Multiparameter magnetic resonance imaging-based radiomics model for the preoperative prediction of lymph-vascular space invasion in cervical squamous carcinoma
Jia Yaju1,2, Yang Xiaotang1, Cui Yanfen1, Quan Shuai3, Hou Lina1
1Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University Shanxi Province Tumor Hospital, Taiyuan 030000, China; 2Department of Medical Imaging, Shanxi Medical University, Taiyuan 030000, China; 3GE Healthcare China, Shanghai 200000, China
Abstract：Objective This study aims to investigate the application value of a combined model based on the multiparameter magnetic resonance imaging (MRI) for preoperative prediction of lymph-vascular space invasion (LVSI) in cervical cancer. Methods A total of 168 patients with cervical squamous carcinoma who were pathologically diagnosed at the Shanxi Cancer Hospital from June 2016 to March 2019 were retrospectively enrolled in this study, with an average age of 22- 76 (52.0±10.1) years old, including 127 FIGO ⅠB cases and 41 FIGO ⅡA cases. All patients underwent a multiparametric pelvic MRI scan before the surgery and a radical hysterectomy combined with pelvic lymph node dissection was performed. Patients were divided into two groups, the training group (n=117) and the validation group (n=51) according to the random ratio of 7∶3. Volume regions of interest (VOIs) were manually delineated slice by slice on the T2 weighted images (T2WI), apparent diffusion coefficient (ADC), and enhanced T1 weighted images (cT1WI) images of each patient. Radiomics features were extracted from each patient. A three-step dimensionality reduction method based on the maximum relevance minimum redundancy (MRMR) and the least absolute shrinkage and selection operator (LASSO) regression methods were used for feature selection and radiomics signature building. The combined radiomics model, including the clinical risk factors and the abovementioned radiomics signature, was constructed via the multivariate logistic regression method, and the corresponding nomogram was constructed. The prediction performance was determined by the calibration, discrimination, and clinical usefulness. Results Postoperative pathological examination confirmed LVSI positive in 42 patients and negative in 126 patients. No significant differences were found for the general clinical information between the training and validation groups (all P values >0.05). Seven key radiomics features were obtained after feature selection based on the T2WI, ADC, and cT1WI, all of which were significantly associated with lymph-vascular space invasion (all P values <0.05). The area under the receiver operating characteristic curve (AUC) values of the three radiomics signatures derived from the independent sequence in the training group were 0.630 (95% confidence interval[CI] 0.557-0.698), 0.686 (95% CI 0.563-0.694), 0.761 (95% CI 0.702-0.818) and the corresponding AUC of the combined radiomics signature was 0.887 (95% CI 0. 842-0. 925), which had the best diagnostic efficacy and was validated in the validation group. The AUCs of radiomics nomogram that incorporated radiomics signatures and tumor differentiation degrees were 0.893 (95% CI 0.851-0.929) and 0.854 (95% CI 0.749-0.943) in the training and validation groups, respectively. The calibration curves showed good calibration performance, and when the risk threshold was 0.50-0.96, the net benefit of using a radiomics nomogram to predict LVSI was better than treating all patients as LVSI positive or LVSI negative as indicated by the decision curves. Conclusion Radiomics nomogram based on multiparameter MRI and clinical features has good predictive value for the LVSI status in patients with cervical cancer.
贾亚菊, 杨晓棠, 崔艳芬, 全帅, 侯丽娜. 基于多参数MR的影像组学融合模型术前预测宫颈鳞癌脉管浸润的应用价值[J]. 中华解剖与临床杂志, 2022, 27(11): 737-744.
Jia Yaju, Yang Xiaotang, Cui Yanfen, Quan Shuai, Hou Lina. Multiparameter magnetic resonance imaging-based radiomics model for the preoperative prediction of lymph-vascular space invasion in cervical squamous carcinoma. Chinese Journal of Anatomy and Clinics, 2022, 27(11): 737-744.
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