant recurrence (0.710, 95% CI 0.645–0.773). IICM+ separated high- versus low-risk groups for overall distant recurrence (HR 5.25, 95% CI 3.50–7.86; <i>P</i> &lt; 0.001) and remained prognostic (HR 3.56, 95% CI 2.22–5.70, <i>P</i> &lt; 0.001) after adjustment for clinicopathologic covariates and RS category. IICM+ also identified RS/IICM+ discordant groups with different observed recurrence risks, supporting additional prognostic stratification beyond RS.</p>

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An Artificial Intelligence (AI) model integrating multiscale foundation model histopathology representations with molecular and clinical features predicts early and late distant recurrence in TAILORx

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Joseph A. Sparano,
  • Norsang Lama,
  • Robert J. Gray,
  • Md Ashequr Rahman,
  • Victoria Wang,
  • Della F. Makower,
  • Yating Cheng,
  • Sisi Shao,
  • Kathy S. Albain,
  • Eghbal Amidi,
  • Ming Chen,
  • Daniel F. Hayes,
  • Anthony Helmstetter,
  • Charles E. Geyer Jr.,
  • Casey Bales,
  • Andrew Hinton,
  • Elizabeth C. Dees,
  • Matthew P. Goetz,
  • John A. Olson Jr.,
  • Sunil S. Badve,
  • Thomas J. Saphner,
  • Timothy J. Whelan,
  • Virginia G. Kaklaman,
  • Matthew Oberley,
  • Milan Radovich,
  • David Spetzler,
  • Eleftherios P. Mamounas,
  • Norman Wolmark,
  • George W. Sledge Jr

摘要

We developed and validated IICM+, a multimodal model integrating clinicopathologic variables, transcriptomic features, and multiscale histopathology-derived image representations to predict distant recurrence in hormone receptor-positive, HER2-negative, node-negative early breast cancer. Image features included tile-level embeddings and slide-level representations generated by a custom multimodal foundation model pretrained on paired histopathology images, RNA sequencing, and pathology reports. Using TAILORx specimens with long-term follow-up, models were trained in a development cohort with five-fold cross-validation (n = 2808) and evaluated in an independent institutional holdout validation set (n = 1621). In holdout validation, IICM+ showed strong prognostic discrimination for overall distant recurrence (C-index 0.735, 95% CI 0.681–0.782), early distant recurrence (0.791, 95% CI 0.715–0.858), and late distant recurrence (0.710, 95% CI 0.645–0.773). IICM+ separated high- versus low-risk groups for overall distant recurrence (HR 5.25, 95% CI 3.50–7.86; P < 0.001) and remained prognostic (HR 3.56, 95% CI 2.22–5.70, P < 0.001) after adjustment for clinicopathologic covariates and RS category. IICM+ also identified RS/IICM+ discordant groups with different observed recurrence risks, supporting additional prognostic stratification beyond RS.