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Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram
Peritoneal recurrence is the most frequent and lethal recurrence pattern in gastric cancer (GC) with serosal invasion after radical surgery. However, current evaluation methods are not adequate for predicting peritoneal recurrence in GC with serosal invasion. Emerging evidence shows that pathomics a...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040964/ https://www.ncbi.nlm.nih.gov/pubmed/36994190 http://dx.doi.org/10.1016/j.isci.2023.106246 |
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author | Chen, Dexin Lai, Jianbo Cheng, Jiaxin Fu, Meiting Lin, Liyan Chen, Feng Huang, Rong Chen, Jun Lu, Jianping Chen, Yuning Huang, Guangyao Yan, Miaojia Ma, Xiaodan Li, Guoxin Chen, Gang Yan, Jun |
author_facet | Chen, Dexin Lai, Jianbo Cheng, Jiaxin Fu, Meiting Lin, Liyan Chen, Feng Huang, Rong Chen, Jun Lu, Jianping Chen, Yuning Huang, Guangyao Yan, Miaojia Ma, Xiaodan Li, Guoxin Chen, Gang Yan, Jun |
author_sort | Chen, Dexin |
collection | PubMed |
description | Peritoneal recurrence is the most frequent and lethal recurrence pattern in gastric cancer (GC) with serosal invasion after radical surgery. However, current evaluation methods are not adequate for predicting peritoneal recurrence in GC with serosal invasion. Emerging evidence shows that pathomics analyses could be advantageous for risk stratification and outcome prediction. Herein, we propose a pathomics signature composed of multiple pathomics features extracted from digital hematoxylin and eosin-stained images. We found that the pathomics signature was significantly associated with peritoneal recurrence. A competing-risk pathomics nomogram including carbohydrate antigen 19-9 level, depth of invasion, lymph node metastasis, and pathomics signature was developed for predicting peritoneal recurrence. The pathomics nomogram had favorable discrimination and calibration. Thus, the pathomics signature is a predictive indicator of peritoneal recurrence, and the pathomics nomogram may provide a helpful reference for predicting an individual’s risk in peritoneal recurrence of GC with serosal invasion. |
format | Online Article Text |
id | pubmed-10040964 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-100409642023-03-28 Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram Chen, Dexin Lai, Jianbo Cheng, Jiaxin Fu, Meiting Lin, Liyan Chen, Feng Huang, Rong Chen, Jun Lu, Jianping Chen, Yuning Huang, Guangyao Yan, Miaojia Ma, Xiaodan Li, Guoxin Chen, Gang Yan, Jun iScience Article Peritoneal recurrence is the most frequent and lethal recurrence pattern in gastric cancer (GC) with serosal invasion after radical surgery. However, current evaluation methods are not adequate for predicting peritoneal recurrence in GC with serosal invasion. Emerging evidence shows that pathomics analyses could be advantageous for risk stratification and outcome prediction. Herein, we propose a pathomics signature composed of multiple pathomics features extracted from digital hematoxylin and eosin-stained images. We found that the pathomics signature was significantly associated with peritoneal recurrence. A competing-risk pathomics nomogram including carbohydrate antigen 19-9 level, depth of invasion, lymph node metastasis, and pathomics signature was developed for predicting peritoneal recurrence. The pathomics nomogram had favorable discrimination and calibration. Thus, the pathomics signature is a predictive indicator of peritoneal recurrence, and the pathomics nomogram may provide a helpful reference for predicting an individual’s risk in peritoneal recurrence of GC with serosal invasion. Elsevier 2023-03-03 /pmc/articles/PMC10040964/ /pubmed/36994190 http://dx.doi.org/10.1016/j.isci.2023.106246 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Chen, Dexin Lai, Jianbo Cheng, Jiaxin Fu, Meiting Lin, Liyan Chen, Feng Huang, Rong Chen, Jun Lu, Jianping Chen, Yuning Huang, Guangyao Yan, Miaojia Ma, Xiaodan Li, Guoxin Chen, Gang Yan, Jun Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram |
title | Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram |
title_full | Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram |
title_fullStr | Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram |
title_full_unstemmed | Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram |
title_short | Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram |
title_sort | predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040964/ https://www.ncbi.nlm.nih.gov/pubmed/36994190 http://dx.doi.org/10.1016/j.isci.2023.106246 |
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