Evidence map›Paper›PMID 40391846›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Non-Invasive Tumor Budding Evaluation and Correlation with Treatment Response in Bladder Cancer: A Multi-Center Cohort Study.

Xiaoyang Li, Chen Zou, Chunhui Wang, Cheng Chang, Yi Lin, Shuai Liang, Haoran Zheng, Libo Liu, Kai Deng, Lin Zhang and 12 more

Abstract readMulticenter Study
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Xiaoyang LiDepartment of Urology, Third Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, 600th Tianhe Road, Guangzhou, 510630, P. R. China.
Chen ZouDepartment of Urology, Third Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, 600th Tianhe Road, Guangzhou, 510630, P. R. China.
Chunhui WangDepartment of Urology, Yan'an Hospital Affiliated to Kunming Medical University, Kunming Medical University, Kunming, 650051, P. R. China.
Cheng ChangDepartment of Urology, the Second Hospital of Dalian Medical University, Dalian Medical University, Dalian, 116027, P. R. China.
Yi LinDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.
Shuai LiangDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.
Haoran ZhengDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.
Libo LiuDepartment of Urology, Henan Provincial People's Hospital, Zhengzhou, 450003, P. R. China.
Kai DengDepartment of Urology, Yan'an Hospital Affiliated to Kunming Medical University, Kunming Medical University, Kunming, 650051, P. R. China.
Lin ZhangDepartment of Urology, Yan'an Hospital Affiliated to Kunming Medical University, Kunming Medical University, Kunming, 650051, P. R. China.
Bohao LiuDepartment of Urology, Third Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, 600th Tianhe Road, Guangzhou, 510630, P. R. China.
Mingchao GaoDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.
Peicong CaiDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.
Jianwen LaoDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.
Longhao XuDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.
Daqin WuDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.
Xiao ZhaoDepartment of Urology, Third Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, 600th Tianhe Road, Guangzhou, 510630, P. R. China.
Xiao WuDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.
Xinyuan LiDepartment of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, 1st Youyi Road, Chongqing, 400016, P. R. China.
Yun LuoDepartment of Urology, Third Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, 600th Tianhe Road, Guangzhou, 510630, P. R. China.
Wenlong ZhongDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.
Tianxin LinDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.ORCID https://orcid.org/0000-0003-3180-8697

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2021A1515110200Foundation of the Third Affiliated Hospital of Sun Yat-sen University YHJH202303Guangdong Provincial Clinical Research Center for Urological Diseases 2020B1111170006Guangzhou Basic and Applied Basic Research Subject-Young Doctor's "Sailing" Project 2024A04J4702Guangzhou Municipal Science and Technology Project SL2022A04J01754National Natural Science Foundation of China 82072831National Natural Science Foundation of China 82373254National Natural Science Foundation of China 92459303National Natural Science Foundation of China U21A20383Science and Technology Planning Project of Guangdong Province 2021A1515011541Science and Technology Planning Project of Guangdong Province 2023A1515010258Sun Yat-Sen Memorial Hospital Clinical Research 5010 Program SYS-5010Z-202401
6 · The paper itself

Abstract

The clinical benefits of neoadjuvant chemoimmunotherapy (NACI) are demonstrated in patients with bladder cancer (BCa); however, more than half fail to achieve a pathological complete response (pCR). This study utilizes multi-center cohorts of 2322 patients with pathologically diagnosed BCa, collected between January 1, 2014, and December 31, 2023, to explore the correlation between tumor budding (TB) status and NACI response and disease prognosis. A deep learning model is developed to noninvasively evaluate TB status based on CT images. The deep learning model accurately predicts the TB status, with area under the curve values of 0.932 (95% confidence interval: 0.898-0.965) in the training cohort, 0.944 (0.897-0.991) in the internal validation cohort, 0.882 (0.832-0.933) in external validation cohort 1, 0.944 (0.908-0.981) in the external validation cohort 2, and 0.854 (0.739-0.970) in the NACI validation cohort. Patients predicted to have a high TB status exhibit a worse prognosis (p < 0.05) and a lower pCR rate of 25.9% (7/20) than those predicted to have a low TB status (pCR rate: 73.9% [17/23]; p < 0.001). Hence, this model may be a reliable, noninvasive tool for predicting TB status, aiding clinicians in prognosis assessment and NACI strategy formulation.

Indexed as

Urinary Bladder NeoplasmsAgedCohort StudiesDeep LearningFemaleHumansMaleMiddle AgedNeoadjuvant TherapyPrognosisTomography, X-Ray ComputedTreatment Outcomebladder cancerdeep learningmulticenter studyneoadjuvant chemoimmunotherapytumor budding

Identifiers

PMID40391846
PMCPMC12165028

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Registered trials

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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.