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Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study.

ABSTRACT: BACKGROUND:To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients. METHODS:In total, 160 surgically resected patients were retrospectively analyzed, and seven predictive models were constructed. Three radiomics predictive models were built from radiomics features based on arterial (A), venous (V) and combination of two phase (A?+?V) images. Then, three Radscores (A-Radscore, V-Radscore and A?+?V-Radscore) were obtained. Another four predictive models were constructed by the three Radscores and clinical risk factors through multivariate logistic regression. A nomogram was developed to predict LVI by incorporating A?+?V-Radscore and clinical risk factors. Kaplan-Meier curve and log-rank test were utilized to analyze the outcome of LVI. RESULTS:Radiomics related to tumor size and intratumoral inhomogeneity were the top-ranked LVI predicting features. The related Radscores showed significant differences according to LVI status (P?

SUBMITTER: Chen X

PROVIDER: S-EPMC7132895 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study.

Chen Xiaofeng X Yang Zhiqi Z Yang Jiada J Liao Yuting Y Pang Peipei P Fan Weixiong W Chen Xiangguang X

Cancer imaging : the official publication of the International Cancer Imaging Society 20200405 1

<h4>Background</h4>To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients.<h4>Methods</h4>In total, 160 surgically resected patients were retrospectively analyzed, and seven predictive models were constructed. Three radiomics predictive models were built from radiomics features based on arterial (A), venous (V) and combination of two phase (A + V) images. Then, thre ...[more]

PMID: 32248822

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Project description:ObjectivesTo develop a radiomics model based on contrast-enhanced CT (CECT) to predict the lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC) and provide decision-making support for clinicians.Patients and methodsThis retrospective study enrolled 334 patients with surgically resected and pathologically confirmed ESCC, including 96 patients with LVI and 238 patients without LVI. All enrolled patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3, with the training cohort containing 234 patients (68 patients with LVI and 166 without LVI) and the testing cohort containing 100 patients (28 patients with LVI and 72 without LVI). All patients underwent preoperative CECT scans within 2 weeks before operation. Quantitative radiomics features were extracted from CECT images, and the least absolute shrinkage and selection operator (LASSO) method was applied to select radiomics features. Logistic regression (Logistic), support vector machine (SVM), and decision tree (Tree) methods were separately used to establish radiomics models to predict the LVI status in ESCC, and the best model was selected to calculate Radscore, which combined with two clinical CT predictors to build a combined model. The clinical model was also developed by using logistic regression. The receiver characteristic curve (ROC) and decision curve (DCA) analysis were used to evaluate the model performance in predicting the LVI status in ESCC.ResultsIn the radiomics model, Sphericity and gray-level non-uniformity (GLNU) were the most significant radiomics features for predicting LVI. In the clinical model, the maximum tumor thickness based on CECT (cThick) in patients with LVI was significantly greater than that in patients without LVI (P<0.001). Patients with LVI had higher clinical N stage based on CECT (cN stage) than patients without LVI (P<0.001). The ROC analysis showed that both the radiomics model (AUC values were 0.847 and 0.826 in the training and testing cohort, respectively) and the combined model (0.876 and 0.867, respectively) performed better than the clinical model (0.775 and 0.798, respectively), with the combined model exhibiting the best performance.ConclusionsThe combined model incorporating radiomics features and clinical CT predictors may potentially predict the LVI status in ESCC and provide support for clinical treatment decisions.

