Dataset Information

A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma.

ABSTRACT: Purpose:To construct and verify a CT-based multidimensional nomogram for the evaluation of lymph node (LN) status in pancreatic ductal adenocarcinoma (PDAC). Materials and Methods:We retrospectively assessed data from 172 patients with clinicopathologically confirmed PDAC surgically resected between February 2014 and November 2016. Patients were assigned to either a training cohort (n = 121) or a validation cohort (n = 51). We acquired radiomics features from the preoperative venous phase (VP) CT images. The maximum relevance-minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) methods were used to select the optimal features. We used multivariable logistic regression to construct a combined radiomics model for visualization in the form of a nomogram. Performance of the nomogram was evaluated by the receiver operating characteristic (ROC) curve approach, calibration testing, and analysis of clinical usefulness. Results:A Rad score consisting of 10 LN status-related radiomics features was found to be significantly associated with the actual LN status (P < 0.01). A nomogram that consisted of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status performed well in terms of predictive power in the training cohort (area under the curve, 0.92), and this was confirmed in the validation cohort (area under the curve, 0.95). The nomogram also performed well in the calibration test and decision curve analysis, demonstrating its potential clinical value. Conclusion:A multidimensional radiomics nomogram consisting of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status may contribute to the non-invasive evaluation of LN status in PDAC patients.

SUBMITTER: Gao J

PROVIDER: S-EPMC7482654 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma.

Gao Jiahao J Han Fang F Jin Yingying Y Wang Xiaoshuang X Zhang Jiawen J

Frontiers in oncology 20200827

<h4>Purpose</h4>To construct and verify a CT-based multidimensional nomogram for the evaluation of lymph node (LN) status in pancreatic ductal adenocarcinoma (PDAC).<h4>Materials and methods</h4>We retrospectively assessed data from 172 patients with clinicopathologically confirmed PDAC surgically resected between February 2014 and November 2016. Patients were assigned to either a training cohort (<i>n</i> = 121) or a validation cohort (<i>n</i> = 51). We acquired radiomics features from the pre ...[more]

PMID: 32974205

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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:BACKGROUND:Accurate lymph node metastasis (LNM) prediction in colorectal cancer (CRC) patients is of great significance for treatment decision making and prognostic evaluation. We aimed to develop and validate a clinical-radiomics nomogram for the individual preoperative prediction of LNM in CRC patients. METHODS:We enrolled 766 patients (458 in the training set and 308 in the validation set) with clinicopathologically confirmed CRC. We included nine significant clinical risk factors (age, sex, preoperative carbohydrate antigen 19-9 (CA19-9) level, preoperative carcinoembryonic antigen (CEA) level, tumor size, tumor location, histotype, differentiation and M stage) to build the clinical model. We used analysis of variance (ANOVA), relief and recursive feature elimination (RFE) for feature selection (including clinical risk factors and the imaging features of primary lesions and peripheral lymph nodes), established classification models with logistic regression analysis and selected the respective candidate models by fivefold cross-validation. Then, we combined the clinical risk factors, primary lesion radiomics features and peripheral lymph node radiomics features of the candidate models to establish combined predictive models. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, decision curve analysis (DCA) and a nomogram were used to evaluate the clinical usefulness of the model. RESULTS:The clinical-primary lesion radiomics-peripheral lymph node radiomics model, with the highest AUC value (0.7606), was regarded as the candidate model and had good discrimination and calibration in both the training and validation sets. DCA demonstrated that the clinical-radiomics nomogram was useful for preoperative prediction in the clinical environment. CONCLUSION:The present study proposed a clinical-radiomics nomogram with a combination of clinical risk factors and radiomics features that can potentially be applied in the individualized preoperative prediction of LNM in CRC patients.

Project description:BackgroundNegative regional lymph nodes do not indicate a lack of distant metastasis. A considerable number of patients with negative regional lymph node pancreatic cancer will skip the step of regional lymph node metastasis and directly develop distant metastasis.MethodsWe retrospectively analyzed the clinicopathological characteristics of patients with negative regional lymph node pancreatic cancer and distant metastasis in the Surveillance, Epidemiology, and End Results database from 2010 to 2015. Multivariate logistic analysis and Cox analysis were used to determine the independent risk factors that promoted distant metastasis and the 1-, 2-, and 3-year cancer-specific survival in this subgroup.ResultsSex, age, pathological grade, surgery, radiotherapy, race, tumor location, and tumor size were significantly correlated with distant metastasis (P < 0.05). Among these factors, pathological grade II and above, tumor site other than the pancreatic head, and tumor size >40 mm were independent risk factors for distant metastasis; age ≥60 years, tumor size ≤21 mm, surgery, and radiation were protective factors against distant metastasis. Age, pathological grade, surgery, chemotherapy, and metastasis site were identified as predictors of survival. Among them, age ≥40 years, pathological grade II and above, and multiple distant metastasis were considered independent risk factors for cancer-specific survival. Surgery and chemotherapy were considered protective factors for cancer-specific survival. The prediction performance of the nomogram was significantly better than that of the traditional American Joint Committee on Cancer tumor, node, metastasis staging system. We also established an online dynamic nomogram calculator, which can predict the survival rate of patients at different follow-up time points.ConclusionPathological grade, tumor location, and tumor size were independent risk factors for distant metastasis in pancreatic ductal adenocarcinoma with negative regional lymph nodes. Older age, smaller tumor size, surgery, and radiotherapy were protective factors against distant metastasis. A new nomogram that was constructed could effectively predict cancer-specific survival in pancreatic ductal adenocarcinoma with negative regional lymph nodes and distant metastasis. Furthermore, an online dynamic nomogram calculator was established.

