Prostate Imaging Reporting And Data System Research Articles

Effect of preoperative PI-RADS assessment on pathological outcomes in patients who underwent radical prostatectomy

ObjectiveTo 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 methodsThis 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.ResultsA 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).ConclusionsPreoperative 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.

Analysis of the relationship between PI-RADS scores and the pathological results of targeted biopsy based on MRI

Objective: To analyze the relationship between Prostate Imaging Reporting and Data System (PI-RADS) scores and the pathological results of transperineal magnetic resonance-ultrasound fusion guided biopsy. Methods: The clinical data, magnetic resonance imaging (MRI) results and prostate puncture biopsies of 517 patients who were assigned to PI-RADS score of 4 or 5 and underwent transperineal magnetic resonance-ultrasound fusion guided biopsy at The First Affiliated Hospital of Nanjing Medical University from June 2019 to March 2022 were retrospectively analyzed. Patients were divided into the PI-RADS 4 and PI-RADS 5 groups according to their PI-RADS scores and were stratified by their prostate specific antigen (PSA) values (PSA<10 ng/ml vs. PSA 10-20 ng/ml). The pathological negative rates from the biopsy, the distribution of the grade groups according to the grading system by World Health Organization/International Society of Urological Pathology (WHO/ISUP), the detection rates of prostate cancer (PCa) and clinically significant prostate cancer (CsPCa)between the groups were compared. Results: 369 patients with a PI-RADS score of 4 and 148 patients with a PI-RADS score of 5 were included in our research. The overall detection rates of PCa and CsPCa were 77.8% (402/517) and 66.7% (345/517), respectively. In the PI-RADS 4 group, patients with prostate negative biopsies or in WHO/ISUP 1, 2, 3, 4, or 5 grade groups accounted for 28.2%, 12.7%, 20.1%, 17.1%, 18.4% and 3.5%, respectively, whereas in the PI-RADS 5 group the rates were 7.4%, 6.8%, 22.3%, 22.3%, 26.4%, and 14.9%, respectively. The difference was statistically significant (P<0.001). The detection rates of PCa and CsPCa in the PI-RADS 4 group [71.8% (265/369) vs. 59.1% (218/369), P<0.001] were lower than those of the PI-RADS 5 group [92.6% (137/148) vs. 85.8% (127/148), P<0.001]. In the PI-RADS 4 group, the proportion of patients classified into WHO/ISUP 4-5 grade groups was lower than that of patients in the PI-RADS 5 group [22.0% (81/369) vs 41.2% (61/148) (P<0.001)]. The detection rates of PCa and CsPCa in the PSA<10 ng/ml stratification were less than that in the PSA 10-20 ng/ml stratification[74.1% (281/379) vs. 87.7% (121/138), P=0.001], and [60.9% (231/379) vs. 82.6% (114/138), P<0.001]. For patients with PSA<10 ng/ml, the detection rates of PCa and CsPCa in the PI-RADS 4 group were less than those in the PI-RADS5 group [70.9% (217/306) vs. 87.7% (64/73), P=0.003], and [56.2% (172/306) vs. 80.8% (59/73), P<0.001]. For those with a PSA value of 10-20 ng/ml, the detection rates of PCa and CsPCa in the PI-RADS 4 group were less than those in the PI-RADS 5 group [76.2% (48/63) vs. 97.3% (73/75), P<0.001], and [73.0% (46/63) vs. 90.7% (68/75), P=0.006]. There were statistically significant differences in the proportions of patients with prostate negative biopsy and those falling into WHO/ISUP grade groups 1, 2, 3, 4, or 5 (P<0.001) between the PI-RADS 4 group and the PI-RADS 5 group in both stratifications. Conclusions: In this study, the detection rates of CsPCa and PCa in the PI-RADS 4 group were less than those in the PI-RADS 5 group. With the increase of PI-RADS scores, the detection rate of high-grade PCa increased. The same results held for patients with PSA<10 ng/ml or with PSA 10-20 ng/ml.

The Value of Prostate-Specific Antigen Density in Combination with Lesion Diameter for the Accuracy of Prostate Cancer Prediction in Prostate Imaging-Reporting and Data System 3 Prostate Lesions

