Jinliang Niu

Radiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasms

To evaluate the efficiency of 2- and 3-class classification predictive tasks constructed from radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic (PK) protocol in discriminating among benign, borderline, and malignant ovarian tumors. One hundred and four ovarian lesions were evaluated using preoperative DCE-MRI. Radiomics features were extracted from 7 types of DCE-MR images. To explore the differential ability of radiomics between three types of ovarian tumors, two- and three-class classification tasks were established. The 2-class classification task was divided into three subtasks: benign vs. borderline (task A), benign vs. malignant (task B), and borderline vs. malignant (task C). For the 3-class classification task, 104 lesions were randomly divided into training (72 lesions) and validation (32 lesions) cohorts. The discrimination abilities of the radiomics signatures were established with the training cohort and tested with the independent validation cohort. The predictive performance of the task was evaluated by receiver operating characteristic (ROC) curve, calibration curve analysis, and decision curve analysis (DCA). For the 2-class classification task, the combination of PK radiomics signatures model (PK model) showed a good diagnostic ability with the highest area under the ROC curves (AUCs) of 0.899, 0.865, and 0.893 for tasks A, B, and C, respectively. Additionally, the 3-class classification task demonstrated a good discrimination performance with AUCs of 0.893, 0.944, and 0.891 for the benign, borderline, and malignant groups, respectively. Radiomics analysis based on the DCE-MRI PK protocol showed promise for discriminating among benign, borderline, and malignant ovarian tumors. • Two-class classification predictive task of DCE-MRI PK protocol enabled the classification of 3 categories of ovarian tumors through the pairwise comparison strategy with a perfect diagnostic ability. • Three-class classification predictive task maintained good performance to effectively judge each category of ovarian tumors directly.

Radiomics based on multisequence magnetic resonance imaging for the preoperative prediction of peritoneal metastasis in ovarian cancer

To develop a radiomics signature based on multisequence magnetic resonance imaging (MRI) to preoperatively predict peritoneal metastasis (PM) in ovarian cancer (OC). Eighty-nine patients with OC were divided into a training cohort including patients (n = 54) with a single lesion and a validation cohort including patients (n = 35) with bilateral lesions. Radiomics features were extracted from the T2-weighted images (T2WIs), fat-suppressed T2WIs, multi-b-value diffusion-weighted images (DWIs), and corresponding parametric maps. A radiomics signature and nomogram incorporating the radiomics signature and clinical predictors were developed and validated on the training and validation cohorts, respectively. The radiomics signature generated by 6 selected features showed a favorable discriminatory ability to predict PM in OC with an area under the curve (AUC) of 0.963 in the training cohort and an AUC of 0.928 in the validation cohort. The nomogram, comprising the radiomics signature, pelvic fluid, and CA-125 level, showed more favorable discrimination with an AUC of 0.969 in the training cohort and 0.944 in the validation cohort. Net reclassification index with values of 0.548 in the training cohort and 0.500 in the validation cohort. Radiomics signature based on multisequence MRI serves as an effective quantitative approach to predict PM in OC patients. A nomogram of radiomics signature and clinical predictors could further improve the prediction ability of PM in patients with OC. • Multisequence MRI-based radiomics showed a favorable discriminatory ability to predict PM in OC. • The nomogram incorporating the radiomics signature and clinical predictors was clinically useful to preoperatively predict PM in patients with OC.