Unknown

Dataset Information

0

Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer.


ABSTRACT: Objective: The aim of this study is to evaluate whether radiomics imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and to develop a nomogram for preoperative prediction of PM status. Methods: We collected CT images of pathological T4 gastric cancer in 955 consecutive patients of two cancer centers to analyze the radiomics features retrospectively and then developed and validated the prediction model built from 292 quantitative image features in the training cohort and two validation cohorts. Lasso regression model was applied for selecting feature and constructing radiomics signature. Predicting model was developed by multivariable logistic regression analysis. Radiomics nomogram was developed by the incorporation of radiomics signature and clinical T and N stage. Calibration, discrimination, and clinical usefulness were used to evaluate the performance of the nomogram. Results: In training and validation cohorts, PM status was associated with the radiomics signature significantly. It was found that the radiomics signature was an independent predictor for peritoneal metastasis in multivariable logistic analysis. For training and internal and external validation cohorts, the area under the receiver operating characteristic curves (AUCs) of radiomics signature for predicting PM were 0.751 (95%CI, 0.703-0.799), 0.802 (95%CI, 0.691-0.912), and 0.745 (95%CI, 0.683-0.806), respectively. Furthermore, for training and internal and external validation cohorts, the AUCs of radiomics nomogram for predicting PM were 0.792 (95%CI, 0.748-0.836), 0.870 (95%CI, 0.795-0.946), and 0.815 (95%CI, 0.763-0.867), respectively. Conclusions: CT-based radiomics signature could predict peritoneal metastasis, and the radiomics nomogram can make a meaningful contribution for predicting PM status in GC patient preoperatively.

SUBMITTER: Huang W 

PROVIDER: S-EPMC7468436 | BioStudies | 2020-01-01

REPOSITORIES: biostudies

Similar Datasets

2020-01-01 | S-EPMC7072015 | BioStudies
2018-01-01 | S-EPMC6131601 | BioStudies
2019-01-01 | S-EPMC6498894 | BioStudies
2020-01-01 | S-EPMC7272670 | BioStudies
2019-01-01 | S-EPMC6485136 | BioStudies
2020-01-01 | S-EPMC7332742 | BioStudies
1000-01-01 | S-EPMC5951199 | BioStudies
2020-01-01 | S-EPMC6958668 | BioStudies
2019-01-01 | S-EPMC6587470 | BioStudies
2020-01-01 | S-EPMC7481634 | BioStudies