Objectives This study was designed to develop a multiregional MRI-based deep learning radiomics nomogram (DLRN) for predicting axillary pathological complete response (apCR) after neoadjuvant chemotherapy (NAC) in breast cancer. Materials and methods In total, 539 patients in our hospital were randomly split into a training cohort (TC; n = 431) and an internal validation cohort (IVC; n = 108), and 703 patients were recruited from three external centers as external validation cohorts (EVC1–3). Uni- and multivariate analyses were performed to select clinicopathological characteristics and establish a clinical model. DLR models were constructed based on DL and handcrafted radiomics features extracted from gross tumor volume (GTV) and GTV incorporating 3-, 5-, 7-, and 9-mm peritumoral regions (GPTV3, GPTV5, GPTV7, and GPTV9, respectively). A DLRN model incorporating the optimal DLR model and clinicopathological predictors was developed. Model performance was assessed employing the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. Results The GPTV5_DLR model surpassed the other DLR models, with an average AUC of 0.876 in the validation cohorts. The DLRN model better predicted apCR after NAC than the clinical model, demonstrating superior AUCs of 0.958 in the TC, 0.906 in the IVC, and 0.876–0.911 in EVC1–3. It also showed improved accuracy and clinical benefits for apCR prediction. Furthermore, the DLRN model achieved robust performance across different age, menstrual status, and clinical stage subgroups. Conclusion The DLRN model, based on the GPTV5_DLR model and clinicopathological features, exhibited high predictive efficiency for apCR after NAC. Critical relevance statement The deep learning radiomics nomogram based on intra- and peritumoral regions could noninvasively predict axillary pCR in breast cancer patients receiving NAC, which might prevent patients from undergoing unnecessary axillary lymph node dissection. Key Points <UnorderedList Mark="Bullet"> <ItemContent> Combining intratumoral and 5-mm peritumoral region radiomics had the highest predictive efficiency for axillary pCR after NAC in breast cancer. </ItemContent> <ItemContent> The deep learning radiomics nomogram based on intra- and peritumoral regions outperformed the clinical model. </ItemContent> <ItemContent> The proposed model could provide a noninvasive and easy-to-use tool to offer decision support for optimizing treatments. </ItemContent> </UnorderedList> Graphical Abstract

Multiregional MRI-based deep learning radiomics to predict axillary response after neoadjuvant chemotherapy in breast cancer patients

期刊论文
出版模式：

开放获取
发表日期：

2026年1月26日

查看全文

Weiyue Chen,
Guihan Lin,
Yi Zhou,
Yongjun Chen,
Changsheng Shi,
Ting Zhao,
Zhihan Yan,
Zhiyi Peng,
Shuiwei Xia,
Min Xu,
Minjiang Chen,
Chenying Lu,
Jiansong Ji

摘要

Objectives

This study was designed to develop a multiregional MRI-based deep learning radiomics nomogram (DLRN) for predicting axillary pathological complete response (apCR) after neoadjuvant chemotherapy (NAC) in breast cancer.

Materials and methods

In total, 539 patients in our hospital were randomly split into a training cohort (TC; n = 431) and an internal validation cohort (IVC; n = 108), and 703 patients were recruited from three external centers as external validation cohorts (EVC1–3). Uni- and multivariate analyses were performed to select clinicopathological characteristics and establish a clinical model. DLR models were constructed based on DL and handcrafted radiomics features extracted from gross tumor volume (GTV) and GTV incorporating 3-, 5-, 7-, and 9-mm peritumoral regions (GPTV₃, GPTV₅, GPTV₇, and GPTV₉, respectively). A DLRN model incorporating the optimal DLR model and clinicopathological predictors was developed. Model performance was assessed employing the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis.

Results

The GPTV₅_DLR model surpassed the other DLR models, with an average AUC of 0.876 in the validation cohorts. The DLRN model better predicted apCR after NAC than the clinical model, demonstrating superior AUCs of 0.958 in the TC, 0.906 in the IVC, and 0.876–0.911 in EVC1–3. It also showed improved accuracy and clinical benefits for apCR prediction. Furthermore, the DLRN model achieved robust performance across different age, menstrual status, and clinical stage subgroups.

Conclusion

The DLRN model, based on the GPTV₅_DLR model and clinicopathological features, exhibited high predictive efficiency for apCR after NAC.

Critical relevance statement

The deep learning radiomics nomogram based on intra- and peritumoral regions could noninvasively predict axillary pCR in breast cancer patients receiving NAC, which might prevent patients from undergoing unnecessary axillary lymph node dissection.

Key Points

Combining intratumoral and 5-mm peritumoral region radiomics had the highest predictive efficiency for axillary pCR after NAC in breast cancer.

The deep learning radiomics nomogram based on intra- and peritumoral regions outperformed the clinical model.

The proposed model could provide a noninvasive and easy-to-use tool to offer decision support for optimizing treatments.

Graphical Abstract