Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2

Bacillus Calmette-Guérin Instillations May Mimic Prostate Cancer on Multiparametric Magnetic Resonance Imaging

Granulomatous prostatitis (GP) is a rare and benign inflammatory condition of the prostate, often mimicking prostate cancer (PCa) in clinical and radiological evaluations. This study examines the characteristics and diagnostic challenges of GP in a cohort of 12 patients who received Bacillus Calmette-Guérin (BCG) therapy following treatment for non-muscle-invasive bladder urothelial carcinoma. In this case series, we analyze their clinical presentations, MRI findings, and histopathological results. Patients presented with elevated PSA levels and firm or nodular prostates on digital rectal examination, complicating the differentiation from PCa. Multiparametric MRI showed lesions with hypointensity on T2-weighted images, hypersignal on diffusion-weighted imaging and hyposignal on the apparent diffusion coefficient map, further mimicking malignancy. Histopathological examination remains the gold standard for diagnosing GP, distinguishing it from PCa through the identification of granulomas and associated inflammatory cells. This study underscores the importance of awareness and accurate diagnosis of GP to avoid unnecessary biopsies and treatments, highlighting the need for a multidisciplinary approach combining clinical, imaging, and pathological data.

Prediction of Clinically Significant Prostate Cancer Using Multiparametric MRI, Biparametric MRI, and Clinical Parameters

INTRODUCTION

Multiparametric MRI (mpMRI) is gold standard for the primary diagnostic work-up of clinically significant prostate cancer (csPCa). The aim of this study was to assess the benefit of the perfusion sequence and the non-inferiority of an MRI without contrast administration (bpMRI) compared to mpMRI while taking clinical parameters into account.

METHODS

In this retrospective, non-interventional study we examined MRI data from 355 biopsy-naïve patients, performed on a 3T MRI system, evaluated by a board-certified radiologist with over 10 years of experience with subsequent mpMRI-TRUS fusion biopsy.

DISCUSSION

Only 16/355 (4.5%) patients benefited from dynamic contrast enhanced. In only 3/355 (0.8%) patients, csPCa would have been missed in bpMRI. BpMRI provided sensitivity and specificity (81.4%; 79.4%) comparable to mpMRI (75.2%; 81.8%). Additionally, bpMRI and mpMRI were independent predictors for the presence of csPCa, individually (OR: 15.36; p < 0.001 vs. 12.15; p = 0.006) and after accounting for established influencing factors (OR: 12.81; p < 0.001 vs. 6.50; p = 0.012). When clinical parameters were considered, a more balanced diagnostic performance between sensitivity and specificity was found for mpMRI and bpMRI. Overall, PSA density showed the highest diagnostic performance (area under the curve = 0.81) for the detection of csPCa.

CONCLUSION

The premise of the study was confirmed. Therefore, bpMRI should be adopted as soon as existing limitations have been lifted by prospective multi-reader studies.

Detection of benign granular cell tumor of the breast via 18F-PSMA-PET/CT in a patient with very high-risk prostate cancer: A case report

Incidental extra-prostatic prostate-specific membrane antigen (PSMA) uptake on initial staging positron emission tomography/computed tomography (PET/CT) scans poses diagnostic challenges, as it can be associated with various benign and malignant lesions. We present the case of a 68-year-old man with very high-risk prostate cancer who was incidentally discovered to have a benign granular cell tumor in the breast initially detected on PSMA-PET/CT. Imaging studies and biopsy were pivotal in the diagnosis, as the tumor's appearance was concerning for breast carcinoma. Recognizing extra-prostatic PSMA uptake in the breast, particularly in patients with prostate cancer, is crucial for guiding appropriate management, accurately interpreting subsequent imaging findings, and assessing radiologic-pathologic correlation.

Limited Utility of Dynamic Contrast Enhancement Imaging Sequences Within the PI-RADS v2.1 Classification Scheme: A Retrospective Cross-Sectional Study of MRI Reports

Background/Objective: We sought to characterize the proportion of peripheral zone lesions "upgraded" within the PI-RADS v2.1 protocol using DCE imaging sequences in a large patient population undergoing multiparametric prostate MRI. Methods: A retrospective review of radiologist reports for 2742 prostate MRI exams at 2 large Alberta teaching hospitals between January 2017 and January 2022 was conducted. Prostate specific antigen (PSA), prostate volume, sequence specific and overall PI-RADS scores, and lesion positivity for DCE were collected if present in the accompanying radiology report. Further, pathology reports of biopsies of the upgraded lesions within upgraded patients were reviewed to see if upgraded lesions were deemed clinically significant by gleason score/grade group. Results: The median age was 63 years, with a median PSA and PSA density of 7.5 ng/mL and 0.13 ng/mL2 respectively. A total of 1809 lesions were reported, with 69.4% of all lesions being DCE positive. Of the lesions within the peripheral zone, 548 were overall PI-RADS 4. A total of 87/2742 (3.2%) of patients were upgraded to a PI-RADS 4 by DCE imaging. Within these patients, 65 had pathology reports available, of which 18 had a clinically significant lesion at the upgrade site. Conclusion: Contrast enhancement is only beneficial for a very small portion of patients undergoing prostate MRI. Given the invasive nature of contrast enhanced studies, potential contrast induced side effects, added imaging time, and the cost of contrast agent, routine use of contrast for prostate MRI is questioned. Further studies are necessary to determine if it should be part of routine prostate MRI imaging protocols.

Prognostic significance of the PI-RADS score in men with prostate cancer undergoing radical prostatectomy

OBJECTIVES

MRI-targeted biopsy (T-Bx) for which Prostate Imaging Reporting and Data System (PI-RADS) assessment categories are useful has been shown to more accurately detect clinically significant prostate cancer. However, the prognostic significance of the PI-RADS in prostate cancer patients needs further investigation. In the present study, we compared radical prostatectomy findings and postoperative oncologic outcomes in men with prostate cancer initially undergoing T-Bx for PI-RADS 3 vs. 4 vs. 5 lesions.

METHODS

We assessed consecutive patients undergoing T-Bx with concurrent systematic biopsy (S-Bx), followed by radical prostatectomy. Within our Surgical Pathology database, we identified a total of 207 men where prostatic adenocarcinoma was detected on either S-Bx or T-Bx, or both.

RESULTS

Prostate cancer was detected on S-Bx only (n = 32; 15%), T-Bx only (n = 39; 19%), or both S-Bx and T-Bx (n = 136; 66%). These patients had PI-RADS 3 (n = 42; 20%), 4 (n = 86; 42%), or 5 (n = 79; 38%) lesions, while T-Bx detected cancer in 31 (74%) of PI-RADS 3 cases, 72 (84%) of PI-RADS 4 cases, and 72 (91%) of PI-RADS 5 cases. There were no significant differences in any of the clinicopathologic features examined, including tumor grade on biopsy or prostatectomy and pT or pN stage, among the PI-RADS 3 vs. 4 vs. 5 groups, except a significantly higher rate of positive margin and significantly larger tumor volume in PI-RADS 5 cases than in PI-RADS 3 cases. Univariate and multivariable analyses revealed significantly higher risks of biochemical recurrence after prostatectomy in patients with PI-RADS 5 lesion than in those with PI-RADS 3 or 4 lesion. Additionally, compared with respective controls, detection of any grade cancer (P = 0.046) or Grade Group 2 or higher cancer (P = 0.005) on T-Bx was associated with a significantly higher risk of recurrence in patients with PI-RADS 5 lesion, but not in those with PI-RADS 3 or 4 lesion.

CONCLUSION

PI-RADS 5 lesions were thus found to independently predict a significantly poorer postoperative prognosis. Moreover, the failure of detection of any grade cancer or clinically significant cancer on T-Bx of PI-RADS 5 lesion may particularly indicate favorable outcomes in radical prostatectomy cases.

Contribution of canine olfaction in the diagnostic strategy of intermediate and high-risk prostate cancer: a double-blind validation study

PURPOSE

Prostate cancer diagnosis is confirmed with a prostate biopsy, which is invasive and unpleasant. Adding canine olfaction into the diagnostic protocol could help avoid unnecessary biopsies. This study aims to determine whether dogs can identify ISUP (International Society of Urological Pathology) > 2 prostate cancer.

MATERIALS AND METHODS

This double-blind, prospective, validation study included men with suspected prostate cancer between November 2022 and April 2023 in France. They were classified into two groups according to their prostate biopsy results; cases (ISUP > 2) and controls (ISUP < 1 or negative). Seven dogs analyzed their urine. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for canine olfactory detection of ISUP ≥ 2 prostate cancer were measured and compared with that of prostate MRI versus prostate biopsy.

RESULTS

The seven dogs analyzed 151 urine samples, 78 from the case group and 73 from the control group. The minimal and maximal observed values were 54% and 86% for sensitivity, and 69% and 88% for specificity. Five dogs had a sensitivity above 73% and six dogs had a specificity above 75%. The kappa coefficient quantifying agreement between the biopsy result and the MRI PI-RADS ≥ 3 was 0.17 [- 0.14; 0.17], 0.20 [0.02-0.33] for PI-RADS ≥ 4 and 0.64 [0.5-0.75] for canine olfaction meaning there is a substantial agreement between the biopsy result and canine olfaction.

CONCLUSIONS

Based on this study, the non-invasive and safe canine olfaction technique seems reliable for diagnosing ISUP ≥ 2 prostate cancer. Combined with prostate MRI, it may improve the decision-making process when choosing to perform prostate biopsies.

