Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

Abstract

Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (18F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.

Feasibility of sentinel lymph node biopsy omission after integration of 18F-FDG dedicated lymph node PET in early breast cancer: a prospective phase II trial

“…In our previous study, we developed a machine learning model integrating LymphPET and clinical characteristics for the prediction of axillary LN status in cT1-2N0-1M0 breast cancer. The performance of this integrated model showed an NPV of 96.88% in the cN0 subgroup; therefore, we believe that the use of a machine learning integrated model can greatly improve the true positive and true negative rates of identifying clinical axillary LN status in early-stage BC 22 .…”

PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review

“…They found that SVM outperformed visual assessment for this purpose (0.77 vs. 0.89, 0.57 vs. 0.94, 0.77 vs. 0.77 and 0.71 vs. 0.85, for AUC, sensitivity, specificity, and accuracy, respectively). Cheng et al [ 42 ] aimed to develop a ML model combining dbPET features and clinical variables to predict pathological involvement of ALN in 420 early-stage BC. The AUC of the integrated model, which included six clinical-pathological factors and five dbPET radiomics parameters, was 0.94 in the training set (n = 203) and 0.93 in the validation set (n = 87) ( p < 0.05 in both cases).…”

Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer

“…Many scholars had applied Ultrasound, MRI, and PET-CT images to carry out radiomics studies [23][24][25], involving radiomics and clinical features to lymph node metastasis of breast cancer. In particular, LNM radiomics studies of tumors (pancreatic ductal adenocarcinoma, gastric cancer, cervical cancer, etc.)…”