scholarly journals Development and validation of a prognostic nomogram for malignant esophageal fistula based on radiomics and clinical factors

2021 ◽  
Author(s):  
Chao Zhu ◽  
Jialin Ding ◽  
Songping Wang ◽  
Qingtao Qiu ◽  
Youxin Ji ◽  
...  
Keyword(s):  
Esophageal Fistula ◽  
Clinical Factors ◽  
Development And Validation

Development and validation of a nomogram for distant metastasis in esophageal cancer based on radiomics and clinical factors.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16071-e16071
Author(s):  
Zhu Chao ◽  
Qingtao Qiu ◽  
Youxin Ji ◽  
Songping Wang ◽  
Jialin Ding ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Predictive Factors ◽  
Distant Metastasis ◽  
Machine Learning Algorithms ◽  
Lasso Regression ◽  
Clinical Factors ◽  
Net Reclassification Improvement ◽  
Contrast Enhanced Ct ◽  
Development And Validation ◽  
Radiomics Signature

e16071 Background: Distant metastasis with an incidence of 25% in esophageal cancer(EC) represents a poor prognosis. However, there was few study for prediction of distant metastasis in EC, due to unsatisfactory specificity of clinical factors and lack of reliable biomarkers. Methods: Two hundred and ninety-nine patients were enrolled and randomly assigned to a training cohort(n = 207) and a validation cohort(n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictive factors and construct a clinical nomogram. Radiomic features were extracted from contrast-enhanced CT performed before treatment, and Lasso regression was used to screen the optimal features, which were developed a radiomics signature subsequently. Four machine learning algorithms were used to establish radiomics models respectively based on the screened features. The joint nomogram incorporating radiomics signature and clinical independent predictors was developed by logical regression algorithm. All models were further validated by discrimination,caliberation, reclassification and clinical usefulness. Results: The joint nomogram had a better performance [AUC(95%CI): 0.827(0.742-0.912)] than clinical nomogram [AUC(95%CI): 0.731(0.626-0.836)]and radiomics predictive models[AUC(95%CI): 0.747(0.642-0.851),SVM algorithms]. Caliberation curve, and decision curve analysis also revealed joint nomogram outperformed the other models. Compared with the clinical nomogram, net reclassification Improvement(NRI) of the joint nomogram was improved by 0.114(0.075, 0.345),and integrated discrimination Improvement (IDI) was improved by 0.071(0.030-0.112), P= 0.001. Conclusions: We constructed and validated the first joint nomogram for distant metastasis in EC based on radiomics signature and clinical independent predictive factors, which could help clinicians to identify patients with high risk of distant metastasis.

scholarly journals Development and validation of a risk prediction model for radiotherapy-related esophageal fistula in esophageal cancer

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Yiyue Xu ◽  
Linlin Wang ◽  
Bo He ◽  
Wanlong Li ◽  
Qiang Wen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Esophageal Cancer ◽  
Risk Prediction ◽  
External Validation ◽  
Individual Risk ◽  
Esophageal Fistula ◽  
Multivariable Analyses ◽  
Independent Risk Factors ◽  
Ecog Ps ◽  
Development And Validation

Abstract Objectives We aimed to identify the risk factors and provide a nomogram for the prediction of radiotherapy-related esophageal fistula in patients with esophageal cancer (EC) using a case-control study. Patients and methods Patients with esophageal fistula who received radiotherapy or chemoradiotherapy between 2003 and 2017 were retrospectively collected in two institutions. In the training cohort (TC), clinical, pathologic, and serum data of 136 patients (cases) who developed esophageal fistula during or after radiotherapy were enrolled and compared with 272 controls (1:2 matched with the diagnosis time of EC, sex, marriage, and race). After the univariable and multivariable logistic regression analyses, the independent risk factors were identified and incorporated into a nomogram. Then the nomogram for the risk prediction was externally validated in the validation cohort (VC; 47 cases and 94 controls) using bootstrap resampling. Results Multivariable analyses demonstrated that ECOG PS, BMI, T4, N2/3 and re-radiotherapy were independent factors for esophageal fistula. Then a nomogram was constructed with the C-index of 0.805 (95% CI, 0.762–0.848) for predicting the risk of developing esophageal fistula in EC patients receiving radiotherapy. Importantly, the C-index maintained 0.764 (95% CI, 0.683–0.845) after the external validation. Conclusions We created and externally validated the first risk nomogram of esophageal fistula associated with radiotherapy. This will aid individual risk stratification of patients with EC developing esophageal fistula.

Development and validation of a prognostic score for overall survival integrating baseline metabolically active tumor volume measured by 18F-FDG PET/CT and clinical factors for metastatic colorectal cancer patients.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3543-3543
Author(s):  
Erwin Woff ◽  
Alain Hendlisz ◽  
Lisa Salvatore ◽  
Federica Marmorino ◽  
Alfredo Falcone ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Multivariate Analysis ◽  
Fdg Pet ◽  
Tumor Volume ◽  
Prognostic Score ◽  
Risk Groups ◽  
Clinical Factors ◽  
Pet Ct ◽  
Fdg Pet Ct ◽  
Development And Validation

3543 Background: This study aimed to develop and validate a prognostic score integrating baseline metabolically active tumor volume (MATV) and clinical factors in metastatic colorectal cancer (mCRC) patients. Methods: The development cohort included chemorefractory mCRC patients enrolled in two prospective multicenter non-randomized trials evaluating sorafenib/regorafenib as last line therapy. The validation cohort included mCRC patients from another center, treated with chemotherapy and bevacizumab as first line. Baseline MATV was defined as the sum of metabolically active volumes of all target lesions identified on the baseline 18F-FDG PET/CT. MATV optimal cutoff for OS prediction was determined from the development cohort with Contal and O’Quigley’s method. MATV, age, gender, BMI, ECOG PS, years since diagnosis, and KRAS status were included in a multivariate analysis. A prognostic score to predict OS was developed from the development cohort using Cox proportional hazards model. Results: MATV and clinical factors were evaluable respectively in 155 and 122 patients of the development and validation cohorts. In univariate analysis, MATV with cutoff set at 100 cm³ identified two risk groups with different median OS (mOS) in both the development (4.5 vs 10.9 months, HR: 2.64; p < 0.001) and validation cohorts (20.9 vs 42.9 months, HR: 2.39; p < 0.001). A multivariate analysis identified four independent negative predictors of OS (high MATV, short time since diagnosis, poor PS, BMI < 25). Combining these factors in a prognostic score for OS (best cutoff:-2) allowed to identify two risk groups with different mOS in the development (4.4 vs 13.4 months, HR: 3.67; p < 0.001) and validation cohorts (25 vs 63.8 months, HR: 2.5; p = 0.001). Conclusions: In mCRC patients, the high prognostic value of baseline MATV found in the development cohort was confirmed by external validation, independently of patients’ treatment. In both the development and validation cohorts the prognostic score for OS allowed to identify two risk groups of mCRC patients with significantly different mOS. MATV and our prognostic score for OS should provide a firm basis for risk stratification, in clinical practice and research trials.

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