A deep learning radiomics model for predicting non-sentinel lymph node metastases in early-stage breast cancer patients

To develop and validate a deep learning radiomics model to predict non-sentinel lymph node (NSLN) metastases in early-stage breast cancer patients with 1–2 positive sentinel lymph node (SLN) metastases. This retrospective and prospective study encompassed 1,647 patients. Clinical, pathological information, and axillary ultrasound (AUS) findings, collected. Radiomic features of breast cancer lesions were extracted from the ultrasound images. We developed predictive models based on clinical factors alone (C model), clinical factors coupled with AUS (CA model), and clinical factors integrated with both AUS and radiomic features (CAR model). The predictive performance of each model was evaluated via the area under the curve (AUC), decision curve analysis (DCA), and calibration curve analysis. The AUC values for the C model, CA model and CAR model in the test cohort were 0.812, 0.850, and 0.994, respectively. Notably, the CAR model exhibited significantly superior predictive capability compared to both the C model and CA model. In subgroups analyses, the CAR model also achieved the optimal predictive performance. The DCA curve confirmed that the CAR model possessed significant clinical implications. The CAR model had the capability to predict NSLN metastases in early-stage breast cancer with 1–2 positive SLN metastases. Axillary lymph node dissection is controversial in patients with 1–2 positive sentinel lymph node metastases because non-sentinel lymph node is negative in some of these patients. In order to identify this group patients, this study built an algorithm with a quite large group of patients. This algorithm built with all valuable informantion of patients including clinical and pathological information, axillary ultrasound findings, as well as ultrasound radiomic features of breast cancer lesions. This algorithm is able to identify above-mentioned patients who would not benefit from axillary lymph node dissection with area under the curve as high as 0.994. The performance of our algorithm is superior to existing models such as MSKCC, Tenon and is robust across different subgroups.本研究旨在开发并验证一款深度学习放射组学模型，用于预测存在1~2枚阳性前哨淋巴结（sentinel lymph node, SLN）转移的早期乳腺癌患者的非前哨淋巴结（non-sentinel lymph node, NSLN）转移风险。 本研究为回顾性与前瞻性结合的队列研究，共纳入1647例患者。研究收集了患者的临床、病理资料及腋窝超声（axillary ultrasound, AUS）影像结果，并从超声图像中提取乳腺癌病灶的放射组学特征。本研究分别构建了仅基于临床因素的预测模型（C模型）、联合临床因素与腋窝超声特征的预测模型（CA模型），以及整合临床因素、腋窝超声特征与放射组学特征的预测模型（CAR模型）。采用受试者工作特征曲线下面积（area under the curve, AUC）、决策曲线分析（decision curve analysis, DCA）以及校准曲线分析，对各模型的预测性能进行评估。 在测试队列中，C模型、CA模型与CAR模型的AUC值分别为0.812、0.850及0.994。值得注意的是，CAR模型的预测性能显著优于C模型与CA模型；在亚组分析中，CAR模型同样取得了最优的预测效果。决策曲线分析结果证实，CAR模型具有显著的临床应用价值。 CAR模型可有效预测存在1~2枚阳性前哨淋巴结转移的早期乳腺癌患者的非前哨淋巴结转移风险。 对于存在1~2枚阳性前哨淋巴结转移的患者，腋窝淋巴结清扫术的临床价值尚存争议，因为此类患者中部分人的非前哨淋巴结并无转移。为精准甄别出这类无需接受腋窝淋巴结清扫的患者，本研究依托大样本量队列构建了一款算法模型。该模型整合了患者的临床与病理资料、腋窝超声影像结果，以及乳腺癌病灶的超声放射组学特征，其受试者工作特征曲线下面积可达0.994，可准确识别出前述无需从腋窝淋巴结清扫术中获益的患者。本研究构建的算法模型性能优于现有MSKCC、Tenon等模型，且在不同亚组中均表现出良好的稳健性。