Search Results (186)

Clinical application of AI/DL-aided acquisitions for quantitative bi-parametric (q-bp)MRI requires validation and harmonization across vendor platforms. An AI/DL-accelerated q-bpMRI, including 5-echo T₂ and 4-b-value apparent diffusion coefficient (ADC) mapping, was implemented on two 3T clinical scanners by two vendors alongside the qualitative standard-of-care (SOC) MRI protocols for six patients with biopsy-confirmed prostate cancer (PCa). AI/DL versus SOC bpMRI image quality was compared for MR-visible PCa lesions on a 4-point Likert-like scale. Quantitative validation and protocol bias assessment were performed using a multiparametric phantom with reference T₂ and diffusion kurtosis values mimicking prostate tissue ranges. Six-minute q-bpMRI achieved acceptable diagnostic quality comparable to the SOC. Better SNR was observed for DL/AI versus SOC ADC with method-dependent distortion susceptibility and resolution enhancement. The measured biases were unaffected by AI/DL reconstruction and related to acquisition protocol parameters: constant for spin-echo T₂ (−7 ms to +5 ms) and ADC (4b-fit: −0.37 µm²/ms and 2b-fit: −0.19 µm²/ms), while nonlinear for echo-planar T₂ (−37 ms to +14 ms). Measured phantom ADC bias dependence on b-value range was consistent with that observed for PCa lesions. Bias correction harmonized lesion T₂ and ADC values across different AI/DL-aided q-bpMRI acquisitions. The developed workflow enables harmonization of AI/DL-accelerated quantitative T₂ and ADC mapping in multi-vendor clinical settings. Full article

In industrial scenarios, the control of permanent magnet synchronous servo motors is mostly achieved with proportional–integral controllers, which require manual adjustment of control parameters. At the same time, the performance of the servo system is usually disturbed by internal characteristic changes, load changes, and external factors. Therefore, preset control parameters may not achieve the desired optimal performance. Many scholars use intelligent algorithms, such as neural networks, to adaptively tune control parameters. However, the offline pre-training of neural networks is often time- and resource-consuming. Due to the lack of a model pre-training process in the neural network online self-tuning process, randomly setting the initial network weight seriously affects the position tracking performance of the servo control system in the start-up phase. In this paper, the physical model and the traditional frequency domain-tuning method of the three-closed-loop permanent magnet synchronous servo system are analyzed. Combined with the neural network PI control parameter self-tuning method and physical symmetry, a physical information-driven optimization framework is proposed. To demonstrate its superiority, the neural network PI controller and the proposed optimization framework are used to control the single-axis sine wave trajectory. The results show that the optimization framework proposed can effectively improve the position tracking control performance of the servo control system in the start-up phase by setting the threshold of the servo control parameters, reduce the position tracking control error to 0.75 rads in the start-up phase, and reduce the position tracking drop caused by a sudden load by 25%. This method achieves the independent optimization adjustment of control parameters under position tracking control, providing a reference for the intelligent control of permanent magnet synchronous servo motors. Full article

Background/Objectives: Improving prostate cancer (PCa) detection remains a key clinical goal. While multiparametric MRI (mp-MRI) fusion-guided biopsy has shown advantages over systematic randomized biopsy, variability persists across studies. This study aimed to compare detection rates between fusion-guided and randomized biopsy techniques and assess the combined predictive value of clinical risk factors. Methods: We retrospectively analyzed 138 male patients aged 50–82 years with PSA (prostate-specific antigen) < 25 ng/mL, undergoing both mp-MRI fusion-guided and systematic randomized biopsies. PI-RADS v2.1 was used for lesion assessment. The patient data included PSA, prostate volume, PI-RADS score, and age. Multicollinearity was evaluated, and a multivariate logistic regression model was developed. ROC analysis assessed predictive performance. Results: Fusion-guided biopsy detected cancer in 68.1% (95% CI: 60.3–75.9%) of cases, randomized biopsy in 76.1% (95% CI: 68.9–83.2%), and the combined approach in 88.4% (95% CI: 83.1–93.7%). McNemar’s test confirmed a significant improvement when combining both methods (p < 0.001). PSA exhibited the strongest individual predictive power (AUC = 0.782, 95% CI: ~0.70–0.86), followed by prostate volume (AUC = 0.631, 95% CI: ~0.53–0.73), PI-RADS score (AUC = 0.619, 95% CI: ~0.51–0.72), and age (AUC = 0.572, 95% CI: ~0.46–0.68). The multivariate model achieved an AUC of 0.751 (95% CI: ~0.66–0.83) and an accuracy of 89.6%. Conclusions: Combining fusion-guided and randomized biopsy techniques enhances prostate cancer detection compared with either method alone. PSA, prostate volume, PI-RADS score, and age contribute independently to risk prediction. Future studies will aim to refine stratification models and explore familial cancer risk factors. Full article

