深度学习预测肌肉浸润性膀胱癌组织病理学中的淋巴管侵犯状态

IF 3.4 2区 医学 Q2 ONCOLOGY
Annals of Surgical Oncology Pub Date : 2025-01-01 Epub Date: 2024-10-29 DOI:10.1245/s10434-024-16422-2
Panpan Jiao, Shaolin Wu, Rui Yang, Xinmiao Ni, Jiejun Wu, Kai Wang, Xiuheng Liu, Zhiyuan Chen, Qingyuan Zheng
{"title":"深度学习预测肌肉浸润性膀胱癌组织病理学中的淋巴管侵犯状态","authors":"Panpan Jiao, Shaolin Wu, Rui Yang, Xinmiao Ni, Jiejun Wu, Kai Wang, Xiuheng Liu, Zhiyuan Chen, Qingyuan Zheng","doi":"10.1245/s10434-024-16422-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Lymphovascular invasion (LVI) is linked to poor prognosis in patients with muscle-invasive bladder cancer (MIBC). Accurately identifying the LVI status in MIBC patients is crucial for effective risk stratification and precision treatment. We aim to develop a deep learning model to identify the LVI status in whole-slide images (WSIs) of MIBC patients.</p><p><strong>Patients and methods: </strong>A cohort from The Cancer Genome Atlas (TCGA) database was used to train a deep learning model, slide-based lymphovascular invasion predictor (SBLVIP), based on multiple-instance learning. This model was externally validated using the Renmin Hospital of Wuhan University (RHWU) and People's Hospital of Hanchuan City (PHHC) cohorts. Kaplan-Meier curves, along with univariate and multivariate Cox models, were employed to evaluate the association between the LVI status predicted by SBLVIP and the survival outcomes of MIBC patients.</p><p><strong>Results: </strong>In the TCGA cohort, the SBLVIP model achieved an average accuracy of 0.804 [95% confidence interval (CI) 0.712-0.895] and an area under the receiver operating characteristic curve (AUC) of 0.77 (95% CI 0.63-0.84) in the training set. In the internal validation set, the model's average accuracy and AUC were 0.774 (95% CI, 0.701-0.846) and 0.76 (95% CI, 0.60-0.83), respectively. In the RHWU cohort, the SBLVIP model achieved an average accuracy of 0.807 (95% CI 0.734-0.880) and an AUC of 0.74 (95% CI 0.55-0.83). In the PHHC cohort, SBLVIP demonstrated an average accuracy of 0.821 (95% CI 0.737-0.909) and an AUC of 0.74 (95% CI 0.58-0.89). Moreover, the LVI status predicted by SBLVIP showed significant independent prognostic value (P = 1 × 10<sup>-6</sup>).</p><p><strong>Conclusions: </strong>We developed a deep learning model named SBLVIP to predict the LVI status in routine WSIs of MIBC patients.</p>","PeriodicalId":8229,"journal":{"name":"Annals of Surgical Oncology","volume":" ","pages":"598-608"},"PeriodicalIF":3.4000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Learning Predicts Lymphovascular Invasion Status in Muscle Invasive Bladder Cancer Histopathology.\",\"authors\":\"Panpan Jiao, Shaolin Wu, Rui Yang, Xinmiao Ni, Jiejun Wu, Kai Wang, Xiuheng Liu, Zhiyuan Chen, Qingyuan Zheng\",\"doi\":\"10.1245/s10434-024-16422-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Lymphovascular invasion (LVI) is linked to poor prognosis in patients with muscle-invasive bladder cancer (MIBC). Accurately identifying the LVI status in MIBC patients is crucial for effective risk stratification and precision treatment. We aim to develop a deep learning model to identify the LVI status in whole-slide images (WSIs) of MIBC patients.</p><p><strong>Patients and methods: </strong>A cohort from The Cancer Genome Atlas (TCGA) database was used to train a deep learning model, slide-based lymphovascular invasion predictor (SBLVIP), based on multiple-instance learning. This model was externally validated using the Renmin Hospital of Wuhan University (RHWU) and People's Hospital of Hanchuan City (PHHC) cohorts. Kaplan-Meier curves, along with univariate and multivariate Cox models, were employed to evaluate the association between the LVI status predicted by SBLVIP and the survival outcomes of MIBC patients.</p><p><strong>Results: </strong>In the TCGA cohort, the SBLVIP model achieved an average accuracy of 0.804 [95% confidence interval (CI) 0.712-0.895] and an area under the receiver operating characteristic curve (AUC) of 0.77 (95% CI 0.63-0.84) in the training set. In the internal validation set, the model's average accuracy and AUC were 0.774 (95% CI, 0.701-0.846) and 0.76 (95% CI, 0.60-0.83), respectively. In the RHWU cohort, the SBLVIP model achieved an average accuracy of 0.807 (95% CI 0.734-0.880) and an AUC of 0.74 (95% CI 0.55-0.83). In the PHHC cohort, SBLVIP demonstrated an average accuracy of 0.821 (95% CI 0.737-0.909) and an AUC of 0.74 (95% CI 0.58-0.89). Moreover, the LVI status predicted by SBLVIP showed significant independent prognostic value (P = 1 × 10<sup>-6</sup>).</p><p><strong>Conclusions: </strong>We developed a deep learning model named SBLVIP to predict the LVI status in routine WSIs of MIBC patients.</p>\",\"PeriodicalId\":8229,\"journal\":{\"name\":\"Annals of Surgical Oncology\",\"volume\":\" \",\"pages\":\"598-608\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Surgical Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1245/s10434-024-16422-2\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/29 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Surgical Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1245/s10434-024-16422-2","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/29 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

