Evaluation of Lymphatic Vessel Invasion Determined by D2-40 Using Preoperative MRI-Based Radiomics for Invasive Breast Cancer

Zengjie Wu; Qing Lin; Hongming Song; Jingjing Chen; Guanqun Wang; Guangming Fu; Chunxiao Cui; Xiaohui Su; Lili Li; Tiantian Bian

doi:10.1016/j.acra.2022.11.024

Evaluation of Lymphatic Vessel Invasion Determined by D2-40 Using Preoperative MRI-Based Radiomics for Invasive Breast Cancer

Acad Radiol. 2023 Nov;30(11):2458-2468. doi: 10.1016/j.acra.2022.11.024. Epub 2022 Dec 29.

Authors

Affiliations

¹ Department of Radiology, the Affiliated Hospital of Qingdao University, Shandong, China.
² Breast Disease Center, the Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao 266000, Shandong, China.
³ Department of Pathology, the Affiliated Hospital of Qingdao University, Shandong, China.
⁴ Breast Disease Center, the Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao 266000, Shandong, China.. Electronic address: tiantianbian5001@qdu.edu.cn.

PMID: 36586760
DOI: 10.1016/j.acra.2022.11.024

Abstract

Rationale and objectives: Preoperative prediction of LVI status can facilitate personalized therapeutic planning. This study aims to investigate the efficacy of preoperative MRI-based radiomics for predicting lymphatic vessel invasion (LVI) determined by D2-40 in patients with invasive breast cancer.

Materials and methods: A total of 203 patients with pathologically confirmed invasive breast cancer, who underwent preoperative breast MRI, were retrospectively enrolled and randomly assigned to the following cohorts: training cohort (n=141) and test cohort (n=62). Then, univariate and multivariate logistic regression were performed to select independent risk factors and build a clinical model. Afterwards, least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select predictive features extracted from the early and delay enhancement dynamic contrast-enhanced (DCE)-MRI images, and a radiomics signature was established. Subsequently, a nomogram model was constructed by incorporating the radiomics score and risk factors. Receiver operating characteristic curves were performed to determine the performance of various models. The efficacy of the various models was evaluated using calibration and decision curves.

Results: Fourteen radiomics features were selected to construct the radiomics model. The size of the lymph node was identified as an independent risk factor of the clinical model. The nomogram model demonstrated the best calibration and discrimination performance in both the training and test cohorts, with an area under the curve of 0.873 (95% confidence interval [CI]: 0.807-0.923) and 0.902 (95% CI: 0.800-0.963), respectively. The decision curve illustrated that the nomogram model added more net benefits, when compared to the radiomics signature and clinical model.

Conclusion: The nomogram model based on preoperative DCE-MRI images exhibits satisfactory efficacy for the noninvasive prediction of LVI determined by D2-40 in invasive breast cancer.

Keywords: Breast cancer; D2-40; Lymphatic vessel invasion; MRI; Radiomics.