Project description:BackgroundTo investigate the value of contrast-enhanced CT (CECT)-derived imaging features in predicting lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC) patients.MethodsOne hundred and ninety-seven patients with postoperative pathologically confirmed esophageal squamous cell carcinoma treated in our hospital between January 2017 and January 2019 were enrolled in our study, including fifty-nine patients with LVI and one hundred and thirty-eight patients without LVI. The CECT-derived imaging features of all patients were analyzed. The CECT-derived imaging features were divided into quantitative features and qualitative features. The quantitative features consisted of the CT attenuation value of the tumor (CTVTumor), the CT attenuation value of the normal esophageal wall (CTVNormal), the CT attenuation value ratio of the tumor-to-normal esophageal wall (TNR), the CT attenuation value difference between the tumor and normal esophageal wall (ΔTN), the maximum thickness of the tumor measured by CECT (Thickness), the maximum length of the tumor measured by CECT (Length), and the gross tumor volume measured by CECT (GTV). The qualitative features consisted of an enhancement pattern, tumor margin, enlarged blood supply or drainage vessels to the tumor (EVFDT), and tumor necrosis. For the clinicopathological characteristics and CECT-derived imaging feature analysis, the chi-squared test was used for categorical variables, the Mann-Whitney U test was used for continuous variables with a nonnormal distribution, and the independent sample t-test was used for the continuous variables with a normal distribution. The trend test was used for ordinal variables. The association between LVI status and CECT-derived imaging features was analyzed by univariable logistic analysis, followed by multivariable logistic regression and receiver operating characteristic (ROC) curve analysis.ResultsThe CTVTumor, TNR, ΔTN, Thickness, Length, and GTV in the group with LVI were higher than those in the group without LVI (P < 0.05). A higher proportion of patients with heterogeneous enhancement pattern, irregular tumor margin, EVFDT, and tumor necrosis were present in the group with LVI (P < 0.05). As revealed by the univariable logistic analysis, the CECT-derived imaging features, including CTVTumor, TNR, ΔTN and enhancement pattern, Thickness, Length, GTV, tumor margin, EVFDT, and tumor necrosis were associated with LVI status (P < 0.05). Only the TNR (OR 8.655; 95% CI 2.125-37.776), Thickness (OR 6.531; 95% CI 2.410-20.608), and tumor margin (OR 4.384; 95% CI 2.004-9.717) were independent risk factors for LVI in the multivariable logistic regression analysis. The ROC curve analysis incorporating the above three CECT-derived imaging features showed that the area under the curve obtained by the multivariable logistic regression model was 0.820 (95% CI 0.754-0.885).ConclusionThe CECT-derived imaging features, including TNR, Thickness, tumor margin, and their combination, can be used as predictors of LVI status for patients with ESCC.

Project description:ObjectiveTo develop and externally validate a CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion (LVI) in patients with colorectal cancer (CRC).Methods357 patients derived from 2 centers with pathologically confirmed CRC were included in this retrospective study. Two-dimensional (2D) and three-dimensional (3D) radiomics features were extracted from portal venous phase CT images. The least absolute shrinkage and selection operator algorithm and logistic regression were used for constructing 2D and 3D radiomics models. The radiomics nomogram was developed by integrating the radiomics score (rad-score) and the clinical risk factor.ResultsThe rad-score was significantly higher in the LVI+ group than in the LVI- group (p < 0.05). The area under the curve (AUC), accuracy, sensitivity and specificity of the 3D radiomics model were higher than those of the 2D radiomics model. The AUCs of 3D and 2D radiomics models in the training set were 0.82 (95% CI: 0.75-0.89) and 0.74 (95% CI: 0.66-0.82); in the internal validation set were 0.75 (95% CI: 0.65-0.85) and 0.67 (95% CI: 0.56-0.78); in the external validation set were 0.75 (95% CI: 0.64-0.86) and 0.57 (95% CI: 0.45-0.69); respectively. The AUCs of the nomogram integrating the optimal 3D rad-score and clinical risk factors (CT-reported T stage, CT-reported lymph node status) in the internal set and external validation set were 0.82 (95% CI: 0.73-0.91) and 0.80 (95% CI: 0.68-0.91), respectively.ConclusionBoth 2D and 3D radiomics models can predict LVI status of CRC. The nomogram combining the optimal 3D rad-score and clinical risk factors further improved predictive performance.Advances in knowledgeThis is the first study to compare the difference in performance of CT-based 2D and 3D radiomics models for the pre-operative prediction of LVI in CRC. The prediction of the nomogram could be improved by combining the 3D radiomics model with the imaging model, suggesting its potential for clinical application.

Dataset Information

Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study.

Publications

Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study.

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