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| Nomogram for prediction of lymphovascular invasion in breast cancer based on DCE-MRI radiomics and conventional MRI features |
| WU Tianbin1, ZHANG Youjian2, LIN Guihan1, CHEN Weiyue1, CHEN Chunmiao1,CHENG Xue1, JI Jiansong1. |
| 1.Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University, Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui 323000,China; 2.Huiying Medical Technology (Beijing) Co, Ltd, Beijing 100089, China |
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| Cite this article: |
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WU Tianbin,ZHANG Youjian,LIN Guihan, et al. Nomogram for prediction of lymphovascular invasion in breast cancer based on DCE-MRI radiomics and conventional MRI features[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2022, 52(11): 882-888.
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Abstract Objective: To develop a nomogram based on arterial contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and conventional MRI features for preoperative prediction of lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC). Methods: A retrospective analysis was made on 300 patients with IBC confirmed by postoperative pathology between July 2016 and May 2021, who were randomly divided into the training (n=238) and validation group (n=62) according to ratio of 8:2. Lesion areas in second-stage DCE-MRI images of all patients were manually segmented and radiomics features were extracted.
Variance threshold, select k best, and LASSO regression were used to screen radiomics features and calculate the radiomics score (rad-score). Logistic regression analysis was used to screen conventional MRI features to establish conventional feature model, while a joint prediction model was built based on radiomics and independent risk factors in conventional MRI features, and a corresponding nomogram was drawn. The performance of the model was evaluated using receiver operating characteristic (ROC) and calibration curves, and the clinical value of the model was assessed using decision curve analysis. Results: A total of 1409 radiomics features were extracted,and 15 radiomics features were screened to correlate with the LVI status of breast cancer, and were involved in the calculation of rad-score values. Among all conventional MRI features, the largest tumor diameter (OR=1.743,P<0.001) and burr sign (OR=6.304, P<0.001) were independent risk factors for LVI positive. In the training and validation group, the area under the ROC curves (AUCs) of the radiomics model was 0.831 and 0.811,respectively; the AUCs of the conventional feature model was 0.779 and 0.770, respectively; and the AUCs of the joint prediction model was improved to 0.889 (95%CI=0.844-0.934) and 0.856 (95%CI=0.759-0.952). The calibration curve showed that the predicted value of the nomogram was in good agreement with the actual value, and the decision curve showed that the nomogram had a higher clinical value. Conclusion: The constructed nomogram based on DCE-MRI radiomics and conventional MRI features has good application value for preoperative prediction of LVI status in IBC patients, and can provide reference for clinical treatment.
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Received: 30 July 2022
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ZHOU Yongjin, GAO Ruijie, JIANG Chunyan, DENG Jingjing, XIA Shuiwei, SHEN Shaobo, WANG Zufei, JI Jiansong. CT-based radiomics nomogram to differentiate non-functional pancreatic neuroendocrine tumors from solid pseudopapillary tumors[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2022, 52(8): 645-651,656. |
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