Computed Tomography-Based Radiomics in Predicting T Stage and Length of Esophageal Squamous Cell Carcinoma

Abstract

BackgroundBecause of the superficial and infiltrative spreading patterns of esophageal squamous cell carcinoma (ESCC), an accurate assessment of tumor extent is challenging using imaging-based clinical staging. Radiomics features extracted from pretreatment computed tomography (CT) or magnetic resonance imaging have shown promise in identifying tumor characteristics. Accurate staging is essential for planning cancer treatment, especially for deciding whether to offer surgery or radiotherapy (chemotherapy) in patients with locally advanced ESCC. Thus, this study aimed to evaluate the predictive potential of contrast-enhanced CT-based radiomics as a non-invasive approach for estimating pathological tumor extent in ESCC patients.MethodsPatients who underwent esophagectomy between October 2011 and September 2017 were retrospectively studied and included 116 patients with pathologically confirmed ESCC. Contrast-enhanced CT from the neck to the abdomen was performed in all patients during the 2 weeks before the operation. Radiomics features were extracted from segmentations, which were contoured by radiologists. Cluster analysis was performed to obtain clusters with similar radiomics characteristics, and chi-squared tests were used to assess differences in clinicopathological features and survival among clusters. Furthermore, a least absolute shrinkage and selection operator was performed to select radiomics features and construct a radiomics model. Receiver operating characteristic analysis was used to evaluate the predictive ability of the radiomics signatures.ResultsAll 116 ESCC patients were divided into two groups according to the cluster analysis. The chi-squared test showed that cluster-based radiomics features were significantly correlated with T stage (p = 0.0254) and tumor length (p = 0.0002). Furthermore, CT radiomics signatures exhibited favorable predictive performance for T stage (area under the curve [AUC] = 0.86, sensitivity = 0.77, and specificity = 0.87) and tumor length (AUC = 0.95, sensitivity = 0.92, and specificity = 0.91).ConclusionsCT contrast radiomics is a simple and non-invasive method that shows promise for predicting pathological T stage and tumor length preoperatively in ESCC patients and may aid in the accurate assessments of patients in combination with the existing examinations.

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

“…The clustering findings revealed that it could be classified into 38 groups, yet only the top 14 were shown in Figure 7A . The largest cluster was “radiomics” (#0) ( 53 – 56 ), while “tumor segmentation”(#6) ( 57 – 61 )was the earliest research in this field. “Endoscopy” (#9) ( 62 – 65 ) and “deep learning” (#2) ( 66 – 70 ) were the latest research hotspots.…”

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

“…Our CT test model showed an AUC of 0.96 (95% CI 0.87–1.00). The results were slightly better than the radiomics model developed by Yang et al (AUC 0.857; 95% CI 0.691–1.000) [ 26 ]. In another study, Wu et al used a radiomics approach to identify early- and late-stage ESCC prior to surgery [ 17 ].…”

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

“…Published studies have mainly investigated the predictive ability of radiomics in the staging, therapy response, and postoperative recurrence of EC[ 16 - 19 ].…”

“…Radiomic characteristics based on CT have good predictive potential for EC staging[ 20 , 21 ]. Yang et al [ 19 ] reported that CT radiomic characteristics were significantly correlated with the tumor (T) stage and tumor length of EC and showed good predictive performance for both; the area under the ROC curve (AUC), sensitivity, and specificitywere 0.86, 0.77 and 0.87, respectively, and 0.95, 0.92 and 0.91. Radiomic features also have good efficacy in predicting EC lymphatic metastasis[ 7 , 22 - 24 ].…”