背景:淋巴管侵犯(LVI)与肌浸润性膀胱癌(MIBC)患者的不良预后有关。准确识别肌层浸润性膀胱癌患者的淋巴管侵犯状态对于有效的风险分层和精准治疗至关重要。我们的目标是开发一种深度学习模型,以识别肌层浸润性膀胱癌患者全滑动图像(WSI)中的LVI状态:我们利用癌症基因组图谱(TCGA)数据库中的一个队列来训练一个基于多实例学习的深度学习模型--基于滑动图的淋巴管侵犯预测模型(SBLVIP)。该模型通过武汉大学人民医院(RHWU)和汉川市人民医院(PHHC)队列进行了外部验证。采用卡普兰-梅耶曲线以及单变量和多变量考克斯模型评估了SBLVIP预测的LVI状态与MIBC患者生存结果之间的关联:在TCGA队列中,SBLVIP模型的平均准确率为0.804[95%置信区间(CI)0.712-0.895],训练集的接收者操作特征曲线下面积(AUC)为0.77(95% CI 0.63-0.84)。在内部验证集中,模型的平均准确率和AUC分别为0.774(95% CI,0.701-0.846)和0.76(95% CI,0.60-0.83)。在 RHWU 队列中,SBLVIP 模型的平均准确率为 0.807(95% CI 0.734-0.880),AUC 为 0.74(95% CI 0.55-0.83)。在 PHHC 队列中,SBLVIP 的平均准确率为 0.821(95% CI 0.737-0.909),AUC 为 0.74(95% CI 0.58-0.89)。此外,SBLVIP预测的LVI状态显示出显著的独立预后价值(P = 1 × 10-6):我们开发了一种名为SBLVIP的深度学习模型,用于预测MIBC患者常规WSI中的LVI状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Predicts Lymphovascular Invasion Status in Muscle Invasive Bladder Cancer Histopathology.

Background: Lymphovascular invasion (LVI) is linked to poor prognosis in patients with muscle-invasive bladder cancer (MIBC). Accurately identifying the LVI status in MIBC patients is crucial for effective risk stratification and precision treatment. We aim to develop a deep learning model to identify the LVI status in whole-slide images (WSIs) of MIBC patients.

Patients and methods: A cohort from The Cancer Genome Atlas (TCGA) database was used to train a deep learning model, slide-based lymphovascular invasion predictor (SBLVIP), based on multiple-instance learning. This model was externally validated using the Renmin Hospital of Wuhan University (RHWU) and People's Hospital of Hanchuan City (PHHC) cohorts. Kaplan-Meier curves, along with univariate and multivariate Cox models, were employed to evaluate the association between the LVI status predicted by SBLVIP and the survival outcomes of MIBC patients.

Results: In the TCGA cohort, the SBLVIP model achieved an average accuracy of 0.804 [95% confidence interval (CI) 0.712-0.895] and an area under the receiver operating characteristic curve (AUC) of 0.77 (95% CI 0.63-0.84) in the training set. In the internal validation set, the model's average accuracy and AUC were 0.774 (95% CI, 0.701-0.846) and 0.76 (95% CI, 0.60-0.83), respectively. In the RHWU cohort, the SBLVIP model achieved an average accuracy of 0.807 (95% CI 0.734-0.880) and an AUC of 0.74 (95% CI 0.55-0.83). In the PHHC cohort, SBLVIP demonstrated an average accuracy of 0.821 (95% CI 0.737-0.909) and an AUC of 0.74 (95% CI 0.58-0.89). Moreover, the LVI status predicted by SBLVIP showed significant independent prognostic value (P = 1 × 10-6).

Conclusions: We developed a deep learning model named SBLVIP to predict the LVI status in routine WSIs of MIBC patients.

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
5.90
自引率
10.80%
发文量
1698
审稿时长
2.8 months
期刊介绍: The Annals of Surgical Oncology is the official journal of The Society of Surgical Oncology and is published for the Society by Springer. The Annals publishes original and educational manuscripts about oncology for surgeons from all specialities in academic and community settings.
×
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