Zhou Tianyang, Yang Mengting, Wang Mijia, Han Linlin, Chen Hong, Wu Nan, Wang Shan, Wang Xinyi, Zhang Yuting, Cui Di, Jin Feng, Qin Pan, Wang Jia
Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
Front Oncol. 2022 Oct 24;12:1046039. doi: 10.3389/fonc.2022.1046039. eCollection 2022.
To determine the feasibility of predicting the rate of an axillary lymph node pathological complete response (apCR) using nomogram and machine learning methods.
A total of 247 patients with early breast cancer (eBC), who underwent neoadjuvant therapy (NAT) were included retrospectively. We compared pre- and post-NAT ultrasound information and calculated the maximum diameter change of the primary lesion (MDCPL): [(pre-NAT maximum diameter of primary lesion - post-NAT maximum diameter of preoperative primary lesion)/pre-NAT maximum diameter of primary lesion] and described the lymph node score (LNS) (1): unclear border (2), irregular morphology (3), absence of hilum (4), visible vascularity (5), cortical thickness, and (6) aspect ratio <2. Each description counted as 1 point. Logistic regression analyses were used to assess apCR independent predictors to create nomogram. The area under the curve (AUC) of the receiver operating characteristic curve as well as calibration curves were employed to assess the nomogram's performance. In machine learning, data were trained and validated by random forest (RF) following Pycharm software and five-fold cross-validation analysis.
The mean age of enrolled patients was 50.4 ± 10.2 years. MDCPL (odds ratio [OR], 1.013; 95% confidence interval [CI], 1.002-1.024; =0.018), LNS changes (pre-NAT LNS - post-NAT LNS; OR, 2.790; 95% CI, 1.190-6.544; =0.018), N stage (OR, 0.496; 95% CI, 0.269-0.915; =0.025), and HER2 status (OR, 2.244; 95% CI, 1.147-4.392; =0.018) were independent predictors of apCR. The AUCs of the nomogram were 0.74 (95% CI, 0.68-0.81) and 0.76 (95% CI, 0.63-0.90) for training and validation sets, respectively. In RF model, the maximum diameter of the primary lesion, axillary lymph node, and LNS in each cycle, estrogen receptor status, progesterone receptor status, HER2, Ki67, and T and N stages were included in the training set. The final validation set had an AUC value of 0.85 (95% CI, 0.74-0.87).
Both nomogram and machine learning methods can predict apCR well. Nomogram is simple and practical, and shows high operability. Machine learning makes better use of a patient's clinicopathological information. These prediction models can assist surgeons in deciding on a reasonable strategy for axillary surgery.
使用列线图和机器学习方法确定预测腋窝淋巴结病理完全缓解（apCR）率的可行性。
回顾性纳入247例接受新辅助治疗（NAT）的早期乳腺癌（eBC）患者。我们比较了NAT前后的超声信息，并计算了原发灶最大直径变化（MDCPL）：[（NAT前原发灶最大直径 - NAT后术前原发灶最大直径）/NAT前原发灶最大直径]，并描述了淋巴结评分（LNS）（1：边界不清（2分）、形态不规则（3分）、无门部（4分）、可见血管（5分）、皮质厚度及（6分）纵横比<2。每项描述计1分）。采用逻辑回归分析评估apCR的独立预测因素以创建列线图。采用受试者工作特征曲线的曲线下面积（AUC）以及校准曲线评估列线图的性能。在机器学习中，数据通过Pycharm软件下的随机森林（RF）进行训练和验证，并进行五折交叉验证分析。
入组患者的平均年龄为50.4±10.2岁。MDCPL（比值比[OR]，1.013；95%置信区间[CI]，1.002 - 1.024；P = 0.018）、LNS变化（NAT前LNS - NAT后LNS；OR，2.790；95% CI，1.190 - 6.544；P = 0.018）、N分期（OR，0.496；95% CI，0.269 - 0.915；P = 0.025）和HER2状态（OR，2.244；95% CI，1.147 - 4.392；P = 0.018）是apCR的独立预测因素。训练集和验证集列线图的AUC分别为0.74（95% CI，0.68 - 0.81）和0.76（95% CI，0.63 - 0.90）。在RF模型中，训练集纳入了每个周期的原发灶最大直径、腋窝淋巴结及LNS、雌激素受体状态、孕激素受体状态、HER2、Ki67以及T和N分期。最终验证集的AUC值为0.85（95% CI，0.74 - 0.87）。
列线图和机器学习方法均能较好地预测apCR。列线图简单实用，操作性强。机器学习能更好地利用患者的临床病理信息。这些预测模型可协助外科医生制定合理的腋窝手术策略。
