Department of Cardiovascular Surgery, Xiangya Hospital, Central South University, Changsha, China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Front Endocrinol (Lausanne). 2022 Aug 5;13:881761. doi: 10.3389/fendo.2022.881761. eCollection 2022.
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（ALND）是维持区域控制的有效方法；然而，它与发生并发症的风险显著相关。同时，患者是否需要进行 ALND 取决于前哨淋巴结活检（SLNB）的结果。然而，SLNB 的假阴性结果已经有报道。自动乳腺容积扫描（ABVS）是乳腺癌的常规检查。本研究纳入了 245 例接受 ABVS 检查的乳腺癌患者的真实队列，共 251 个肿瘤病变。分析了 ABVS 表现与 SLNB 结果的关系，以及与选择淋巴结转移相关的 ALND 结果。最后，构建了一个列线图预测模型来预测乳腺癌腋窝淋巴结肿瘤负担。具有 luminal B 型分子亚型、最大病变直径≥5cm、肿瘤侵犯 Coopers 韧带和肿瘤侵犯乳头的乳腺癌患者具有较重的淋巴结肿瘤负担。采用分子分类、肿瘤大小和 Cooper 韧带状态构建腋窝淋巴结肿瘤负担的临床预测模型。训练队列和验证队列的一致性指数（或 AUC）分别为 0.743 和 0.711，与 SLNB（0.768）接近。ABVS 列线图的最佳截断值为 81.146 分。结合 ABVS 特征和 SLNB 后，预测模型的 AUC 为 0.889，最佳截断值为 178.965 分。校准曲线表明，所构建的列线图临床预测模型与真实结果高度一致。使用分子分类、肿瘤大小和 Cooper 韧带状态构建的临床预测模型可有效预测腋窝淋巴结肿瘤负担较重的概率，可作为 SLNB 的重要补充。因此，该模型可能用于乳腺癌的诊断和治疗中的个体化决策。
