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Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study
Small cell carcinoma of the bladder (SCCB) is a rare urological tumor. The prognosis of SCCB is abysmal. Therefore, this study aimed to construct nomograms that predict overall survival (OS) and cancer-specific survival (CSS) in SCCB patients. Information on patients diagnosed with SCCB during 2004–...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229646/ https://www.ncbi.nlm.nih.gov/pubmed/37253772 http://dx.doi.org/10.1038/s41598-023-35761-w |
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author | Zhanghuang, Chenghao Zhang, Zhaoxia Wang, Jinkui Yao, Zhigang Ji, Fengming Wu, Chengchuang Ma, Jing Yang, Zhen Xie, Yucheng Tang, Haoyu Yan, Bing |
author_facet | Zhanghuang, Chenghao Zhang, Zhaoxia Wang, Jinkui Yao, Zhigang Ji, Fengming Wu, Chengchuang Ma, Jing Yang, Zhen Xie, Yucheng Tang, Haoyu Yan, Bing |
author_sort | Zhanghuang, Chenghao |
collection | PubMed |
description | Small cell carcinoma of the bladder (SCCB) is a rare urological tumor. The prognosis of SCCB is abysmal. Therefore, this study aimed to construct nomograms that predict overall survival (OS) and cancer-specific survival (CSS) in SCCB patients. Information on patients diagnosed with SCCB during 2004–2018 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models analyzed Independent risk factors affecting patients' OS and CSS. Nomograms predicting the OS and CSS were constructed based on the multivariate Cox regression model results. The calibration curve verified the accuracy and reliability of the nomograms, the concordance index (C-index), and the area under the curve (AUC). Decision curve analysis (DCA) assessed the potential clinical value. 975 patients were included in the training set (N = 687) and the validation set (N = 288). Multivariate COX regression models showed that age, marital status, AJCC stage, T stage, M stage, surgical approach, chemotherapy, tumor size, and lung metastasis were independent risk factors affecting the patients' OS. However, distant lymph node metastasis instead AJCC stage is the independent risk factor affecting the CSS in the patients. We successfully constructed nomograms that predict the OS and CSS for SCCB patients. The C index of the training set and the validation set of the OS were 0.747 (95% CI 0.725–0.769) and 0.765 (95% CI 0.736–0.794), respectively. The C index of the CSS were 0.749 (95% CI 0.710–0.773) and 0.786 (95% CI 0.755–0.817), respectively, indicating that the predictive models of the nomograms have excellent discriminative power. The calibration curve and the AUC also show good accuracy and discrimination of the nomograms. To sum up, We established nomograms to predict the OS and CSS of SCCB patients. The nomograms have undergone internal cross-validation and show good accuracy and reliability. The DCA shows that the nomograms have an excellent clinical value that can help doctors make clinical-assisted decision-making. |
format | Online Article Text |
id | pubmed-10229646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102296462023-06-01 Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study Zhanghuang, Chenghao Zhang, Zhaoxia Wang, Jinkui Yao, Zhigang Ji, Fengming Wu, Chengchuang Ma, Jing Yang, Zhen Xie, Yucheng Tang, Haoyu Yan, Bing Sci Rep Article Small cell carcinoma of the bladder (SCCB) is a rare urological tumor. The prognosis of SCCB is abysmal. Therefore, this study aimed to construct nomograms that predict overall survival (OS) and cancer-specific survival (CSS) in SCCB patients. Information on patients diagnosed with SCCB during 2004–2018 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models analyzed Independent risk factors affecting patients' OS and CSS. Nomograms predicting the OS and CSS were constructed based on the multivariate Cox regression model results. The calibration curve verified the accuracy and reliability of the nomograms, the concordance index (C-index), and the area under the curve (AUC). Decision curve analysis (DCA) assessed the potential clinical value. 975 patients were included in the training set (N = 687) and the validation set (N = 288). Multivariate COX regression models showed that age, marital status, AJCC stage, T stage, M stage, surgical approach, chemotherapy, tumor size, and lung metastasis were independent risk factors affecting the patients' OS. However, distant lymph node metastasis instead AJCC stage is the independent risk factor affecting the CSS in the patients. We successfully constructed nomograms that predict the OS and CSS for SCCB patients. The C index of the training set and the validation set of the OS were 0.747 (95% CI 0.725–0.769) and 0.765 (95% CI 0.736–0.794), respectively. The C index of the CSS were 0.749 (95% CI 0.710–0.773) and 0.786 (95% CI 0.755–0.817), respectively, indicating that the predictive models of the nomograms have excellent discriminative power. The calibration curve and the AUC also show good accuracy and discrimination of the nomograms. To sum up, We established nomograms to predict the OS and CSS of SCCB patients. The nomograms have undergone internal cross-validation and show good accuracy and reliability. The DCA shows that the nomograms have an excellent clinical value that can help doctors make clinical-assisted decision-making. Nature Publishing Group UK 2023-05-30 /pmc/articles/PMC10229646/ /pubmed/37253772 http://dx.doi.org/10.1038/s41598-023-35761-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhanghuang, Chenghao Zhang, Zhaoxia Wang, Jinkui Yao, Zhigang Ji, Fengming Wu, Chengchuang Ma, Jing Yang, Zhen Xie, Yucheng Tang, Haoyu Yan, Bing Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study |
title | Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study |
title_full | Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study |
title_fullStr | Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study |
title_full_unstemmed | Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study |
title_short | Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study |
title_sort | surveillance of prognostic risk factors in patients with sccb using artificial intelligence: a retrospective study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229646/ https://www.ncbi.nlm.nih.gov/pubmed/37253772 http://dx.doi.org/10.1038/s41598-023-35761-w |
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