Predictive Value of 18F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer

Background: To develop and validate a radiomics model based on F-18-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cNO); Methods: A total of 180 patients (mean age, 55 years; range, 31-82 years) with pathologically proven IDC and a preoperative F-18-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, t-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661-0.929 (mean AUC, 0.817), and the accuracy range was 65.3-93.9% (mean accuracy, 81.2%). The p-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative F-18-FDG PET/CT radiomic features.

Automated summary

A radiomics model based on F-18-FDG PET/CT images was developed to predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) preoperatively. The RF model showed the best prediction results among the four models tested.