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Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data
BACKGROUND: Primary sclerosing cholangitis (PSC) patients have a risk of developing cholangiocarcinoma (CCA). Establishing predictive models for CCA in PSC is important. METHODS: In a large cohort of 1,459 PSC patients seen at Mayo Clinic (1993–2020), we quantified the impact of clinical/laboratory...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114387/ https://www.ncbi.nlm.nih.gov/pubmed/37076803 http://dx.doi.org/10.1186/s12876-023-02759-7 |
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author | Hu, Chang Iyer, Ravishankar K. Juran, Brian D. McCauley, Bryan M. Atkinson, Elizabeth J. Eaton, John E. Ali, Ahmad H. Lazaridis, Konstantinos N. |
author_facet | Hu, Chang Iyer, Ravishankar K. Juran, Brian D. McCauley, Bryan M. Atkinson, Elizabeth J. Eaton, John E. Ali, Ahmad H. Lazaridis, Konstantinos N. |
author_sort | Hu, Chang |
collection | PubMed |
description | BACKGROUND: Primary sclerosing cholangitis (PSC) patients have a risk of developing cholangiocarcinoma (CCA). Establishing predictive models for CCA in PSC is important. METHODS: In a large cohort of 1,459 PSC patients seen at Mayo Clinic (1993–2020), we quantified the impact of clinical/laboratory variables on CCA development using univariate and multivariate Cox models and predicted CCA using statistical and artificial intelligence (AI) approaches. We explored plasma bile acid (BA) levels’ predictive power of CCA (subset of 300 patients, BA cohort). RESULTS: Eight significant risk factors (false discovery rate: 20%) were identified with univariate analysis; prolonged inflammatory bowel disease (IBD) was the most important one. IBD duration, PSC duration, and total bilirubin remained significant (p < 0.05) with multivariate analysis. Clinical/laboratory variables predicted CCA with cross-validated C-indexes of 0.68–0.71 at different time points of disease, significantly better compared to commonly used PSC risk scores. Lower chenodeoxycholic acid, higher conjugated fraction of lithocholic acid and hyodeoxycholic acid, and higher ratio of cholic acid to chenodeoxycholic acid were predictive of CCA. BAs predicted CCA with a cross-validated C-index of 0.66 (std: 0.11, BA cohort), similar to clinical/laboratory variables (C-index = 0.64, std: 0.11, BA cohort). Combining BAs with clinical/laboratory variables leads to the best average C-index of 0.67 (std: 0.13, BA cohort). CONCLUSIONS: In a large PSC cohort, we identified clinical and laboratory risk factors for CCA development and demonstrated the first AI based predictive models that performed significantly better than commonly used PSC risk scores. More predictive data modalities are needed for clinical adoption of these models. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12876-023-02759-7. |
format | Online Article Text |
id | pubmed-10114387 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101143872023-04-20 Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data Hu, Chang Iyer, Ravishankar K. Juran, Brian D. McCauley, Bryan M. Atkinson, Elizabeth J. Eaton, John E. Ali, Ahmad H. Lazaridis, Konstantinos N. BMC Gastroenterol Research BACKGROUND: Primary sclerosing cholangitis (PSC) patients have a risk of developing cholangiocarcinoma (CCA). Establishing predictive models for CCA in PSC is important. METHODS: In a large cohort of 1,459 PSC patients seen at Mayo Clinic (1993–2020), we quantified the impact of clinical/laboratory variables on CCA development using univariate and multivariate Cox models and predicted CCA using statistical and artificial intelligence (AI) approaches. We explored plasma bile acid (BA) levels’ predictive power of CCA (subset of 300 patients, BA cohort). RESULTS: Eight significant risk factors (false discovery rate: 20%) were identified with univariate analysis; prolonged inflammatory bowel disease (IBD) was the most important one. IBD duration, PSC duration, and total bilirubin remained significant (p < 0.05) with multivariate analysis. Clinical/laboratory variables predicted CCA with cross-validated C-indexes of 0.68–0.71 at different time points of disease, significantly better compared to commonly used PSC risk scores. Lower chenodeoxycholic acid, higher conjugated fraction of lithocholic acid and hyodeoxycholic acid, and higher ratio of cholic acid to chenodeoxycholic acid were predictive of CCA. BAs predicted CCA with a cross-validated C-index of 0.66 (std: 0.11, BA cohort), similar to clinical/laboratory variables (C-index = 0.64, std: 0.11, BA cohort). Combining BAs with clinical/laboratory variables leads to the best average C-index of 0.67 (std: 0.13, BA cohort). CONCLUSIONS: In a large PSC cohort, we identified clinical and laboratory risk factors for CCA development and demonstrated the first AI based predictive models that performed significantly better than commonly used PSC risk scores. More predictive data modalities are needed for clinical adoption of these models. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12876-023-02759-7. BioMed Central 2023-04-19 /pmc/articles/PMC10114387/ /pubmed/37076803 http://dx.doi.org/10.1186/s12876-023-02759-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Hu, Chang Iyer, Ravishankar K. Juran, Brian D. McCauley, Bryan M. Atkinson, Elizabeth J. Eaton, John E. Ali, Ahmad H. Lazaridis, Konstantinos N. Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data |
title | Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data |
title_full | Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data |
title_fullStr | Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data |
title_full_unstemmed | Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data |
title_short | Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data |
title_sort | predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114387/ https://www.ncbi.nlm.nih.gov/pubmed/37076803 http://dx.doi.org/10.1186/s12876-023-02759-7 |
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