Objectives: To evaluate the diagnostic accuracy of AI-assisted biparametric MRI (AI-bpMRI) in detecting prostate cancer (PCa) as a possible replacement for multiparametric MRI (mpMRI) depending on readers’ experience. Methods: This fully crossed, multireader multicase, single-centre, consecutive study retrospectively included men with suspected PCa. Three radiologists with different levels of experience independently scored each participant’s biparametric (bp) MRI, mpMRI, and AI-bpMRI according to the PI-RADS V2.1 classification. The AI-assisted image processing was based on a sequential deep learning network. Histopathological findings were used as a reference. The study evaluated the mean areas under the receiver operating characteristic curves (AUCs) using the jackknife method for covariance. AUCs were tested for non-inferiority of AI-bpMRI to mpMRI (non-inferiority margin: −0.05). Results: A total of 105 men (mean age 66 ± 7 years) were evaluated. AI-bpMRI was non-inferior to mpMRI in detecting both Gleason score (GS) ≥ 3 + 4 PCa (AUC difference: 0.03 [95% CI: −0.03, 0.08], p = 0.37) and GS ≥ 3 + 3 PCa (AUC difference: 0.04 [95% CI: −0.01, 0.09], p = 0.14) and was superior to bpMRI in detecting GS ≥ 3 + 3 PCa (AUC difference: 0.07 [95% CI: 0.02, 0.12], p = 0.004). The benefit of AI-bpMRI was greatest for the readers with low or medium experience (AUC difference in detecting GS ≥ 3 + 4 compared to mpMRI: 0.06 [95% CI: −0.03, 0.14], p = 0.19 and 0.06 [95% CI: −0.03, 0.14], p = 0.19, respectively). Conclusions: This study indicates that AI-bpMRI detects PCa with a diagnostic accuracy comparable to that of mpMRI. Full article

Background/Objectives: Magnetic resonance imaging (MRI) and MRI–ultrasound (US) fusion-targeted biopsy have improved prostate cancer diagnosis, particularly for clinically significant disease. However, the added value of combining systematic biopsy with targeted biopsy remains debated. This study aimed to evaluate the diagnostic accuracy of MRI–US fusion-targeted and systematic transperineal biopsies in detecting prostate cancer and explore the correlation between PI-RADS score and histology. Methods: We retrospectively analyzed 356 patients with 452 MRI-detected lesions who underwent both MRI–US fusion-targeted and transperineal systematic biopsies between 2020 and 2023. Clinically significant prostate cancer (csPCa) was defined as International Society of Urological Pathology (ISUP) grade ≥ 2. Diagnostic performance metrics (sensitivity, specificity, and accuracy) were calculated for each technique using the combined result as a reference. Subgroup analysis was performed for patients under active surveillance. Results: Prostate cancer was diagnosed in 323 of 452 lesions (71%) and csPCa in 223 lesions (49%). Targeted biopsy demonstrated higher sensitivity (93.7%) and accuracy (79.9%) than systematic biopsy (85.7% sensitivity and 77.6% accuracy), although systematic biopsy provided slightly higher specificity. Systematic biopsy alone identified 8.2% of PCa cases missed by targeted biopsy and upgraded 9.9% of lesions to csPCa. csPCa detection increased with PI-RADS score (23% in PI-RADS 3 and 73% in PI-RADS 5). In active surveillance patients, csPCa was found in 65% of lesions. Conclusions: MRI–US fusion-targeted biopsy improves csPCa detection, but systematic biopsy remains valuable, especially for identifying additional or higher-grade disease. The combined approach provides an optimal diagnostic yield, supporting its continued use in both initial and repeat biopsy settings. Full article

