Abstract:
ObjectiveTo investigate the value of T2WI combined with DCE-MRI radiomics features in the preoperative prediction of axillary lymph node metastasis of invasive breast cancer.
MethodsThe clinicopathological and MRI data of 168 patients with invasive breast cancer confirmed by surgical pathology were retrospectively analyzed. According to the pathological results, the patients were divided into the lymph node metastasis group(n=64) and non-lymph node metastasis group(n=104), and randomly divided into the training group(n=134) and verification group(n=34) in an 8:2 ratio. The ROI was manually delineated on T2WI and DCE sequences for image segmentation and image omics feature extraction. The Select K Best, LASSO regression and iterative screening features were used to reduce the dimensionality of high-dimensional omics features and retain the high associated features with axillary lymph node metastasis. The three imaging omics prediction models of T2WI, DCE and T2WI combined WITH DCE were established using the logistic regression. The area under the ROC curve(AUC) was used to evaluate the effectiveness of models, and the optimal model was used to generate a column chart.
ResultsThe AUC of T2WI, DCE, and T2WI combined with DCE in the training group was 0.75, 0.75, 0.80, respectively. The AUC of T2WI, DCE, and T2WI combined with DCE in the validation group was 0.75, 0.73 and 0.79, respectively. The predictive performance in T2WI combined with DCE predictive model was the best.
ConclusionsThe predictive model of T2WI combined with DCE has certain value in the preoperative prediction of axillary lymph node metastasis of invasive breast cancer. It can accurately and noninvasively predict the status of axillary lymph node metastasis.
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