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.
Highlights
Esophageal cancer is the eighth most common cancer and the sixth leading cause of cancer-related death worldwide, which presents a major global health challenge [1, 2]
Each patient’s pathological assessment was reviewed, and tumors were staged according to the eighth edition of the tumor node metastasis (TNM) staging classification for carcinoma of the esophagus and esophagogastric junction of the American Joint Committee on Cancer [11]
Two patients were at TNM stage I, 54 patients were at stage II, 49 patients were at stage III, and 11 patients were at stage IV
Summary
Esophageal cancer is the eighth most common cancer and the sixth leading cause of cancer-related death worldwide, which presents a major global health challenge [1, 2]. Multidisciplinary treatment strategies are recommended for ESCC patients to improve therapeutic outcomes, especially for those in advanced tumor stages. Because of the crucial role of accurate staging for guiding treatment decisions, a sophisticated staging approach, which includes endoscopy, endoscopic ultrasound (EU), and contrast-enhanced computed tomography (CT), is in routine use. For primary tumors, an accurate determination of tumor depth and length can be extremely difficult in some cases, primarily due to the superficial and infiltrative spreading pattern of ESCCs. there is an urgent need for the development of a novel approach for improved staging accuracy. Because 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. This study aimed to evaluate the predictive potential of contrast-enhanced CT-based radiomics as a noninvasive approach for estimating pathological tumor extent in ESCC patients
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