Objectives: This study aimed to investigate the consistency of lesion identification by Prostate Imaging Reporting and Data System (PI-RADS) and the related clinical and histological characteristics in a high-volume tertiary care center. Materials and methods: The analysis used real-world data from 111 patients between 2018 and 2022. Each patient underwent two multiparametric magnetic resonance imaging (MRI) scans of the prostate at different institutions with a median interval of 42 days between the scans, followed by an MRI-fused biopsy conducted 7 days after the second MRI. Results: The PI-RADS classifications assigned to the index lesions in the in-house prostate MRI were as follows: PI-RADS V, 33.3% (n = 37); PI-RADS IV, 49.5% (n = 55); PI-RADS III, 12.6% (n = 14); and PI-RADS II, 4.5% (n = 5). Cancer detection rates for randomized and/or targeted biopsies were 91.9% (n = 34) for PI-RADS V, 65.5% (n = 36) for PI-RADS IV, 21.4% (n = 3) for PI-RADS III, and 20% (n = 1) for PI-RADS II. Overall, malignant histology was observed in 64.9% (n = 72) of the targeted lesions and 57.7% (n = 64) of the randomized biopsies. In the first performed, external MRI, 18% (n = 20) and 10.8% (n = 12) of the patients were classified in the higher and lower PI-RADS categories, respectively. The biopsy plan was adjusted for 57 patients (51.4%); nevertheless, any cancer could have possibly been identified regardless of the adjustments. Conclusion: The 6-week interval between the MRI scans did not affect the quality of the biopsy results significantly. Full article

Background/Objectives: Accurate histopathological grading of prostate cancer at the time of biopsy is essential for guiding treatment decisions and minimizing the risks of both overtreatment and undertreatment. A key challenge in prostate cancer diagnostics is the phenomenon of upgrading, wherein the cancer appears more aggressive in the radical prostatectomy specimen than initially indicated by biopsy. Such discrepancies can compromise therapeutic planning. This study investigates whether transperineal combined fusion biopsy (ComBx), incorporating MRI-targeted and systematic sampling, achieves greater concordance with final prostatectomy histopathology compared to conventional transrectal ultrasound-guided systematic biopsy (TRUS-Bx). Methods: This retrospective cohort study analyzed 500 men aged 46 to 79 years (mean age 65) who underwent prostate biopsies between 2017 and 2022 at a single tertiary institution. Patients were stratified into two groups: 250 underwent TRUS-Bx using a 12-core systematic approach, and 250 underwent ComBx guided by software-based MRI–ultrasound fusion targeting PI-RADS ≥ 3 lesions, followed by systematic sampling. Histopathological grading from biopsies was compared with final pathology following radical prostatectomy. Concordance, upgrading, and downgrading rates were analyzed using appropriate statistical methods. Results: Prostate cancer was diagnosed in 113 patients in the TRUS-Bx group and 152 in the ComBx group. Among these, 89 TRUS-Bx and 68 ComBx patients underwent radical prostatectomy at our center. Histological upgrading occurred statistically significantly more often in the TRUS-Bx group (35%) compared to the ComBx group (16%) (p = 0.004). Concordance between biopsy and prostatectomy grading was statistically significantly higher in the ComBx group (63%) than in the TRUS-Bx group (49%) (p = 0.042). No significant difference in downgrading rates was observed between groups. Conclusions: Transperineal combined fusion biopsy substantially improves concordance with final prostatectomy histology and significantly reduces the risk of upgrading compared to transrectal systematic biopsy. These findings support the adoption of ComBx as a more reliable diagnostic strategy for accurate grading of clinically significant prostate cancer, with implications for improving treatment precision and patient outcomes. Full article

