He Shuyan, Deng Biao, Chen Jiaqi, Li Jiamin, Wang Xuefeng, Li Guanxing, Long Siyu, Wan Jian, Zhang Yan
Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, China.
Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Xiashan District, Guangzhou, Guangdong Province, China.
BMC Med Imaging. 2025 May 19;25(1):169. doi: 10.1186/s12880-025-01711-3.
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.
This multi-center study evaluates the feasibility of preoperative DBT-based radiomics, using tumor and peritumoral features, to predict ALN metastasis in breast cancer.
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.
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 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 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.
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.
Not applicable.
在乳腺癌的预后中，腋窝淋巴结（ALN）状态至关重要。传统的腋窝淋巴结清扫术（ALND）虽能提供全面信息，但风险较高。前哨淋巴结活检（SLND）作为替代方法，侵入性较小，但仍存在过度治疗的风险。近年来，数字乳腺断层合成（DBT）技术作为一种新的乳腺癌精确诊断工具应运而生，它对被致密腺组织掩盖的病变具有较高的检测能力。
本多中心研究评估基于术前DBT的影像组学利用肿瘤及瘤周特征预测乳腺癌腋窝淋巴结转移的可行性。
我们回顾性收集了两个中心536例术前乳腺癌患者的DBT影像数据。具体而言，390例来自一家医院，146例来自另一家医院。这些数据分别被分配到内部训练集和外部验证集。我们在DBT图像的头尾位（CC）和内外斜位（MLO）视图上进行三维感兴趣区域（ROI）勾画，并提取影像组学特征。使用方差分析（ANOVA）和最小绝对收缩和选择算子（LASSO）等方法，我们从肿瘤及其周围3毫米、5毫米和10毫米区域提取的影像组学特征中进行选择，并构建了一个影像组学特征集。然后，我们开发了一个包含最佳影像组学特征和临床病理因素的联合模型。使用曲线下面积（AUC）评估联合模型的性能，并将其与放射科医生的诊断结果直接比较。
结果显示，肿瘤周围区域的影像组学特征的AUC普遍低于肿瘤本身的。其中，Signature模型表现最佳，使用逻辑回归（LR）分类器生成RadScore时AUC达到0.806。结合Ki67和RadScore的列线图显示AUC（0.813）略高于单独的Signature模型（0.806）。通过整合相关临床信息，列线图增强了潜在的临床实用性。此外，它在预测准确性方面优于放射科医生的评估，突出了其在临床决策中的附加价值。
基于肿瘤及周围区域DBT成像的影像组学可为指导乳腺癌腋窝淋巴结转移的治疗策略提供一种非侵入性辅助工具。
不适用。
