Multiparametric magnetic resonance imaging-based radiomics model for the preoperative prediction of lymph node metastasis in cervical cancer
Hou Lina1, Cui Yanfen1, Guo Lingyun1, Ren Jialiang2, Li Dandan3
1Department of Radiology, Shanxi Province Tumor Hospital, Taiyuan 030000, China; 2GE Healthcare China, Beijing 100000, China; 3Department of Medical Imaging, Shanxi Medical University, Taiyuan 030000, China
Abstract：Objective This study aimed to investigate the value of the combined radiomics model on the basis of multiparametric MRI and clinical features to predict lymph node(LN) metastasis in patients with cervical cancer. Methods A total of 168 consecutive patients with cervical cancer in the Shanxi Province Tumor Hospital from June 2016 to March 2019 were enrolled in our retrospective study. The patients were divided into a training set of 115 cases and a validation set of 53 cases via the completely stochastic method at a ratio of 7∶3. The volume of interest was delineated manually and separately by two radiologists in MR imaging, and repeatability was assessed. LN were dichotomized in accordance with the pathological Results of the operation. The clinical and the imaging data were divided into corresponding groups. A total of 3 111 imaging features were extracted from T2-weighted image (T2WI), apparent diffusion coefficient (ADC), and contrast-enhanced T1-weighted image (cT1WI) for each patient. Four-step procedures, namely, the minimum redundancy-maximum relevance and the least absolute shrinkage and selection operator regression, were applied for the feature selection and radiomics signature building. Stratified analyses were also performed. 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 through its calibration, discrimination, and clinical usefulness.Results No significant difference in baseline data existed between the training and the validation groups (all P values>0.05). The radiomics signature derived from the combination of T2WI, ADC, and cT1W images, which was composed of six LN status-related features (i.e., three wavelet and three LoG features), was significantly associated with lymph node metastasis (all P values<0.05). The radiomic signature derived from single images yielded the area under the ROC curve (AUC) values of 0.763 and 0.829 in the training set, showing good prediction performance. The radiomics signature from the aforementioned sets yielded the highest AUC (0.859), thereby showing better prediction performance than that of the signatures derived from either of them alone in both sets, as validated in the validation cohort. The radiomics nomogram that incorporated the radiomics signature and the MRI-reported LN status, also showed good calibration and discrimination in both sets, with AUCs of 0.865 and 0.861, respectively. When the threshold probability was more than 10%, the use of the radiomics nomogram to predict LN metastasis provided a better net benefit than those of the scheme in which all patients are LN-positive + or the scheme in which all patients were LN-negative and the MRI-reported LN status. The decision curve analysis confirmed its clinical usefulness.Conclusions The proposed MRI-based radiomics nomogram has good performance in predicting lymph node metastasis and may be useful in supplementing morphological evaluation to determine LN status in patients with cervical cancer.
侯丽娜, 崔艳芬, 郭凌云, 任嘉粱, 李丹丹. 多参数MRI的影像组学融合模型在术前预测宫颈癌淋巴结转移的应用价值[J]. 中华解剖与临床杂志, 2020, 25(3): 213-220.
Hou Lina, Cui Yanfen, Guo Lingyun, Ren Jialiang, Li Dandan. Multiparametric magnetic resonance imaging-based radiomics model for the preoperative prediction of lymph node metastasis in cervical cancer. Chinese Journal of Anatomy and Clinics, 2020, 25(3): 213-220.
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2020, Vol. 25 
