Radiomics and Ki-67 index predict survival in clear cell renal cell carcinoma.

OBJECTIVE: To develop and validate predictive models based on Ki-67 index, radiomics, and Ki-67 index combined with radiomics for survival analysis of patients with clear cell renal cell carcinoma. METHODS: This study enrolled 148 patients who were pathologically diagnosed as ccRCC between March 2010 and December 2018 at our institute. All tissue sections were collected and immunohistochemical staining was performed to calculate Ki-67 index. All patients were randomly divided into the training and validation sets in a 7:3 ratio. Regions of interests (ROIs) were segmented manually. Radiomics features were selected from ROIs in unenhanced, corticomedullary, and nephrographic phases. Multivariate Cox models based on the Ki-67 index and radiomics and univariate Cox models based on the Ki-67 index or radiomics alone were built; the predictive power was evaluated by the concordance (C)-index, integrated area under the curve, and integrated Brier Score. RESULTS: Five features were selected to establish the prediction models of radiomics and combined model. The C-indexes of Ki-67 index model, radiomics model, and combined model were 0.741, 0.718, and 0.782 for disease-free survival (DFS); 0.941, 0.866, and 0.963 for overall survival, respectively. The predictive power of combined model was the best in both training and validation sets. CONCLUSION: The survival prediction performance of combined model was better than Ki-67 model or radiomics model. The combined model is a promising tool for predicting the prognosis of patients with ccRCC in the future. ADVANCES IN KNOWLEDGE: Both Ki-67 and radiomics have showed giant potential in prognosis prediction. There are few studies to investigate the predictive ability of Ki-67 combined with radiomics. This study intended to build a combined model and provide a reliable prognosis for ccRCC in clinical practice.

Nomogram Based on Preoperative Absolute Lymphocyte Count to Predict Local Recurrence in Patients with Non-Small Cell Lung Cancer After Microwave Ablation.

Purpose: This study aims to investigate the prognostic value of preoperative absolute lymphocyte count (preALC) for non-small cell lung cancer (NSCLC) after microwave ablation (MWA) and build a combined nomograph with clinical features to predict the local recurrence. Patients and Methods: A total of 118 NSCLC patients who underwent microwave ablation were enrolled in this study. The median local recurrence-free survival (LRFS) was 35.5 months. Independent prognostic factors obtained by multivariate analysis were included in the prediction model. The prognostic value of the model was assessed by the area under the time-dependent receiver operating characteristic curve (T-AUC). Results: Histological subtype and preALC were independent risk factors for local relapse-free survival. According to the time-dependent receiver operating characteristic curve (T-ROC), the optimal cut-off value of preALC was 1.965×109/L, the sensitivity was 0.837, and the specificity was 0.594. The area under the T-ROC curve (AUC) of preALC was 0.703. To establish a nomogram to predict the local recurrence rate of NSCLC after MWA based on the prognostic factors revealed by Cox regression. Conclusion: Preoperative lymphocyte count reduction is associated with poor prognosis of NSCLC. The nomogram model combined with preALC can provide a good individualized prediction of local recurrence after microwave ablation.

Effect of visceral adipose tissue on the accuracy of preoperative T-staging of gastric cancer.

BACKGROUND: Due to the anatomical characteristics of the tumor and the specific variables of the patients, the accuracy of preoperative T-staging of gastric cancer needs to be further improved. This study investigated the effect of visceral adipose tissue (VAT) on the accuracy of clinical T-staging of gastric cancer. METHODS: The clinical data of 455 patients who underwent gastrectomy from January 2013 to December 2018 were analyzed retrospectively. Taking the postoperative pathological results as the reference standard, the patients were divided into accurate staging group and mistaken staging group according to the comparison of clinical T stage (cT) and pathological T stage (pT). The individual characteristics of the two groups were compared, including visceral fat content at L2/L3 level calculated on computed tomography, age, sex, tumor size, tumor location (cardia, stomach body, stomach antrum), and degree of differentiation. Multivariate logistic regression was used to determine the independent factors affecting the accuracy of cT staging. RESULTS: Among the 455 patients, 355 patients (78.0 %) had accurate preoperative cT staging and 100 patients (22.0 %) had inaccurate preoperative cT staging. The average area of VAT in the accurate staging group was (129.8 ± 72.6) cm2 and that in the mistaken staging group was (74.6 ± 61.6) cm2 (P < 0.001). The optimal cut-off value of VAT was 97.8 cm2 calculated according to the Yoden index. Multivariate logistic regression analysis showed that VAT, tumor location and tumor size were independent predictors of cT accuracy. CONCLUSIONS: Patients with lower visceral fat content (<97.8 cm2) based on L2/L3 level had a higher risk of false staging in preoperative clinical T staging.

A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas.

PURPOSE: To establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas. MATERIALS AND METHODS: A total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation. RESULTS: The radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p < 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram. CONCLUSION: Our CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.