Evidence map›Paper›PMID 39472420›Full record

ArticleAnnals of surgical oncology2025

Deep Learning Predicts Lymphovascular Invasion Status in Muscle Invasive Bladder Cancer Histopathology.

Panpan Jiao, Shaolin Wu, Rui Yang, Xinmiao Ni, Jiejun Wu, Kai Wang, Xiuheng Liu, Zhiyuan Chen, Qingyuan Zheng

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Article in Annals of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. MRI-based deep learning combined with radiomics for the preoperative prediction of lymphovascular invasion in patients with bladder cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Article
  2. Development and validation of MRI-based models to predict lymph node metastasis in bladder cancer: a multi-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Article
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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Panpan Jiao *Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
Shaolin Wu *Department of Nephrology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China.
Rui YangDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
Xinmiao NiDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
Jiejun WuDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
Kai WangDepartment of Urology, People's Hospital of Hanchuan City, Xiaogan, China.
Xiuheng LiuDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
Zhiyuan ChenDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China. drchenzy@whu.edu.cn.
Qingyuan ZhengDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China. zqy710890394@whu.edu.cn.ORCID http://orcid.org/0000-0003-4682-3857

Funding

China Primary Health Care Foundation 025Hubei Province Central Guiding Local Science and Technology Development Project 2022BGE232
6 · The paper itself

Abstract

backgroundLymphovascular 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

methodsA 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.

resultsIn 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

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

Indexed as

Deep LearningNeoplasm InvasivenessUrinary Bladder NeoplasmsAgedFemaleFollow-Up StudiesHumansLymphatic MetastasisLymphatic VesselsMaleMiddle AgedPrognosisROC CurveSurvival RateDeep learningLymphovascular invasionMultiple instance learningMuscle-invasive bladder cancer

Identifiers

PMID39472420

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.