Scholarlink

<p>Patients with triple-negative breast cancer (TNBC) often have a poor prognosis. Existing staging systems cannot accurately assess patients’ recurrence risk, due to the tumor’s significant heterogeneity. Given the lack of a reliable prognostic assessment method, this study aimed to propose a combined multimodal prognostic model. We systematically integrated the clinicopathological indicators with multi-scale radiomics features in a large-scale TNBC cohort (n = 700). Remarkably, “intratumoral habitat heterogeneity analysis” and “peritumoral microenvironment characteristics” were innovatively incorporated in our model to reflect the potential biological behavior of individual tumors. The best consistency index (C-index) illuminated the superior prognostic capabilities of the combined model. Time-dependent ROC analysis demonstrated robust AUC at the 1-, 3-, and 5-year disease-free survival (DFS). Overall, our predictive model could provide a more comprehensive description of the biological aggressiveness of TNBC, overcoming the limitations of traditional staging systems in individualized prognosis assessment and better assisting doctors in developing personalized treatment and follow-up plans.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multidimensional MRI radiomics-based model predicts recurrence risk in triple-negative breast cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

查看全文
  • Zirui Wang,
  • Teng Ma,
  • Yifan Li,
  • Xinyi Sun,
  • Yongmei Wang,
  • Yan Mao,
  • Tianyi Ma,
  • Xu Qiao

摘要

Patients with triple-negative breast cancer (TNBC) often have a poor prognosis. Existing staging systems cannot accurately assess patients’ recurrence risk, due to the tumor’s significant heterogeneity. Given the lack of a reliable prognostic assessment method, this study aimed to propose a combined multimodal prognostic model. We systematically integrated the clinicopathological indicators with multi-scale radiomics features in a large-scale TNBC cohort (n = 700). Remarkably, “intratumoral habitat heterogeneity analysis” and “peritumoral microenvironment characteristics” were innovatively incorporated in our model to reflect the potential biological behavior of individual tumors. The best consistency index (C-index) illuminated the superior prognostic capabilities of the combined model. Time-dependent ROC analysis demonstrated robust AUC at the 1-, 3-, and 5-year disease-free survival (DFS). Overall, our predictive model could provide a more comprehensive description of the biological aggressiveness of TNBC, overcoming the limitations of traditional staging systems in individualized prognosis assessment and better assisting doctors in developing personalized treatment and follow-up plans.