Establishment and Validation of The Axillary Lymph Node Burden Using Cone-Beam Computerized Tomography and Ultrasound-Based Prediction Models in T1-2 Breast Cancer Patients

Background: This study aimed to develop and validate models to preoperatively predict the risk of the lymph node (LN) burden based on the Z0011 clinical trial to assist breast cancer surgical decision-making. Methods: Data on 1394 consecutive patients who presented at Sun Yat-sen University Cancer Center for Cone-beam breast computerized tomography (CBBCT) examinations between April 3, 2019, and July 17, 2020, were retrospectively collected. 387 patients who met the inclusion criteria were included and randomly divided into training and validation cohorts. Clinical-pathological information of all patients was recorded, and images were reinterpreted in this study. A bidirectional stepwise method followed by multi-variable analysis was used to incorporate preoperative features and build optimal model sets with the training cohort for prediction of N 0 versus N + and N ＜3 versus N ≥3 . Results: The ROC curves of two models were generated with the training cohort, and their calibration abilities were estimated using 1000 bootstrap resamples. The bias-corrected C-index of the models were 0.779 (95% CI, 0.752–0.793) in model one and 0.809 (95% CI, 0.794–0.833) in model two for the training cohort. Decision curves and clinical impact curves were plotted to evaluate prediction performance for further clinical application. Delong’s test showed comparable performance of both cohorts. Conclusions: Our models were developed as reliable and noninvasive tools for the preoperative prediction of nodal status, and we hope that they can serve as useful tools for the early planning of treatment strategies for breast cancer patients.