Introduction: The aim of the study was to investigate the value of prostate-specific antigen density (PSAD) and lesion diameter (LD) combination in prostate cancer (PCa) detection. Methods: 181 patients who were detected to have prostate imaging-reporting and data system (PI-RADS) 3 lesions in mpMRI and underwent prostate biopsies were included in the study. Demographic, clinical, and pathological data of all patients were evaluated. The patients were divided into four groups according to PSAD and LD status (PSAD <0.15 ng/mL/cc + LD <1 cm, PSAD <0.15 ng/mL/cc + LD ≥1 cm, PSAD ≥0.15 ng/mL/cc + LD <1 cm, and PSAD ≥0.15 ng/mL/cc + LD ≥1 cm). Diagnostic ability for PCa and clinical significant PCa (csPCa) was evaluated by PSAD and LD. Results: PSAD ≥0.15 ng/mL/cc (OR = 6; 95% Cl = 2.847–12.647; p < 0.001), LD ≥1 cm (OR = 7.341; 95% confidence interval [CI] = 2.91–18.52; p < 0.001), and combination of PSAD ≥0.15 ng/mL/cc and LD ≥1 cm (OR = 10.023; 95% CI = 4.32–23.252; p < 0.001) were associated with PCa detection rates. The most sensitivity, specificity, negative, and positive predictive values were found in PSAD ≥0.15 ng/mL/cc + LD ≥1 cm group for both PCa and csPCa detection (48.8%, 92%, 85.2%, and 65.6% for any PCa detection; 66.7%, 85.2%, 97.3%, and 24.2% for csPCa detection, respectively). Conclusion: The presence of PSAD ≥0.15 ng/mL/cc or LD ≥1 cm in mpMRI of patients with PI-RADS 3 lesions is associated significantly with the finding of PCa and particularly with the detection of csPCa.

Recent Developments in Speeding up Prostate MRI.

The increasing incidence of prostate cancer cases worldwide has led to a tremendous demand for multiparametric MRI (mpMRI). In order to relieve the pressure on healthcare, reducing mpMRI scan time is necessary. This review focuses on recent techniques proposed for faster mpMRI acquisition, specifically shortening T2W and DWI sequences while adhering to the PI-RADS (Prostate Imaging Reporting and Data System) guidelines. Speeding up techniques in the reviewed studies rely on more efficient sampling of data, ranging from the acquisition of fewer averages or b-values to adjustment of the pulse sequence. Novel acquisition methods based on undersampling techniques are often followed by suitable reconstruction methods typically incorporating synthetic priori information. These reconstruction methods often use artificial intelligence for various tasks such as denoising, artifact correction, improvement of image quality, and in the case of DWI, for the generation of synthetic high b-value images or apparent diffusion coefficient maps. Reduction of mpMRI scan time is possible, but it is crucial to maintain diagnostic quality, confirmed through radiological evaluation, to integrate the proposed methods into the standard mpMRI protocol. Additionally, before clinical integration, prospective studies are recommended to validate undersampling techniques to avoid potentially inaccurate results demonstrated by retrospective analysis. This review provides an overview of recently proposed techniques, discussing their implementation, advantages, disadvantages, and diagnostic performance according to PI-RADS guidelines compared to conventional methods. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Can MRI Help Inform Which Men With a History of Multifocal High-Grade Prostatic Intraepithelial Neoplasia or Atypical Small Acinar Proliferation Remain at an Elevated Risk for Clinically Significant Prostate Cancer?

We investigated the association of MRI findings in men with a previous diagnosis of atypical small acinar proliferation (ASAP) or multifocal high-grade intraepithelial neoplasia (HGPIN) with pathologic findings on repeat biopsy. We retrospectively reviewed patients with ASAP/multifocal HGPIN undergoing a repeat biopsy in the Michigan Urological Surgery Improvement Collaborative registry. We included men with and without an MRI after the index biopsy demonstrating ASAP/multifocal HGPIN but before the repeat biopsy. Men with an MRI prior to the index biopsy were excluded. We compared the proportion of men with ≥ GG2 CaP (Grade Group 2 prostate cancer) on repeat biopsy among the following groups with the χ2 test: no MRI, PIRADS (Prostate Imaging-Reporting and Data System) ≥ 4, and PIRADS ≤ 3. Multivariable models were used to estimate the adjusted association between MRI findings and ≥ GG2 CaP on repeat biopsy. Among the 207 men with a previous diagnosis of ASAP/multifocal HGPIN that underwent a repeat biopsy, men with a PIRADS ≥ 4 lesion had a higher proportion of ≥ GG2 CaP (56%) compared with men without an MRI (12%, P < .001). A lower proportion of men with PIRADS ≤ 3 lesions had ≥ GG2 CaP (3.0%) compared with men without an MRI (12%, P = .13). In the adjusted model, men with a PIRADS 4 to 5 lesion had higher odds (OR: 11.4, P < .001) of ≥ GG2 CaP on repeat biopsy. MRI is a valuable diagnostic tool to triage which men with a history of ASAP or multifocal HGPIN on initial biopsy should undergo or avoid repeat biopsy without missing clinically significant CaP.

