ArticleWorld journal of urology2023
External validation of the computerized analysis of TRUS of the prostate with the ANNA/C-TRUS system: a potential role of artificial intelligence for improving prostate cancer detection.
Article in World journal of urology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics vs radiologist in prostate cancer. Results from a systematic review.World journal of urology · 2023Pooled it
- Trial
- Artificial intelligence for prostate cancer detection and risk stratification using transrectal ultrasound: a narrative review.Gland surgery · 2026Review
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects.BMC cancer · 2024Review
- Current Approach to Complications and Difficulties during Transrectal Ultrasound-Guided Prostate Biopsies.Journal of clinical medicine · 2024Review
- Machine learning based on radiomics features combing B-mode transrectal ultrasound and contrast-enhanced ultrasound to improve peripheral zone prostate cancer detection.Abdominal radiology (New York) · 2024Article
- Reliability of Systematic and Targeted Biopsies versus Prostatectomy.Bioengineering (Basel, Switzerland) · 2023Article
- [Digitalization in urology-challenge and opportunity].Urologie (Heidelberg, Germany) · 2023Review
- Special Issue "Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning".Journal of clinical medicine · 2022Article
- Multiparametric ultrasound of prostate: role in prostate cancer diagnosis.Therapeutic advances in urologyReview
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14 authors.
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Abstract
purposeProstate cancer (PCa) imaging has been revolutionized by the introduction of multi-parametric Magnetic Resonance Imaging (mpMRI). Transrectal ultrasound (TRUS) has always been considered a low-performance modality. To overcome this, a computerized artificial neural network analysis (ANNA/C-TRUS) of the TRUS based on an artificial intelligence (AI) analysis has been proposed. Our aim was to evaluate the diagnostic performance of the ANNA/C-TRUS system and its ability to improve conventional TRUS in PCa diagnosis.
methodsWe retrospectively analyzed data from 64 patients with PCa and scheduled for radical prostatectomy who underwent TRUS followed by ANNA/C-TRUS analysis before the procedure. The results of ANNA/C-TRUS analysis with whole mount sections from final pathology.
resultsOn a per-sectors analysis, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) and accuracy were 62%, 81%, 80%, 64% and 78% respectively. The values for the detection of clinically significant prostate cancer were 69%, 77%, 88%, 50% and 75%. The diagnostic values for high grade tumours were 70%, 74%, 91%, 41% and 74%, respectively. Cancer volume (≤ 0.5 or greater) did not influence the diagnostic performance of the ANNA/C-TRUS system.
conclusionsANNA/C-TRUS represents a promising diagnostic tool and application of AI for PCa diagnosis. It improves the ability of conventional TRUS to diagnose prostate cancer, preserving its simplicity and availability. Since it is an AI system, it does not hold the inter-observer variability nor a learning curve. Multicenter biopsy-based studies with the inclusion of an adequate number of patients are needed to confirm these results.
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