Dataset Information

Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study.

ABSTRACT: Background: Lymph node (LN) metastasis is the most important prognostic factor in esophageal squamous cell carcinoma (ESCC). Traditional clinical factor and existing methods based on CT images are insufficiently effective in diagnosing LN metastasis. A more efficient method to predict LN status based on CT image is needed. Methods: In this multicenter retrospective study, 411 patients with pathologically confirmed ESCC were registered from two hospitals. Quantitative image features including handcrafted-, computer vision-(CV-), and deep-features were extracted from preoperative arterial phase CT images for each patient. A handcrafted-, CV-, and deep-radiomics signature were built, respectively. Then, multiple radiomics models were constructed by merging independent clinical risk factor into radiomics signatures. The performance of models were evaluated with respect to the discrimination, calibration, and clinical usefulness. Finally, an independent external validation cohort was used to validate the model's predictive performance. Results: Five, seven, and nine features were selected for building handcrafted-, CV-, and deep-radiomics signatures from extracted features, respectively. Those signatures were statistically significant different between LN-positive and LN-negative patients in all cohorts (p < 0.001). The developed multiple level CT radiomics model that integrates multiple radiomics signatures with clinical risk factor, was superior to traditional clinical factors and the results reported by existing methods, and achieved satisfactory discrimination performance with C-statistic of 0.875 in development cohort, 0.874 in internal validation cohort and 0.840 in independent external validation cohort. Nomogram and decision curve analysis (DCA) further confirmed our method may serve as an effective tool for clinicians to evaluate the risk of LN metastasis in patients with ESCC and further choose treatment strategy. Conclusions: The proposed multiple level CT radiomics model which integrate multiple level radiomics features into clinical risk factor can be used for preoperative predicting LN metastasis of patients with ESCC.

SUBMITTER: Wu L

PROVIDER: S-EPMC6985546 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study.

Wu Lei L Yang Xiaojun X Cao Wuteng W Zhao Ke K Li Wenli W Ye Weitao W Chen Xin X Zhou Zhiyang Z Liu Zaiyi Z Liang Changhong C

Frontiers in oncology 20200121

<b>Background:</b> Lymph node (LN) metastasis is the most important prognostic factor in esophageal squamous cell carcinoma (ESCC). Traditional clinical factor and existing methods based on CT images are insufficiently effective in diagnosing LN metastasis. A more efficient method to predict LN status based on CT image is needed. <b>Methods:</b> In this multicenter retrospective study, 411 patients with pathologically confirmed ESCC were registered from two hospitals. Quantitative image features ...[more]

PMID: 32039021

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Project description:PURPOSE:To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS:A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS:Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell's Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION:The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.

Project description:PurposeTo investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC).Methods and materialsTwo hundred and seventy-four eligible patients with ESCC were divided into a training set (n =193) and a validation set (n =81). The least absolute shrinkage and selection operator algorithm (LASSO) was used to select radiomics features. The predictive models were constructed with radiomics features and clinical factors through multivariate logistic regression analysis. The predictive performance and clinical application value of the models were evaluated by area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Delong Test was used to evaluate the differences in AUC among models.ResultsSixteen and eighteen features were respectively selected from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images. The model established using only clinical factors (Model 1) has an AUC value of 0.655 (95%CI 0.552-0.759) with a sensitivity of 0.585, a specificity of 0.725 and an accuracy of 0.654. The models contained clinical factors with radiomics features of NECT or/and CECT (Model 2,3,4) have significantly improved prediction performance. The values of AUC of Model 2,3,4 were 0.766, 0.811 and 0.809, respectively. It also achieved a great AUC of 0.800 in the model built with only radiomics features derived from NECT and CECT (Model 5). DCA suggested the potential clinical benefit of model prediction of LN metastasis of ESCC. A comparison of the receiver operating characteristic (ROC) curves using the Delong test indicated that Models 2, 3, 4, and 5 were superior to Model 1(P< 0.05), and no difference was found among Model 2, 3, 4 and Model 5(P > 0.05).ConclusionRadiomics models based on CT at different phases could accurately predict the lymph node metastasis in patients with ESCC, and their predictive efficiency was better than the clinical model based on tumor size criteria. NECT-based radiomics model could be a reasonable option for ESCC patients due to its lower price and availability for renal failure or allergic patients.

Project description:BackgroundAdequate safe margin in tongue cancer radical surgery is one of the most important prognostic factors. However, the role of peritumoral tissues in predicting lymph node metastasis (LNM) and prognosis using radiomics analysis remains unclear.PurposeTo investigate whether magnetic resonance imaging (MRI)-based radiomics analysis with peritumoral extensions contributes toward the prediction of LNM and prognosis in tongue cancer.Study typeRetrospective.PopulationTwo hundred and thirty-six patients (38.56% female) with tongue cancer (training set, N = 157; testing set, N = 79; 37.58% and 40.51% female for each).Field strength/sequence1.5 T; T2-weighted turbo spin-echo images.AssessmentRadiomics models (Rprim , Rprim+3 , Rprim+5 , Rprim+10 , Rprim+15 ) were developed with features extracted from the primary tumor without or with peritumoral extensions (3, 5, 10, and 15 mm, respectively). Clinicopathological characteristics selected from univariate analysis, including MRI-reported LN status, radiological extrinsic lingual muscle invasion, and pathological depth of invasion (DOI) were further incorporated into radiomics models to develop combined radiomics models (CRprim , CRprim+3 , CRprim+5 , CRprim+10 , CRprim+15 ). Finally, the model performance was validated in the testing set. DOI was measured from the adjacent normal mucosa to the deepest point of tumor invasion.Statistical testsChi-square test, regression analysis, receiver operating characteristic curve (ROC) analysis, decision analysis, spearman correlation analysis. The Delong test was used to compare area under the ROC (AUC). P < 0.05 was considered statistically significant.ResultsOf all the models, the CRprim+10 reached the highest AUC of 0.995 in the training set and 0.872 in the testing set. Radiomics features were significantly correlated with pathological DOI (correlation coefficients, -0.157 to -0.336). The CRprim+10 was an independent indicator for poor disease-free survival (hazard ratio, 5.250) and overall survival (hazard ratio, 17.464) in the testing set.Data conclusionRadiomics analysis with a 10-mm peritumoral extension had excellent power to predict LNM and prognosis in tongue cancer.

Dataset Information

Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study.

Publications

Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study.

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