建立肺腺癌组织学精准亚型的人工智能模型,并将其应用于定量和空间分析。

IF 1.9 4区 医学 Q3 ONCOLOGY Japanese journal of clinical oncology Pub Date : 2024-09-04 DOI:10.1093/jjco/hyae066
Eisuke Miura, Katsura Emoto, Tokiya Abe, Akinori Hashiguchi, Tomoyuki Hishida, Keisuke Asakura, Michiie Sakamoto
{"title":"建立肺腺癌组织学精准亚型的人工智能模型,并将其应用于定量和空间分析。","authors":"Eisuke Miura, Katsura Emoto, Tokiya Abe, Akinori Hashiguchi, Tomoyuki Hishida, Keisuke Asakura, Michiie Sakamoto","doi":"10.1093/jjco/hyae066","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify lung adenocarcinoma images into seven histological subtypes and adopted the model for whole-slide images to investigate the relationship between the distribution of histological subtypes and clinicopathological factors.</p><p><strong>Methods: </strong>Using histological subtype images, which are typical for pathologists, we trained and validated an artificial intelligence model. Then, the model was applied to whole-slide images of resected lung adenocarcinoma specimens from 147 cases.</p><p><strong>Result: </strong>The model achieved an accuracy of 99.7% in training sets and 90.4% in validation sets consisting of typical tiles of histological subtyping for pathologists. When the model was applied to whole-slide images, the predominant subtype according to the artificial intelligence model classification matched that determined by pathologists in 75.5% of cases. The predominant subtype and tumor grade (using the WHO fourth and fifth classifications) determined by the artificial intelligence model resulted in similar recurrence-free survival curves to those determined by pathologists. Furthermore, we stratified the recurrence-free survival curves for patients with different proportions of high-grade components (solid, micropapillary and cribriform) according to the physical distribution of the high-grade component. The results suggested that tumors with centrally located high-grade components had a higher malignant potential (P < 0.001 for 5-20% high-grade component).</p><p><strong>Conclusion: </strong>The new artificial intelligence model for histological subtyping of lung adenocarcinoma achieved high accuracy, and subtype quantification and subtype distribution analyses could be achieved. Artificial intelligence model therefore has potential for clinical application for both quantification and spatial analysis.</p>","PeriodicalId":14656,"journal":{"name":"Japanese journal of clinical oncology","volume":" ","pages":"1009-1023"},"PeriodicalIF":1.9000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Establishment of artificial intelligence model for precise histological subtyping of lung adenocarcinoma and its application to quantitative and spatial analysis.\",\"authors\":\"Eisuke Miura, Katsura Emoto, Tokiya Abe, Akinori Hashiguchi, Tomoyuki Hishida, Keisuke Asakura, Michiie Sakamoto\",\"doi\":\"10.1093/jjco/hyae066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify lung adenocarcinoma images into seven histological subtypes and adopted the model for whole-slide images to investigate the relationship between the distribution of histological subtypes and clinicopathological factors.</p><p><strong>Methods: </strong>Using histological subtype images, which are typical for pathologists, we trained and validated an artificial intelligence model. Then, the model was applied to whole-slide images of resected lung adenocarcinoma specimens from 147 cases.</p><p><strong>Result: </strong>The model achieved an accuracy of 99.7% in training sets and 90.4% in validation sets consisting of typical tiles of histological subtyping for pathologists. When the model was applied to whole-slide images, the predominant subtype according to the artificial intelligence model classification matched that determined by pathologists in 75.5% of cases. The predominant subtype and tumor grade (using the WHO fourth and fifth classifications) determined by the artificial intelligence model resulted in similar recurrence-free survival curves to those determined by pathologists. Furthermore, we stratified the recurrence-free survival curves for patients with different proportions of high-grade components (solid, micropapillary and cribriform) according to the physical distribution of the high-grade component. The results suggested that tumors with centrally located high-grade components had a higher malignant potential (P < 0.001 for 5-20% high-grade component).</p><p><strong>Conclusion: </strong>The new artificial intelligence model for histological subtyping of lung adenocarcinoma achieved high accuracy, and subtype quantification and subtype distribution analyses could be achieved. Artificial intelligence model therefore has potential for clinical application for both quantification and spatial analysis.</p>\",\"PeriodicalId\":14656,\"journal\":{\"name\":\"Japanese journal of clinical oncology\",\"volume\":\" \",\"pages\":\"1009-1023\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Japanese journal of clinical oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/jjco/hyae066\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Japanese journal of clinical oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/jjco/hyae066","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

