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Background <p>In the prognosis of breast cancer, the status of axillary lymph nodes (ALN) is critically important. While traditional axillary lymph node dissection (ALND) provides comprehensive information, it is associated with high risks. Sentinel lymph node biopsy (SLND), as an alternative, is less invasive but still poses a risk of overtreatment. In recent years, digital breast tomosynthesis (DBT) technology has emerged as a new precise diagnostic tool for breast cancer, leveraging its high detection capability for lesions obscured by dense glandular tissue.</p> Purpose <p>This multi-center study evaluates the feasibility of preoperative DBT-based radiomics, using tumor and peritumoral features, to predict ALN metastasis in breast cancer.</p> Methods <p>We retrospectively collected DBT imaging data from 536 preoperative breast cancer patients across two centers. Specifically, 390 cases were from one Hospital, and 146 cases were from another Hospital. These data were assigned to internal training and external validation sets, respectively. We performed 3D region of interest (ROI) delineation on the cranio-caudal (CC) and mediolateral oblique (MLO) views of DBT images and extracted radiomic features. Using methods such as analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO), we selected radiomic features extracted from the tumor and its surrounding 3&#xa0;mm, 5&#xa0;mm, and 10&#xa0;mm regions, and constructed a radiomic feature set. We then developed a combined model that includes the optimal radiomic features and clinical pathological factors. The performance of the combined model was evaluated using the area under the curve (AUC), and it was directly compared with the diagnostic results of radiologists.</p> Results <p>The results showed that the AUC of the radiomic features from the surrounding regions of the tumor were generally lower than those from the tumor itself. Among them, the Signature<sub>tuomor+10&#xa0;mm</sub> model performed best, achieving an AUC of 0.806 using a logistic regression (LR) classifier to generate the RadScore.The nomogram incorporating both Ki67 and RadScore demonstrated a slightly higher AUC (0.813) compared to the Signature<sub>tuomor+10&#xa0;mm</sub> model alone (0.806). By integrating relevant clinical information, the nomogram enhances potential clinical utility. Moreover, it outperformed radiologists’ assessments in predictive accuracy, highlighting its added value in clinical decision-making.</p> Conclusions <p>Radiomics based on DBT imaging of the tumor and surrounding regions can provide a non-invasive auxiliary tool to guide treatment strategies for ALN metastasis in breast cancer.</p> Clinical trial number <p>Not applicable.</p>

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Preoperative DBT-based radiomics for predicting axillary lymph node metastasis in breast cancer: a multi-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月19日

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  • Shuyan He,
  • Biao Deng,
  • Jiaqi Chen,
  • Jiamin Li,
  • Xuefeng Wang,
  • Guanxing Li,
  • Siyu Long,
  • Jian Wan,
  • Yan Zhang

摘要

Background

In the prognosis of breast cancer, the status of axillary lymph nodes (ALN) is critically important. While traditional axillary lymph node dissection (ALND) provides comprehensive information, it is associated with high risks. Sentinel lymph node biopsy (SLND), as an alternative, is less invasive but still poses a risk of overtreatment. In recent years, digital breast tomosynthesis (DBT) technology has emerged as a new precise diagnostic tool for breast cancer, leveraging its high detection capability for lesions obscured by dense glandular tissue.

Purpose

This multi-center study evaluates the feasibility of preoperative DBT-based radiomics, using tumor and peritumoral features, to predict ALN metastasis in breast cancer.

Methods

We retrospectively collected DBT imaging data from 536 preoperative breast cancer patients across two centers. Specifically, 390 cases were from one Hospital, and 146 cases were from another Hospital. These data were assigned to internal training and external validation sets, respectively. We performed 3D region of interest (ROI) delineation on the cranio-caudal (CC) and mediolateral oblique (MLO) views of DBT images and extracted radiomic features. Using methods such as analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO), we selected radiomic features extracted from the tumor and its surrounding 3 mm, 5 mm, and 10 mm regions, and constructed a radiomic feature set. We then developed a combined model that includes the optimal radiomic features and clinical pathological factors. The performance of the combined model was evaluated using the area under the curve (AUC), and it was directly compared with the diagnostic results of radiologists.

Results

The results showed that the AUC of the radiomic features from the surrounding regions of the tumor were generally lower than those from the tumor itself. Among them, the Signaturetuomor+10 mm model performed best, achieving an AUC of 0.806 using a logistic regression (LR) classifier to generate the RadScore.The nomogram incorporating both Ki67 and RadScore demonstrated a slightly higher AUC (0.813) compared to the Signaturetuomor+10 mm model alone (0.806). By integrating relevant clinical information, the nomogram enhances potential clinical utility. Moreover, it outperformed radiologists’ assessments in predictive accuracy, highlighting its added value in clinical decision-making.

Conclusions

Radiomics based on DBT imaging of the tumor and surrounding regions can provide a non-invasive auxiliary tool to guide treatment strategies for ALN metastasis in breast cancer.

Clinical trial number

Not applicable.