A new nomogram for recurrence-free survival prediction of gastrointestinal stromal tumors: Comparison with current risk classification methods. Issue 6 (June 2019)

• Record Type: Journal Article
• Authors: Chen, Tao; Xu, Lili; Ye, Liangying; Qiu, Haibo; Hu, Yanfeng; Liu, Hao; Zhou, Zhiwei; Li, Guoxin; Yu, Jiang
• Is Part Of: European journal of surgical oncology. Volume 45:Issue 6(2019)
• Page Start: 1109
• Page End: 1114
• Publication Date: 2019-06
• Subjects: Gastrointestinal stromal tumors -- Risk stratification -- Prognosis -- Nomogram -- Ki-67; GISTs gastrointestinal stromal tumor -- AUC area under the receiver operating characteristic curve -- NIH National Institute of Health -- AFIP Armed Forces Institute of Pathology -- MSKCC Memorial Sloan Kettering Cancer Center -- RFS recurrence-free survival -- LI labeling index -- HPFs high-power fields; Oncology -- Periodicals; Cancer -- Surgery -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- surgery -- Periodicals; Cancer -- Chirurgie -- Périodiques; Cancérologie -- Périodiques; Oncologie; Chirurgie (geneeskunde); Electronic journals; Electronic journals -- Sciences; Electronic journals -- Medicine; Electronic journals; 616.994059005
• DOI: 10.1016/j.ejso.2018.12.014
• Languages: English
• ISSNs: 0748-7983

Abstract

Abstract: Background: This study aimed to build a new risk stratification nomogram for gastrointestinal stromal tumors (GISTs) focused on a popular factor Ki-67 to enable individualized and precise predictions of the most suitable candidates for imatinib therapy. Methods: We retrospectively collected clinicopathologic data of the patients diagnosed with GISTs from January 1998 to December 2015 at Southern Medical University Nanfang Hospital as the experiment group. And patients with GISTs at the Sun Yat-sen University Cancer Center from January 2007 to December 2012 were included as the validation group. The nomogram was built using Kaplan-Meier method and the Cox proportional hazards regression model. The receiver operating characteristic (ROC) curves were established to compare the discriminative ability of the new nomogram with other risk stratification systems, including the modified National Institute of Health (modified NIH) criteria, Armed Forces Institute of Pathology (AFIP) criteria, Memorial Sloan Kettering Cancer Center (MSKCC) prognostic nomogram, and contour maps. Results: In univariate analysis, the tumor size, site, mitotic count, tumor rupture and Ki-67 labeling index were significant factors (all P < 0.05) and included in the Cox model to build our nomogram. According to the ROC curve, our new nomogram showed the largest AUC value (0.778) compared with that of the other classification methods (contour maps, AUC = 0.743; AFIP, AUC = 0.719; MSKCC, AUC = 0.712; and modified NIH, AUC = 0.719). Conclusion: Our new nomogram exhibits an excellent performance and might become a potential risk stratification to support therapeutic decision-making for GISTs.