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<p>A substantial proportion of patients with non-metastatic triple-negative breast cancer (TNBC) experience disease progression and death despite treatment. However, no tool currently exists to discriminate those at higher risk of death. To identify high-risk TNBC, we conducted a retrospective analysis of 749 patients from two independent cohorts. We built a prediction model that leverages breast magnetic resonance imaging (MRI) features to predict risk groups based on a 50-gene Transcriptomics Signature (TS). The TS distinguished patients with high-risk for death in multivariate survival analysis (Transcriptomic cohort: [HR] = 13.6, 95% confidence interval [CI] = 1.56-1, <i>p</i> = 0.02; SCAN-B cohort: HR = 1.45, CI 1.04-2.03, <i>p</i> = 0.02). The model identified a 20-feature radiomic signature derived from breast MRI that predicted the TS-based risk groups. This imaging-based classifier was applied to a validation cohort (log rank <i>p</i> = 0.013, accuracy 0.72, AUC 0.71, F1 0.74, precision 0.67, and recall 0.82), detecting a 25% absolute survival difference between high- and low-risk groups after 5 years.</p>

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A 20-feature radiomic signature of triple-negative breast cancer identifies patients at high risk of death

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月26日

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  • Humaira Noor,
  • Yuanning Zheng,
  • Adam B. Mantz,
  • Ryle Zhou,
  • Andrew Kozlov,
  • Wendy B. DeMartini,
  • Shu-Tian Chen,
  • Satoko Okamoto,
  • Debra M. Ikeda,
  • Melinda L. Telli,
  • Allison W. Kurian,
  • James M. Ford,
  • Shaveta Vinayak,
  • Mina Satoyoshi,
  • Vishal Joshi,
  • Sarah A. Mattonen,
  • Kevin Lee,
  • Olivier Gevaert,
  • George W. Sledge,
  • Haruka Itakura

摘要

A substantial proportion of patients with non-metastatic triple-negative breast cancer (TNBC) experience disease progression and death despite treatment. However, no tool currently exists to discriminate those at higher risk of death. To identify high-risk TNBC, we conducted a retrospective analysis of 749 patients from two independent cohorts. We built a prediction model that leverages breast magnetic resonance imaging (MRI) features to predict risk groups based on a 50-gene Transcriptomics Signature (TS). The TS distinguished patients with high-risk for death in multivariate survival analysis (Transcriptomic cohort: [HR] = 13.6, 95% confidence interval [CI] = 1.56-1, p = 0.02; SCAN-B cohort: HR = 1.45, CI 1.04-2.03, p = 0.02). The model identified a 20-feature radiomic signature derived from breast MRI that predicted the TS-based risk groups. This imaging-based classifier was applied to a validation cohort (log rank p = 0.013, accuracy 0.72, AUC 0.71, F1 0.74, precision 0.67, and recall 0.82), detecting a 25% absolute survival difference between high- and low-risk groups after 5 years.