Objective To assess the effect of preoperative MRI with standardized prostate imaging-reporting and data system (PI-RADS) assessment on pathological outcomes in prostate cancer (PCa) patients who underwent radical pro...
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Objective To assess the effect of preoperative MRI with standardized prostate imaging-reporting and data system (PI-RADS) assessment on pathological outcomes in prostate cancer (PCa) patients who underwent radical prostatectomy (RP). Patients and methods This retrospective cohort study included patients who had undergone prostate MRI and subsequent RP for PCa between January 2017 and December 2022. The patients were divided into the PI-RADS group and the non-PI-RADS group according to evaluation scheme of presurgery MRI. The preoperative characteristics and postoperative outcomes were retrieved and analyzed. The pathological outcomes included pathological T stage (pT2 vs. pT3-4) and positive surgical margins (PSMs). Patients were further stratified according to statistically significant preoperative variables to assess the difference in pathological outcomes. A propensity score matching based on the above preoperative characteristics was additionally performed. Results A total of 380 patients were included in this study, with 201 patients in the PI-RADS group and 179 in the non-PI-RADS group. The two groups had similar preoperative characteristics, except for clinical T stage (cT). As for pathological outcomes, the PI-RADS group showed a significantly lower percentage of pT3-4 (21.4% vs. 48.0%, p < 0.001), a lower percentage of PSMs (31.3% vs. 40.9%, p = 0.055), and a higher concordance between the cT and pT (79.1% vs. 64.8%, p = 0.003). The PI-RADS group also showed a lower proportion of pT3-4 (p < 0.001) in the cT1-2 subgroup and the cohort after propensity score matching. The PSM rate of cT3 patients was reduced by 39.2% in the PI-RADS group but without statistical significance (p = 0.089). Conclusions Preoperative MRI with standardized PI-RADS assessment could benefit the decision-making of patients by reducing the rate of pathologically confirmed non-organ-confined PCa after RP and slightly reducing the PSM rate compared with non-PI-RADS assessment.
Purpose: prostate cancer (PCa) is the most common solid organ cancer and second leading cause of death in men. Multiparametric magnetic resonance imaging (mpMRI) enables detection of the most aggressive, clinically si...
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Purpose: prostate cancer (PCa) is the most common solid organ cancer and second leading cause of death in men. Multiparametric magnetic resonance imaging (mpMRI) enables detection of the most aggressive, clinically significant PCa (csPCa) tumors that require further treatment. A suspicious region of interest (ROI) detected on mpMRI is now assigned a prostate imaging-reporting and data system (PIRADS) score to standardize interpretation of mpMRI for PCa detection. However, there is significant inter-reader variability among radiologists in PIRADS score assignment and a minimal input semi-automated artificial intelligence (AI) system is proposed to harmonize PIRADS scores with mpMRI data. Approach: The proposed deep learning model (the seed point model) uses a simulated single-click seed point as input to annotate the lesion on mpMRI. This approach is in contrast to typical medical AI-based approaches that require annotation of the complete lesion. The mpMRI data from 617 patients used in this study were prospectively collected at a major tertiary U.S. medical center. The model was trained and validated to classify whether an mpMRI image had a lesion with a PIRADS score greater than or equal to PIRADS 4. Results: The model yielded an average receiver-operator characteristic (ROC) area under the curve (ROC-AUC) of 0.704 over a 10-fold cross-validation, which is significantly higher than the previously published benchmark. Conclusions: The proposed model could aid in PIRADS scoring of mpMRI, providing second reads to promote quality as well as offering expertise in environments that lack a radiologist with training in prostate mpMRI interpretation. The model could help identify tumors with a higher PIRADS for better clinical management and treatment of PCa patients at an early stage. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)
BACKGROUND AND OBJECTIVE:Conventional core needle biopsy for prostate cancer diagnosis can lead to diagnostic uncertainty and complications, prompting exploration of alternative risk assessment approaches that use cli...
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BACKGROUND AND OBJECTIVE:Conventional core needle biopsy for prostate cancer diagnosis can lead to diagnostic uncertainty and complications, prompting exploration of alternative risk assessment approaches that use clinical and imaging features. Our aim was to evaluate the effectiveness of machine learning (ML) models in reducing unnecessary biopsies.
