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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
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.
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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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