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<p>Pathological complete response (pCR) can guide surgical strategy and postoperative treatments in triple-negative breast cancer (TNBC). In this study, we developed a Breast Cancer Response Prediction (BCRP) model to predict the pCR in patients with TNBC. The BCRP model integrated multi-dimensional longitudinal quantitative imaging features, clinical factors and features from the Breast Imaging Data and Reporting System (BI-RADS). Multi-dimensional longitudinal quantitative imaging features, including deep learning features and radiomics features, were extracted from multiview B-mode and colour Doppler ultrasound images before and after treatment. The BCRP model achieved the areas under the receiver operating curves (AUCs) of 0.94 [95% confidence interval (CI), 0.91–0.98] and 0.84 [95%CI, 0.75–0.92] in the training and external test cohorts, respectively. Additionally, the low BCRP score was an independent risk factor for event-free survival (<i>P</i> &lt; 0.05). The BCRP model showed a promising ability in predicting response to neoadjuvant chemotherapy in TNBC, and could provide valuable information for survival.</p>

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Multimodal prediction based on ultrasound for response to neoadjuvant chemotherapy in triple negative breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月25日

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  • Maohua Lyu,
  • Shouheng Yi,
  • Chunyan Li,
  • Yu Xie,
  • Yu Liu,
  • Zeyan Xu,
  • Zhitao Wei,
  • Huan Lin,
  • Yunlin Zheng,
  • Chunwang Huang,
  • Xi Lin,
  • Zaiyi Liu,
  • Shufang Pei,
  • Biao Huang,
  • Zhenwei Shi

摘要

Pathological complete response (pCR) can guide surgical strategy and postoperative treatments in triple-negative breast cancer (TNBC). In this study, we developed a Breast Cancer Response Prediction (BCRP) model to predict the pCR in patients with TNBC. The BCRP model integrated multi-dimensional longitudinal quantitative imaging features, clinical factors and features from the Breast Imaging Data and Reporting System (BI-RADS). Multi-dimensional longitudinal quantitative imaging features, including deep learning features and radiomics features, were extracted from multiview B-mode and colour Doppler ultrasound images before and after treatment. The BCRP model achieved the areas under the receiver operating curves (AUCs) of 0.94 [95% confidence interval (CI), 0.91–0.98] and 0.84 [95%CI, 0.75–0.92] in the training and external test cohorts, respectively. Additionally, the low BCRP score was an independent risk factor for event-free survival (P < 0.05). The BCRP model showed a promising ability in predicting response to neoadjuvant chemotherapy in TNBC, and could provide valuable information for survival.