{"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}
引用次数: 0
Abstract
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.
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.