Development and validation of a CT-based radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma
| Autor: | Mengyi Dong, Ting Xia, Zaiyi Liu, Changhong Liang, Yexing Li, Chu Han, Huihui Wang, Huasheng Yao, Xiaomei Huang, Lan He, Jia Huang, Yuan Zhang, Zongjian Yi, Jian He |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Stomach neoplasm
X-ray computed tomography Cancer Research medicine.medical_specialty business.industry Retrospective cohort study Regression analysis Nomogram Adenocarcinoma histologic grade Logistic regression Confidence interval 03 medical and health sciences 0302 clinical medicine Oncology Lasso (statistics) 030220 oncology & carcinogenesis stomach neoplasm Mann–Whitney U test Medicine Original Article Radiology business nomograms |
| Zdroj: | Chinese Journal of Cancer Research |
| ISSN: | 1993-0631 1000-9604 |
| Popis: | Objectives To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma (GA). Methods This retrospective study enrolled 592 patients with clinicopathologically confirmed GA (low-grade: n=154; high-grade: n=438) from January 2008 to March 2018 who were divided into training (n=450) and validation (n=142) sets according to the time of computed tomography (CT) examination. Radiomic features were extracted from the portal venous phase CT images. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression model were used for feature selection, data dimension reduction and radiomics signature construction. Multivariable logistic regression analysis was applied to develop the prediction model. The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration and discrimination. Results A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA (P |
| Databáze: | OpenAIRE |
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