The Role of Radiomics in the Prediction of Clinically Significant Prostate Cancer in the PI-RADS v2 and v2.1 Era: A Systematic Review

Early detection of clinically significant prostate cancer (csPCa) has substantially improved with the latest PI-RADS versions. However, there is still an overdiagnosis of indolent lesions (iPCa), and radiomics has emerged as a potential solution. The aim of this systematic review is to evaluate the role of handcrafted and deep radiomics in differentiating lesions with csPCa from those with iPCa and benign lesions on prostate MRI assessed with PI-RADS v2 and/or 2.1. The literature search was conducted in PubMed, Cochrane, and Web of Science databases to select relevant studies. Quality assessment was carried out with Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), Radiomic Quality Score (RQS), and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) tools. A total of 14 studies were deemed as relevant from 411 publications. The results highlighted a good performance of handcrafted and deep radiomics methods for csPCa detection, but without significant differences compared to radiologists (PI-RADS) in the few studies in which it was assessed. Moreover, heterogeneity and restrictions were found in the studies and quality analysis, which might induce bias. Future studies should tackle these problems to encourage clinical applicability. Prospective studies and comparison with radiologists (PI-RADS) are needed to better understand its potential.

MRI T2w Radiomics-Based Machine Learning Models in Imaging Simulated Biopsy Add Diagnostic Value to PI-RADS in Predicting Prostate Cancer: A Retrospective Diagnostic Study

BACKGROUND

Currently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases.

METHOD

A retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation.

RESULTS

All the models showed significant differences compared to the shape control model (all p < 0.001, except SVM model PI-RADS+2 Features p = 0.004, SVM model PI-RADS+3 Features p = 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%.

CONCLUSIONS

The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa.

Development and validation of a novel nomogram to avoid unnecessary biopsy in patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml

OBJECTIVES

To develop and validate a prediction model for identifying non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml to avoid unnecessary biopsy.

PATIENTS AND METHODS

Eligible patients who underwent transperineal biopsies at West China Hospital between 2018 and 2022 were included. The patients were randomly divided into training cohort (70%) and validation cohort (30%). Logistic regression was used to screen for independent predictors of non-PCa, and a nomogram was constructed based on the regression coefficients. The discrimination and calibration were assessed by the C-index and calibration plots, respectively. Decision curve analysis (DCA) and clinical impact curves (CIC) were applied to measure the clinical net benefit.

RESULTS

A total of 1580 patients were included, with 634 non-PCa. Age, prostate volume, prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) and lesion zone were independent predictors incorporated into the optimal prediction model, and a corresponding nomogram was constructed ( https://nomogramscu.shinyapps.io/PI-RADS-4-5/ ). The model achieved a C-index of 0.931 (95% CI, 0.910-0.953) in the validation cohort. The DCA and CIC demonstrated an increased net benefit over a wide range of threshold probabilities. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% of unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2% and 25.1% of foregone biopsies, respectively).

CONCLUSION

The developed nomogram has favorable predictive capability and clinical utility can help identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies.

The learning curve for transperineal MRI/TRUS fusion prostate biopsy: A prospective evaluation of a stepwise approach

OBJECTIVE

To evaluate the learning curve of a transperineal (TP) magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) fusion prostate biopsy (PBx).

MATERIALS AND METHODS

Consecutive patients undergoing MRI followed by TP PBx from May/2017 to January/2023, were prospectively enrolled (IRB# HS-13-00663). All participants underwent MRI followed by 12 to 14 core systematic PBx (SB), with at least 2 additional targeted biopsy (TB) cores per PIRADS ≥3. The biopsies were performed transperineally using an organ tracking image-fusion system. The cohort was divided into chronological quintiles. An inflection point analysis was performed to determine proficiency. Operative time was defined from insertion to removal of the TRUS probe from the patient's rectum. Grade Group ≥2 defined clinically significant prostate cancer (CSPCa). Statistically significant if P < 0.05.

RESULTS

A total of 370 patients were included and divided into quintiles of 74 patients. MRI findings and PIRADS distribution were similar between quintiles (P = 0.08). The CSPCa detection with SB+TB was consistent across quintiles: PIRADS 1 and 2 (range, 0%-18%; P = 0.25); PIRADS 3 to 5 (range, 46%-70%; P = 0.12). The CSPCa detection on PIRADS 3 to 5 TB alone, for quintiles 1 to 5, was respectively 44%, 58%, 66%, 41%, and 53% (P = 0.08). The median operative time significantly decreased for PIRADS 1 and 2 (33 min to 13 min; P < 0.01) and PIRADS 3 to 5 (48 min to 19 min; P < 0.01), reaching a plateau after 156 cases. Complications were not significantly different across quintiles (range, 0-5.4%; P = 0.3).

CONCLUSIONS

The CSPCa detection remained consistently satisfactory throughout the learning curve of the Transperineal MRI/TRUS fusion prostate biopsy. However, the operative time significantly decreased with proficiency achieved after 156 cases.

Long-term Results of Hypofractionated Radiotherapy With Intra-prostatic Boosts in Men With Intermediate- and High-risk Prostate Cancer: A Phase II Trial

AIMS

In the conventionally fractionated phase III FLAME prostate trial, focal boosts improved local control and biochemical disease-free survival (bDFS). We explored the toxicity and effectiveness of a moderately hypofractionated schedule with focal boosts.

MATERIAL AND METHODS

BIOPROP20 is a phase II single-arm non-randomised trial for intermediate- to very high-risk localised prostate cancer patients with bulky tumour volumes. Multi-parametric magnetic resonance imaging (MRI) and 18F-choline positron emission tomography-computed tomography (PET-CT) scans were used for staging and boost volume definition. Patients were treated with 60Gy in 20 fractions with a boost dose up to 68Gy. Five patients with positive lymph nodes on the PET-CT scan received radiotherapy to pelvic lymph nodes (45Gy to elective nodes, boosted up to 50Gy to involved nodes). Primary outcomes were acute (≤18 weeks) and late urinary and gastrointestinal toxicity, prospectively recorded up to 5 years with Common Terminology Criteria for Adverse Events v4 (CTCAE). Secondary outcomes were biochemical or clinical progression, metastasis-free survival (MFS), and overall survival (OS).

RESULTS

61 patients completed radiotherapy with hormone therapy (range: 6-36 months). Cumulative acute and late gastrointestinal toxicity was low at 6.6% and 5.0%, respectively. Cumulative acute and late urinary toxicity was 49.2% and 30.1%, respectively; the prevalence reduced to 5.9% at 5 years. At 5 years: 6 patients had biochemical progression (bDFS: 88.5%; 95% CI: 80.2-97.6%), the MFS was 82.4% (95% CI: 73.0-92.9%), 5 patients died (OS: 91.2%; 95% CI: 84.1-98.9%), one with prostate cancer. The prostate, boost, nodal planning volumes, and the organs at risk (rectum, bowel, urethra, and bladder) met the optimal protocol dose constraints. There was a trend to increased urinary toxicity with increasing urethral (RR: 1.95, 95% CI: 0.73-5.22, p = 0.18), but not bladder dose.

CONCLUSION

Focal boosts with a 20 fraction hypofractionated prostate radiotherapy schedule are associated with an acceptable risk of gastrointestinal and urinary toxicity and achieve good cancer control.

CLINICALTRIALS

GOV IDENTIFIER

NCT02125175.

A contemporary review: mpMRI in prostate cancer screening and diagnosis

Prostate cancer (PCa) screening has evolved beyond PSA and digital rectal exam to include multiparametric prostate MRI (mpMRI). Incorporating this advanced imaging tool has further limited the well-established problem of overdiagnosis, aiding in the identification of higher grade, clinically significant cancers. For this reason, mpMRI has become an important part of the diagnostic pathway and is recommended across guidelines in biopsy naïve patients or for patients with prior negative biopsy. This contemporary review evaluates the most recent literature on the role of mpMRI in the screening and diagnosis of prostate cancer. Barriers to utilization of mpMRI still exist including variable access, high cost, and requisite expertise, encouraging evaluation of novel techniques such as biparametric MRI. Future screening and diagnostic practice patterns will undoubtedly evolve as our understanding of novel biomarkers and artificial intelligence improves.

Interactive Cascaded Network for Prostate Cancer Segmentation from Multimodality MRI with Automated Quality Assessment

The accurate segmentation of prostate cancer (PCa) from multiparametric MRI is crucial in clinical practice for guiding biopsy and treatment planning. Existing automated methods often lack the necessary accuracy and robustness in localizing PCa, whereas interactive segmentation methods, although more accurate, require user intervention on each input image, thereby limiting the cost-effectiveness of the segmentation workflow. Our innovative framework addresses the limitations of current methods by combining a coarse segmentation network, a rejection network, and an interactive deep network known as Segment Anything Model (SAM). The coarse segmentation network automatically generates initial segmentation results, which are evaluated by the rejection network to estimate their quality. Low-quality results are flagged for user interaction, with the user providing a region of interest (ROI) enclosing the lesions, whereas for high-quality results, ROIs were cropped from the automatic segmentation. Both manually and automatically defined ROIs are fed into SAM to produce the final fine segmentation. This approach significantly reduces the annotation burden and achieves substantial improvements by flagging approximately 20% of the images with the lowest quality scores for manual annotation. With only half of the images manually annotated, the final segmentation accuracy is statistically indistinguishable from that achieved using full manual annotation. Although this paper focuses on prostate lesion segmentation from multimodality MRI, the framework can be adapted to other medical image segmentation applications to improve segmentation efficiency while maintaining high accuracy standards.

Urine biomarkers can predict prostate cancer and PI-RADS score prior to biopsy

Prostate-Specific Antigen (PSA) based screening of prostate cancer (PCa) needs refinement. The aim of this study was the identification of urinary biomarkers to predict the Prostate Imaging-Reporting and Data System (PI-RADS) score and the presence of PCa prior to prostate biopsy. Urine samples from patients with elevated PSA were collected prior to prostate biopsy (cohort = 99). The re-analysis of mass spectrometry data from 45 samples was performed to identify urinary biomarkers to predict the PI-RADS score and the presence of PCa. The most promising candidates, i.e. SPARC-like protein 1 (SPARCL1), Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), Alpha-1-microglobulin/bikunin precursor (AMBP), keratin 13 (KRT13), cluster of differentiation 99 (CD99) and hornerin (HRNR), were quantified by ELISA and validated in an independent cohort of 54 samples. Various biomarker combinations showed the ability to predict the PI-RADS score (AUC = 0.79). In combination with the PI-RADS score, the biomarkers improve the detection of prostate carcinoma-free men (AUC = 0.89) and of those with clinically significant PCa (AUC = 0.93). We have uncovered the potential of urinary biomarkers for a test that allows a more stringent prioritization of mpMRI use and improves the decision criteria for prostate biopsy, minimizing patient burden by decreasing the number of unnecessary prostate biopsies.