Histopathological results after MRТ/US fusion guided biopsy of prostate lesions classified asPIRADS 3

Introduction: Мagnetic resonance tomography (MRT) has become an established part of prostate cancer diagnosis. MRT/US fusion guided biopsy enables obtaining tissue samples directly from lesions suspected for prostate cancer. The introduction of the Prostate Imaging-Reporting and Data System (PI-RADS) precises the cases in which performance of target biopsy is needed by categorization of suspected prostate lesions according to the probability of detecting prostate cancer. It is debatable whether PI-RADS 3 lesions in MRТ (MRТ) represent a significant risk for prostate cancer. Objectivе: The objective of this study is to assess the incidence of carcinoma-positive histological results in PIRADS 3 compliant lesions from MRТ. Material and methods: Medical records of 99 patients, who underwent transperineal MRТ/US fusion prostate biopsy of suspicious lesions on MRТ, classified as PI-RADS 3, between January 2019 and December 2022 were retrospectively analyzed. Results: Patients` mean age was 65.6±6.8 years. Mean volume of the prostate gland was 54.2±20.2 cc and the mean value of prostate-specific antigen was 18.5±6.3. Pathologic finding from digital rectal examination of the prostate gland appeared in 18 (18.2%) patients. Mean size of the lesion from MRI was 12.9±5.24 mm. The suspect lesions were localized mainly in the peripheral zone of the prostate gland – 60 (60.6%), secondly in the transitional zone – 35 (35.4%). Seminal vesicles invasion and lymphade-nomegaly were rare – 1 (1%) and 2 (2%) cases. Histopathological results in 63 (63.6%) patients was benign prostatic hyperplasia. 24 (24.2%) lesions were classified as 3+3=6 according to Gleason Score, 4 (4.0%) as 3+4=7 and 8 (8.1%) as 4+3=7. Conclusion: The results of this retrospective study shows that PI-RADS 3 lesions take an intermediate position in regard to histopathological findings. The resulting almost equal number of patients diagnosed with BPH and prostate cancer is proof of this. PI-RADS 3 lesions carry a relatively small risk of clinically significant cancer (csPCa).

Cancer Detection Rate of MRI Ultrasound Fusion Prostate Biopsy in 1,039 Patients and Number Needed to Biopsy in Targeted Lesion

Objective: To determine the cancer detection rate (CDR) of magnetic resonance imaging ultrasound fusion prostate biopsy (MRI/US fusion prostate biopsy) in Thailand. The secondary aim was to estimate the number needed to biopsy (NNB) for each prostate lesion in a targeted biopsy, classified by Prostate Imaging-Reporting and Data System (PI-RADS). Materials and Methods: The data of 1,039 consecutive patients who underwent a MRI/US fusion prostate biopsy at Siriraj Hospital between September 2017 and February 2021 was included and retrospectively reviewed. Those included had previous negative biopsies and were biopsy naïve. The data was analyzed to find the detection rate and NNB. Results: The overall detection rate of MRI/US fusion prostate biopsy was 58.71%, whereas that of systematic biopsy was 45.72%. Clinically significant prostate cancer (csPCa) (Gleason score ≥ 3+4) detection rate of MRI/US fusion prostate biopsy was 51.01%. When categorized by PI-RADS category 3, 4, and 5 were 12.16%, 44.98%, 85.71% respectively. NNB in targeted biopsy of PI-RADS 3, 4, and 5 were 8, 7, and 3 times sequentially. Positive predictive factors for prostate cancer (PCa) detection were age, prostate-specific antigen density (PSAD) and PI-RADS, whereas prostate volume was a negative predictive factor. Conclusion: This study supports the role of MRI/US fusion prostate biopsy in PCa detection and should not avoid systematic biopsies. The higher the PI-RADS was, the greater the csPCa detection rate. NNB is helpful in guiding the least amount of biopsied cores for each lesion.

Optimal size threshold for PIRADSv2 category 5 upgrade and its positive predictive value: is it predictive of “very high” likelihood of clinically-significant cancer?

