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Prognostic Nomogram for Gastrointestinal Stromal Tumors after Surgery Based on the SEER Database

We aimed to determine prognostic factors and develop an effective and practical nomogram for predicting cancer-specific survival in gastrointestinal stromal tumor (GIST) patients. Postoperative data were obtained from the SEER database (2000-2018). Patients were divided into training and validation...

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Autores principales: Sun, Qianhui, Chen, Yunru, Li, Tingting, Zhu, Xiaoyu, Zhu, Guanghui, Ni, Baoyi, Gao, Ruike, Li, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9678471/
https://www.ncbi.nlm.nih.gov/pubmed/36420093
http://dx.doi.org/10.1155/2022/5639174
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author Sun, Qianhui
Chen, Yunru
Li, Tingting
Zhu, Xiaoyu
Zhu, Guanghui
Ni, Baoyi
Gao, Ruike
Li, Jie
author_facet Sun, Qianhui
Chen, Yunru
Li, Tingting
Zhu, Xiaoyu
Zhu, Guanghui
Ni, Baoyi
Gao, Ruike
Li, Jie
author_sort Sun, Qianhui
collection PubMed
description We aimed to determine prognostic factors and develop an effective and practical nomogram for predicting cancer-specific survival in gastrointestinal stromal tumor (GIST) patients. Postoperative data were obtained from the SEER database (2000-2018). Patients were divided into training and validation cohorts at random (7 : 3). Prognostic factors were screened, and a prognostic nomogram was established using log-rank testing and Cox regression. We used DCA, ROC curves, C-index, and calibration curves to evaluate our model's predictive performance. The clinical value of the nomogram and the modified National Institute of Health (M-NIH) classification were compared using the NRI and IDI. The Kaplan-Meier method was applied to examine survival by risk group, and log-rank tests were applied to compare variations in survival curves. Independent prognostic risk factors associated with cancer-specific survival on multivariate Cox proportional hazards regression analysis were age, race, and tumor location, size, grade, and stage. Clinically relevant variables need to be considered in addition to statistically significant variables when developing prognostic models to aid clinical decision-making. We included two additional variables (mitotic rate and chemotherapy) when constructing the prognostic model. The C-index was 0.766 (95% confidence interval (CI): 0.737-0.794) in the training cohort and 0.795 (95% CI: 0.754-0.836) in the internal validation group suggesting robustness. The areas under the ROC curve for three-year and five-year survival were >0.700, indicating satisfactory discrimination. The calibration curves showed good agreement between the predictions of the nomogram and the actual results. The NRI (0.346 for 3-year and 0.265 for 5-year cancer-specific survival for patients with GIST (GSS) prediction; validation cohort: 0.356 for 3-year and 0.246 for 5-year GSS prediction) and IDI values (0.047 for 3-year and 0.060 for 5-year GSS prediction; validation cohort: 0.071 for 3-year and 0.084 for 5-year GSS prediction) suggested that the established nomogram performed significantly better than the M-NIH classification. The DCA indicated that the nomogram was clinically useful and had a high discriminative ability in identifying patients who were at high risk of poor outcomes. According to nomogram findings, patients were divided into three groups (high, moderate, and low risk), with significantly different prognoses in both cohorts. Our nomogram satisfactorily predicted survival in postsurgical GIST patients, which may assist clinicians to evaluate the postoperative status and guide subsequent treatments.
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spelling pubmed-96784712022-11-22 Prognostic Nomogram for Gastrointestinal Stromal Tumors after Surgery Based on the SEER Database Sun, Qianhui Chen, Yunru Li, Tingting Zhu, Xiaoyu Zhu, Guanghui Ni, Baoyi Gao, Ruike Li, Jie Biomed Res Int Research Article We aimed to determine prognostic factors and develop an effective and practical nomogram for predicting cancer-specific survival in gastrointestinal stromal tumor (GIST) patients. Postoperative data were obtained from the SEER database (2000-2018). Patients were divided into training and validation cohorts at random (7 : 3). Prognostic factors were screened, and a prognostic nomogram was established using log-rank testing and Cox regression. We used DCA, ROC curves, C-index, and calibration curves to evaluate our model's predictive performance. The clinical value of the nomogram and the modified National Institute of Health (M-NIH) classification were compared using the NRI and IDI. The Kaplan-Meier method was applied to examine survival by risk group, and log-rank tests were applied to compare variations in survival curves. Independent prognostic risk factors associated with cancer-specific survival on multivariate Cox proportional hazards regression analysis were age, race, and tumor location, size, grade, and stage. Clinically relevant variables need to be considered in addition to statistically significant variables when developing prognostic models to aid clinical decision-making. We included two additional variables (mitotic rate and chemotherapy) when constructing the prognostic model. The C-index was 0.766 (95% confidence interval (CI): 0.737-0.794) in the training cohort and 0.795 (95% CI: 0.754-0.836) in the internal validation group suggesting robustness. The areas under the ROC curve for three-year and five-year survival were >0.700, indicating satisfactory discrimination. The calibration curves showed good agreement between the predictions of the nomogram and the actual results. The NRI (0.346 for 3-year and 0.265 for 5-year cancer-specific survival for patients with GIST (GSS) prediction; validation cohort: 0.356 for 3-year and 0.246 for 5-year GSS prediction) and IDI values (0.047 for 3-year and 0.060 for 5-year GSS prediction; validation cohort: 0.071 for 3-year and 0.084 for 5-year GSS prediction) suggested that the established nomogram performed significantly better than the M-NIH classification. The DCA indicated that the nomogram was clinically useful and had a high discriminative ability in identifying patients who were at high risk of poor outcomes. According to nomogram findings, patients were divided into three groups (high, moderate, and low risk), with significantly different prognoses in both cohorts. Our nomogram satisfactorily predicted survival in postsurgical GIST patients, which may assist clinicians to evaluate the postoperative status and guide subsequent treatments. Hindawi 2022-11-14 /pmc/articles/PMC9678471/ /pubmed/36420093 http://dx.doi.org/10.1155/2022/5639174 Text en Copyright © 2022 Qianhui Sun et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Sun, Qianhui
Chen, Yunru
Li, Tingting
Zhu, Xiaoyu
Zhu, Guanghui
Ni, Baoyi
Gao, Ruike
Li, Jie
Prognostic Nomogram for Gastrointestinal Stromal Tumors after Surgery Based on the SEER Database
title Prognostic Nomogram for Gastrointestinal Stromal Tumors after Surgery Based on the SEER Database
title_full Prognostic Nomogram for Gastrointestinal Stromal Tumors after Surgery Based on the SEER Database
title_fullStr Prognostic Nomogram for Gastrointestinal Stromal Tumors after Surgery Based on the SEER Database
title_full_unstemmed Prognostic Nomogram for Gastrointestinal Stromal Tumors after Surgery Based on the SEER Database
title_short Prognostic Nomogram for Gastrointestinal Stromal Tumors after Surgery Based on the SEER Database
title_sort prognostic nomogram for gastrointestinal stromal tumors after surgery based on the seer database
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9678471/
https://www.ncbi.nlm.nih.gov/pubmed/36420093
http://dx.doi.org/10.1155/2022/5639174
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