The additional value of 68Ga-PSMA PET/CT SUVmax in predicting ISUP GG ≥ 2 and ISUP GG ≥ 3 prostate cancer in biopsy

BACKGROUND

Prebiopsy magnetic resonance imaging (MRI) increases the detection rate of clinically significant prostate cancer (csPCa). Prostate-specific membrane antigen-positron emission tomography/computed tomography (PSMA PET/CT) maximum standardized uptake value (SUVmax) of the prostate may offer additional value in predicting the likelihood of csPCa in biopsy.

METHODS

A single-center cohort study involving patients with biopsy-proven PCa who underwent both MRI and PSMA PET/CT between 2020 and 2021. Logistic regression models were developed for International Society of Urological Pathology (ISUP) Grade Group (GG) ≥ 2 and GG ≥ 3 using noninvasive prebiopsy parameters: age, (log-)prostate-specific antigen (PSA) density, PI-RADS 5 lesion presence, extraprostatic extension (EPE) on MRI, and SUVmax of the prostate. Models with and without SUVmax were compared using Likelihood ratio tests and area under the curve (AUC). DeLong's test was used to compare the AUCs.

RESULTS

The study included 386 patients, with 262 (68%) having ISUP GG ≥ 2 and 180 (47%) having ISUP GG ≥ 3. Including SUVmax significantly improved both models' goodness of fit (p < 0.001). The GG ≥ 2 model had a higher AUC with SUVmax 89.16% (95% confidence interval [CI]: 86.06%-92.26%) than without 87.34% (95% CI: 83.93%-90.76%) (p = 0.026). Similarly, the GG ≥ 3 model had a higher AUC with SUVmax 82.51% (95% CI: 78.41%-86.6%) than without 79.33% (95% CI: 74.84%-83.83%) (p = 0.003). The SUVmax inclusion improved the GG ≥ 3 model's calibration at higher probabilities.

CONCLUSION

SUVmax of the prostate on PSMA PET/CT potentially improves diagnostic accuracy in predicting the likelihood of csPCa in prostate biopsy.

Targeted microwave ablation for prostate cancer (FOSTINE1b): a prospective 'ablate-and-resect' study

OBJECTIVE

To assess histopathological outcomes, as well as feasibility and safety of targeted microwave ablation (TMA) via the Trinity® system (KOELIS, La Tronche, France).

PATIENTS AND METHODS

Prospective, single-institution, interventional Phase IIa study with an 'ablate-and-resect' design. In all, 11 patients diagnosed with localised prostate cancer (PCa) underwent TMA via the Trinity system under conscious sedation in an outpatient setting using a single transrectal TATO® 18-G antenna with different treatment regimens. Magnetic resonance imaging (MRI) and robot-assisted radical prostatectomy (RARP) were conducted at 7 days and 1 month after TMA, respectively. Nine patients received RARP, and two patients chose to withdraw their consent following TMA. These men chose an active surveillance protocol upon confirmation of a low-risk prostate cancer diagnosis. Functional outcomes and adverse events were evaluated at baseline and follow-up visits using validated questionnaires. Prostate volumetry and confirmation of necrosis were carried out through MRI and whole-mount histopathological examination.

RESULTS

The TMA was successfully executed, and all patients were discharged on the same day. No severe adverse events (Common Terminology Criteria for Adverse Events Grade ≥3) were reported at the 7-day and 1-month follow-up visits. Additionally, no declines were observed in urinary, sexual and ejaculation functional outcomes. T1-weighted MRI revealed clear and well-defined ablation zones. The RARP was executed without difficulty, particularly during the dissection of the posterior plane. As a result, no intraoperative complications were encountered. Histopathological assessment on surgical specimens confirmed the absence of viable cells, indicating complete necrosis of the ablative zone if a power intensity >10 W was used during TMA. Ablation zone volumetry revealed no notable distinctions between the three-dimensional segmentation of the virtual ablation zone at TMA (median volume: 2 mL) and MRI (median volume: 1.923 mL). Conversely, a significant reduction was noted in the surgical specimen (median volume: 0.221 mL).

CONCLUSIONS

Targeted microwave ablation via the Trinity system for localised PCa treatment proves to be a secure and feasible procedure, with complete necrosis evidence within the ablation zone on surgical specimens.

Multiparametric-magnetic resonance imaging (mp-MRI) of the prostate and Urolift: Identifying artefact size, location and clinical implications

Objectives

We sought to define the degree of artefact caused by prostatic urethral lift (PUL) on multiparametric-magnetic resonance imaging (mp-MRI) to determine the location, size of artefact and if the device could potentially obscure a diagnosis of prostate cancer.

Methods

Ten patients were prospectively enrolled to undergo PUL for treatment of benign prostatic hyperplasia and follow-up imaging. A standard mp-MRI protocol using a 3.0 Tesla scanner was performed prior to and following Urolift insertion. Pre- and post-PUL images were compared to measure maximum artefact diameter around each implant in each MRI parameter. A transverse relaxation time weighted (T2) artefact reduction protocol was also evaluated. The location of each artefact was then compared to a separate database of 225 consecutive patients who underwent magnetic resonance guided prostate biopsies.

Results

Artefact occurred around the stainless steel urethral implant component only. Mean T2 artefact maximum diameter was 7.7 mm (sd = 1.71 mm), with an artefact reduction protocol reducing this to 5.4 mm (sd = 1.43). Mean dynamic-contrast-enhancement artefact was 10 mm (sd = 2.5 mm), and mean diffusion-weighted-imaging artefact was 28.2 mm (sd = 7.8 mm). All artefacts were confined to the posterior transition zone only. In the 225 consecutive patients who had undergone magnetic resonance guided prostate biopsies, there were 55 positive biopsies with prostate cancer, with 13 cases found in the transition zones and no cancer identified solely in the posterior transitional zone.

Conclusions

The stainless steel urethral component of the PUL does cause artefact, which is confined to the posterior transition zone only. PUL artefact occurs in an area of the prostate that has a very low incidence of a single focus of prostate cancer. If there is concern for prostate cancer in the posterior TZ (e.g. if every other area is clear with a high PSA), this area can undergo targeted biopsy.

Diffusion weighted MRI and apparent diffusion coefficient as a prognostic biomarker in evaluating chemotherapy-antiangiogenic treated stage IV non-small cell lung cancer: A prospective, single-arm, open-label, clinical trial (BevMar)

PURPOSE

When treating Lung Cancer, it is necessary to identify early treatment failure to enable timely therapeutic adjustments. The Aim of this study was to investigate whether changes in tumor diffusion during treatment with chemotherapy and bevacizumab could serve as a predictor of treatment failure.

MATERIAL AND METHODS

A prospective single-arm, open-label, clinical trial was conducted between September 2014 and December 2020, enrolling patients with stage IV non-small cell lung cancer (NSCLC). The patients were treated with chemotherapy-antiangiogenic combination. Diffusion weighted magnetic resonance imaging (DW-MRI) was performed at baseline, two, four, and sixteen weeks after initiating treatment. The differences in apparent diffusion coefficient (ADC) values between pre- and post-treatment MRIs were recorded as Delta values (ΔADC). We assessed whether ΔADC could serve as a prognostic biomarker for overall survival (OS), with a five year follow up.

RESULTS

18 patients were included in the final analysis. Patients with a ΔADC value ≥ -3 demonstrated a significantly longer OS with an HR of 0.12 (95 % CI; 0.03- 0.61; p = 0.003) The median OS in patients with a ΔADC value ≥ -3 was 18 months, (95 % C.I; 7-46) compared to 7 months (95 % C.I; 5-9) in those with a ΔADC value < -3.

CONCLUSION

Our findings suggest that early changes in tumor ADC values, may be indicative of a longer OS. Therefore, DW-MRI could serve as an early biomarker for assessing treatment response in patients receiving chemotherapy combined with antiangiogenic therapy.

Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI

OBJECTIVE

To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.

MATERIALS AND METHODS

This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher's exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE.

RESULTS

A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24).

CONCLUSION

Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

The importance and future of prostate MRI report templates: improving oncological care

The radiologist's report is crucial for guiding care post-imaging, with ongoing advancements in report construction. Recent studies across various modalities and organ systems demonstrate enhanced clarity and communication through structured reports. This article will explain the benefits of disease-state specific reporting templates using prostate MRI as the model system. We identify key reporting components for prostate cancer detection and staging as well as imaging in active surveillance and following therapy. We discuss relevant reporting systems including PI-QUAL, PI-RADS, PRECISE, PI-RR and PI-FAB systems. Additionally, we examine optimal reporting structure including disruptive technologies such as graphical reporting and using artificial intelligence to improve report clarity and applicability.

