[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123811-en":3,"doc-seo-123811-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123811,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Clinical application of machine learning-based pathomics signature of gastric atrophy","Gastric atrophy diagnosis remains highly subjective, limiting consistency across clinicians. This study established a machine learning model using pathological features to improve diagnostic agreement. Retrospectively collected HE-stained biopsy slides were segmented and analyzed with CellProfiler to extract quantitative pathomics features. LASSO selected key features, and multiple ML algorithms built diagnostic models. Model performance was evaluated with training, testing, and external validation cohorts. The resulting pathological score based on the LR model correlated with endoscopic atrophy grading and gastric cancer risk.","TYPE Original Research PUBLISHED 27 February 2024 DOI 10.3389/fonc.2024.1289265  \nOPEN ACCESS  \nEDITED BY  \nMingzhou Guo,  \nPeople’s Liberation Army General Hospital, China  \nREVIEWED BY Shahin Sayed, Nairobi, Kenya Gang Sun,  \nThe First Affiliated Hospital of People’s Liberation Army General Hospital, China  \n*CORRESPONDENCE Hongwei Xu  \n [xhwsdslyy@sina.com](xhwsdslyy@sina.com)  \nRECEIVED 05 September 2023  \nACCEPTED 05 February 2024  \nPUBLISHED 27 February 2024  \nCITATION  \nLan Y, Han B, Zhai T, Xu Q, Li Z, Liu M, Xue Y and Xu H (2024) Clinical application of machine learning‐based pathomics signature of gastric atrophy.  \nFront. Oncol. 14:1289265 .  \ndoi: 10.3389/fonc.2024.1289265  \nCOPYRIGHT  \n© 2024 Lan, Han, Zhai, Xu, Li, Liu, Xue and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nClinical application of machine learning‐based pathomics signature of gastric atrophy  \nYadi Lan 1, Bing Han 1, Tianyu Zhai 1, Qianqian Xu 1, Zhiwei Li 2, Mingyue Liu 1, Yining Xue 2 and Hongwei Xu 1,2*  \n1 Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China, 2 Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China  \nBackground: The diagnosis of gastric atrophy is highly subjective, and we aimed to establish a model of gastric atrophy based on pathological features to improve diagnostic consistency.  \nMethods: We retrospectively collected the HE-stained pathological slides of gastric biopsies and used CellProfiler software for image segmentation and feature extraction of ten representative images for each sample. Subsequently, we employed the Least absolute shrinkage and selection operator (LASSO) to select features and different machine learning (ML) algorithms to construct the diagnostic models for gastric atrophy.  \nResults: We selected 289 gastric biopsy specimens for training, testing, and external validation. We extracted 464 pathological features and screened ten features by LASSO to establish the diagnostic model for moderate-to-severe atrophy. The range of area under the curve (AUC) for various machine learning algorithms was 0.835-1.000 in the training set, 0.786-0.949 in the testing set, and 0 .689-0. 818 in the external validation set. LR model had the highest AUC value, with 0 . 900 (95% CI: 0 .852-0. 947) in the training set, 0 . 901 (95% CI: 0 .807- 0.996) in the testing set, and 0.818 (95% CI: 0.714-0.923) in the external validation set. The atrophy pathological score based on the LR model was associated with endoscopic atrophy grading (Z=-2.478, P=0.013) and gastric cancer (GC) (OR=5 .70, 95% CI: 2 .63-12.33, P\u003C0 . 001) .  \nConclusion: The ML model based on pathological features could improve the diagnostic consistency of gastric atrophy, which is also associated with endoscopic atrophy grading and GC.  \nKEYWORDS  \npathology, machine learning, gastric, atrophy, cancer  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nAccording to the latest global cancer statistics, gastric cancer (GC) is one of the most common cancers and a significant contributor to cancer-related mortality due to late diagnosis (1) . Intestinal gastric adenocarcinoma (GA) is the predominant type of GC, following a process known as the Correa cascade, progressing from chronic inflammation to atrophy and intestinal metaplasia, then to dysplasia, and finally to GC (2) . Consequently, the early identification of atrophy and intestinal metaplasia plays a crucial role in the timely diagnosis and treatment of ","cbCairKXl5VaAhxN","https://ap.wps.com/l/cbCairKXl5VaAhxN","pdf",4813750,1,9,"English","en",105,"# Introduction\n## Background and rationale\n## Pathomics and machine learning approach\n# Methods\n## Study population\n## Feature extraction and model construction\n# Results\n## Model performance across cohorts\n## Association with endoscopic grading and gastric cancer\n# Conclusion","[{\"question\":\"Why is gastric atrophy diagnosis difficult in clinical practice?\",\"answer\":\"Gastric atrophy grading from biopsy is subjective and depends on strict sampling requirements, leading to low inter- and intra-observer agreement.\"},{\"question\":\"How were the machine learning diagnostic models constructed?\",\"answer\":\"HE-stained slides were digitized, segmented, and processed with CellProfiler to extract quantitative pathomics features. LASSO selected features, and multiple ML algorithms built diagnostic models.\"},{\"question\":\"What did the model show regarding diagnostic performance?\",\"answer\":\"Across training, testing, and external validation sets, AUC values varied by algorithm, with the LR model showing the highest AUC performance in each cohort.\"},{\"question\":\"What clinical associations were observed for the model score?\",\"answer\":\"The atrophy pathological score from the LR model was associated with endoscopic atrophy grading and with gastric cancer risk.\"}]","Clinical application of machine learning-based pathomics signature of gastric atrophy | PDF",1785818671,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"clinical-application-of-machine-learning-based-pathomics-signature-of-gastric-atrophy","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/clinical-application-of-machine-learning-based-pathomics-signature-of-gastric-atrophy/123811/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why is gastric atrophy diagnosis difficult in clinical practice?","Question",{"text":75,"@type":76},"Gastric atrophy grading from biopsy is subjective and depends on strict sampling requirements, leading to low inter- and intra-observer agreement.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine learning diagnostic models constructed?",{"text":80,"@type":76},"HE-stained slides were digitized, segmented, and processed with CellProfiler to extract quantitative pathomics features. LASSO selected features, and multiple ML algorithms built diagnostic models.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the model show regarding diagnostic performance?",{"text":84,"@type":76},"Across training, testing, and external validation sets, AUC values varied by algorithm, with the LR model showing the highest AUC performance in each cohort.",{"name":86,"@type":73,"acceptedAnswer":87},"What clinical associations were observed for the model score?",{"text":88,"@type":76},"The atrophy pathological score from the LR model was associated with endoscopic atrophy grading and with gastric cancer risk.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]