在数字/非 WSI 图像上检测结直肠癌淋巴结转移沉积物的模型

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Talat Zehra, Sarosh Moeen, Mahin Shams, Muhammad Raza, Amna Khurshid, Asad Jafri, Jamshid Abdul-Ghafar
{"title":"在数字/非 WSI 图像上检测结直肠癌淋巴结转移沉积物的模型","authors":"Talat Zehra, Sarosh Moeen, Mahin Shams, Muhammad Raza, Amna Khurshid, Asad Jafri, Jamshid Abdul-Ghafar","doi":"10.1186/s13000-024-01547-5","DOIUrl":null,"url":null,"abstract":"Colorectal cancer (CRC) constitutes around 10% of global cancer diagnoses and death due to cancer. Treatment involves the surgical resection of the tumor and regional lymph nodes. Assessment of multiple lymph node demands meticulous examination by skilled pathologists, which can be arduous, prompting consideration for an artificial intelligence (AI)-supported workflow due to the growing number of slides to be examined, demanding heightened precision and the global shortage of pathologists. This was a retrospective cross-sectional study including digital images of glass slides containing sections of positive and negative lymph nodes obtained from radical resection of primary CRC. Lymph nodes from 165 previously diagnosed cases were selected from Agha Khan University Hospital, from Jan 2021 to Jan 2022. The images were prepared at 10X and uploaded into an open source software, Q path and deep learning model Ensemble was applied for the identification of tumor deposits in lymph node. Out of the 87 positive lymph nodes detected by AI, 73(84%) were true positive and 14(16%) were false positive. The total number of negative lymph nodes detected by AI was 78. Out of these, 69(88.5%) were true negative and 9 (11.5%) were false negative. The sensitivity was 89% and specificity 83.1%. The odds ratio was 40 with a confidence interval of 16.26–98.3. P-value was < 0.05 (< 0.0001). Though it was a small study but its results were really appreciating and we encourage more such studies with big sample data in future.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Model for detecting metastatic deposits in lymph nodes of colorectal carcinoma on digital/ non-WSI images\",\"authors\":\"Talat Zehra, Sarosh Moeen, Mahin Shams, Muhammad Raza, Amna Khurshid, Asad Jafri, Jamshid Abdul-Ghafar\",\"doi\":\"10.1186/s13000-024-01547-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Colorectal cancer (CRC) constitutes around 10% of global cancer diagnoses and death due to cancer. Treatment involves the surgical resection of the tumor and regional lymph nodes. Assessment of multiple lymph node demands meticulous examination by skilled pathologists, which can be arduous, prompting consideration for an artificial intelligence (AI)-supported workflow due to the growing number of slides to be examined, demanding heightened precision and the global shortage of pathologists. This was a retrospective cross-sectional study including digital images of glass slides containing sections of positive and negative lymph nodes obtained from radical resection of primary CRC. Lymph nodes from 165 previously diagnosed cases were selected from Agha Khan University Hospital, from Jan 2021 to Jan 2022. The images were prepared at 10X and uploaded into an open source software, Q path and deep learning model Ensemble was applied for the identification of tumor deposits in lymph node. Out of the 87 positive lymph nodes detected by AI, 73(84%) were true positive and 14(16%) were false positive. The total number of negative lymph nodes detected by AI was 78. Out of these, 69(88.5%) were true negative and 9 (11.5%) were false negative. The sensitivity was 89% and specificity 83.1%. The odds ratio was 40 with a confidence interval of 16.26–98.3. P-value was < 0.05 (< 0.0001). Though it was a small study but its results were really appreciating and we encourage more such studies with big sample data in future.\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13000-024-01547-5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13000-024-01547-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0

摘要

结直肠癌(CRC)约占全球癌症诊断和癌症死亡人数的 10%。治疗包括手术切除肿瘤和区域淋巴结。由于需要检查的切片数量越来越多,对精确度的要求越来越高,而且全球病理学家短缺,因此需要考虑采用人工智能(AI)支持的工作流程。这是一项回顾性横断面研究,包括从原发性 CRC 根治性切除术中获得的含有阳性和阴性淋巴结切片的玻璃载玻片的数字图像。2021 年 1 月至 2022 年 1 月期间,研究人员从阿迦汗大学医院选取了 165 个既往确诊病例的淋巴结。图像以 10 倍放大,上传到开源软件 Q path,并应用深度学习模型 Ensemble 来识别淋巴结中的肿瘤沉积物。在人工智能检测出的 87 个阳性淋巴结中,73 个(84%)为真阳性,14 个(16%)为假阳性。人工智能检测出的阴性淋巴结总数为 78 个。其中 69 个(88.5%)为真阴性,9 个(11.5%)为假阴性。灵敏度为 89%,特异性为 83.1%。几率比为 40,置信区间为 16.26-98.3。P值小于0.05(小于0.0001)。虽然这是一项小型研究,但其结果确实令人赞赏,我们鼓励今后开展更多此类大样本数据研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model for detecting metastatic deposits in lymph nodes of colorectal carcinoma on digital/ non-WSI images
Colorectal cancer (CRC) constitutes around 10% of global cancer diagnoses and death due to cancer. Treatment involves the surgical resection of the tumor and regional lymph nodes. Assessment of multiple lymph node demands meticulous examination by skilled pathologists, which can be arduous, prompting consideration for an artificial intelligence (AI)-supported workflow due to the growing number of slides to be examined, demanding heightened precision and the global shortage of pathologists. This was a retrospective cross-sectional study including digital images of glass slides containing sections of positive and negative lymph nodes obtained from radical resection of primary CRC. Lymph nodes from 165 previously diagnosed cases were selected from Agha Khan University Hospital, from Jan 2021 to Jan 2022. The images were prepared at 10X and uploaded into an open source software, Q path and deep learning model Ensemble was applied for the identification of tumor deposits in lymph node. Out of the 87 positive lymph nodes detected by AI, 73(84%) were true positive and 14(16%) were false positive. The total number of negative lymph nodes detected by AI was 78. Out of these, 69(88.5%) were true negative and 9 (11.5%) were false negative. The sensitivity was 89% and specificity 83.1%. The odds ratio was 40 with a confidence interval of 16.26–98.3. P-value was < 0.05 (< 0.0001). Though it was a small study but its results were really appreciating and we encourage more such studies with big sample data in future.
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
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信