Objective:To investigate the clinical value of the prediction models constructed by CT based imaging features and radiomics for World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading in pre-operative patients with T1 clear cell renal cell carcinoma (ccRCC).Methods:Ninety patients with ccRCC diagnosed at Hangzhou Hospital of Traditional Chinese Medicine from January 2016 to December 2023 were enrolled as the training set, and 43 patients diagnosed at the Sir Run Run Shaw Hospital from January 2017 to December 2018 were enrolled as the external validation set. According to the WHO/ISUP grading system, grades Ⅰ and Ⅱ were defined as the low grade group, and grades Ⅲ and Ⅳ were defined as the high grade group. In the training set, 64 patients were in the low grade group and 26 patients in the high grade group. In the external validation set, 33 patients were in the low grade group and 10 patients in the high grade group. The multivariate logistic regression was used to establish an imaging factor model based on CT imaging features in the training set. The 3-dimensional regions of interest were manually contoured at the cortical phase of enhanced CT, and the radiomics features were extracted. Linear correlation between features and L1 regularization were used for feature selection, and then linear support vector classification was used to construct the radiomics model. After that, a combined diagnostic model of nomogram combining the radiomics score and imaging factors was constructed using multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve was used to evaluate the effectiveness of each model. The Delong test was used for comparison of the areas under the ROC curve.Results:The imaging factor model, the radiomics model, and the combined diagnostic model of nomogram were successfully constructed to predict the WHO/ ISUP grading in stage T1 ccRCC. The AUC value of the imaging factor model in the training and validation sets was 0.742 (95% CI: 0.623-0.860) and 0.664 (95% CI: 0.448-0.879), respectively. The AUC values of the radiomics model in the two sets were 0.914 (95% CI: 0.844-0.983) and 0.879 (95% CI: 0.718-1.000), and of the combined diagnostic model of nomogram in the two sets were 0.929 (95% CI: 0.858-0.999) and 0.882 (95% CI: 0.710-1.000), respectively. The AUCs of the radiomics model and combined diagnostic model of nomogram were significantly higher than that of the imaging factor model (both P＜0.05). There was no statistical difference in the AUCs between the combined diagnostic model of nomogram and the radiomics model (both P＞0.05). Conclusion:The CT-based radiomics model and combined diagnostic model of nomogram incorporating radiomics signature and imaging features showed favorable predictive efficacy for the preoperative prediction of WHO/ISUP grading in stage T1 ccRCC.

Objective:To investigate the clinical value of the prediction models constructed by CT based imaging features and radiomics for World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading in pre-operative patients with T1 clear cell renal cell carcinoma (ccRCC).Methods:Ninety patients with ccRCC diagnosed at Hangzhou Hospital of Traditional Chinese Medicine from January 2016 to December 2023 were enrolled as the training set, and 43 patients diagnosed at the Sir Run Run Shaw Hospital from January 2017 to December 2018 were enrolled as the external validation set. According to the WHO/ISUP grading system, grades Ⅰ and Ⅱ were defined as the low grade group, and grades Ⅲ and Ⅳ were defined as the high grade group. In the training set, 64 patients were in the low grade group and 26 patients in the high grade group. In the external validation set, 33 patients were in the low grade group and 10 patients in the high grade group. The multivariate logistic regression was used to establish an imaging factor model based on CT imaging features in the training set. The 3-dimensional regions of interest were manually contoured at the cortical phase of enhanced CT, and the radiomics features were extracted. Linear correlation between features and L1 regularization were used for feature selection, and then linear support vector classification was used to construct the radiomics model. After that, a combined diagnostic model of nomogram combining the radiomics score and imaging factors was constructed using multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve was used to evaluate the effectiveness of each model. The Delong test was used for comparison of the areas under the ROC curve.Results:The imaging factor model, the radiomics model, and the combined diagnostic model of nomogram were successfully constructed to predict the WHO/ ISUP grading in stage T1 ccRCC. The AUC value of the imaging factor model in the training and validation sets was 0.742 (95% CI: 0.623-0.860) and 0.664 (95% CI: 0.448-0.879), respectively. The AUC values of the radiomics model in the two sets were 0.914 (95% CI: 0.844-0.983) and 0.879 (95% CI: 0.718-1.000), and of the combined diagnostic model of nomogram in the two sets were 0.929 (95% CI: 0.858-0.999) and 0.882 (95% CI: 0.710-1.000), respectively. The AUCs of the radiomics model and combined diagnostic model of nomogram were significantly higher than that of the imaging factor model (both P＜0.05). There was no statistical difference in the AUCs between the combined diagnostic model of nomogram and the radiomics model (both P＞0.05). Conclusion:The CT-based radiomics model and combined diagnostic model of nomogram incorporating radiomics signature and imaging features showed favorable predictive efficacy for the preoperative prediction of WHO/ISUP grading in stage T1 ccRCC.

Background: Gastroenteropancreatic neuroendocrine neoplasm (GEP-NEN) is a rare heterogeneous tumor. Liver metastasis seriously affects the prognosis of GEP-NEN. However, few tools are existed to predict GEP-NEN complicated with synchronous liver metastasis. Aims: To analyze the risk factors of synchronous liver metastasis in patients with GEP-NEN and establish a nomogram to predict synchronous liver metastasis in patients with GEP-NEN. Methods: A total of 10 973 pathologically confirmed patients with GEP-NEN from Jan. 2010 to Dec. 2017 were collected from SEER database and divided randomly into training set (n=7 511) and test set (n=3 462). Both groups were divided into liver metastasis group and non-liver metastasis group according to the occurrence of liver metastasis. Multifactorical logistic regression analysis was used to identify the risk factors of liver metastasis in patients with GEP-NEN. R software was used to establish and verify the nomogram of liver metastasis in GEP-NEN patients. Results: Liver metastasis was associated with gender, age, race, primary tumor site, degree of differentiation, tumor diameter, T3/4 stage, and lymph node metastasis in patients with GEP-NEN. The results of multivariate logistic regression analysis showed that primary tumor site (small intestine and pancreas), differentiation degree (poorly differentiated and undifferentiated), diameter of tumor ≥ 5 cm, T3/4 stage and lymph node metastasis were independent risk factors affecting liver metastasis in patients with GEP-NEN (P< 0.001). The concordance index of internal validation for nomogram was 0.838 (95% CI: 0.826-0.849), and the concordance index of external validation was 0.847 (95% CI: 0.829-0.864). Conclusions: GEP-NEN patients with primary tumor site in small intestine or pancreas, poor differentiation and undifferentiation, diameter of tumor ≥5 cm, T3/4 stage and lymph node metastasis are more likely to develop liver metastasis which suggested that such patients need to be alert for the occurrence of liver metastasis and need more aggressive treatment. The calibration curves fits are good for both the training and test sets, and can help clinicians to make individualized prediction for whether the GEP-NEN patient has synchronous liver metastasis at the initial diagnosis.