Background/Objectives: ISUP grade group discordance between prostate biopsy and radical prostatectomy (RP) impacts treatment decisions in over a third (~25–40%) of prostate cancer (PCa) patients. We aimed to identify ISUP grade migration predictors and assess the impact of preoperative imaging (MRI) in a contemporary Romanian PCa cohort. Methods: We retrospectively analyzed 142 PCa patients undergoing RP following biopsy between January 2021 and December 2024 at Pius Brinzeu County Hospital, Timișoara: 90 without and 52 with preoperative MRI. Clinical parameters, MRI findings (PI-RADS), and biopsy characteristics were evaluated. Machine learning models (gradient boosting, random forest) were developed with SHAP analysis for interpretability. Results: Grade migration occurred in 69/142 patients (48.6%): upstaging in 55 (38.7%) and downstaging in 14 (9.9%). In the non-MRI cohort, 37/90 (41.1%) were upstaged and 9/90 (10.0%) were downstaged, versus 18/52 (34.6%) upstaged and 5/52 (9.6%) downstaged in the MRI cohort. The MRI group showed a 6.5% absolute reduction in upstaging (34.6% vs. 41.1%), a promising non-significant trend (p = 0.469) that requires further investigation. Grade 1 patients showed the highest upstaging (69.4%), while Grades 3–4 showed the highest downstaging (11/43, 25.6%). PI-RADS 4 lesions had the highest upstaging (43.5%). PSA density > 0.20 ng/mL² emerged as the strongest predictor. Gradient boosting achieved superior performance (AUC = 0.812) versus logistic regression (AUC = 0.721), representing a 13% improvement in discrimination. SHAP analysis revealed PSA density as the most influential (importance: 0.287). Grade migration associated with adverse pathology: extracapsular extension (52.7% vs. 28.7%, p = 0.008) and positive margins (38.2% vs. 21.8%, p = 0.045). Conclusions: ISUP grade migration affects 48.6% of Romanian patients, with 38.7% upstaged and 9.9% downstaged. The 69.4% upstaging in Grade 1 patients emphasizes the need for enhanced risk stratification tools, while 10% downstaging suggests potential overtreatment. Machine learning with SHAP analysis provides superior predictive performance (13% AUC improvement) while offering clinically interpretable risk assessments. PSA density dominates risk assessment, while PI-RADS 4 lesions warrant closer scrutiny than previously recognized. Full article

Secondary lymphoma of the prostate is described as the involvement of the prostate gland by lymphomatous spread from a primary site. This condition is exceedingly rare and often presents diagnostic and therapeutic challenges. The symptoms often mimic those of benign prostatic hyperplasia or prostate cancer, including LUTS (lower urinary tract symptoms) and even complete urinary retention. Here, we present a rare case of a 62-year-old male patient undergoing chemotherapy for stage IV mantle cell stomach lymphoma and subsequently secondary prostatic involvement. The patient presented with complete urinary retention, accompanied by biochemical (PSA = 11.7 ng/mL) and imaging (Magnetic Resonance Imaging-PIRADS V lesion) suspicion for prostate cancer. Histopathologic analysis of the MRI-targeted prostate fusion biopsy revealed secondary prostatic lymphoma. The chosen treatment was transurethral resection of the prostate (TUR-P) for relief of symptoms, which significantly improved urinary function (postoperative IPSS = 5 and Qmax = 17 mL/s). This case underscores the importance of considering prostatic lymphoma in the differential diagnosis of bladder outlet obstruction, especially in patients with a known lymphoma history. This report also provides a focused review of the literature on secondary prostatic lymphoma, highlighting the diagnostic challenges, treatment options, and clinical outcomes. Full article

Prostate cancer remains one of the most prevalent malignancies among men, and emerging evidence proposed a potential role for periprostatic adipose tissue (PPAT) in tumor progression. However, its relationship with imaging-based risk stratification systems such as PI-RADS remains uncertain. This retrospective observational study aimed to evaluate whether periprostatic and subcutaneous fat thickness are associated with PI-RADS scores or PSA levels in biopsy-naïve patients. We retrospectively reviewed 104 prostate MRI scans performed between January 2020 and January 2024. Fat thickness was measured on axial T2-weighted images, and statistical analyses were conducted using Spearman’s correlation and multiple linear regression. In addition to linear measurements, we also assessed periprostatic fat volume and posterior fat thickness derived from imaging data. No significant correlations were observed between fat thickness (either periprostatic or subcutaneous) and PI-RADS score or PSA values. Similarly, periprostatic fat volume showed only a weak, non-significant correlation with PI-RADS, while posterior fat thickness demonstrated a weak but statistically significant positive association. Additionally, subgroup comparisons between low-risk (PI-RADS < 4) and high-risk (PI-RADS ≥ 4) patients showed no meaningful differences in fat measurements. These findings suggest that simple linear fat thickness measurements may not enhance imaging-based risk assessment in prostate cancer, though regional and volumetric assessments could offer modest added value. Full article