Clinical Validation of Multiparametric Ultrasound for Detecting Clinically Significant Prostate Cancer Using Computer-Aided Diagnosis: A Direct Comparison with the Magnetic Resonance Imaging Pathway

We present the protocol for a study testing the hypothesis that a computer-aided diagnosis (CAD) system for three-dimensional multiparametric ultrasound (3D mpUS) is noninferior to magnetic resonance imaging (MRI) in guiding prostate biopsies for detection of clinically significant prostate cancer (csPCa). The prospective study has a fully paired design for assessment of diagnostic accuracy and is registered on ClinicalTrials.gov as NCT06281769. A total of 438 biopsy-naïve men scheduled for prostate MRI evaluation because of an abnormal digital rectal examination and/or elevated serum prostate-specific antigen will be included. All patients will undergo both MRI (multiparametric or biparametric) and 3D mpUS with CAD (PCaVision). Suspicious lesions will be independently identified using each imaging technique. MRI targeted biopsy (TBx) and/or PCaVision TBx will be performed if suspicious lesions are identified on imaging. When both PCaVision and MRI identify lesions in an individual patient, the TBx order for this patient will be randomized. Three TBx samples per lesion will be taken for a maximum of two lesions per modality. The primary objective is the detection rate for csPCa (International Society of Urological Pathology grade group [GG] ≥2) with the PCaVision versus the MRI TBx pathway. The noninferiority margin for the absolute difference in detection rates is set at a difference of 5%. Secondary outcomes are the proportion of men in whom TBx could have been safely omitted in each pathway. Additional diagnostic accuracy analyses will be performed for different definitions of PCa (GG ≥3; GG ≥2 with cribriform growth and/or intraductal carcinoma; and GG 1). The frequency of insufficient image quality for the two pathways will also be assessed. Lastly, we will determine the diagnostic performance for csPCa detection at various 3D mpUS image quality thresholds for PCaVision.

Radiomic nomogram based on bi-parametric magnetic resonance imaging to predict the International Society of Urological Pathology grading ≥ 3 prostate cancer: a multicenter study

PURPOSE

To create a reliable radiomic nomogram for the prediction of the International Society of Urological Pathology (ISUP) grading ≥ 3 prostate cancer (PCa) patients.

METHODS

patients with verified PCa were obtained from three different hospitals. The patients were divided into training, internal validation, and two external validation groups. A radiomic signature (rad-score) extracted from T2WI, diffusion-weighted imaging, and apparent diffusion coefficient (ADC) maps were constructed in the training cohort. Eight clinical features were performed to develop a clinical model using univariate and multivariate logistic regression. The combined model incorporated the radiomic signature and clinical model. The model's performance was assessed by the receiver operating characteristic (ROC) curve.

RESULTS

Rad-score, magnetic resonance imaging T-stage, and ADC value were significant predictors of ISUP ≥ 3 PCa. A nomogram of these three factors was shown to have greater diagnostic accuracy than using only the radiomic signature or clinical model alone. The area under the ROC curve was 0.85, 0.88, 0.81, 0.81 for the training, internal, and two external validation cohorts, respectively. In the stratified analysis based on the MR scanner model, the area under the ROC curve of predicting ISUP ≥ 3 PCa for GE, Siemens, and combined groups were 0.84, 0.83, and 0.84, respectively, in the combined training group and an internal validation group.

CONCLUSIONS

The proposed nomogram has the potential to predict the differentiation degree of ISUP PCa patients.

Phantoms for Quantitative Body MRI: a review and discussion of the phantom value

In this paper, we review the value of phantoms for body MRI in the context of their uses for quantitative MRI methods research, clinical trials, and clinical imaging. Certain uses of phantoms are common throughout the body MRI community, including measuring bias, assessing reproducibility, and training. In addition to these uses, phantoms in body MRI methods research are used for novel methods development and the design of motion compensation and mitigation techniques. For clinical trials, phantoms are an essential part of quality management strategies, facilitating the conduct of ethically sound, reliable, and regulatorily compliant clinical research of both novel MRI methods and therapeutic agents. In the clinic, phantoms are used for development of protocols, mitigation of cost, quality control, and radiotherapy. We briefly review phantoms developed for quantitative body MRI, and finally, we review open questions regarding the most effective use of a phantom for body MRI.

AI-based automated evaluation of image quality and protocol tailoring in patients undergoing MRI for suspected prostate cancer

PURPOSE

To develop and validate an artificial intelligence (AI) application in a clinical setting to decide whether dynamic contrast-enhanced (DCE) sequences are necessary in multiparametric prostate MRI.

METHODS

This study was approved by the institutional review board and requirement for study-specific informed consent was waived. A mobile app was developed to integrate AI-based image quality analysis into clinical workflow. An expert radiologist provided reference decisions. Diagnostic performance parameters (sensitivity and specificity) were calculated and inter-reader agreement was evaluated.

RESULTS

Fully automated evaluation was possible in 87% of cases, with the application reaching a sensitivity of 80% and a specificity of 100% in selecting patients for multiparametric MRI. In 2% of patients, the application falsely decided on omitting DCE. With a technician reaching a sensitivity of 29% and specificity of 98%, and resident radiologists reaching sensitivity of 29% and specificity of 93%, the use of the application allowed a significant increase in sensitivity.

CONCLUSION

The presented AI application accurately decides on a patient-specific MRI protocol based on image quality analysis, potentially allowing omission of DCE in the diagnostic workup of patients with suspected prostate cancer. This could streamline workflow and optimize time utilization of healthcare professionals.

Inter-reader Agreement for Prostate Cancer Detection Using Micro-ultrasound: A Multi-institutional Study

Background and objective

Micro-ultrasound (MUS) uses a high-frequency transducer with superior resolution to conventional ultrasound, which may differentiate prostate cancer from normal tissue and thereby allow targeted biopsy. Preliminary evidence has shown comparable sensitivity to magnetic resonance imaging (MRI), but consistency between users has yet to be described. Our objective was to assess agreement of MUS interpretation across multiple readers.

Methods

After institutional review board approval, we prospectively collected MUS images for 57 patients referred for prostate biopsy after multiparametric MRI from 2022 to 2023. MUS images were interpreted by six urologists at four institutions with varying experience (range 2-6 yr). Readers were blinded to MRI results and clinical data. The primary outcome was reader agreement on the locations of suspicious lesions, measured in terms of Light's κ and positive percent agreement (PPA). Reader sensitivity for identification of grade group (GG) ≥2 prostate cancer was a secondary outcome.

Key findings and limitations

Analysis revealed a κ value of 0.30 (95% confidence interval [CI] 0.21-0.39). PPA was 33% (95% CI 25-42%). The mean patient-level sensitivity for GG ≥2 cancer was 0.66 ± 0.05 overall and 0.87 ± 0.09 when cases with anterior lesions were excluded. Readers were 12 times more likely to detect higher-grade cancers (GG ≥3), with higher levels of agreement for this subgroup (κ 0.41, PPA 45%). Key limitations include the inability to prospectively biopsy reader-delineated targets and the inability of readers to perform live transducer maneuvers.

Conclusions and clinical implications

Inter-reader agreement on the location of suspicious lesions on MUS is lower than rates previously reported for MRI. MUS sensitivity for cancer in the anterior gland is lacking.

Patient summary

The ability to find cancer on imaging scans can vary between doctors. We found that there was frequent disagreement on the location of prostate cancer when doctors were using a new high-resolution scan method called micro-ultrasound. This suggests that the performance of micro-ultrasound is not yet consistent enough to replace MRI (magnetic resonance imaging) for diagnosis of prostate cancer.

Comparison of Multiparametric MRI and the Combination of PSMA Plus MRI for the Intraprostatic Diagnosis of Prostate Cancer: A Systematic Review and Meta-Analysis

PURPOSE

The aim of this study was to perform a head-to-head comparison of multiparametric MRI (mpMRI) and the combination of prostate-specific membrane antigen (PSMA) PET plus MRI (PSMA + MRI) for detecting intraprostatic clinically significant prostate cancer (csPCa).

PATIENTS AND METHODS

Relevant databases were searched through November 2023. Only studies directly comparing mpMRI and PSMA + MRI (PET/MRI or PET/CT + mpMRI) were included. A meta-analysis with a random-effects model was used to estimate pooled sensitivity, specificity, and area under the curve for each approach.

RESULTS

A total of 19 studies were included. On a patient-level analysis, PSMA + MRI had higher sensitivity (9 studies) than mpMRI for csPCa detection (96% [95% confidence interval (CI): 92%, 98%] vs 89% [95% CI: 81%, 94%]; P = 0.04). The patient-level specificity (4 studies) of PSMA + MRI was 55% (95% CI: 31%-76%) compared with 50% (95% CI: 44%-57%) of mpMRI ( P = 0.67). Region-level sensitivity (10 studies) was 85% (95% CI: 74%-92%) for PSMA + MRI and 71% (95% CI: 58%-82%) for mpMRI ( P = 0.09), whereas specificity (4 studies) was 87% (95% CI: 76%-94%) and 90% (95% CI: 82%-95%), respectively ( P = 0.59). Lesion-level sensitivity and specificity were similar between modalities with pooled data from less than 4 studies.

CONCLUSIONS

PSMA + MRI had superior pooled sensitivity and similar specificity for the detection of csPCa compared with mpMRI in this meta-analysis of head-to-head studies.

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI

Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purpose To develop a DL model to predict the presence of csPCa by using patient-level labels without information about tumor location and to compare its performance with that of radiologists. Materials and Methods Data from patients without known csPCa who underwent MRI from January 2017 to December 2019 at one of multiple sites of a single academic institution were retrospectively reviewed. A convolutional neural network was trained to predict csPCa from T2-weighted images, diffusion-weighted images, apparent diffusion coefficient maps, and T1-weighted contrast-enhanced images. The reference standard was pathologic diagnosis. Radiologist performance was evaluated as follows: Radiology reports were used for the internal test set, and four radiologists' PI-RADS ratings were used for the external (ProstateX) test set. The performance was compared using areas under the receiver operating characteristic curves (AUCs) and the DeLong test. Gradient-weighted class activation maps (Grad-CAMs) were used to show tumor localization. Results Among 5735 examinations in 5215 patients (mean age, 66 years ± 8 [SD]; all male), 1514 examinations (1454 patients) showed csPCa. In the internal test set (400 examinations), the AUC was 0.89 and 0.89 for the DL classifier and radiologists, respectively (P = .88). In the external test set (204 examinations), the AUC was 0.86 and 0.84 for the DL classifier and radiologists, respectively (P = .68). DL classifier plus radiologists had an AUC of 0.89 (P < .001). Grad-CAMs demonstrated activation over the csPCa lesion in 35 of 38 and 56 of 58 true-positive examinations in internal and external test sets, respectively. Conclusion The performance of a DL model was not different from that of radiologists in the detection of csPCa at MRI, and Grad-CAMs localized the tumor. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Johnson and Chandarana in this issue.

