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.
Background: We aimed to identify the suitable indication and delineate the target volume based on the pattern of abdominal lymph node recurrence (ALNR) after radical surgery for guiding postoperative radiotherapy in thoracic esophageal squamous cell cancer (TESCC). Methods: Clinical data of patients with locally advanced TESCC after radical surgery without perioperative anti-tumor therapies from June 2011 to June 2016 were reviewed. Logistic regression analysis was used to find out the high-risk factors of ALNR. The pattern of ALNR was analysed and a template CT in the Pinnacle treatment plan system was used to reconstruct the distribution of the sites of ALNR. Results: A total of 63 (19.57%) patients with 276 lymph nodes of ALNR were identified in 322 patients. Univariate logistic regression indicated that pathological tumor location, width of tumor, T stage, N stage, TNM stage, ratio of lymph node metastasis (LNM), vessel carcinoma embolus, cancerous node, LNM in the middle and lower mediastinum, LNM in the abdominal region, ratio of LNM in the abdominal region were risk factors of ALNR. Multivariate logistic regression analysis showed that only LNM in the abdominal region was an independent risk factor. The odds ratio was 7.449 (95% CI=2.552-22.297, P<0.001). Station 16a2, station 9, station 16b1, and station 8 were the major regions of ALNR. The recurrence rates were 10.56%, 9.63%, 7.14% and 5.28% in these stations, respectively. Conclusion: Positive pathological abdominal lymph nodes should be the major indication for abdominal irradiation in postoperative radiotherapy for locally advanced TESCC. We recommended that the target volume includes station 8, station 9, station 16a2 and station 16b1 and proposed a specific delineation of the clinical target volume based on the distribution of ALNR on template CT images.
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