Evidence map›Paper›PMID 39047698›Full record

ArticleMedical principles and practice : international journal of the Kuwait University, Health Science Centre2024

A Comparison of Systematic, Targeted, and Combined Biopsy Using Machine Learning for Prediction of Prostate Cancer Risk: A Multi-Center Study.

Mostafa A Arafa, Islam Omar, Karim H Farhat, Mona Elshinawy, Farrukh Khan, Faisal A Alkhathami, Alaa Mokhtar, Abdulaziz Althunayan, Danny M Rabah, Abdel-Hameed A Badawy

Abstract readMulticenter StudyComparative Study
PubMed Full text at PMC Publisher
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Article in Medical principles and practice : international journal of the Kuwait University, Health Science Centre, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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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

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Mostafa A ArafaThe Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Islam OmarKlipsch School of Electrical and Computer Engineering, New Mexico State University, Las Cruces, New Mexico, USA.
Karim H FarhatThe Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Mona ElshinawyEngineering Technology and Surveying Engineering Department, New Mexico State University, Las Cruces, New Mexico, USA.
Farrukh KhanDepartment of Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Faisal A AlkhathamiDepartment of Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Alaa MokhtarDepartment of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.
Abdulaziz AlthunayanThe Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Danny M RabahThe Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Abdel-Hameed A BadawyKlipsch School of Electrical and Computer Engineering, New Mexico State University, Las Cruces, New Mexico, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe aims of the study were to construct a new prognostic prediction model for detecting prostate cancer (PCa) patients using machine-learning (ML) techniques and to compare those models across systematic and target biopsy detection techniques.

methodsThe records of the two main hospitals in Riyadh, Saudi Arabia, were analyzed for data on diagnosed PCa from 2019 to 2023. Four ML algorithms were utilized for the prediction and classification of PCa.

resultsA total of 528 patients with prostate-specific antigen (PSA) greater than 3.5 ng/mL who had undergone transrectal ultrasound-guided prostate biopsy were evaluated. The total number of confirmed PCa cases was 234. Age, prostate volume, PSA, body mass index (BMI), multiparametric magnetic resonance imaging (mpMRI) score, number of regions of interest detected in MRI, and the diameter of the largest size lesion were significantly associated with PCa. Random Forest (RF) and XGBoost (XGB) (ML algorithms) accurately predicted PCa. Yet, their performance for classification and prediction of PCa was higher and more accurate for cases detected by targeted and combined biopsy (systematic and targeted together) compared to systematic biopsy alone. F1, the area under the curve (AUC), and the accuracy of XGB and RF models for targeted biopsy and combined biopsy ranged from 0.94 to 0.97 compared to the AUC of systematic biopsy for RF and XGB algorithms, respectively.

conclusionsThe RF model generated and presented an excellent prediction capability for the risk of PCa detected by targeted and combined biopsy compared to systematic biopsy alone. ML models can prevent missed PCa diagnoses by serving as a screening tool.

Indexed as

Machine LearningProstate-Specific AntigenProstatic NeoplasmsAgedAlgorithmsHumansImage-Guided BiopsyMaleMiddle AgedMultiparametric Magnetic Resonance ImagingProstateRetrospective StudiesRisk AssessmentSaudi ArabiaProstate-Specific AntigenMachine learningProstate cancer riskSystematic biopsyTargeted biopsy

Identifiers

PMID39047698
PMCPMC11460957

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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.