Update on PI-RADS Version 2.1 Diagnostic Performance Benchmarks for Prostate MRI: Systematic Review and Meta-Analysis

Background Prostate MRI for the detection of clinically significant prostate cancer (csPCa) is standardized by the Prostate Imaging Reporting and Data System (PI-RADS), currently in version 2.1. A systematic review and meta-analysis infrastructure with a 12-month update cycle was established to evaluate the diagnostic performance of PI-RADS over time. Purpose To provide estimates of diagnostic accuracy and cancer detection rates (CDRs) of PI-RADS version 2.1 categories for prostate MRI, which is required for further evidence-based patient management. Materials and Methods A systematic search of PubMed, Embase, Cochrane Library, and multiple trial registers (English-language studies published from March 1, 2019, to August 30, 2022) was performed. Studies that reported data on diagnostic accuracy or CDRs of PI-RADS version 2.1 with csPCa as the primary outcome were included. For the meta-analysis, pooled estimates for sensitivity, specificity, and CDRs were derived from extracted data at the lesion level and patient level. Sensitivity and specificity for PI-RADS greater than or equal to 3 and PI-RADS greater than or equal to 4 considered as test positive were investigated. In addition to individual PI-RADS categories 1-5, subgroup analyses of subcategories (ie, 2+1, 3+0) were performed. Results A total of 70 studies (11 686 lesions, 13 330 patients) were included. At the patient level, with PI-RADS greater than or equal to 3 considered positive, meta-analysis found a 96% summary sensitivity (95% CI: 95, 98) and 43% specificity (95% CI: 33, 54), with an area under the summary receiver operating characteristic (SROC) curve of 0.86 (95% CI: 0.75, 0.93). For PI-RADS greater than or equal to 4, meta-analysis found an 89% sensitivity (95% CI: 85, 92) and 66% specificity (95% CI: 58, 74), with an area under the SROC curve of 0.89 (95% CI: 0.85, 0.92). CDRs were as follows: PI-RADS 1, 6%; PI-RADS 2, 5%; PI-RADS 3, 19%; PI-RADS 4, 54%; and PI-RADS 5, 84%. The CDR was 12% (95% CI: 7, 19) for transition zone 2+1 lesions and 19% (95% CI: 12, 29) for 3+0 lesions (P = .12). Conclusion Estimates of diagnostic accuracy and CDRs for PI-RADS version 2.1 categories are provided for quality benchmarking and to guide further evidence-based patient management. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Tammisetti and Jacobs in this issue.

Martin B, Jacobsen NE, Evans H, Gao Y, Hung R, Abele J, Dromparis P, Lima JF, Bismar T, Michelakis E, Sutendra G, Wuest F, Tu W, Adam BA, Fung C, Tamm A, Kinnaird A. Fluorine-18 Prostate-Specific Membrane Antigen-1007 PET/CT vs Multiparametric MRI for Locoregional Staging of Prostate Cancer

Importance

Prostate-specific membrane antigen (PSMA) demonstrates overexpression in prostate cancer and correlates with tumor aggressiveness. PSMA positron emission tomography (PET) is superior to conventional imaging for the metastatic staging of prostate cancer per current research but studies of second-generation PSMA PET radioligands for locoregional staging are limited.

Objective

To determine the accuracy of fluorine-18 PSMA-1007 PET/computed tomography (18F-PSMA-1007 PET/CT) compared to multiparametric magnetic resonance imaging (MRI) in the primary locoregional staging of intermediate-risk and high-risk prostate cancers.

Design, Setting, and Participants

The Next Generation Trial was a phase 2 prospective validating paired cohort study assessing the accuracy of 18F-PSMA-1007 PET/CT and MRI for locoregional staging of prostate cancer, with results of histopathologic examination as the reference standard comparator. Radiologists, nuclear medicine physicians, and pathologists were blinded to preoperative clinical, pathology, and imaging data. Patients underwent all imaging studies and radical prostatectomies at 2 tertiary care hospitals in Alberta, Canada. Eligible participants included men with intermediate-risk or high-risk prostate cancer who consented to radical prostatectomy. Participants who underwent radical prostatectomy were included in the final analysis. Patients were recruited between March 2022 and June 2023, and data analysis occurred between July 2023 and December 2023.

Exposures

All participants underwent both 18F-PSMA-1007 PET/CT and MRI within 2 weeks of one another and before radical prostatectomy.

Main Outcomes and Measures

The primary outcome was the correct identification of the prostate cancer tumor stage by each imaging test. The secondary outcomes were correct identification of the dominant nodule, laterality, extracapsular extension, and seminal vesical invasion.

Results

Of 150 eligible men with prostate cancer, 134 patients ultimately underwent radical prostatectomy (mean [SD] age at prostatectomy, 62.0 [5.7] years). PSMA PET was superior to MRI for the accurate identification of the final pathological tumor stage (61 [45%] vs 38 [28%]; P = .003). PSMA PET was also superior to MRI for the correct identification of the dominant nodule (126 [94%] vs 112 [83%]; P = .01), laterality (86 [64%] vs 60 [44%]; P = .001), and extracapsular extension (100 [75%] vs 84 [63%]; P = .01), but not for seminal vesicle invasion (122 [91%] vs 115 [85%]; P = .07).

Conclusions and Relevance

In this phase 2 prospective validating paired cohort study, 18F-PSMA-1007 PET/CT was superior to MRI for the locoregional staging of prostate cancer. These findings support PSMA PET in the preoperative workflow of intermediate-risk and high-risk tumors.

Comparison of model-based versus deep learning-based image reconstruction for thin-slice T2-weighted spin-echo prostate MRI

PURPOSE

To compare a previous model-based image reconstruction (MBIR) with a newly developed deep learning (DL)-based image reconstruction for providing improved signal-to-noise ratio (SNR) in high through-plane resolution (1 mm) T2-weighted spin-echo (T2SE) prostate MRI.

METHODS

Large-area contrast and high-contrast spatial resolution of the reconstruction methods were assessed quantitatively in experimental phantom studies. The methods were next evaluated radiologically in 17 subjects at 3.0 Tesla for whom prostate MRI was clinically indicated. For each subject, the axial T2SE raw data were directed to MBIR and to the DL reconstruction at three vendor-provided levels: (L)ow, (M)edium, and (H)igh. Thin-slice images from the four reconstructions were compared using evaluation criteria related to SNR, sharpness, contrast fidelity, and reviewer preference. Results were compared using the Wilcoxon signed-rank test using Bonferroni correction, and inter-reader comparisons were done using the Cohen and Krippendorf tests.

RESULTS

Baseline contrast and resolution in phantom studies were equivalent for all four reconstruction pathways as desired. In vivo, all three DL levels (L, M, H) provided improved SNR versus MBIR. For virtually, all other evaluation criteria DL L and M were superior to MBIR. DL L and M were evaluated as superior to DL H in fidelity of contrast. For 44 of the 51 evaluations, the DL M reconstruction was preferred.

CONCLUSION

The deep learning reconstruction method provides significant SNR improvement in thin-slice (1 mm) T2SE images of the prostate while retaining image contrast. However, if taken to too high a level (DL High), both radiological sharpness and fidelity of contrast diminish.

Development and validation of a clinical decision support system based on PSA, microRNAs, and MRI for the detection of prostate cancer

OBJECTIVES

The aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone.

METHODS

This retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG ≥ 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated.

RESULTS

MRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73-98%) and NPV of 93% (95%CI 82-98%), and for csPCa identification, with sensitivity of 91% (95%CI 72-99%) and NPV of 95% (95%CI 84-99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone.

CONCLUSION

MRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naïve patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results.

CLINICAL RELEVANCE STATEMENT

The use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa.

KEY POINTS

• Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA. • The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance. • Results of this study were in line with previous works on MRI and microRNA.

Prior Negative Biopsy, PSA Density, and Anatomic Location Impact Cancer Detection Rate of MRI-Targeted PI-RADS Index Lesions

BACKGROUND

MRI fusion prostate biopsy has improved the detection of clinically significant prostate cancer (CSC). Continued refinements in predicting the pre-biopsy probability of CSC are essential for optimal patient counseling. We investigated potential factors related to improved cancer detection rates (CDR) of CSC in patients with PI-RADS ≥ 3 lesions.

METHODS

The pathology of 980 index lesions in 980 patients sampled by transrectal mpMRI-targeted prostate biopsy across four medical centers between 2017-2020 was reviewed. PI-RADS lesion distribution included 291 PI-RADS-5, 374 PI-RADS-4, and 315 PI-RADS-3. We compared CDR of index PI-RADS ≥ 3 lesions based on location (TZ) vs. (PZ), PSA density (PSAD), and history of prior negative conventional transrectal ultrasound-guided biopsy (TRUS).

RESULTS

Mean age, PSA, prostate volume, and level of prior negative TRUS biopsy were 66 years (43-90), 7.82 ng/dL (5.6-11.2), 54 cm3 (12-173), and 456/980 (46.5%), respectively. Higher PSAD, no prior history of negative TRUS biopsy, and PZ lesions were associated with higher CDR. Stratified CDR highlighted significant variance across subgroups. CDR for a PI-RADS-5 score, PZ lesion with PSAD ≥ 0.15, and prior negative biopsy was 77%. Conversely, the CDR rate for a PI-RADS-4 score, TZ lesion with PSAD < 0.15, and prior negative biopsy was significantly lower at 14%.