Background: prostate fusion biopsies are key in the diagnosis of prostate cancer (PCa); however, the fusion imaging system is not always user-friendly or reliable. The aim of this study was to assess the feasibility, accuracy, and effectiveness of transperineal fusion biopsies performed with a novel fusion imaging device equipped with AI-driven auto-contouring. Methods: data from 148 patients who underwent MRI-targeted and systematic prostate fusion biopsy with UroFusion (Esaote) were prospectively collected. All biopsies were performed in-office, under local anaesthesia. Results: cancer detection rate was 64% overall and 56% for clinically significant PCa (csPCa, ISUP ≥ 2). PCa was detected in 35%, 65% and 84% of lesions scored as PI-RADS 3, 4 and 5, respectively. Outfield positive systematic cores were found in the contralateral lobe in one third of cases. Median device-time to obtain fusion imaging was 5 min and median biopsy duration was 15 min. Median difference in volume estimation between ultrasound and MRI auto-contouring was only 1 cc. Detection rate did not differ between experienced and novice, supervised users. Conclusions: in this initial prospective experience, fusion biopsies performed with UroFusion AI-driven auto-contouring system appeared time-efficient, accurate, well tolerated, and user-friendly, with comparable outcomes between experienced and novice users. Systematic biopsies remain highly recommended given the non-negligible rates of positive outfield cores. Full article

Background: Accurate upfront risk stratification in suspected clinically significant prostate cancer (csPCa) may reduce unnecessary prostate biopsies. Integrating clinical and Magnetic Resonance Imaging (MRI) variables using deep learning could improve prediction. Methods: We retrospectively analysed 538 men who underwent MRI and biopsy between April 2019-September 2024. A fully connected neural network was trained using 5-fold cross-validation. Model 1 included clinical features (age, prostate-specific antigen [PSA], PSA density, digital rectal examination, family history, prior negative biopsy, and ongoing therapy). Model 2 used MRI-derived Prostate Imaging Reporting and Data System (PI-RADS) categories. Model 3 used all previous variables as well as lesion size, location, and prostate volume as determined on MRI. Results: Model 3 achieved the highest area under the receiver operating characteristic curve (AUC = 0.822), followed by Model 2 (AUC = 0.778) and Model 1 (AUC = 0.716). Sensitivities for detecting clinically significant prostate cancer (csPCa) were 87.4%, 91.6%, and 86.8% for Models 1, 2, and 3, respectively. Although Model 3 had slightly lower sensitivity than Model 2, it showed higher specificity, reducing false positives and avoiding 43.4% and 21.2% more biopsies compared to Models 1 and 2. Decision curve analysis showed M2 had the highest net benefit at risk thresholds ≤ 20%, while M3 was superior above 20%. Conclusions: Model 3 improved csPCa risk stratification, particularly in biopsy-averse settings, while Model 2 was more effective in cancer-averse scenarios. These models support personalized, context-sensitive biopsy decisions. Full article

Background/Objectives: There is a growing need for efficient prostate MRI protocols due to their increasing use in managing prostate cancer (PCa) and potential inclusion in screening. Deep learning reconstruction (DLR) may enhance MR acquisitions and improve image quality compared to conventional acceleration techniques. This systematic review examines DLR approaches to prostate MRI. Methods: A search of PubMed, Web of Science, and Google Scholar identified eligible studies comparing DLR to conventional reconstruction for prostate imaging. A narrative synthesis was performed to summarize the impact of DLR on acquisition time, image quality, and diagnostic performance. Results: Thirty-three studies showed that DLR can reduce acquisition times for T₂w and DWI imaging while maintaining or improving image quality. It did not significantly affect clinical tasks, such as biopsy decisions, and performed comparably to human readers in PI-RADS scoring and the detection of extraprostatic extension. However, AI models trained on conventional data might be less accurate with DLR images. The heterogeneity in image quality metrics among the studies prevented quantitative synthesis. Discussion: DLR has the potential to achieve substantial time savings in prostate MRI while maintaining image quality, which is especially relevant because of increased MRI demands. Future research should address the effect of DLR on clinically relevant downstream tasks, including AI algorithms’ performances and biopsy decisions, and explore task-specific accelerated protocols for screening, image-guided biopsy, and treatment. Full article

