Evidence map›Paper›PMID 39448755›Full record

ArticleScientific reports2024

Precise grading of non-muscle invasive bladder cancer with multi-scale pyramidal CNN.

Aya T Shalata, Ahmed Alksas, Mohamed Shehata, Sherry Khater, Osama Ezzat, Khadiga M Ali, Dibson Gondim, Ali Mahmoud, Eman M El-Gendy, Mohamed A Mohamed and 3 more

Abstract read
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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. The Stalk Sign in Bladder Cancer: CEUS Versus MRI.Diagnostics (Basel, Switzerland) · 2026
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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

13 authors.

Aya T ShalataBiomedical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
Ahmed AlksasDepartment of Bioengineering, University of Louisville, Louisville, KY, USA.
Mohamed ShehataDepartment of Bioengineering, University of Louisville, Louisville, KY, USA.
Sherry KhaterUrology and Nephrology Center, Mansoura University, Mansoura, Egypt.
Osama EzzatUrology and Nephrology Center, Mansoura University, Mansoura, Egypt.
Khadiga M AliPathology Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt.
Dibson GondimDepartment of Pathology and Laboratory Medicine, University of Louisville, Louisville, KY, USA.
Ali MahmoudDepartment of Bioengineering, University of Louisville, Louisville, KY, USA.
Eman M El-GendyComputers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
Mohamed A MohamedElectronics and Communication Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
Norah S AlghamdiDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Mohammed GhazalElectrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi, UAE.
Ayman El-BazDepartment of Bioengineering, University of Louisville, Louisville, KY, USA. aselba01@louisville.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The grading of non-muscle invasive bladder cancer (NMIBC) continues to face challenges due to subjective interpretations, which affect the assessment of its severity. To address this challenge, we are developing an innovative artificial intelligence (AI) system aimed at objectively grading NMIBC. This system uses a novel convolutional neural network (CNN) architecture called the multi-scale pyramidal pretrained CNN to analyze both local and global pathology markers extracted from digital pathology images. The proposed CNN structure takes as input three levels of patches, ranging from small patches (e.g.,

Indexed as

Neoplasm GradingNeural Networks, ComputerUrinary Bladder NeoplasmsAlgorithmsArtificial IntelligenceHumansNeoplasm InvasivenessNon-Muscle Invasive Bladder NeoplasmsArtificial intelligence (AI) systemDigital pathologyEnsemble machine learningHistopathological imagesNon-muscle invasive bladder cancer (NMIBC)Pyramidal convolutional neural networks (CNN)ShuffleNet

Identifiers

PMID39448755
PMCPMC11502747

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.