METHODS:We conducted a retrospective analysis of data for 1884 patients across two academic centers who underwent prostate magnetic resonance imaging and biopsy between 2016 and 2020 or 2004 and 2011. Twelve ML models were assessed for prediction of clinically significant prostate cancer (csPCa; Gleason grade group ≥2) using combinations of clinical features, including patient age, prostate-specific antigen level and density, prostate imaging-reporting and data system/Likert score, lesion volume, and gland volume. The models were trained and validated using a tenfold split for intrasite, intersite, and combined-site data sets. Model effectiveness was evaluated using the area under the receiver operating characteristic curve and decision curve analysis.
KEY FINDINGS AND LIMITATIONS:The best-performing ML model would reduce the number of biopsies by 13.07% at a false-negative rate of 1.91%. Performance was consistent across sites, although the study is limited by the small number of centers and the absence of specific clinical data.
CONCLUSIONS AND CLINICAL IMPLICATIONS:ML-enhanced clinical models provide an effective and generalizable approach for prediction of csPCa using standard clinical data. These models allow personalized risk assessment and follow-up, support clinical decision-making, and improve workflow efficiency.
PATIENT SUMMARY:Models that are enhanced by machine learning can predict the severity of prostate cancer and help doctors in tailoring treatments for individual patients. This approach can simplify health care decisions and improve clinical efficiency.
This report details a single-center experience of using magnetic resonance imaging-guided transurethral ultrasound ablation (TULSA) for whole-gland prostate treatment. Nine men with organ-confined low-to-intermediate-...
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This report details a single-center experience of using magnetic resonance imaging-guided transurethral ultrasound ablation (TULSA) for whole-gland prostate treatment. Nine men with organ-confined low-to-intermediate-risk prostate cancer underwent the TULSA procedure. The primary endpoint of reduction of more than 75% was achieved in 8 of 9 patients. and all patients demonstrated a histologic benefit at 12-month biopsy. No major urinary or gastrointestinal side effects were observed. and there Were no postprocedural changes in erectile firmness. These findings suggest that TULSA is potentially safe and efficacious for patients with low-to-intermediate-risk disease.
Granulomatous prostatitis (GP) is an unusual and benign inflammatory condition of the prostate, where autoimmunity has been recognized as a key factor in the pathogenesis of GP in a subset of patients. Clinically, GP ...
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Granulomatous prostatitis (GP) is an unusual and benign inflammatory condition of the prostate, where autoimmunity has been recognized as a key factor in the pathogenesis of GP in a subset of patients. Clinically, GP poses diagnostic challenges as it may strongly mimic prostate cancer from a clinical, biochemical and radiological point of view. The occurrence of GP in patients suffering from psoriasis, a systemic autoimmune disease, has never been investigated. We describe the case of GP in a patient with psoriatic arthritis presenting with an increased prostate specific antigen level, and evidence of a nodular lesion visualized by prostate multiparametric magnetic resonance imaging, which was highly suspicious for aggressive prostate cancer. Lay abstract: Granulomatous prostatitis is an uncommon inflammatory condition of the prostate that can mimic prostate cancer due to increased prostate specific antigen levels and suspect findings from both digital rectal exploration and prostate magnetic resonance imaging. This condition is considered an autoimmune disorder in many cases. We report the association between granulomatous prostatitis and psoriasis, another autoimmune disease.
prostate cancer is the second most common cancer in men worldwide [1], and is the fifth leading cause of cancer death in men with 307,500 deaths in 2012 [1]. Approximately two-thirds of prostate cancer cases are dispr...
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prostate cancer is the second most common cancer in men worldwide [1], and is the fifth leading cause of cancer death in men with 307,500 deaths in 2012 [1]. Approximately two-thirds of prostate cancer cases are disproportionately diagnosed in the developed world, largely due to prostate cancer screening practices [1]. However, some detected cancers are so low grade and slow growing that they are unlikely to affect the individual in his lifetime [2]. Treatment with radical prostatectomy, brachytherapy, or external bean radiotherapy carries risks including erectile dysfunction and urinary incontinence [2].
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