To identify the optimal size metric and threshold for Prostate Imaging Reporting and Data System (PIRADS) 5 upgrade, calculate its positive predictive value (PPV) for clinically-significant prostate cancer (csPCA), and determine if it is indicative of a "very high" likelihood of csPCA. One hundred and forty-three PIRADS 4 or 5 lesions were evaluated. Lesion diameters were used to calculate lesion volume (LV). Pearson correlation between maximum lesion diameter (MLD) and LV was calculated. Area under the curve (AUC) for discriminating csPCA (Gleason grade ≥ 3+4) was calculated using MLD and LV. Optimal size thresholds (using Youden index) and highly predictive size thresholds were identified for the whole prostate (WP), peripheral zone (PZ), and transitional zone (TZ). There was high correlation between MLD and LV (r=0.77-0.81), with comparable AUCs for MLD and LV in the identification of csPCA in the WP (0.73, 0.72), PZ (0.73, 0.73), and TZ (0.79, 0.75). Optimal MLD thresholds were 1.4, 1.4, and 1.6 cm in the WP, PZ, and TZ respectively, with PPVs of 76%, 81%, and 69%, respectively. An MLD threshold of 2.7 cm would be needed in the WP to achieve a PPV approaching 90%, with sensitivity decreasing to 10%. There is high correlation between MLD and LV with comparable discrimination of csPCA using each. PIRADSv2's 1.5 cm MLD threshold is near the optimal threshold for PIRADS 5 upgrade but has moderate PPV. A much higher threshold would be needed to increase its PPV, with significant sacrifice in sensitivity.

Significant Prostate Cancer in Patients with PI-RADS Category 3 Lesions: A Single-Center, Retrospective Cohort Study.

The Prostate Imaging-Reporting and Data System (PI-RADS) category 3 is the most ambiguous lesion with a variable clinically significant prostate cancer (CsPCa) detection rate. Prostate-specific antigen density (PSAD) has been investigated as an adjunctive factor to improve the diagnostic efficiency of PI-RADS categories. This study aimed to investigate the utility of PSAD as an adjunctive factor in predicting CsPCA risk in patients with PI-RADS 3 lesions. The patients with an initial PI-RADS 3 category lesion (n=142) scheduled for systematic and magnetic resonance imaging-guided prostate biopsy between 2018 and 2022 were retrospectively evaluated. Demographic and clinical variables, including PSAD, were collected. The rate of CsPCa was the primary outcome. The impact of PSAD on the CsPCa detection rate was the secondary outcome. The median age was 62 years. The rate of CsPCa was 8.5% (n=12). The patients with CsPCa have significantly lower prostate volüme and higher PSAD levels than those without CsPCa (p=0.016 and p=0.012). The cut-off values of PSAD in predicting CsPCa in all PI-RADS 3 patients and patients with CsPCa and clinically insignificant prostate cancer (n=26) were ≥0.181 ng/ml2. The sensitivity and specificity values for PSAD ≥0.181 ng/ml2 were of 75% (95% CI: 42.8%-94.5%) and 81.5% (95% CI: 73.4%-88.0%) in predicting CsPCa among PI-RADS 3 category. Conclusion: PSAD values higher than 0.181 ng/ml2 can be used as an adjunctive clinical parameter in predicting CsPCa in patients with PI-RADS 3 lesions and differentiating CsPCa from clinically insignificant prostate cancer cases.

Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection.

Aging is the most important risk factor for prostate cancer (PC). Imaging techniques can be useful to measure age-related changes associated with the transition to diverse pathological states. However, biomarkers of aging from prostate magnetic resonance imaging (MRI) remain to be explored. To develop an aging biomarker from prostate MRI and to examine its relationship with clinically significant PC (csPC, Gleason score ≥7) risk occurrence. Retrospective. Four hundred and sixty-eight (65.97 ± 6.91 years) biopsied males, contributing 7243 prostate MRI slices. A deep learning (DL) model was trained on 3223 MRI slices from 81 low-grade PC (Gleason score ≤6) and 131 negative patients, defined as non-csPC. The model was tested on 90 negative, 52 low-grade (142 non-csPC), and 114 csPC patients. 3-T, axial T2-weighted spin sequence. Chronological age was defined as the age of the participant at the time of the visit. Prostate-specific antigen (PSA), prostate volume, Gleason, and Prostate Imaging-Reporting and Data System (PI-RADS) scores were also obtained. Manually annotated prostate masks were used to crop the MRI slices, and a DL model was trained with those from non-csPC patients to estimate the age of the patients. Following, we obtained the prostate age gap (PAG) on previously unseen csPC and non-csPC cropped MRI exams. PAG was defined as the estimated model age minus the patient's age. Finally, the relationship between PAG and csPC risk occurrence was assessed through an adjusted multivariate logistic regression by PSA levels, age, prostate volume, and PI-RADS ≥ 3 score. T-test, Mann-Whitney U test, permutation test, receiver operating characteristics (ROC), area under the curve (AUC), and odds ratio (OR). A P value <0.05 was considered statistically significant. After adjusting, there was a significant difference in the odds of csPC (OR = 3.78, 95% confidence interval [CI]: 2.32-6.16). Further, PAG showed a significantly larger bootstrapped AUC to discriminate between csPC and non-csPC than that of adjusted PI-RADS ≥ 3 (AUC = 0.981, 95% CI: 0.975-0.987). PAG may be associated with the risk of csPC and could outperform other PC risk factors. 3 TECHNICAL EFFICACY: Stage 3.

