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Radiomic analysis in contrast-enhanced CT: predict treatment response to chemoradiotherapy in esophageal carcinoma.

ABSTRACT: To investigate the capability of computed-tomography (CT) radiomic features to predict the therapeutic response of Esophageal Carcinoma (EC) to chemoradiotherapy (CRT).Pretreatment contrast-enhanced CT images of 49 EC patients (33 responders, 16 nonresponders) who received with CRT were retrospectively analyzed. The region of tumor was contoured by two radiologists. A total of 214 features were extracted from the tumor region. Kruskal-Wallis test and receiver operating characteristic (ROC) analysis were performed to evaluate the capability of each feature on treatment response classification. Support vector machine (SVM) and artificial neural network (ANN) algorithms were used to build models for prediction of the treatment response. The statistical difference between the performances of the models was assessed using McNemar's test.Radiomic-based classification showed significance in differentiating responders from nonresponders. Five features were found to discriminate nonresponders from responders (AUCs from 0.686 to 0.727). Considering these features, two features (Histogram2D_skewness: P = 0.015. Histogram2D_kurtosis: P = 0.039) were significant for differentiating SDs (stable disease) from PRs (partial response) and one feature (Histogram2D_skewness: P = 0.027) for differentiating SDs from CRs (complete response). Both classifiers showed potential in predicting the treatment response with higher accuracy (ANN: 0.972, SVM: 0.891). No statistically significant difference was observed in the performance of the two classifiers (P = 0.250).CT-based radiomic features can be used as imaging biomarkers to predict tumor response to CRT in EC patients.

SUBMITTER: Hou Z

PROVIDER: S-EPMC5732818 | biostudies-other | 2017 Nov

REPOSITORIES: biostudies-other

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Radiomic analysis in contrast-enhanced CT: predict treatment response to chemoradiotherapy in esophageal carcinoma.

Hou Zhen Z Ren Wei W Li Shuangshuang S Liu Juan J Sun Yu Y Yan Jing J Wan Suiren S

Oncotarget 20171106 61

<h4>Objectives</h4>To investigate the capability of computed-tomography (CT) radiomic features to predict the therapeutic response of Esophageal Carcinoma (EC) to chemoradiotherapy (CRT).<h4>Methods</h4>Pretreatment contrast-enhanced CT images of 49 EC patients (33 responders, 16 nonresponders) who received with CRT were retrospectively analyzed. The region of tumor was contoured by two radiologists. A total of 214 features were extracted from the tumor region. Kruskal-Wallis test and receiver o ...[more]

PMID: 29262652

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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:ObjectivesTo assess and compare the diagnostic value of contrast-enhanced MRI (CEMRI) and contrast-enhanced CT (CECT) for evaluating the response of hepatocellular carcinoma (HCC) after transarterial chemoembolisation (TACE).DesignSystematic review and meta-analysis.Data sourcesPubMed, Embase, the Cochrane Library, CNKI and Wanfang databases were systematically searched from inception to 1 August 2023.Eligibility criteriaStudies with any outcome that demonstrates the diagnostic performance of CEMRI and CECT for HCC after TACE were included.Data extraction and synthesisTwo authors independently extracted the data and assessed the quality of included studies. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2. The diagnostic performance of CEMRI and CECT for the response of HCC was investigated by collecting true and false positives, true and false negatives, or transformed-derived data from each study to calculate specificity and sensitivity. Other outcomes are the positive likelihood ratio/negative likelihood ratio (NLR), the area under the receiver operating characteristic curve (AUC) for diagnostic tests and the diagnostic OR (DOR). Findings were summarised and synthesised qualitatively according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.ResultsThis study included 5843 HCC patients diagnosed with CEMRI or CECT and treated with TACE from 36 studies. The mean proportion of men in the total sample was 76.3%. The pool sensitivity, specificity and AUC of CEMRI in diagnosing HCC after TACE were 0.92 (95% CI: 0.86 to 0.96), 0.94 (95% CI: 0.86 to 0.98) and 0.98 (95% CI: 0.96 to 0.99). The pool sensitivity, specificity and AUC of CECT in diagnosing HCC after TACE were 0.74 (95% CI: 0.68 to 0.80), 0.98 (95% CI: 0.93 to 1.00) and 0.90 (95% CI: 0.88 to 0.93).ConclusionsIn conclusion, this study found that both CEMRI and CECT had relatively high predictive power for assessing the response of HCC after TACE. Furthermore, the diagnostic value of CEMRI may be superior to CECT in terms of sensitivity, AUC, DOR and NLR.

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Radiomic analysis in contrast-enhanced CT: predict treatment response to chemoradiotherapy in esophageal carcinoma.

Publications

Radiomic analysis in contrast-enhanced CT: predict treatment response to chemoradiotherapy in esophageal carcinoma.

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