Ling Long

Tumor Stiffness Measurement at Multifrequency MR Elastography to Predict Lymphovascular Space Invasion in Endometrial Cancer

A diagnostic model combining tumor stiffness measured at MR elastography with clinical-radiologic variables showed the best performance for predicting lymphovascular space invasion in participants with endometrial cancer.

MRI-based traditional radiomics and computer-vision nomogram for predicting lymphovascular space invasion in endometrial carcinoma

To determine the capabilities of MRI-based traditional radiomics and computer-vision (CV) nomogram for predicting lymphovascular space invasion (LVSI) in patients with endometrial carcinoma (EC). A total of 184 women (mean age, 52.9±9.0 [SD] years; range, 28-82 years) with EC were retrospectively included. Traditional radiomics features and CV features were extracted from preoperative T2-weighted and dynamic contrast-enhanced MR images. Two models (Model 1, the radiomics model; Model 2, adding CV radiomics signature into the Model 1) were built. The performance of the models was evaluated by the area under the curve (AUC) of the receiver operator characteristic (ROC) in the training and test cohorts. A nomogram based on clinicopathological metrics and radiomics signatures was developed. The predictive performance of the nomogram was assessed by AUC of the ROC in the training and test cohorts. For predicting LVSI, the AUC values of Model 1 in the training and test cohorts were 0.79 (95% confidence interval [CI]: 0.702-0.889; accuracy: 65.9%; sensitivity: 88.8%; specificity: 57.8%) and 0.75 (95% CI: 0.585-0.914; accuracy: 69.5%; sensitivity: 85.7%; specificity: 62.5%), respectively. The AUC values of Model 2 in the training and test cohorts were 0.93 (95% CI: 0.875-0.991; accuracy: 94.9%; sensitivity: 91.6%; specificity: 96.0%) and 0.81 (95% CI: 0.666-0.962; accuracy: 71.7%; sensitivity: 92.8%; specificity: 62.5%), respectively. The discriminative ability of Model 2 was significantly improved compared to Model 1 (Net Reclassification Improvement [NRI]=0.21; P=0.04). Based on histologic grade, FIGO stage, Rad-score and CV-score, AUC values of the nomogram to predict LVSI in the training and test cohorts were 0.98 (95% CI: 0.955-1; accuracy: 91.6%; sensitivity: 91.6%; specificity: 96.0%) and 0.92 (95% CI: 0.823-1; accuracy: 91.3%; sensitivity: 78.5%; specificity: 96.8%), respectively. MRI-based traditional radiomics and computer-vision nomogram are useful for preoperative risk stratification in patients with EC and may facilitate better clinical decision-making.

Risk factors associated with overall survival in patients with cervical cancer: a prospective cohort study in Western China comparing random survival forest and Cox proportional hazards models

Cervical cancer (CCa) significantly affects female fertility and quality of life. This study aimed to construct and validate a random survival forest (RSF) model to identify the factors that affect the overall survival (OS) in patients with CCa in China and compare its performance with that of the Cox proportional hazards model (Cox model). Data on CCa patients were collected from Chongqing University Cancer Hospital. The performance and discrimination ability of the models were evaluated via the C-index, integrated Brier score (IBS), accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The Kaplan-Meier (K-M) survival curve was used to analyze the difference in OS between patients with high and low risk predicted by RSF model. A total of 3,982 patients were included in this study. Comparing to Cox model, the RSF model ranked important variables and identified radiotherapy (RT) as an important treatment measure. A comprehensive analysis of the evaluation indices confirmed that the RSF model outperformed the Cox model (IBS: 0.152 vs. 0.162, C-index: 0.863 vs. 0.764). The RSF model metrics for the validation cohort (VC) were as follows: 1-, 3-, and 5-year AUC (0.908, 0.884, and 0.869), sensitivity (0.746), specificity (0.825), and accuracy (0.808). The OS of low-risk patients predicted by RSF was greater than that of high-risk patients. The RSF model demonstrated excellent discrimination, calibrated predictions, and stratified risk for CCa patients. Furthermore, it outperformed the Cox model in predicting risks, thus enabling the delivery of personalised treatment and follow-up strategies.