{"title":"食管癌病理分类与分期的深度学习模型。","authors":"Himanshu Agrawal, Nikhil Gupta","doi":"10.5306/wjco.v16.i8.109893","DOIUrl":null,"url":null,"abstract":"<p><p>This letter comments on Wei <i>et al</i>'s study applying the Wave-Vision Transformer for oesophageal cancer classification. Highlighting its superior accuracy and efficiency, we discuss its potential clinical impact, limitations in dataset diversity, and the need for explainable artificial intelligence to enhance adoption in pathology and personalized treatment.</p>","PeriodicalId":23802,"journal":{"name":"World journal of clinical oncology","volume":"16 8","pages":"109893"},"PeriodicalIF":3.2000,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12400239/pdf/","citationCount":"0","resultStr":"{\"title\":\"Deep learning models for pathological classification and staging of oesophageal cancer.\",\"authors\":\"Himanshu Agrawal, Nikhil Gupta\",\"doi\":\"10.5306/wjco.v16.i8.109893\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This letter comments on Wei <i>et al</i>'s study applying the Wave-Vision Transformer for oesophageal cancer classification. Highlighting its superior accuracy and efficiency, we discuss its potential clinical impact, limitations in dataset diversity, and the need for explainable artificial intelligence to enhance adoption in pathology and personalized treatment.</p>\",\"PeriodicalId\":23802,\"journal\":{\"name\":\"World journal of clinical oncology\",\"volume\":\"16 8\",\"pages\":\"109893\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12400239/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"World journal of clinical oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5306/wjco.v16.i8.109893\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"World journal of clinical oncology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5306/wjco.v16.i8.109893","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Deep learning models for pathological classification and staging of oesophageal cancer.
This letter comments on Wei et al's study applying the Wave-Vision Transformer for oesophageal cancer classification. Highlighting its superior accuracy and efficiency, we discuss its potential clinical impact, limitations in dataset diversity, and the need for explainable artificial intelligence to enhance adoption in pathology and personalized treatment.
期刊介绍:
The WJCO is a high-quality, peer reviewed, open-access journal. The primary task of WJCO is to rapidly publish high-quality original articles, reviews, editorials, and case reports in the field of oncology. In order to promote productive academic communication, the peer review process for the WJCO is transparent; to this end, all published manuscripts are accompanied by the anonymized reviewers’ comments as well as the authors’ responses. The primary aims of the WJCO are to improve diagnostic, therapeutic and preventive modalities and the skills of clinicians and to guide clinical practice in oncology. Scope: Art of Oncology, Biology of Neoplasia, Breast Cancer, Cancer Prevention and Control, Cancer-Related Complications, Diagnosis in Oncology, Gastrointestinal Cancer, Genetic Testing For Cancer, Gynecologic Cancer, Head and Neck Cancer, Hematologic Malignancy, Lung Cancer, Melanoma, Molecular Oncology, Neurooncology, Palliative and Supportive Care, Pediatric Oncology, Surgical Oncology, Translational Oncology, and Urologic Oncology.