Zone-specific computer-aided diagnosis system aimed at characterizing ISUP ≥ 2 prostate cancers on multiparametric magnetic resonance images: evaluation in a cohort of patients on active surveillance.

To assess a region-of-interest-based computer-assisted diagnosis system (CAD) in characterizing aggressive prostate cancer on magnetic resonance imaging (MRI) from patients under active surveillance (AS). A prospective biopsy database was retrospectively searched for patients under AS who underwent MRI and subsequent biopsy at our institution. MRI lesions targeted at baseline biopsy were retrospectively delineated to calculate the CAD score that was compared to the Prostate Imaging-Reporting and Data System (PI-RADS) version 2 score assigned at baseline biopsy. 186 patients were selected. At baseline biopsy, 51 and 15 patients had International Society of Urological Pathology (ISUP) grade ≥ 2 and ≥ 3 cancer respectively. The CAD score had significantly higher specificity for ISUP ≥ 2 cancers (60% [95% confidence interval (CI): 51-68]) than the PI-RADS score (≥ 3 dichotomization: 24% [CI: 17-33], p = 0.0003; ≥ 4 dichotomization: 32% [CI: 24-40], p = 0.0003). It had significantly lower sensitivity than the PI-RADS ≥ 3 dichotomization (85% [CI: 74-92] versus 98% [CI: 91-100], p = 0.015) but not than the PI-RADS ≥ 4 dichotomization (94% [CI:85-98], p = 0.104). Combining CAD findings and PSA density could have avoided 47/184 (26%) baseline biopsies, while missing 3/51 (6%) ISUP 2 and no ISUP ≥ 3 cancers. Patients with baseline negative CAD findings and PSAd < 0.15ng/mL2 who stayed on AS after baseline biopsy had a 9% (4/44) risk of being diagnosed with ISUP ≥ 2 cancer during a median follow-up of 41months, as opposed to 24% (18/74) for the others. The CAD could help define AS patients with low risk of aggressive cancer at baseline assessment and during subsequent follow-up.

Variability in prostate cancer detection among radiologists and urologists using MRI fusion biopsy.

The aim of this study is to evaluate the impact of radiologist and urologist variability on detection of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) with magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) fusion prostate biopsies. The Prospective Loyola University MRI (PLUM) Prostate Biopsy Cohort (January 2015 to December 2020) was used to identify men receiving their first MRI and MRI/TRUS fusion biopsy for suspected PCa. Clinical, MRI and biopsy data were stratified by radiologist and urologist to evaluate variation in Prostate Imaging-Reporting and Data System (PI-RADS) grading, lesion number and cancer detection. Multivariable logistic regression (MVR) models and area under the curve (AUC) comparisons assessed the relative impact of individual radiologists and urologists. A total of 865 patients (469 biopsy-naïve) were included across 5 urologists and 10 radiologists. Radiologists varied with grading 15.4% to 44.8% of patients with MRI lesions as PI-RADS 3. PCa detection varied significantly by radiologist, from 34.5% to 66.7% (p= 0.003) for PCa and 17.2% to 50% (p= 0.001) for csPCa. Urologists' PCa diagnosis rates varied between 29.2% and 55.8% (p= 0.013) and between 24.6% and 39.8% (p= 0.36) for csPCa. After adjustment for case-mix on MVR, a fourfold to fivefold difference in PCa detection was observed between the highest-performing and lowest-performing radiologist (OR 0.22, 95%CI 0.10-0.47, p< 0.001). MVR demonstrated improved AUC for any PCa and csPCa detection when controlling for radiologist variation (p= 0.017 and p= 0.038), but controlling for urologist was not significant (p= 0.22 and p= 0.086). Any PCa detection (OR 1.64, 95%CI 1.06-2.55, p= 0.03) and csPCa detection (OR 1.57, 95%CI 1.00-2.48, p= 0.05) improved over time (2018-2020 vs. 2015-2017). Variability among radiologists in PI-RADS grading is a key area for quality improvement significantly impacting the detection of PCa and csPCa. Variability for performance of MRI-TRUS fusion prostate biopsies exists by urologist but with less impact on overall detection of csPCa.

Patient-related characteristics predict prostate cancers in men with PI-RADS 4-5 to further optimize the diagnostic performance of MRI.

