Prediction of axillary lymph node pathological complete response to neoadjuvant therapy using nomogram and machine learning methods

Tianyang Zhou; Mengting Yang; Mijia Wang; Linlin Han; Hong Chen; Nan Wu; Shan Wang; Xinyi Wang; Yuting Zhang; Di Cui; Feng Jin; Pan Qin; Jia Wang

doi:10.3389/fonc.2022.1046039

Prediction of axillary lymph node pathological complete response to neoadjuvant therapy using nomogram and machine learning methods

Front Oncol. 2022 Oct 24:12:1046039. doi: 10.3389/fonc.2022.1046039. eCollection 2022.

Authors

Tianyang Zhou¹, Mengting Yang², Mijia Wang¹, Linlin Han³, Hong Chen¹, Nan Wu¹, Shan Wang¹, Xinyi Wang¹, Yuting Zhang¹, Di Cui⁴, Feng Jin⁵, Pan Qin², Jia Wang¹

Affiliations

¹ Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
² Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
³ Health Management Center, The Second Hospital of Dalian Medical University, Dalian, China.
⁴ Information Center, The Second Hospital of Dalian Medical University, Dalian, China.
⁵ Department of Breast Surgery, The First Affiliated Hospital of China Medical University, Shenyang, China.

Abstract

Purpose: To determine the feasibility of predicting the rate of an axillary lymph node pathological complete response (apCR) using nomogram and machine learning methods.

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.

Results: 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; p=0.018), LNS changes (pre-NAT LNS - post-NAT LNS; OR, 2.790; 95% CI, 1.190-6.544; p=0.018), N stage (OR, 0.496; 95% CI, 0.269-0.915; p=0.025), and HER2 status (OR, 2.244; 95% CI, 1.147-4.392; p=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).

Conclusion: 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.

Keywords: axillary lymph node pathological complete response; breast cancer; machine learning; neoadjuvant therapy; nomogram.