ReviewDer Urologe. Ausg. A2021
[Machine learning and multiparametric MRI for early diagnosis of prostate cancer].
Review in Der Urologe. Ausg. A, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed, 12 citations in OpenAlex.
- Recent Advances in Multimodal Assessment Scoring Systems for Prostate Cancer: An Integrated Pathological and Imaging Perspective.Diagnostics (Basel, Switzerland) · 2026Review
- Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading.NPJ precision oncology · 2025Article
- Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis.Discover oncology · 2024Article
- [Artificial intelligence (AI) in radiology? : Do we need as many radiologists in the future?]Der Urologe. Ausg. A · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last few years, the early diagnosis of prostate cancer has continued to shift from systematic biopsies to multiparametric MRI (mpMRI)-guided/MRI-transrectal ultrasound (TRUS) fusion biopsies and guidelines are already reflecting these changes. While MRI-TRUS fusion biopsies have already resulted in significant improvements in diagnostic sensitivity and, thus, correct diagnosis of clinically significant prostate cancer (sPC), its use to avoid biopsies in certain men is still controversial. Optimal use of mpMRI requires a high degree of reader expertise due to the difficulty of image interpretation and poses the problem of training sufficient numbers of radiologists while demand is increasing. Recently, artificial intelligence (AI) has been utilized to create fully automatic analysis tools for interpretation of mpMRI of the prostate, rivaling the performance of clinical radiologist interpretation in retrospective research studies, demonstrating the promising potential of AI for diagnostic prostate MRI in the future. This article will provide an overview of machine and deep learning and its application in mpMRI of the prostate for early diagnosis of prostate cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.