To develop a prediction model based on patient-related characteristics for detecting prostate cancer (PCa) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 4-5 in multiparametric magnetic resonance imaging (mp-MRI), aiming to optimize pre-biopsy risk stratification in MRI. The patient-related characteristics including the lesion location, age, prostate-specific antigen (PSA), free prostate-specific antigen (fPSA), fPSA/PSA, prostate-specific antigen density (PSAD) and body mass index (BMI) were collected for patients who underwent mp-MRI and prostate biopsy between February 2014 and October 2022. Univariate and multivariate logistic regression analyses were conducted to select independent predictors of PCa and further create a prediction model. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) were also calculated. A total of 833 patients were included in this study. In the subgroup PI-RADS 4, the independent characteristics of lesion location, age, fPSA/PSA and PSAD were selected to create the prediction model with an AUC of 0.748 (95% CI 0.694-0.803), sensitivity of 61.88%, specificity of 85.32%, PPV of 92.52%, and NPV of 43.26%. Besides, the prediction model in PI-RADS 5 was created using PSA and PSAD with an AUC of 0.893 (95% CI 0.844-0.941), sensitivity of 81.40%, specificity of 84.85%, PPV of 98.37% and NPV of 28.87%. The patient-related clinical characteristics were significant predictors of PCa and the prediction model based on selected characteristics could achieve a medium risk prediction of PCa in PI-RADS 4-5.

Prostate cancer detection using magnetic resonance imaging-transrectal ultrasound fusion biopsy: A retrospective cohort study

ABSTRACT Introduction: Transrectal ultrasound (TRUS) guided systematic prostate biopsy is conventionally used for the diagnosis of carcinoma prostate (CaP). However, magnetic resonance imaging (MRI) guided biopsies have been shown to have superior diagnostic performance. MRI-TRUS fusion biopsy improves the detection by combining the systematic and the targeted biopsies (TB). In this study, we evaluated the role of fusion biopsy in the detection of CaP as well as clinically significant carcinoma prostate (CsCaP). Methods: In this retrospective study, the patients who underwent fusion biopsy from January 2016 to July 2022 were evaluated. Patients underwent multiparametric MRI and the suspicious lesions were reported as per the Prostate Imaging Reporting and Data System (PIRADS) version 2. The clinical, imaging, and biopsy parameters were recorded and evaluated. Results: A total of 330 patients with PIRADS ≥3 underwent MRI-TRUS fusion biopsy and prostate cancer was detected in 187 patients (56.67%). With an increase in the PIRADS score, there was a significant rise in the detection of CaP (P &lt; 0.001) and CsCaP (P &lt; 0.0000001). Prostatitis was observed in 13%–18.1% of the patients with a lesion on MRI irrespective of the PIRADS score. The systematic and TB were comparable for the detection of CaP (P = 0.88) and CsCaP (P = 0.26). With a prostate-specific antigen density (PSAD) cutoff of 0.15 ng/mL/cc and 0.22 ng/mL/cc, biopsy could be safely avoided in 14.2% and 20.3% of the patients, missing only 0.3% of CaP and 0.9% of CsCaP, respectively. Different subgroups based on PSA levels, prostate volume, lesion dimension, and PIRADS score did not show a significant difference between the systematic and the targeted cores for the detection of CsCaP. Conclusion: This single center study of MRI-TRUS fusion prostate biopsy shows that in men with clinical suspicion of prostate cancer a pre-biopsy MRI and MRI-TRUS fusion combined systematic and targeted prostate biopsy improves the detection of prostate cancer and CsCaP. Patients with a PIRADS 3 lesion with a PSA density &lt;0.22 can safely avoid prostate biopsy, without a significant risk of missing clinically significant prostate cancer.

Development and internal validation of a nomogram predicting significant prostate cancer: Is it clinically applicable in low prevalent prostate cancer countries? A multicenter study.

Accurately identifying aggressive prostate tumors and studying them as a separate outcome are urgently needed. Nomogram is a predictive tool using an algorithm, it has been widely applied in clinical practice to predict prognosis. We aimed to develop and internally validate a nomogram predicting clinically significant prostate cancer (csPCa). Data were retrieved from the records of the two main hospitals in Riyadh, during the period 2019-2022. Significant variables associated with csPCa cases were used to develop and internally validate a novel nomogram, utilizing the C index, and calibration curves. Decision curve analysis (DCA) was used to assess its clinical utility. Prostate imaging reporting and data system (PI-RADS), smaller prostate volume, and prostate-specific antigen (PSA) > 10 ng/mL were significantly associated with the risk csPCa, respectively. The model developed by the nomogram showed an excellent accuracy for csPCa discrimination, as indicated by area under the curve (0.83), and calibration curves. DCA showed that our model was superior and surpassed all other models with a larger net benefit for various threshold probabilities. Based on our model, at a probability threshold of 30%, biopsying patients is the equivalent of a strategy that led to an absolute 5% reduction in the number of biopsies without missing any csPCa. The developed nomogram consisting of PI-RAD, total PSA, and prostate volume showed a robust predictive capacity for csPCa before prostate biopsy that may be valuable for clinical judgment to prevent needless biopsy. Yet, the small percentage (5%) of yielded unnecessary biopsies that could be saved by using such a model, cast an important question on its merit and clinical applicability.

