Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer

• Published in: European radiology 2022-04, Vol.32 (4), p.2313-2325
• Main Authors: Jiang, Meng, Li, Chang-Li, Luo, Xiao-Mao, Chuan, Zhi-Rui, Chen, Rui-Xue, Tang, Shi-Chu, Lv, Wen-Zhi, Cui, Xin-Wu, Dietrich, Christoph F.
• Format: Article
• Language: English
• Subjects: Algorithms; Axilla - pathology; Breast; Breast cancer; Breast Neoplasms - pathology; Calibration; Decision analysis; Decision making; Diagnostic Radiology; Elasticity Imaging Techniques; Feature extraction; Female; Humans; Imaging; Internal Medicine; Interventional Radiology; Lymph nodes; Lymph Nodes - diagnostic imaging; Lymph Nodes - pathology; Lymphatic system; Medicine; Medicine & Public Health; Metastases; Neuroradiology; Nomograms; Patients; Performance evaluation; Radiology; Radiomics; Redundancy; Retrospective Studies; Risk analysis; Risk factors; Shear; Training; Ultrasonic imaging; Ultrasound
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-021-08330-w

Objectives To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer. Methods Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status–related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration. Results SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (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 a 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 ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742–0.913) in the training cohort and 0.810 (95%CI, 0.755–0.864) in the validation cohort. Conclusion The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making. Key Points • Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy. • This multicentre retrospective study showed that radiomics nomogram based on shear - wave elastography provides incremental information for risk stratification. • Treatment can be given with more precision based on the model.