ArticleBJUI compass2021
The diagnostic accuracy of multiparametric MRI for detection and localization of prostate cancer depends on the affected region.
Article in BJUI compass, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Diagnosis of Prostate Cancer: A Comparative Evaluation of Biological Techniques.Diseases (Basel, Switzerland) · 2026Review
- Multimodality focal therapy for prostate cancer: outcome of theProstate international · 2025Article
- Haralick Texture Analysis for Differentiating Suspicious Prostate Lesions from Normal Tissue in Low-Field MRI.Bioengineering (Basel, Switzerland) · 2025Article
- Diagnostic value of [Frontiers in oncology · 2025Article
- Role of Apparent Diffusion Coefficient Value and Apparent Diffusion Coefficient Ratio as Prognostic Factors for Prostate Cancer Aggressiveness.Diagnostics (Basel, Switzerland) · 2024Article
- phi and phiD predict adverse pathological features after radical prostatectomy for prostate cancer in Chinese population.Cancer medicine · 2024Article
- Risk score model to automatically detect prostate cancer patients by integrating diagnostic parameters.Frontiers in oncology · 2024Article
- The diagnostic accuracy of multiparametric MRI for detection and localization of prostate cancer depends on the affected region.BJUI compass · 2021Article
- Review
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To determine the diagnostic accuracy of 3T multiparametric magnetic resonance imaging (mpMRI) for detecting and locating prostate cancer (PCa) on Dickinson's 27-sector map, using histopathology specimens from radical prostatectomy (RP) as the reference standard. Patients and methods: The authors studied a continuous series of 140 patients who underwent RP over three consecutive years. Prior to RP, all patients had mpMRI for detection and localization of PCa and further assessment by biopsy. To minimize the potential of disease progression, 25 patients were excluded because the interval between mpMRI and RP exceeded 6 months, which left 115 patients eligible for analysis. The mpMRI findings were reported using the Prostate Imaging-Reporting and Data System (PI-RADS) v2, considering PI-RADS ≥ 3 to indicate PCa. The histopathology findings from RP specimens were graded using the Gleason scoring system, considering Gleason ≥ 6 to indicate PCa. The location of the tumors was mapped on Dickinson's 27-sector map for both mpMRI and histopathology and compared by rigid sector-by-sector matching. Results: The cohort of 115 patients eligible for analysis was aged 66.5 ± 6.0 years at RP. Of the 3105 sectors analyzed, there were 412 true positives (13%), 28 false positives (1%), 68 false negatives (2%), and 2597 true negatives (84%). Across the 27 sectors of the prostate, mpMRI sensitivity ranged from 50% to 100% and specificity from 96% to 100%, while PPV ranged from 50% to 100%, and NPV from 91% to 100%. For the anterior prostate, mpMRI had a sensitivity of 80% (CI, 71%-86%), specificity of 99% (CI, 99%-100%), PPV of 91% (CI, 83%-95%), and NPV of 99% (CI, 98%-99%). For the posterior prostate, mpMRI had a sensitivity of 88% (CI, 84%-91%), specificity of 98% (CI, 97%-99%), PPV of 94% (CI, 92%-96%), and NPV of 96% (CI, 94%-97%). Overall, mpMRI had a sensitivity of 86%, specificity of 99%, PPV of 94%, and NPV of 97%. Conclusions: The accuracy of mpMRI in detecting and locating prostate tumors depends on the affected region, but its high NPV across all sectors suggests that negative findings may not need corroboration by other techniques.
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