Background/Objectives: It remains unclear whether certain areas of the prostate are more difficult to accurately sample using MRI/US-fusion-guided freehand single-access transperineal prostate biopsy (FSA-TP). The aim of this study was to evaluate the detection rates of clinically significant (cs) and clinically insignificant (ci) prostate cancer (PCa) in each prostate zone during FSA-TP MRI-target biopsies (MRI-TBs) and systematic biopsies (SB). Methods: This monocentric observational study included a cohort of 277 patients with no prior history of PCa who underwent 3 MRI-TB cores and 14 SB cores with an FSA-TP from January to December 2023. The intraclass correlation coefficient (ICC) was assessed to evaluate the correlation between the Prostate Imaging–Reporting and Data System (PI-RADS) of the index lesion and the International Society of Urological Pathology (ISUP) grade stratified according to prostate zone and region of index lesion at MRI. Multivariate logistic regression analysis was conducted to identify factors associated with PCa and csPCa in patients with discordant results between MRI-TB and SB. Results: FSA-TP-MRI-TB demonstrated higher detection rates of both ciPCa and csPCa in the anterior, apical, and intermediate zones when each of the three MRI-TB cores was analysed separately (p < 0.01). However, when all MRI-TB cores were combined, no significant differences were observed in detection rates across prostate zones (apex, mid, base; p = 0.57) or regions (anterior vs. posterior; p = 0.34). Concordance between radiologic and histopathologic findings, as measured by the intraclass correlation coefficient (ICC), was similar across all zones (apex ICC: 0.33; mid ICC: 0.34; base ICC: 0.38) and regions (anterior ICC: 0.42; posterior ICC: 0.26). Univariate analysis showed that in patients with PCa detected on SB but with negative MRI-TB, older age was the only significant predictor (p = 0.04). Multivariate analysis revealed that patients with PCa detected on MRI-TB but with negative SB, only PSA remained a significant predictor (OR 1.2, 95% CI 1.1–1.4; p = 0.01). In cases with csPCa detected on MRI-TB but with negative SB, age (OR: 1.0, 95% CI 1.0–1.1; p = 0.02), positive digital rectal examination (OR: 2.0, 95% CI 1.1–3.8; p = 0.03), PI-RADS score >3 (OR: 4.5, 95% CI 1.7–12.1; p < 0.01), and larger lesion size (OR: 1.1, 95% CI 1.1–1.2; p < 0.01) were significant predictors. Conclusions: FSA-TP using 14 SB cores and 3 MRI-TB cores ensures comprehensive sampling of all prostate regions, including anterior and apical zones, without significant differences in detection rates between nodules across different zones. Only in a small percentage of patients was csPCa detected exclusively by SB, highlighting the small but important complementary value of combining SB and MRI-TB. Full article

Background/Objectives: Prostate cancer (PCa) remains a major contributor to cancer-related morbidity and mortality worldwide. Current diagnostic strategies, largely based on PSA screening, lack specificity and sensitivity, leading to unnecessary invasive procedures and elevated healthcare costs. This real-world study evaluated the EpiSwitch^® PSE assay, a blood-based test analyzing 3D genome conformation signatures, ability to avoid unnecessary biopsies and the resulting clinical and economical benefits. Methods: 187 patients undergoing evaluation for PCa were tested with the EpiSwitch^® PSE assay. Biopsy confirmation was available for 53 patients, while predictive modeling assessed 134 patients using EpiSwitch PSE results and clinical variables. Results: Among the 187 patients evaluated, predictive modeling showed that up to 79.1% (106/134) of patients could safely defer biopsy based on a low-likelihood EpiSwitch PSE result, while an alternative model showed a 66.4% (89/134) biopsy avoidance rate. The PSE result demonstrated strong concordance with biopsy-confirmed diagnoses and was the most influential predictor in multivariate analysis, followed by PI-RADS score. The test achieved a 100% technical success rate, with an average turnaround time of 4.4 days. Conclusions: Incorporating the EpiSwitch PSE assay into clinical workflows enhances decision-making efficiency, reduces unnecessary biopsies, and improves healthcare resource utilization. These findings support the assay’s strong clinical utility and economic value, highlighting its potential for broader adoption as a minimally invasive reflex test and a pre-biopsy triage tool for the early and accurate detection of prostate cancer. Future studies should include prospective, multicenter trials to confirm these results across broader populations and evaluate longitudinal outcomes of patients managed with PSE-guided care. Full article