背景:肺腺癌的组织学亚型是一个重要的预后因素。我们开发了一种新的人工智能模型,将肺腺癌图像分为七种组织学亚型,并将该模型用于全滑动图像,研究组织学亚型分布与临床病理因素之间的关系:方法:利用病理学家典型的组织学亚型图像,我们训练并验证了一个人工智能模型。然后,将该模型应用于 147 例切除的肺腺癌标本的全切片图像:结果:该模型在训练集中的准确率为 99.7%,在验证集中的准确率为 90.4%。当该模型应用于整张切片图像时,根据人工智能模型分类得出的主要亚型与病理学家确定的亚型相符的病例占 75.5%。人工智能模型确定的主要亚型和肿瘤分级(采用世界卫生组织第四和第五分级)与病理学家确定的无复发生存曲线相似。此外,我们还根据高级别成分的物理分布情况,对高级别成分(实性、微乳头状和楔形)比例不同的患者的无复发生存曲线进行了分层。结果表明,高级别成分位于中心位置的肿瘤具有更高的恶性潜能(P 结论):用于肺腺癌组织学亚型分析的新型人工智能模型具有较高的准确性,可实现亚型量化和亚型分布分析。因此,人工智能模型在量化和空间分析方面具有临床应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Establishment of artificial intelligence model for precise histological subtyping of lung adenocarcinoma and its application to quantitative and spatial analysis.

Background: The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify lung adenocarcinoma images into seven histological subtypes and adopted the model for whole-slide images to investigate the relationship between the distribution of histological subtypes and clinicopathological factors.

Methods: Using histological subtype images, which are typical for pathologists, we trained and validated an artificial intelligence model. Then, the model was applied to whole-slide images of resected lung adenocarcinoma specimens from 147 cases.

Result: The model achieved an accuracy of 99.7% in training sets and 90.4% in validation sets consisting of typical tiles of histological subtyping for pathologists. When the model was applied to whole-slide images, the predominant subtype according to the artificial intelligence model classification matched that determined by pathologists in 75.5% of cases. The predominant subtype and tumor grade (using the WHO fourth and fifth classifications) determined by the artificial intelligence model resulted in similar recurrence-free survival curves to those determined by pathologists. Furthermore, we stratified the recurrence-free survival curves for patients with different proportions of high-grade components (solid, micropapillary and cribriform) according to the physical distribution of the high-grade component. The results suggested that tumors with centrally located high-grade components had a higher malignant potential (P < 0.001 for 5-20% high-grade component).

Conclusion: The new artificial intelligence model for histological subtyping of lung adenocarcinoma achieved high accuracy, and subtype quantification and subtype distribution analyses could be achieved. Artificial intelligence model therefore has potential for clinical application for both quantification and spatial analysis.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.70
自引率
8.30%
发文量
177
审稿时长
3-8 weeks
期刊介绍: Japanese Journal of Clinical Oncology is a multidisciplinary journal for clinical oncologists which strives to publish high quality manuscripts addressing medical oncology, clinical trials, radiology, surgery, basic research, and palliative care. The journal aims to contribute to the world"s scientific community with special attention to the area of clinical oncology and the Asian region. JJCO publishes various articles types including: ・Original Articles ・Case Reports ・Clinical Trial Notes ・Cancer Genetics Reports ・Epidemiology Notes ・Technical Notes ・Short Communications ・Letters to the Editors ・Solicited Reviews
期刊最新文献
Projection of the number of new brain and central nervous system cancer cases in the world. Comparative analysis of oncological outcomes between trimodal therapy and radical cystectomy in muscle-invasive bladder cancer utilizing propensity score matching. Individual survival prediction model for patients with leptomeningeal metastasis. Authors' reply to 'RE: A real-world survey on expensive drugs used as first-line chemotherapy in patients with HER2-negative unresectable advanced/recurrent gastric cancer in the stomach cancer study group of the Japan clinical oncology group'. Predictors of nodal upstaging in clinical N1 nonsmall cell lung cancer.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1