Project description:This study aimed to evaluate the feasibility of applying a clinical multimodal radiomics nomogram based on ultrasonography (US) and multiparametric magnetic resonance imaging (MRI) for the prediction of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) preoperatively. We performed retrospective evaluations of 133 patients with pathologically confirmed PTC, who were assigned to the training cohort and validation cohort (7 : 3), and extracted radiomics features from the preoperative US, T2-weighted (T2WI),diffusion-weighted (DWI), and contrast-enhanced T1-weighted (CE-T1WI) images. Optimal subsets were selected using minimum redundancy, maximum relevance, and recursive feature elimination in the support vector machine (SVM). For LNM prediction, the radiomics model was constructed by SVM, and Multi-Omics Graph cOnvolutional NETworks (MOGONET) was used for the effective classification of multiradiomics data. Multivariable logistic regression incorporating multiradiomics signatures and clinical risk factors was used to generate a nomogram, whose performance and clinical utility were assessed. Results showed that the nine most predictive features were separately selected from US, T2WI, DWI, and CE-T1WI images, and 18 features were selected in the combined model. The combined radiomics model showed better performance than models based on US, T2WI, DWI, and CE-T1WI. In a comparison of the combined radiomics and MOGONET model, receiver operating curve analysis showed that the area under the curve (AUC) value (95% CI) was 0.84 (0.76-0.93) and 0.84 (0.71-0.96) for the MOGONET model in the training and validation cohorts, respectively. The corresponding values (95% CI) for the combined radiomics model were 0.82 (0.74-0.90) and 0.77 (0.61-0.94), respectively. The MOGONET model had better performance and better prediction specificity compared with the combined radiomics model. The nomogram including the MOGONET signature showed a better predictive value (AUC: 0.81 vs. 0.88) in the training and validation (AUC: 0.74vs. 0.87) cohorts, as compared with the clinical model. Calibration curves showed good agreement in both cohorts. The applicability of the clinical multimodal radiomics (CMR) nomogram in clinical settings was validated by decision curve analysis. In patients with PTC, the CMR nomogram could improve the prediction of cervical LNM preoperatively and may be helpful in clinical decision-making.

Project description:Background and purposeThe preoperative LN (lymph node) status of patients with LUAD (lung adenocarcinoma) is a key factor for determining if systemic nodal dissection is required, which is usually confirmed after surgery. This study aimed to develop and validate a nomogram for preoperative prediction of LN metastasis in LUAD based on a radiomics signature and deep learning signature.Materials and methodsThis retrospective study included a training cohort of 200 patients, an internal validation cohort of 40 patients, and an external validation cohort of 60 patients. Radiomics features were extracted from conventional CT (computed tomography) images. T-test and Extra-trees were performed for feature selection, and the selected features were combined using logistic regression to build the radiomics signature. The features and weights of the last fully connected layer of a CNN (convolutional neural network) were combined to obtain a deep learning signature. By incorporating clinical risk factors, the prediction model was developed using a multivariable logistic regression analysis, based on which the nomogram was developed. The calibration, discrimination and clinical values of the nomogram were evaluated.ResultsMultivariate logistic regression analysis showed that the radiomics signature, deep learning signature, and CT-reported LN status were independent predictors. The prediction model developed by all the independent predictors showed good discrimination (C-index, 0.820; 95% CI, 0.762 to 0.879) and calibration (Hosmer-Lemeshow test, P=0.193) capabilities for the training cohort. Additionally, the model achieved satisfactory discrimination (C-index, 0.861; 95% CI, 0.769 to 0.954) and calibration (Hosmer-Lemeshow test, P=0.775) when applied to the external validation cohort. An analysis of the decision curve showed that the nomogram had potential for clinical application.ConclusionsThis study presents a prediction model based on radiomics signature, deep learning signature, and CT-reported LN status that can be used to predict preoperative LN metastasis in patients with LUAD.

Project description:BackgroundGrowing evidence suggests that tumor metastasis necessitates multi-step microenvironmental regulation. Lymph node metastasis (LNM) influences both pre- and post-operative bladder cancer (BLCA) treatment strategies. Given that current LNM diagnosis methods are still insufficient, we intend to investigate the microenvironmental changes in BLCA with and without LNM and develop a prediction model to confirm LNM status.Method"Estimation of Stromal and Immune cells in Malignant Tumors using Expression data" (ESTIMATE) algorithm was used to characterize the tumor microenvironment pattern of TCGA-BLCA cohort, and dimension reduction, feature selection, and StrLNM signature construction were accomplished using least absolute shrinkage and selection operator (LASSO) regression. StrLNM signature was combined with the genomic mutation to establish an LNM nomogram by using multivariable logistic regression. The performance of the nomogram was evaluated in terms of calibration, discrimination, and clinical utility. The testing set from the TCGA-BLCA cohort was used for internal validation. Moreover, three independent cohorts were used for external validation, and BLCA patients from our cohort were also used for further validation.ResultsThe StrLNM signature, consisting of 22 selected features, could accurately predict LNM status in the TCGA-BLCA cohort and several independent cohorts. The nomogram performed well in discriminating LNM status, with the area under curve (AUC) of 75.1% and 65.4% in training and testing datasets from the TCGA-BLCA cohort. Furthermore, the StrLNM nomogram demonstrated good calibration with p >0.05 in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis (DCA) revealed that the StrLNM nomogram had a high potential for clinical utility. Additionally, 14 of 22 stably expressed genes were identified by survival analysis and confirmed by qPCR in BLCA patient samples in our cohort.ConclusionIn summary, we developed a nomogram that included an StrLNM signature and facilitated the preoperative prediction of LNM status in BLCA patients.

Dataset Information

A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma.

Publications

A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma.

Similar Datasets

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