Nuclear Medicine & Radiation Therapy

ISSN: 2155-9619

Open Access

Textural Features on Computed Tomography Scans Predict Overall Survival in Patients with Esophageal Cancer


Jingya Wang, Laurence E Court, Arvind Rao, Roland Bassett, Joon K Lee, Luke Hunter, Bevan Myles, Zhongxing Liao and Steven H Lin

Purpose: To predict overall survival (OS) in non-metastatic esophageal cancer using texture analysis of pre-therapy computed tomography (CT) images.
Materials and Methods: Records from 762 non-metastatic esophageal cancer patients with non-contrast CT scans (obtained from 1998-2011) before receiving chemoradiation were retrospectively reviewed. 328 quantitative image features were extracted from the esophageal gross tumor volume (GTV). A random survival forest model compared how well five of these features (entropy, histogram 10th percentile, volume, volume-to-area ratio, fraction GTV pruned after thresholding) predicted OS versus all 328 features. Cox proportional hazards modeling was used to derive scores, based on these five features, which could stratify patients by survival in a training set consisting of 50% of the 762 cases, chosen randomly from the data. This model was then tested in a validation set (remaining 50% of cases). Multivariate analysis was done with the image-derived score and other prognostic variables. Results: CT texture analysis based on the five image-derived features yielded a similar concordance rate for predicting OS (56%) as did all 328 features (56%), and in fact showed higher concordance for predicting OS than disease stage alone (44%). This image-derived score was also able to significantly stratify OS (P<0.05) in both the training and validation set, as well as independently predict OS in multivariate analysis (HR 1.61, 95% CI 1.13-2.29, P=0.009), along with stage, treatment with surgery, tumor grade, and radiation modality.
Conclusions: Texture features from pretreatment CT images can independently predict OS in patients with non-metastatic esophageal carcinoma.


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