Evidence map›Paper›PMID 40508192›Full record

ReviewInternational journal of molecular sciences2025

Transforming Prostate Cancer Care: Innovations in Diagnosis, Treatment, and Future Directions.

Sanaz Vakili, Iman Beheshti, Amir Barzegar Behrooz, Marek J Łos, Rui Vitorino, Saeid Ghavami

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2025. 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.

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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

6 authors.

Sanaz VakiliDepartment of Human Anatomy and Cell Science, University of Manitoba College of Medicine, Winnipeg, MB R3E 3P5, Canada.
Iman BeheshtiDepartment of Human Anatomy and Cell Science, University of Manitoba College of Medicine, Winnipeg, MB R3E 3P5, Canada.ORCID 0000-0003-4750-3433
Amir Barzegar BehroozDepartment of Human Anatomy and Cell Science, University of Manitoba College of Medicine, Winnipeg, MB R3E 3P5, Canada.ORCID 0000-0002-8113-5809
Marek J ŁosBiotechnology Center, Silesian University of Technology, 44-100 Gliwice, Poland.ORCID 0000-0001-9518-1411
Rui VitorinoDepartment of Medical Sciences, Institute of Biomedicine iBiMED, University of Aveiro, 3810-193 Aveiro, Portugal.ORCID 0000-0003-3636-5805
Saeid GhavamiDepartment of Human Anatomy and Cell Science, University of Manitoba College of Medicine, Winnipeg, MB R3E 3P5, Canada.ORCID 0000-0001-5948-508X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prostate cancer remains a major global health challenge, ranking as the second most common malignancy in men worldwide. Advances in diagnostic and therapeutic strategies have transformed its management, enhancing patient outcomes and quality of life. This review highlights recent breakthroughs in imaging, including multiparametric MRI and PSMA-PET, which have improved cancer detection and staging. Biomarker-based diagnostics, such as PHI and 4K Score, offer precise risk stratification, reducing unnecessary biopsies. Innovations in treatment, including robotic-assisted surgery, novel hormone therapies, immunotherapy, and PARP inhibitors, are redefining care for localized and advanced prostate cancer. Artificial intelligence (AI) and machine learning (ML) are emerging as powerful tools to optimize diagnostics, risk prediction, and treatment personalization. Additionally, advances in radiation therapy, such as IMRT and SBRT, provide targeted and effective options for high-risk patients. While these innovations have significantly improved survival and minimized overtreatment, challenges remain in optimizing therapy sequencing and addressing disparities in care. The integration of AI, theranostics, and gene-editing technologies holds immense promise for the future of prostate cancer management.

Indexed as

Prostatic NeoplasmsArtificial IntelligenceBiomarkers, TumorHumansMachine LearningMaleBiomarkers, Tumoradvanced therapiesartificial intelligencebiomarkersdiagnosisprecision medicineprostate cancer

Identifiers

PMID40508192
PMCPMC12154402

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.