Developing a diagnostic model for predicting prostate cancer: a retrospective study based on Chinese multicenter clinical data.

The overdiagnosis of prostate cancer (PCa) caused by nonspecific elevation serum prostate-specific antigen (PSA) and the overtreatment of indolent PCa have become a global problem that needs to be solved urgently. We aimed to construct a prediction model and provide a risk stratification system to reduce unnecessary biopsies. In this retrospective study, clinical data of 1807 patients from three Chinese hospitals were used. The final model was built using stepwise logistic regression analysis. The apparent performance of the model was assessed by receiver operating characteristic curves, calibration plots, and decision curve analysis. Finally, a risk stratification system of clinically significant prostate cancer (csPCa) was created, and diagnosis-free survival analyses were performed. Following multivariable screening and evaluation of the diagnostic performances, a final diagnostic model comprised of the PSA density and Prostate Imaging-Reporting and Data System (PI-RADS) score was established. Model validation in the development cohort and two external cohorts showed excellent discrimination and calibration. Finally, we created a risk stratification system using risk thresholds of 0.05 and 0.60 as the cut-off values. The follow-up results indicated that the diagnosis-free survival rate for csPCa at 12 months and 24 months postoperatively was 99.7% and 99.4%, respectively, for patients with a risk threshold below 0.05 after the initial negative prostate biopsy, which was significantly better than patients with higher risk. Our diagnostic model and risk stratification system can achieve a personalized risk calculation of csPCa. It provides a standardized tool for Chinese patients and physicians when considering the necessity of prostate biopsy.

Factors predicting transrectal ultrasound-guided systematic prostate biopsy failure

Objectives: To determine the factors that predict the failure of systematic prostate biopsy by examining the clinical, laboratory, and radiological parameters of patients for whom prostate cancer was detected by magnetic resonance imaging (MRI)-targeted biopsy but not by systematic biopsy. Methods: Patients were included in this study if they had undergone combined targeted and systematic biopsy and had cancer detected in the targeted biopsy. They were biopsy-naive patients and had lesions with a Prostate Imaging Reporting and Data System (PIRADS) score ≥ 3 in the peripheral zone on MRI. The clinical, biochemical, and radiological findings of the groups with and without cancer detected in the systematic biopsy were compared. Results: A total of 100 patients had an index lesion in the peripheral zone and cancer detected by MRI-targeted biopsy. In 43 (43%) of the patients, no cancer was detected in the systematic biopsy, whereas it was detected in the other 57 (57%). Statistically significant differences were found between the two groups in terms of prostate volume and PSA density (p &amp;lt; 0.001 and p &amp;lt; 0.001, respectively). Moreover, the findings of univariate and multivariate logistic regression analyses indicated that prostate volume and lesion size are independent predictors of systematic biopsy failure. Conclusions: The success of systematic biopsy may be lower in patients with high prostate volume and low peripheral zone index lesion size.

The role of PSMA PET/CT to predict upgrading in patients undergoing radical prostatectomy for ISUP grade group 1 prostate cancer.

To investigate the additive role of prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) independent from multiparametric magnetic resonance imaging (mpMRI) and clinical-pathological parameters to predict pathological upgrading in patients with ISUP grade group (GG) 1 prostate cancer (PCa) at prostate biopsy. A total of 41 patients who underwent robotic radical prostatectomy (RP) for GG1 disease at prostate biopsy with preoperative PSMA PET/CT and mpMRI images available for central review were included in the study. Univariate and multivariate logistic regression analyses were performed to determine the independent predictors of pathological upgrading (GG ≥ 2). Final RP pathology revealed upgrading in 26 patients (65.9%); to GG 2 disease in 25 cases and GG 4 disease in one case. International Society of Urological Pathology (ISUP) upgrading rates for prostate imaging-reporting and data system (PIRADS)-5, PIRADS-4, and PIRADS ≤ 3 lesions were 78%, 74%, and 38%, respectively. Fourteen out of 15 (93.3%) patients with an SUVmax ≥ 5.6 and all patients with an SUVmax ≥ 6.5 (n = 10) had pathological upgrading. The upgrading rate in patients with SUV < 5.6 was 46.2% (12/26). Intraprostatic SUVmax ≥ 5.6 was found as the only independent predictor of pathological upgrading in multivariate analysis. High prostatic PSMA uptake was found to be a very reliable predictor of pathological upgrading, but low PSMA uptake cannot exclude pathological upgrading. Intraprostatic PSMA uptake along with previously known mpMRI and biopsy-related parameters should be considered when making a treatment decision in patients with GG1 PCa at prostate biopsy.

