Table_1_Identification of the lymph node metastasis-related automated breast volume scanning features for predicting axillary lymph node tumor burden of invasive breast cancer via a clinical prediction model.docx

Breast cancer has become the malignant tumor with the highest incidence in women. Axillary lymph node dissection (ALND) is an effective method of maintaining regional control; however, it is associated with a significant risk of complications. Meanwhile, whether the patients need ALND or not is according to sentinel lymph node biopsy (SLNB). However, the false-negative results of SLNB had been reported. Automated breast volume scanning (ABVS) is a routine examination in breast cancer. A real-world cohort consisting of 245 breast cancer patients who underwent ABVS examination were enrolled, including 251 tumor lesions. The ABVS manifestations were analyzed with the SLNB results, and the ALND results for selecting the lymph node metastasis were related to ABVS features. Finally, a nomogram was used to construct a breast cancer axillary lymph node tumor burden prediction model. Breast cancer patients with a molecular subtype of luminal B type, a maximum lesion diameter of ≥5 cm, tumor invasion of the Cooper’s ligament, and tumor invasion of the nipple had heavy lymph node tumor burden. Molecular classification, tumor size, and Cooper’s ligament status were used to construct a clinical prediction model of axillary lymph node tumor burden. The consistency indexes (or AUC) of the training cohort and the validation cohort were 0.743 and 0.711, respectively, which was close to SLNB (0.768). The best cutoff value of the ABVS nomogram was 81.146 points. After combination with ABVS features and SLNB, the AUC of the prediction model was 0.889, and the best cutoff value was 178.965 points. The calibration curve showed that the constructed nomogram clinical prediction model and the real results were highly consistent. The clinical prediction model constructed using molecular classification, tumor size, and Cooper’s ligament status can effectively predict the probability of heavy axillary lymph node tumor burden, which can be the significant supplement to the SLNB. Therefore, this model may be used for individual decision-making in the diagnosis and treatments of breast cancer.

乳腺癌已成为女性发病率最高的恶性肿瘤。腋窝淋巴结清扫术（Axillary lymph node dissection, ALND）是维持区域病灶控制的有效手段，但该术式伴随显著的并发症风险。当前患者是否需要接受ALND，需依据前哨淋巴结活检术（Sentinel lymph node biopsy, SLNB）的结果判定，但现有研究已报道SLNB存在假阴性结果。自动乳腺容积扫描（Automated breast volume scanning, ABVS）是乳腺癌临床常规检查项目。本研究纳入了245例接受过ABVS检查的乳腺癌患者真实世界队列，共包含251个肿瘤病灶。研究人员对ABVS影像学表现与SLNB结果进行关联分析，并将与淋巴结转移相关的ALND结局与ABVS特征进行关联。最终通过列线图构建了乳腺癌腋窝淋巴结肿瘤负荷预测模型。当乳腺癌患者为管腔B型分子亚型、病灶最大直径≥5cm、肿瘤侵犯库珀韧带（Cooper’s ligament）或乳头时，其腋窝淋巴结肿瘤负荷较高。研究以分子分型、肿瘤直径及库珀韧带受累状态构建了腋窝淋巴结肿瘤负荷的临床预测模型。训练队列与验证队列的一致性指数（AUC）分别为0.743与0.711，与SLNB的0.768相近。该ABVS列线图的最佳截断值为81.146分。将ABVS特征与SLNB结果结合后，预测模型的AUC升至0.889，最佳截断值为178.965分。校准曲线显示，本研究构建的列线图临床预测模型与真实临床结果具有高度一致性。基于分子分型、肿瘤直径及库珀韧带状态构建的临床预测模型，可有效预测高腋窝淋巴结肿瘤负荷的发生概率，可作为SLNB的重要补充手段。因此，该模型有望为乳腺癌诊疗中的个体化决策提供参考依据。