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

Abstract

ObjectivesTo investigate the capability of computed-tomography (CT) radiomic features to predict the therapeutic response of Esophageal Carcinoma (EC) to chemoradiotherapy (CRT).MethodsPretreatment 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.ResultsRadiomic-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).ConclusionsCT-based radiomic features can be used as imaging biomarkers to predict tumor response to CRT in EC patients.

Radiomics in esophageal and gastric cancer

“…The largest study in 49 patients found that histogram skewness, histogram kurtosis, GLSZM long-zone emphasis, and 2 Gabor transformed parameters MSA-54 and MSE-54, discriminated non-responders from responders using an artificial neural network-derived prediction model [35]. The two remaining studies have assessed prognostication.…”

Improving Treatment Response Prediction for Chemoradiation Therapy of Pancreatic Cancer Using a Combination of Delta-Radiomics and the Clinical Biomarker CA19-9

“…Radiomics analysis has been associated with several clinical end points, several researchers showed that it has a potential to be developed into an imaging biomarker using different imaging modalities. We showed earlier that CT based radiomics has a potential in a variety of applications (11)(12)(13)(14)(15)(16)(17)(18)(19). Researchers also showed that it has promise using MR images for instance, Wibmer et al (33) and Vignti et al (34) showed the radiomics features as potential imaging biomarkers in prostate cancer.…”

“…CT-derived radiomic texture features have shown promising prognostic value in a variety of cancer treatments. For instance, Hou et al performed radiomic analysis using contrast-enhanced CT and found that the identified radiomic features have the potential to predict treatment response in esophageal carcinoma with an AUC of 0.97 (11). Coroller et al showed that CT-based radiomics can be developed as a prognostic biomarker to predict distant metastasis in lung cancer (12).…”

A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer

“…To date, several studies have reported the application of radiomics features in predicting the treatment response and prognosis for patients with esophageal cancer [10,13,14,[19][20][21][22] . Hou et al [22] extracted 214 radiomics features from the pretreatment enhanced CT images of 49 patients with esophageal cancer. A model based on 5 radiomics features was developed with an AUC of 0.686-0.727 and the classi cation accuracy is 0.891 and 0.972, respectively.…”