CONCLUSIONS

For index PI-RADS ≥ 3 lesions, CDR varied significantly based on location, prior history of negative TRUS biopsy, and PSAD. Such considerations are critical when counseling on the merits and potential yield of prostate needle biopsy.

Transperineal 3-Core Magnetic Resonance Imaging Ultrasound Fusion Targeted Plus Laterally 6-Core Systematic Biopsy in Prostate Cancer Diagnosis

INTRODUCTION

It is important to explore strategies reducing the number of SB cores taken to minimize biopsy-related morbidity and patient's discomfort during biopsy. This study aims to optimize prostate biopsy procedures by reducing the number of systematic biopsy (SB) cores while preserving cancer detection rates in the era of combined biopsy.

PATIENTS AND METHODS

We prospectively recruited patients with ≥1 magnetic resonance imaging (MRI) lesions and they underwent transperineal combined 12-core SB+3-core targeted prostate biopsy (TB, reference standard). New strategy was defined as a laterally 6-core SB+3-core TB. Patients were served as their own control. Detection rates for overall prostate cancer (PCa) and clinically significant PCa (csPCa) were compared among the standard SB, MRI-TB, 6-core SB +3-core TB, and reference standard. Pathology consistency was assessed using the Kappa test.

RESULTS

A total of 204 men were included, of which 111 (54.41%) and 92 (45.10%) harbored overall PCa and csPCa. Referenced combined biopsy detected significantly 6.86% (P = .0005) or 4.90% (P = .0044) more csPCa than performing only SB or 3-core TB, but was comparable to the new biopsy strategy. (45.10% vs. 43.14%, P = .1336) Similar results persisted when limiting patients in biopsy-naïve men or stratified by Prostate Imaging Reporting and Data System scores, PSAD, and index lesion parameters. Additionally, performing 6-core SB+3-core TB demonstrated high consistency with reference standard in grade group distribution (Kappa coefficient: 0.952 for all, 0.961 for biopsy-naïve men) and achieved superior sensitivity of 95.7% (All: 95% CI: 89.2%-99.8%) and 96.9% (Biopsy-naïve: 95% CI: 91.1%-99.7%), respectively.

CONCLUSIONS

The 6-core SB+3-core TB approach maintains expected detection rates while reducing the total core count, offering a promising alternative to the reference standard, which may help to tailor transperineal combined biopsy procedures.

Clinical risk prediction model and external validation of positive surgical margin in laparoscopic radical prostatectomy based on MRI lesion location

OBJECTIVE

To clarify the composition of lesions in different magnetic resonance imaging (MRI) partitions of positive surgical margins (PSM) after laparoscopic radical prostatectomy, explore the influence of lesion location on PSM, and construct a clinical prediction model to predict the risk of PSM.

MATERIALS AND METHODS

This retrospective cohort study included 309 patients who underwent laparoscopic radical prostatectomy from 2018 to 2021 in our center was performed. 129 patients who met the same criteria from January to September 2022 were external validation cohorts.

RESULTS

The incidence of PSM in transition zone (TZ) lesions was higher than that in peripheral zone (PZ) lesions. The incidence of PSM in the middle PZ was lower than that in other regions. Prostate specific antigen (PSA), clinical T-stage, the number of positive cores, international society of urological pathology (ISUP) grade (biopsy), MRI lesion location, extracapsular extension, seminal vesicle invasion (SVI), pseudo-capsule invasion (PCI), long diameter of lesions, lesion volume, lesion volume ratio, PSA density were related to PSM. MRI lesion location and PCI were independent risk factors for PSM. Least absolute shrinkage and selection operator (LASSO) regression was used to construct a clinical prediction model for PSM, including five variables: the number of positive cores, SVI, MRI lesion location, long diameter of lesions, and PSA.

CONCLUSION

The positive rate of surgical margin in middle PZ was significantly lower than that in other regions, and MRI lesion location was an independent risk factor for PSM.

Prospective close monitoring of the effect of vascular-targeted photodynamic therapy and high intensity focused ultrasound of localized prostate cancer by multiparametric magnetic resonance imaging

PURPOSE

The aim of this study is to describe the anatomical and functional changes observed in multiparametric magnetic resonance imaging (mpMRI) during follow-up after focal therapy (FT) for localized prostate cancer (PCa).

MATERIALS AND METHODS

In this prospective study, we analyzed pre- and postoperatively acquired mpMRI of 10 patients after FT (7 days; 3, 6, 9, 12 months). 7/10 (70%) patients underwent vascular-targeted photodynamic therapy (VTP). 3/10 (30%) patients underwent high-intensity focused ultrasound (HIFU). MpMR image analysis was performed using a semi-automatic software for segmentation of the prostate gland (PG) and tumor zones. Signal intensities (SI) of T2-weighted (T2w), T1-weighted (T1w),diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) images as well as volumes of the prostate gland (PGV) and tumor volumes (TV) were evaluated at each time point.

RESULTS

The results showed a significant increase of PGV 7 days after FT (p = 0.042) and a significant reduction of PGV between 7 days and 6, 9 and 12 months after FT (p < 0.001). The TV increased significantly 7 days after FT (p < 0.001) and decreased significantly between 7 days and 12 months after FT (p < 0.001). There was a significant increase in SI of the ADC in the ablation zone after 6, 9 and 12 months after FT (p < 0.001). 1/9 patients (11%) had recurrent tumor on rebiopsy characterized as a a small focal lesion on mpMRI with strong diffusion restriction (low SI on ADC map and high SI on b-value DWI).

CONCLUSION

MpMRI is able to represent morphologic changes of the ablated zone after FT and might be helpful to detect recurrent tumor.

Cognitive Targeted Prostate Biopsy Alone for Diagnosing Clinically Significant Prostate Cancer in Selected Biopsy-Naïve Patients: Results from a Retrospective Pilot Study

(1) Background: To identify a particular setting of biopsy-naïve patients in which it would be reasonable to offer only cognitive targeted prostate biopsy (PBx) with a transrectal approach. (2) Methods: We designed an observational retrospective pilot study. Patients with a prostatic specific antigen (PSA) level > 10 ng/mL, either a normal or suspicious digital rectal examination (DRE), and a lesion with a PI-RADS score ≥ 4 in the postero-medial or postero-lateral peripheral zone were included. All patients underwent a transrectal PBx, including both systematic and targeted samples. The detection rate of clinically significant prostate cancer (csPCa) (Gleason Score ≥ 7) was chosen as the primary outcome. We described the detection rate of csPCa in systematic PBx, targeted PBx, and overall PBx. (3) A total of 92 patients were included. Prostate cancer was detected in 84 patients (91.30%) with combined biopsies. A csPCa was diagnosed in all positive cases (100%) with combined biopsies. Systematic PBxs were positive in 80 patients (86.96%), while targeted PBxs were positive in 84 men (91.30%). Targeted PBx alone would have allowed the diagnosis of csPCa in all positive cases; systematic PBx alone would have missed the diagnosis of 8/84 (9.52%) csPCa cases (4 negative patients and 4 not csPCa) (p = 0.011). (4) Conclusions: Cognitive targeted PBx with a transrectal approach could be offered alone to diagnose csPCa in biopsy-naïve patients with PSA ≥ 10 ng/mL, either normal or suspicious DRE, and a lesion with PI-RADS score ≥ 4 in the postero-medial or postero-lateral peripheral zone.

A comparative study of 18F-PSMA-1007 PET/CT and pelvic MRI in newly diagnosed prostate cancer

PURPOSE

To evaluate the difference in the diagnostic efficacy of 18F-PSMA-1007 PET/CT and pelvic MRI in primary prostate cancer, as well as the correlation between the two methods and histopathological parameters and serum PSA levels.

METHODS

A total of 41 patients with suspected prostate cancer who underwent 18F-PSMA-1007 PET/CT imaging in our department from 2018 to 2023 were retrospectively collected. All patients underwent 18F-PSMA-1007 PET/CT and MRI scans. The sensitivity, PPV and diagnostic accuracy of MRI and 18F-PSMA-1007 PET/CT in the diagnosis of prostate cancer were calculated after comparing the results of MRI and 18F-PSMA-1007 PET/CT with biopsy. The Spearman test was used to calculate the correlation between 18F-PSMA-1007 PET/CT, MRI parameters, histopathological indicators, and serum PSA levels.

RESULTS

Compared with histopathological results, the sensitivity, PPV and diagnostic accuracy of 18F-PSMA-1007 PET/CT in the diagnosis of prostate cancer were 95.1%, 100.0% and 95.1%, respectively. The sensitivity, PPV and diagnostic accuracy of MRI in the diagnosis of prostate cancer were 82.9%, 100.0% and 82.9%, respectively. There was a mild to moderately positive correlation between Gleason (Gs) score, Ki-67 index, serum PSA level and 18F-PSMA-1007 PET/CT parameters (p < 0.05). There was a moderately negative correlation between the expression of AMACR (P504S) and 18F-PSMA-1007 PET/CT parameters (p < 0.05). The serum PSA level and the Gs score were moderately positively correlated with the MRI parameters (p < 0.05). There was no correlation between histopathological parameters and MRI parameters (p > 0.05).

CONCLUSION

Compared with MRI, 18F-PSMA-1007 PET/CT has higher sensitivity and diagnostic accuracy in the detection of malignant prostate tumors. In addition, the Ki-67 index and AMACR (P504S) expression were only correlated with 18F-PSMA-1007 PET/CT parameters. Gs score and serum PSA level were correlated with 18F-PSMA-1007 PET/CT and MRI parameters. 18F-PSMA-1007 PET/CT examination can provide certain reference values for the clinical diagnosis, evaluation, and treatment of malignant prostate tumors.

