Enhancing Prostate Cancer Detection in PI-RADS 3 Cases: An In-depth Analysis of Radiological Indicators from Multiparametric MRI.
İlker MersinlioğluAyse KevenZülbiye Eda TezelAhmet Faruk GürbüzMetin ÇubukPublished in: RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin (2024)
Prostate cancer (PCa) diagnosis using multiparametric magnetic resonance imaging (mpMRI) remains challenging, especially in Prostate Imaging Reporting and Data System 3 (PI-RADS 3) lesions, which present an intermediate risk of malignancy. This study aims to evaluate the diagnostic efficacy of various radiological parameters in PI-RADS 3 lesions to improve the decision-making process for prostate biopsies.This retrospective study included 76 patients with PI-RADS 3 lesions who underwent mpMRI and transrectal prostate biopsy at a tertiary university hospital between 2015 and 2022. Radiological parameters such as signal intensity, lesion size, border definition, morphological features, lesion location, and prostate volume were analyzed. Apparent diffusion coefficient (ADC) values and the patients' clinical data including age, prostate-specific antigen (PSA), and histopathological findings were also evaluated. Results: Among the 76 patients meeting the inclusion criteria, prostate cancer was detected in 17, with only one case being clinically significant (csPCa). Factors increasing malignancy risk in PI-RADS 3 lesions included poorly defined lesion borders, ADC values below 1180 μm²/sec, and prostate volume below 50.5 cc. The study highlighted the need for additional radiological and clinical parameters in the risk classification of PI-RADS 3 cases.This retrospective study included 76 patients with PI-RADS 3 lesions who underwent mpMRI and transrectal prostate biopsy at a tertiary university hospital between 2015 and 2022. Radiological parameters such as signal intensity, lesion size, border definition, morphological features, lesion location, and prostate volume were analyzed. Apparent diffusion coefficient (ADC) values and the patients' clinical data including age, prostate-specific antigen (PSA), and histopathological findings were also evaluated.Among the 76 patients meeting the inclusion criteria, prostate cancer was detected in 17, with only one case being clinically significant (csPCa). Factors increasing malignancy risk in PI-RADS 3 lesions included poorly defined lesion borders, ADC values below 1180 μm²/sec, and prostate volume below 50.5 cc. The study highlighted the need for additional radiological and clinical parameters in the risk classification of PI-RADS 3 cases.The findings suggest that incorporating additional radiological parameters into the evaluation of PI-RADS 3 lesions can enhance the accuracy of prostate cancer diagnosis. This approach could minimize unnecessary biopsies and ensure that significant malignancies are not overlooked. Future multicenter, large-scale studies are recommended to establish more definitive risk stratification criteria. · The study emphasizes the complexity of diagnosing prostate cancer in PI-RADS 3 lesions and the importance of detailed radiological assessment.. · It highlights the significance of specific radiological parameters, including lesion border definition and ADC values, in predicting malignancy.. · The research provides valuable insight for clinicians in order to make informed decisions regarding prostate biopsies, particularly in ambiguous PI-RADS 3 cases.. · Mersinlioğlu İ, Keven A, Tezel ZE et al. Enhancing Prostate Cancer Detection in PI-RADS 3 Cases: An In-depth Analysis of Radiological Indicators from Multiparametric MRI. Fortschr Röntgenstr 2024; DOI 10.1055/a-2374-2531.
Keyphrases
- prostate cancer
- radical prostatectomy
- end stage renal disease
- magnetic resonance imaging
- diffusion weighted imaging
- chronic kidney disease
- ejection fraction
- newly diagnosed
- prognostic factors
- peritoneal dialysis
- machine learning
- benign prostatic hyperplasia
- emergency department
- squamous cell carcinoma
- patient reported outcomes
- radiation therapy
- magnetic resonance
- artificial intelligence
- decision making
- high intensity
- cross sectional
- locally advanced
- label free
- rectal cancer