Feasibility of an ADC-based radiomics model for predicting pelvic lymph node metastases in patients with stage?IB-IIA cervical squamous cell carcinoma.
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ABSTRACT: OBJECTIVES:To investigate the prediction value of a radiomics model based on apparent diffusion coefficient (ADC) maps for pelvic lymph node metastasis (PLNM) in patients with stage?IB-IIA cervical squamous cell carcinoma (CSCC). METHODS:A total of 153 stage?IB-IIA CSCC patients who underwent preoperative MRI including DWI from January 2015 to October 2017 were retrospectively studied and divided into a training cohort ( n = 102) and a validation cohort ( n = 51). Radiomics features were extracted from the ADC maps. The one-way ANOVA method, Mann-Whitney U test and Pearson's correlation analysis were used for selecting radiomics features. Logistic regression analyses were used to develop the model. ROC analyses were used to evaluate the prediction performance of the model. RESULTS:Clinical stage, tumor diameter, and MR-reported lymph node (LN) status were significantly associated with LN status ( p < 0.05 for both the training and validation cohorts). The radiomics model, which incorporated clinical stage, MR-reported LN status, and grey-level non-uniformity, showed good predictive performance in the training group (AUC 0.864; 95%?CI, 0.782 - 0.924) and the validation group (AUC 0.870; 95%?CI, 0.747 - 0.948). The performance of the radiomics model was significantly better than that of each predictive factor alone. CONCLUSION:The presented radiomics model, a non-invasive preoperative prediction tool, has the potential to have more predictive efficacy than clinical and radiological factors for differentiating between metastatic and non-metastatic lymph nodes. ADVANCES IN KNOWLEDGE:A radiomics model derived from the ADC maps of primary lesions demonstrated good performance for predicting PLNM in stage IB-IIA CSCC patients and may help to improve clinical decision-making.
SUBMITTER: Yu YY
PROVIDER: S-EPMC6580913 | biostudies-literature | 2019 May
REPOSITORIES: biostudies-literature
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