Clinically significant prostate cancer detection rate in biopsy-naïve patients with mpMRI and microultrasound topographically discordant lesions: A single-center retrospective analysis

INTRODUCTION AND OBJECTIVES

Multiparametric magnetic resonance imaging (mpMRI) has improved the detection of clinically significant prostate cancer (csPCa), and microultrasound (micro-US) shows promise in enhancing detection rates. We compared mpMRI-guided targeted biopsy (MTBx) and micro-US-guided targeted biopsy (micro-US-TBx) in biopsy-naïve patients with discordant lesions at micro-US and mpMRI to detect csPCa (grade group ≥2) and clinically insignificant PCa (ciPCa; grade group 1) and assessed the role of nontargeted systematic biopsy (SBx).

MATERIAL AND METHODS

We analyzed 178 biopsy-naive men with suspected PCa and discordant lesions at mpMRI and micro-US. All patients underwent mpMRI followed by micro-US, the latter being performed immediately before the biopsy. Imaging findings were interpreted blindly, followed by targeted and SBx. Median age was 63 years (IQR, 57-70), median prostate-specific antigen level was 7 ng/mL (IQR, 5-9 ng/mL), and median prostate volume was 49 cm^3 (IQR, 35-64 cm^3). Overall, 86/178 (48%) patients were diagnosed with PCa, 51/178 (29%) with csPCa.

RESULTS

Micro-USTBx detected csPCa in 36/178 men (20%; 95% CI: 26-46), and MTBx detected csPCa in 28/178 men (16%; 95% CI: 36-50), resulting in a -8% difference (95% CI: -10, 4; P = 0.022) and a relative detection rate of 0.043. Micro-USTBx detected ciPCa in 9/178 men (5%; 95% CI: 3, 15), while MTBx detected ciPCa in 12/178 men (7%; 95% CI: 5, 20), resulting in a -3% difference (95% CI: -2 to 4; P = 0.2) and a relative detection rate of 0.1. SBx detected ciPCa in 29 (16%) men. mpMRI plus micro-US detected csPCa in 51/178 men, with no additional cases with the addition of SBx. Similarly, MTBx plus micro-USTBx plus SBx detected ciPCa in 35/178 men (20%; 95% CI: 18, 37) compared to 9 (5%) in the micro-US pathway (P = 0.002) and 14/178 (8%; 95% CI: 6, 26) in the mpMRI plus micro-US pathway (P = 0.004).

CONCLUSIONS

In conclusion, a combined micro-US/mpMRI approach could characterize primary disease in biopsy-naïve patients with discordant lesions, potentially avoiding SBx. Further studies are needed to validate our findings and assess micro-US's role in reducing unnecessary biopsies.

Biparametric vs. Multiparametric MRI in the Detection of Cancer in Transperineal Targeted-Biopsy-Proven Peripheral Prostate Cancer Lesions Classified as PI-RADS Score 3 or 3+1: The Added Value of ADC Quantification

BACKGROUND

Biparametric MRI (bpMRI) has an important role in the diagnosis of prostate cancer (PCa), by reducing the cost and duration of the procedure and adverse reactions. We assess the additional benefit of the ADC map in detecting prostate cancer (PCa). Additionally, we examine whether the ADC value correlates with the presence of clinically significant tumors (csPCa).

METHODS

104 peripheral lesions classified as PI-RADS v2.1 score 3 or 3+1 at the mpMRI underwent transperineal MRI/US fusion-guided targeted biopsy.

RESULTS

The lesions were classified as PI-RADS 3 or 3+1; at histopathology, 30 were adenocarcinomas, 21 of which were classified as csPCa. The ADC threshold that maximized the Youden index in order to predict the presence of a tumor was 1103 (95% CI (990, 1243)), with a sensitivity of 0.8 and a specificity of 0.59; both values were greater than those found using the contrast medium, which were 0.5 and 0.54, respectively. Similar results were also found with csPCa, where the optimal ADC threshold was 1096 (95% CI (988, 1096)), with a sensitivity of 0.86 and specificity of 0.59, compared to 0.49 and 0.59 observed in the mpMRI.

CONCLUSIONS

Our study confirms the possible use of a quantitative parameter (ADC value) in the risk stratification of csPCa, by reducing the number of biopsies and, therefore, the number of unwarranted diagnoses of PCa and the risk of overtreatment.

Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study

OBJECTIVES

Exploring the value of adding correlation analysis (radiomic features (RFs) of pelvic metastatic lymph nodes and primary lesions) to screen RFs of primary lesions in the feature selection process of establishing prediction model.

METHODS

A total of 394 prostate cancer (PCa) patients (263 in the training group, 74 in the internal validation group and 57 in the external validation group) from two tertiary hospitals were included in the study. The cases with pelvic lymph node metastasis (PLNM) positive in the training group were diagnosed by biopsy or MRI with a short-axis diameter ≥ 1.5 cm, PLNM-negative cases in the training group and all cases in validation group were underwent both radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). The RFs of PLNM-negative lesion and PLNM-positive tissues including primary lesions and their metastatic lymph nodes (MLNs) in the training group were extracted from T2WI and apparent diffusion coefficient (ADC) map to build the following two models by fivefold cross-validation: the lesion model, established according to the primary lesion RFs selected by t tests and absolute shrinkage and selection operator (LASSO); the lesion-correlation model, established according to the primary lesion RFs selected by Pearson correlation analysis (RFs of primary lesions and their MLNs, correlation coefficient > 0.9), t test and LASSO. Finally, we compared the performance of these two models in predicting PLNM.

RESULTS

The AUC and the DeLong test of AUC in the lesion model and lesion-correlation model were as follows: training groups (0.8053, 0.8466, p = 0.0002), internal validation group (0.7321, 0.8268, p = 0.0429), and external validation group (0.6445, 0.7874, p = 0.0431), respectively.

CONCLUSION

The lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.

A Comparison of Systematic, Targeted, and Combined Biopsy Using Machine Learning for Prediction of Prostate Cancer Risk: A Multi-Center Study

OBJECTIVES

The aims of the study were to construct a new prognostic prediction model for detecting prostate cancer (PCa) patients using machine-learning (ML) techniques and to compare those models across systematic and target biopsy detection techniques.

METHODS

The records of the two main hospitals in Riyadh, Saudi Arabia, were analyzed for data on diagnosed PCa from 2019 to 2023. Four ML algorithms were utilized for the prediction and classification of PCa.

RESULTS

A total of 528 patients with prostate-specific antigen (PSA) greater than 3.5 ng/mL who had undergone transrectal ultrasound-guided prostate biopsy were evaluated. The total number of confirmed PCa cases was 234. Age, prostate volume, PSA, body mass index (BMI), multiparametric magnetic resonance imaging (mpMRI) score, number of regions of interest detected in MRI, and the diameter of the largest size lesion were significantly associated with PCa. Random Forest (RF) and XGBoost (XGB) (ML algorithms) accurately predicted PCa. Yet, their performance for classification and prediction of PCa was higher and more accurate for cases detected by targeted and combined biopsy (systematic and targeted together) compared to systematic biopsy alone. F1, the area under the curve (AUC), and the accuracy of XGB and RF models for targeted biopsy and combined biopsy ranged from 0.94 to 0.97 compared to the AUC of systematic biopsy for RF and XGB algorithms, respectively.

CONCLUSIONS

The RF model generated and presented an excellent prediction capability for the risk of PCa detected by targeted and combined biopsy compared to systematic biopsy alone. ML models can prevent missed PCa diagnoses by serving as a screening tool.

Refining clinically relevant cut-offs of prostate specific antigen density for risk stratification in patients with PI-RADS 3 lesions

BACKGROUND

Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions, identified through multiparametric magnetic resonance imaging (mpMRI), present a clinical challenge due to their equivocal nature in predicting clinically significant prostate cancer (csPCa). Aim of the study is to improve risk stratification of patients with PI-RADS 3 lesions and candidates for prostate biopsy.

METHODS

A cohort of 4841 consecutive patients who underwent MRI and subsequent MRI-targeted and systematic biopsies between January 2016 and April 2023 were retrospectively identified from independent prospectively maintained database. Only patients who have PI-RADS 3 lesions were included in the final analysis. A multivariable logistic regression analysis was performed to identify covariables associated with csPCa defined as International Society of Urological Pathology (ISUP) grade group ≥2. Performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Significant predictors were then selected for further exploration using a Chi-squared Automatic Interaction Detection (CHAID) analysis.

RESULTS

Overall, 790 patients had PI-RADS 3 lesions and 151 (19%) had csPCa. Significant associations were observed for age (OR: 1.1 [1.0-1.1]; p = 0.01) and PSA density (OR: 1643 [2717-41,997]; p < 0.01). The CHAID analysis identified PSAd as the sole significant factor influencing the decision tree. Cut-offs for PSAd were 0.13 ng/ml/cc (csPCa detection rate of 1% vs. 18%) for the two-nodes model and 0.09 ng/ml/cc and 0.16 ng/ml/cc for the three-nodes model (csPCa detection rate of 0.5% vs. 2% vs. 17%).

CONCLUSIONS

For individuals with PI-RADS 3 lesions on prostate mpMRI and a PSAd below 0.13, especially below 0.09, prostate biopsy can be omitted, in order to avoid unnecessary biopsy and overdiagnosis of non-csPCa.

Comparison of synthesized and acquired high b-value diffusion-weighted MRI for detection of prostate cancer

BACKGROUND

High b-value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). This study qualitatively and quantitatively compares synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa.

METHODS

One hundred fifty-one consecutive patients who underwent prostate MRI and biopsy were included in the study. Axial DWI with b = 0, 500, 1000, and 2000 s/mm2 using a 3T clinical scanner using a 32-channel phased-array body coil were acquired. We retrospectively synthesized DWI for b = 2000 s/mm2 via extrapolation based on mono-exponential decay, using b = 0 and b = 500 s/mm2 (sDWI500) and b = 0, b = 500 s/mm2, and b = 1000 s/mm2 (sDWI1000). Differences in signal intensity between sDWI and aDWI were evaluated within different regions of interest (prostate alone, prostate plus 5 mm, 30 mm and 70 mm margin and full field of view). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC).

