Abstract
Background: Accurate prediction of axillary lymph node (ALN) involvement in early-stage breast cancer is important for determining appropriate axillary treatment and therefore avoiding unnecessary axillary surgery and complications. This study aimed to develop and validate an ultrasound radiomics nomogram for preoperative evaluation of the ALN burden. Methods: Data of 303 patients from Wuhan Tongji Hospital (training cohort) and 130 cases from Hunan Provincial Tumour Hospital (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomic features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. Then, the minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used to select ALN status-related features and construct the SWE and BMUS radiomic signatures. Proportional odds ordinal logistic regression was performed using the radiomic signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated the performance of the nomogram using C-index, calibration, and compared it with clinical model.Results: Multivariate analysis indicated that SWE signature, US-reported LN status and molecular subtype were independent risk factors associated with ALN status. The radiomics nomogram based on these variables showed good calibration and discrimination in the training set (overall C-index: 0.842; 95%CI, 0.773–0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765–0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥1)), it achieved C-index of 0.845 (95%CI, 0.777–0.914) for the training cohort and 0.817 (95%CI, 0.769–0.865) for the validation cohort. The tool could also discriminate between low (N + (1–2)) and heavy metastatic burden of ALN (N + (≥3)), with C-index of 0.827 (95%CI, 0.742–0.913) for the training cohort and 0.810 (95%CI, 0.755–0.864) for the validation cohort. Conclusions: The presented radiomics nomogram shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for preoperative decision-making.
MicroRNA-1291 Is Associated With Locoregional Metastases in Patients With Early-Stage Breast Cancer