Evaluating Tumor‐Infiltrating Lymphocytes in Breast Cancer Using Preoperative MRI‐Based Radiomics. Issue 3 (28th August 2021) - Bian, Tiantian; Wu, Zengjie; Lin, Qing; Mao, Yan; Wang, Haibo; Chen, Jingjing; Chen, Qianqian; Fu, Guangming; Cui, Chunxiao; Su, Xiaohui
Abstract : Background: Evaluating tumor‐infiltrating lymphocytes (TILs) in patients with breast cancer using radiomics has been rarely explored. Purpose: To establish a radiomics nomogram based on dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) for preoperatively evaluating TIL level. Study Type: Retrospective. Population: A total of 154 patients with breast cancer were divided into a training cohort ( N = 87) and a test cohort ( N = 67), who were further divided into low TIL (<50%) and high TIL (≥50%) subgroups according to the histopathological results. Field Strength/Sequence: 3.0 T; axial T2‐weighted imaging (fast spin echo), diffusion‐weighted imaging (spin echo‐echo planar imaging), and the volume imaging for breast assessment DCE sequence (gradient recalled echo). Assessment: A radiomics signature was developed from the training dataset and independent risk factors were selected by multivariate logistic regression to build a clinical model. A nomogram model was built by combining radiomics score and risk factors. The performance of the nomogram was assessed using calibration curves and decision curves. The area under the receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity were calculated. Statistical Tests: The least absolute shrinkage and selection operator, univariate and multivariate logistic regression analysis, t ‐tests and chi‐squared tests or Fisher's exact test, Hosmer–Lemeshow test, ROC analysis, and decision curve analysis were conducted. P < 0.05 was considered statistically significant. Results: The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (radiomics: area under the curve [AUC] 0.86; nomogram: AUC 0.88) and test (radiomics: AUC 0.83; nomogram: AUC 0.84) datasets compared with clinical model (training: AUC 0.76; test: AUC 0.72). The decision curve demonstrated that the nomogram model exhibited better performance than the clinical model, with a threshold probability between 0.15 and 0.9. Data Conclusion: The nomogram model based on preoperative MRI exhibited an excellent ability for the noninvasive evaluation of TILs in breast cancer. Level of Evidence: 4 Technical Efficacy Stage: 2