A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients

• Published in: BMC cancer 2023-01, Vol.23 (1), p.15-15, Article 15
• Main Authors: Liu, Shasha, Du, Siyao, Gao, Si, Teng, Yuee, Jin, Feng, Zhang, Lina
• Format: Article
• Language: English
• Subjects: Axillary lymph node; Breast cancer; Breast neoplasms; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - drug therapy; Breast Neoplasms - pathology; Care and treatment; DCE-MRI; Female; Humans; Lymph nodes; Lymph Nodes - diagnostic imaging; Lymph Nodes - pathology; Magnetic Resonance Imaging; Neoadjuvant chemotherapy; Neoadjuvant Therapy; Pathological complete response; Patient outcomes; Radiomics; Retrospective Studies
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-022-10496-5

The objective of this paper is to explore the value of a delta-radiomic model of the axillary lymph node (ALN) using dynamic contrast-enhanced (DCE) MRI for early prediction of the axillary pathological complete response (pCR) of breast cancer patients after neoadjuvant chemotherapy (NAC). A total of 120 patients with ALN-positive breast cancer who underwent breast MRI before and after the first cycle of NAC between October 2018 and May 2021 were prospectively included in this study. Patients were divided into a training (n = 84) and validation (n = 36) cohort based on the temporal order of their treatments. Radiomic features were extracted from the largest slice of targeted ALN on DCE-MRI at pretreatment and after one cycle of NAC, and their changes (delta-) were calculated and recorded. Logistic regression was then applied to build radiomic models using the pretreatment (pre-), first-cycle(1st-), and changes (delta-) radiomic features separately. A clinical model was also built and combined with the radiomic models. The models were evaluated by discrimination, calibration, and clinical application and compared using DeLong test. Among the three radiomic models, the ALN delta-radiomic model performed the best with AUCs of 0.851 (95% CI: 0.770-0.932) and 0.822 (95% CI: 0.685-0.958) in the training and validation cohorts, respectively. The clinical model yielded moderate AUCs of 0.742 (95% CI: 0.637-0.846) and 0.723 (95% CI: 0.550-0.896), respectively. After combining clinical features to the delta-radiomics model, the efficacy of the combined model (AUC = 0.932) in the training cohort was significantly higher than that of both the delta-radiomic model (Delong p = 0.017) and the clinical model (Delong p