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A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis

BACKGROUND: Distant metastasis (DM) is relatively rare in superficial gastric cancer (SGC), especially in patients without lymph node metastasis. This study aimed to explore the main clinical risk factors for DM in patients with superficial gastric cancer‐no lymph node metastasis (SGC‐NLNM) and the...

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Autores principales: Chen, Jingyu, Wu, Lunpo, Zhang, Zizhen, Zheng, Sheng, Lin, Yifeng, Ding, Ning, Sun, Jiawei, Shi, Liuhong, Xue, Meng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7897959/
https://www.ncbi.nlm.nih.gov/pubmed/33350173
http://dx.doi.org/10.1002/cam4.3680
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author Chen, Jingyu
Wu, Lunpo
Zhang, Zizhen
Zheng, Sheng
Lin, Yifeng
Ding, Ning
Sun, Jiawei
Shi, Liuhong
Xue, Meng
author_facet Chen, Jingyu
Wu, Lunpo
Zhang, Zizhen
Zheng, Sheng
Lin, Yifeng
Ding, Ning
Sun, Jiawei
Shi, Liuhong
Xue, Meng
author_sort Chen, Jingyu
collection PubMed
description BACKGROUND: Distant metastasis (DM) is relatively rare in superficial gastric cancer (SGC), especially in patients without lymph node metastasis. This study aimed to explore the main clinical risk factors for DM in patients with superficial gastric cancer‐no lymph node metastasis (SGC‐NLNM) and the prognostic factors for patients with DM. METHODS: Records of patients with SGC‐NLNM between 2004 and 2015 were collected from the public Surveillance, Epidemiology, and End Results (SEER) database. Both univariate and multivariate logistic regressions were performed to analyze the clinical risk factors for DM. The Kaplan–Meier method and Cox regression model were used to identify prognostic factors for patients with DM. A nomogram was built based on multivariate logistic regression and evaluated by the C‐index, the calibration, and the area under the receiver operating characteristic curve (AUC). RESULTS: We developed and validated a nomogram to predict DM in patients with SGC‐NLNM, showing that race, age, primary site, depth, size, and grade were independent risk factors. The built nomogram had a good discriminatory performance, with a C‐index of 0.836 (95% confidence interval [CI]: 0.813–0.859). Calibration plots showed that the predicted DM probability was identical to the actual observations in both the training and validation sets. AUC was 0.846 (95% CI: 0.820–0.871) and 0.801 (95% CI: 0.751–0.850) in the training and validation sets, respectively. The results of the survival analysis revealed that surgery (hazard ratio [HR] = 0.249; 95% CI, 0.125–0.495), chemotherapy (HR = 0.473; 95% CI, 0.353–0.633), and grade (HR = 1.374; 95% CI, 1.018–1.854) were independent prognostic factors associated with cancer‐specific survival (CSS), but radiotherapy was not (log‐rank test, p = 0.676). CONCLUSIONS: We constructed a sensitive and discriminative nomogram to identify high‐risk patients with SGC‐NLNM who may harbor dissemination at initial diagnosis. The tumor size and primary site were the largest contributors to DM prediction. Compared with radiotherapy, aggressive surgery, and chemotherapy may be better options for patients with DM.
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spelling pubmed-78979592021-02-23 A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis Chen, Jingyu Wu, Lunpo Zhang, Zizhen Zheng, Sheng Lin, Yifeng Ding, Ning Sun, Jiawei Shi, Liuhong Xue, Meng Cancer Med Clinical Cancer Research BACKGROUND: Distant metastasis (DM) is relatively rare in superficial gastric cancer (SGC), especially in patients without lymph node metastasis. This study aimed to explore the main clinical risk factors for DM in patients with superficial gastric cancer‐no lymph node metastasis (SGC‐NLNM) and the prognostic factors for patients with DM. METHODS: Records of patients with SGC‐NLNM between 2004 and 2015 were collected from the public Surveillance, Epidemiology, and End Results (SEER) database. Both univariate and multivariate logistic regressions were performed to analyze the clinical risk factors for DM. The Kaplan–Meier method and Cox regression model were used to identify prognostic factors for patients with DM. A nomogram was built based on multivariate logistic regression and evaluated by the C‐index, the calibration, and the area under the receiver operating characteristic curve (AUC). RESULTS: We developed and validated a nomogram to predict DM in patients with SGC‐NLNM, showing that race, age, primary site, depth, size, and grade were independent risk factors. The built nomogram had a good discriminatory performance, with a C‐index of 0.836 (95% confidence interval [CI]: 0.813–0.859). Calibration plots showed that the predicted DM probability was identical to the actual observations in both the training and validation sets. AUC was 0.846 (95% CI: 0.820–0.871) and 0.801 (95% CI: 0.751–0.850) in the training and validation sets, respectively. The results of the survival analysis revealed that surgery (hazard ratio [HR] = 0.249; 95% CI, 0.125–0.495), chemotherapy (HR = 0.473; 95% CI, 0.353–0.633), and grade (HR = 1.374; 95% CI, 1.018–1.854) were independent prognostic factors associated with cancer‐specific survival (CSS), but radiotherapy was not (log‐rank test, p = 0.676). CONCLUSIONS: We constructed a sensitive and discriminative nomogram to identify high‐risk patients with SGC‐NLNM who may harbor dissemination at initial diagnosis. The tumor size and primary site were the largest contributors to DM prediction. Compared with radiotherapy, aggressive surgery, and chemotherapy may be better options for patients with DM. John Wiley and Sons Inc. 2020-12-22 /pmc/articles/PMC7897959/ /pubmed/33350173 http://dx.doi.org/10.1002/cam4.3680 Text en © 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Cancer Research
Chen, Jingyu
Wu, Lunpo
Zhang, Zizhen
Zheng, Sheng
Lin, Yifeng
Ding, Ning
Sun, Jiawei
Shi, Liuhong
Xue, Meng
A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis
title A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis
title_full A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis
title_fullStr A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis
title_full_unstemmed A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis
title_short A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis
title_sort clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis
topic Clinical Cancer Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7897959/
https://www.ncbi.nlm.nih.gov/pubmed/33350173
http://dx.doi.org/10.1002/cam4.3680
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