Evidence map›Paper›PMID 38791888›Full record

ReviewCancers2024

Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.

Ali Talyshinskii, B M Zeeshan Hameed, Prajwal P Ravinder, Nithesh Naik, Princy Randhawa, Milap Shah, Bhavan Prasad Rai, Theodoros Tokas, Bhaskar K Somani

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Review
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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

9 authors.

Ali TalyshinskiiDepartment of Urology and Andrology, Astana Medical University, Astana 010000, Kazakhstan.ORCID 0000-0002-3521-8937
B M Zeeshan HameedDepartment of Urology, KMC Manipal Hospitals, Mangalore 575001, India.
Prajwal P RavinderDepartment of Urology, Kasturba Medical College, Mangaluru, Manipal Academy of Higher Education, Manipal 576104, India.
Nithesh NaikDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.ORCID 0000-0003-0356-7697
Princy RandhawaDepartment of Mechatronics, Manipal University Jaipur, Jaipur 303007, India.ORCID 0000-0001-6356-8718
Milap ShahDepartment of Urology, Aarogyam Hospital, Ahmedabad 380014, India.
Bhavan Prasad RaiDepartment of Urology, Freeman Hospital, Newcastle upon Tyne NE7 7DN, UK.
Theodoros TokasDepartment of Urology, Medical School, University General Hospital of Heraklion, University of Crete, 14122 Heraklion, Greece.ORCID 0000-0003-0928-0507
Bhaskar K SomaniDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.ORCID 0000-0002-6248-6478

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications.

methodsA search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas.

resultsA total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [

conclusionDL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.

Indexed as

ADTartificial intelligencebiopsydeep learningMRIPCa detectionPCa reconstructionPET/CTprostate cancerprostate reconstruction

Identifiers

PMID38791888
PMCPMC11119252

What OpenQuestion holds

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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.