Abstract
Background: The existing risk stratification systems for gastrointestinal stromal tumors (GISTs) are mainly based on tumor size measurements without consideration of the cystic or necrotic components within tumors and mitosis index. We aimed to develop and validate a new model to predict recurrence-free survival (RFS) after surgical resection of primary gastric GISTs. Methods: In this study, 210 consecutive patients who underwent preoperative contrast-enhanced CT and surgery were retrospectively analyzed and divided into training (n = 132) and validation (n = 78) datasets. Semiautoquantitative CT parameters were evaluated. Moreover, morphological CT features and pathological mitosis index were reviewed. Univariate and multivariate Cox regression analyses were performed to develop a novel model whose performance was evaluated and compared with that of the modified National Institutes of Health (NIH) risk stratification. Findings: The model comprised direct invasion (p= 0·003; hazard ratio (HR) = 3·965), enhanced tissue volume (p= 0·012; HR = 1·432), and mitosis index (p= 0·012; HR = 1·013), which showed better predictive performance than the modified NIH risk stratification in the training (concordance index (C-index) = 0·837 vs. 0·751; p= 0·005) and validation (C-index = 0·830 vs. 0·726; p= 0·028) datasets for 3- and 5-year RFS. Decision curve analysis revealed that the model had a greater overall net benefit than the modified NIH risk stratification. Interpretation: Our prognostic model’s performance was superior to that of the modified NIH risk stratification for predicting RFS after curative resection of primary gastric GISTs. Funding: This paper is supported by China International Medical Foundation (grant number Z-2014-07- 2003-04); Shanghai Municipal Key Clinical Specialty (grant number shslczdzk03202); Clinical Research Plan of SHDC (grant number SHDC2020CR1029B). Declaration of Interest: None to declare. Ethical Approval: Ethics approval was obtained for this study in Zhongshan Hospital, Fudan university (approval number B2016-075R).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.