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A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma
BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use. METHODS: In the population-based cohort study, we collected clinical characteristics from the Surveillanc...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10643950/ https://www.ncbi.nlm.nih.gov/pubmed/37969363 http://dx.doi.org/10.21037/tcr-23-422 |
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author | Li, Wei Zhang, Minghang Cai, Siyu Li, Siqi Yang, Biao Zhou, Shijie Pan, Yuanming Xu, Shaofa |
author_facet | Li, Wei Zhang, Minghang Cai, Siyu Li, Siqi Yang, Biao Zhou, Shijie Pan, Yuanming Xu, Shaofa |
author_sort | Li, Wei |
collection | PubMed |
description | BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use. METHODS: In the population-based cohort study, we collected clinical characteristics from the Surveillance, Epidemiology, and End Results (SEER) database. According to the time of diagnosis, the SEER data were divided into 2 cohorts: the training cohort (from 2010 to 2016) and the test cohort (from 2017 to 2019). The training cohort was used to train a deep learning-based predictive model derived from DeepSurv theory, which was validated by both the training and the test cohorts. All clinical characteristics were included and analyzed using Cox proportional risk regression or Kaplan-Meier curve to determine the risk factors and protective factors of MPM. RESULTS: The survival model included 3,130 cases (2,208 in the training cohort and 922 in the test cohort). As for model’s performance, the area under the receiver operating characteristics curve (AUC) was 0.7037 [95% confidence interval (CI): 0.7030–0.7045] in the training cohort and 0.7076 (95% CI: 0.7067–0.7086) in the test cohort. Older age; male sex, sarcomatoid mesothelioma; and T4, N2, and M1 stage tended to be the risk factors for survival. Meanwhile, epithelioid mesothelioma, surgery, radiotherapy, and chemotherapy tended to be the protective factors. The median overall survival (OS) of patients who underwent surgery combined with radiotherapy was the longest, followed by those who underwent a combination of surgery, radiotherapy, and chemotherapy. CONCLUSIONS: Our deep learning-based model precisely could predict the survival of patients with MPM; moreover, multimode combination therapy might provide more meaningful survival benefits. |
format | Online Article Text |
id | pubmed-10643950 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-106439502023-11-15 A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma Li, Wei Zhang, Minghang Cai, Siyu Li, Siqi Yang, Biao Zhou, Shijie Pan, Yuanming Xu, Shaofa Transl Cancer Res Original Article BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use. METHODS: In the population-based cohort study, we collected clinical characteristics from the Surveillance, Epidemiology, and End Results (SEER) database. According to the time of diagnosis, the SEER data were divided into 2 cohorts: the training cohort (from 2010 to 2016) and the test cohort (from 2017 to 2019). The training cohort was used to train a deep learning-based predictive model derived from DeepSurv theory, which was validated by both the training and the test cohorts. All clinical characteristics were included and analyzed using Cox proportional risk regression or Kaplan-Meier curve to determine the risk factors and protective factors of MPM. RESULTS: The survival model included 3,130 cases (2,208 in the training cohort and 922 in the test cohort). As for model’s performance, the area under the receiver operating characteristics curve (AUC) was 0.7037 [95% confidence interval (CI): 0.7030–0.7045] in the training cohort and 0.7076 (95% CI: 0.7067–0.7086) in the test cohort. Older age; male sex, sarcomatoid mesothelioma; and T4, N2, and M1 stage tended to be the risk factors for survival. Meanwhile, epithelioid mesothelioma, surgery, radiotherapy, and chemotherapy tended to be the protective factors. The median overall survival (OS) of patients who underwent surgery combined with radiotherapy was the longest, followed by those who underwent a combination of surgery, radiotherapy, and chemotherapy. CONCLUSIONS: Our deep learning-based model precisely could predict the survival of patients with MPM; moreover, multimode combination therapy might provide more meaningful survival benefits. AME Publishing Company 2023-09-22 2023-10-31 /pmc/articles/PMC10643950/ /pubmed/37969363 http://dx.doi.org/10.21037/tcr-23-422 Text en 2023 Translational Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Li, Wei Zhang, Minghang Cai, Siyu Li, Siqi Yang, Biao Zhou, Shijie Pan, Yuanming Xu, Shaofa A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma |
title | A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma |
title_full | A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma |
title_fullStr | A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma |
title_full_unstemmed | A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma |
title_short | A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma |
title_sort | deep learning-based model (deepmpm) to help predict survival in patients with malignant pleural mesothelioma |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10643950/ https://www.ncbi.nlm.nih.gov/pubmed/37969363 http://dx.doi.org/10.21037/tcr-23-422 |
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