Urine spermine and multiparametric magnetic resonance imaging for prediction of prostate cancer in Japanese men.

To investigate the role of urine spermine and spermine risk score in predicting prostate cancer (PCa) diagnoses in combination with multiparametric magnetic resonance imaging (mpMRI). Three hundred forty seven consecutive men with elevated prostate-specific antigen (PSA) with mpMRI examination were prospectively enrolled in this study. In 265 patients with PSA levels between 4 and20ng/ml, pre-biopsy urine samples were analyzed for spermine levels with ultra-high performance liquid chromatography (UPLC-MS/MS). Transperineal image-guided prostate biopsies with 16-18 cores were performed. Logistic regressions were used to form different models for the prediction of the PCa, and the performances were compared using the area under the curve (AUC). The median serum PSA level and prostate volume were 7.4ng/mL and 33.9mL, respectively. PCa and high-grade PCa (ISUP group ≥2, HGPCa) were diagnosed in 66.0% (175/265) and 132/265 (49.8%) cases, respectively. The urine spermine levels were significantly lower in men with PCa (0.87 vs. 2.20, P<0.001). Multivariate analyses showed that age, PSA, PV, urine spermine level, and Prostate Imaging Reporting and Data System (PI-RADS) findings were independent predictors for PCa. The Spermine Risk Score is a multivariable model including PSA, age, prostate volume, and urine spermine. Adding the Spermine Risk Score to PI-RADS improved the AUC from 0.73 to 0.86 in PCaand from 0.72 to 0.83 in high grade PCa (HGPCa) prediction (both P<0.001). At 90% sensitivity for HGPCa prediction using Spermine Risk Score, 31.1% of unnecessary biopsies could be avoided. In men with equivocal MRI PI-RADS score 3, the AUC for HGPCa prediction was 0.58, 0.79, and 0.87 for PSA, PSA density, and Spermine Risk Score, respectively. Urine Spermine Risk Score, including mpMRI could accurately identify men at high risk of HGPCa and reduce unnecessary prostate biopsies. Spermine Risk Score could more accurately predict HGPCa than PSA density in men with MRI showing equivocal PI-RADS 3 lesions.

Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI.

Background Deep learning (DL) reconstructions can enhance image quality while decreasing MRI acquisition time. However, DL reconstruction methods combined with compressed sensing for prostate MRI have not been well studied. Purpose To use an industry-developed DL algorithm to reconstruct low-resolution T2-weighted turbo spin-echo (TSE) prostate MRI scans and compare these with standard sequences. Materials and Methods In this prospective study, participants with suspected prostate cancer underwent prostate MRI with a Cartesian standard-resolution T2-weighted TSE sequence (T2C) and non-Cartesian standard-resolution T2-weighted TSE sequence (T2NC) between August and November 2022. Additionally, a low-resolution Cartesian DL-reconstructed T2-weighted TSE sequence (T2DL) with compressed sensing DL denoising and resolution upscaling reconstruction was acquired. Image sharpness was assessed qualitatively by two readers using a five-point Likert scale (from 1 = nondiagnostic to 5 = excellent) and quantitatively by calculating edge rise distance. The Friedman test and one-way analysis of variance with post hoc Bonferroni and Tukey tests, respectively, were used for group comparisons. Prostate Imaging Reporting and Data System (PI-RADS) score agreement between sequences was compared by using Cohen κ. Results This study included 109 male participants (mean age, 68 years ± 8 [SD]). Acquisition time of T2DL was 36% and 29% lower compared with that of T2C and T2NC (mean duration, 164 seconds ± 20 vs 257 seconds ± 32 and 230 seconds ± 28; P < .001 for both). T2DL showed improved image sharpness compared with standard sequences using both qualitative (median score, 5 [IQR, 4-5] vs 4 [IQR, 3-4] for T2C and 4 [IQR, 3-4] for T2NC; P < .001 for both) and quantitative (mean edge rise distance, 0.75 mm ± 0.39 vs 1.15 mm ± 0.68 for T2C and 0.98 mm ± 0.65 for T2NC; P < .001 and P = .01) methods. PI-RADS score agreement between T2NC and T2DL was excellent (κ range, 0.92-0.94 [95% CI: 0.87, 0.98]). Conclusion DL reconstruction of low-resolution T2-weighted TSE sequences enabled accelerated acquisition times and improved image quality compared with standard acquisitions while showing excellent agreement with conventional sequences for PI-RADS ratings. Clinical trial registration no. NCT05820113 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Turkbey in this issue.