RESULTS

Within the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were -46 ± 35% for sDWI1000 and -67 ± 24% for sDWI500. AUC for aDWI, sDWI500, sDWI1000, and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively.

CONCLUSION

sDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.

Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics

Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

Integration of PSAd and multiparametric MRI to forecast biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml

Introduction

This study aims to investigate whether the transrectal ultrasound-guided combined biopsy (CB) improves the detection rates of prostate cancer (PCa) and clinically significant PCa (csPCa) in biopsy-naïve patients. We also aimed to compare the Prostate Imaging Reporting and Data System (PI-RADS v2.1) score, ADC values, and PSA density (PSAd) in predicting csPCa by the combined prostate biopsy.

Methods

This retrospective and single-center study included 389 biopsy-naïve patients with PSA level 4~20 ng/ml, of whom 197 underwent prebiopsy mpMRI of the prostate. The mpMRI-based scores (PI-RADS v2.1 scores and ADC values) and clinical parameters were collected and evaluated by logistic regression analyses. Multivariable models based on the mpMRI-based scores and clinical parameters were developed by the logistic regression analyses to forecast biopsy outcomes of CB in biopsy-naïve patients. The ROC curves measured by the AUC values, calibration plots, and DCA were performed to assess multivariable models.

Results

The CB can detect more csPCa compared with TRUSB (32.0% vs. 53%). The Spearman correlation revealed that Gleason scores of the prostate biopsy significantly correlated with PI-RADS scores and ADC values. The multivariate logistic regression confirmed that PI-RADS scores 4, 5, and prostate volume were important predictors of csPCa. The PI-RADS+ADC+PSAd (PAP) model had the highest AUCs of 0.913 for predicting csPCa in biopsy-naïve patients with PSA level 4~20 ng/ml. When the biopsy risk threshold of the PAP model was greater than or equal to 0.10, 51% of patients could avoid an unnecessary biopsy, and only 5% of patients with csPCa were missed.

Conclusion

The prebiopsy mpMRI and the combined prostate biopsy have a high CDR of csPCa in biopsy-naïve patients. A multivariable model based on the mpMRI-based scores and PSAd could provide a reference for clinicians in forecasting biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml and make a more comprehensive assessment during the decision-making of the prostate biopsy.

Diagnostic Performance of [18F]AlF-Thretide PET/CT in Patients with Newly Diagnosed Prostate Cancer Using Histopathology as Reference Standard

This study aimed to assess the diagnostic value of [18F]AlF-thretide PET/CT in patients with newly diagnosed prostate cancer (PCa). Methods: In total, 49 patients with biopsy-proven PCa were enrolled in this prospective study. All patients underwent [18F]AlF-thretide PET/CT, and the scoring system of the PRIMARY trial was used for PET image analysis. The dosimetry evaluation of [18F]AlF-thretide was performed on 3 patients. Pathologic examination was used as the reference standard to evaluate the location, number, size, and Gleason score of tumors, for comparison with the [18F]AlF-thretide PET/CT results. PSMA expression was evaluated by immunohistochemical staining. Results: All patients tolerated the [18F]AlF-thretide PET/CT well. The total effective dose of [18F]AlF-thretide was 1.16E-02 mSv/MBq. For patient-based analysis of intraprostatic tumors, 46 of 49 (93.9%) patients showed pathologic uptake on [18F]AlF-thretide PET/CT. For lesion-based analysis of intraprostatic tumors, the sensitivity and positive predictive value for [18F]AlF-thretide PET/CT were 58.2% and 90.5%, respectively. Delayed images can detect more lesions than standard images (n = 57 vs. 49, P = 0.005), and the SUVmax and tumor-to-background ratio of the former were higher than those of the latter (SUVmax: 14.5 ± 16.7 vs. 11.4 ± 13.6, P < 0.001; tumor-to-background ratio: 37.1 ± 42.3 vs. 23.1 ± 27.4, P < 0.001). The receiver-operating-characteristic curve analysis showed that the areas under the curve for PRIMARY score-predicted true-positive and false-positive lesions were significantly higher than those for the SUVmax of standard images (P = 0.015) and seemed higher than those for the SUVmax of delayed images (P = 0.257). [18F]AlF-thretide PET/CT showed a higher detection rate than multiparametric MRI for all intraprostatic foci (53.5% vs. 40.8%, P = 0.012) and clinically significant PCa (75.0% vs. 61.4%, P = 0.031). Conclusion: [18F]AlF-thretide PET/CT showed high diagnostic value for patients with primary PCa and can be used as an excellent imaging modality for preoperative evaluation of PCa patients.

Biparametric versus multiparametric MRI for the detection of clinically significant prostate cancer in a diverse, multiethnic population

PURPOSE

There is not yet satisfactory performance data comparing multiparametric MRI (mpMRI) versus biparametric MRI (bpMRI) for detecting prostate cancer (PCa), particularly in high-risk populations. We compared both protocols for detecting overall PCa and clinically significant PCa (CS-PCa; defined as Grade Group ≥ 2) in a multiethnic urban population.

METHODS

We retrospectively reviewed electronic medical record data from men who underwent image-guided fusion prostate biopsy (FB) between 2016 and 2021 at our institution. Patient characteristics, Prostate Imaging Reporting and Data System (PI-RADS) scores, and FB outcomes were analyzed based on MRI protocol. Multivariate mixed-effects logistic regression models were used to examine associations of bpMRI versus mpMRI for detecting overall PCa and CS-PCa in targeted lesions, among all patients and stratified by race/ethnicity.

RESULTS

Overall, 566 men (44.0% Non-Hispanic Black [NHB]; 27.0% Hispanic) with 975 PI-RADS 3-5 lesions on MRI underwent FB. Of these, 312 (55%) men with 497 lesions underwent mpMRI and 254 (45%) men with 478 lesions underwent bpMRI. On multivariate analyses among all men, the odds of detecting overall PCa (OR = 1.18, 95% CI: 1.05-3.11, p = 0.031) and CS-PCa (OR = 2.15, 95% CI: 1.16-4.00, p = 0.014) on FB were higher for lesions identified on bpMRI than mpMRI. When stratified by race/ethnicity, the odds of detecting overall PCa (OR = 1.86; p = 0.15) and CS-PCa (OR = 2.20; p = 0.06) were not statistically different between lesions detected on bpMRI or mpMRI.

CONCLUSION

BpMRI has similar diagnostic performance to mpMRI in detecting overall and CS-PCa within a racially/ethnically diverse population. BpMRI can be utilized for evaluating suspected CS-PCa among NHB and Hispanic men.

Transperineal Versus Transrectal Magnetic Resonance Imaging-targeted and Systematic Prostate Biopsy to Prevent Infectious Complications: The PREVENT Randomized Trial

BACKGROUND AND OBJECTIVE

The transrectal biopsy approach is traditionally used to detect prostate cancer. An alternative transperineal approach is historically performed under general anesthesia, but recent advances enable transperineal biopsy to be performed under local anesthesia. We sought to compare infectious complications of transperineal biopsy without antibiotic prophylaxis versus transrectal biopsy with targeted prophylaxis.

METHODS

We assigned biopsy-naïve participants to undergo transperineal biopsy without antibiotic prophylaxis versus transrectal biopsy with targeted prophylaxis (rectal culture screening for fluoroquinolone-resistant bacteria and antibiotic targeting to culture and sensitivity results) through a multicenter, randomized trial. The primary outcome was post-biopsy infection captured by a prospective medical review and patient report on a 7-d survey. The secondary outcomes included cancer detection, noninfectious complications, and a numerical rating scale (0-10) for biopsy-related pain and discomfort during and 7-d after biopsy.

KEY FINDINGS AND LIMITATIONS

A total of 658 participants were randomized, with zero transperineal versus four (1.4%) transrectal biopsy infections (difference -1.4%; 95% confidence interval [CI] -3.2%, 0.3%; p = 0.059). The rates of other complications were very low and similar. Importantly, detection of clinically significant cancer was similar (53% transperineal vs 50% transrectal, adjusted difference 2.0%; 95% CI -6.0, 10). Participants in the transperineal arm experienced worse periprocedural pain (0.6 adjusted difference [0-10 scale], 95% CI 0.2, 0.9), but the effect was small and resolved by 7-d.

CONCLUSIONS AND CLINICAL IMPLICATIONS

Office-based transperineal biopsy is tolerable, does not compromise cancer detection, and did not result in infectious complications. Transrectal biopsy with targeted prophylaxis achieved similar infection rates, but requires rectal cultures and careful attention to antibiotic selection and administration. Consideration of these factors and antibiotic stewardship should guide clinical decision-making.

PATIENT SUMMARY

In this multicenter randomized trial, we compare prostate biopsy infectious complications for the transperineal versus transrectal approach. The absence of infectious complications with transperineal biopsy without the use of preventative antibiotics is noteworthy, but not significantly different from transrectal biopsy with targeted antibiotic prophylaxis.

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

BACKGROUND

Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine practice at scale.

METHODS

In this international, paired, non-inferiority, confirmatory study, we trained and externally validated an AI system (developed within an international consortium) for detecting Gleason grade group 2 or greater cancers using a retrospective cohort of 10 207 MRI examinations from 9129 patients. Of these examinations, 9207 cases from three centres (11 sites) based in the Netherlands were used for training and tuning, and 1000 cases from four centres (12 sites) based in the Netherlands and Norway were used for testing. In parallel, we facilitated a multireader, multicase observer study with 62 radiologists (45 centres in 20 countries; median 7 [IQR 5-10] years of experience in reading prostate MRI) using PI-RADS (2.1) on 400 paired MRI examinations from the testing cohort. Primary endpoints were the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) of the AI system in comparison with that of all readers using PI-RADS (2.1) and in comparison with that of the historical radiology readings made during multidisciplinary routine practice (ie, the standard of care with the aid of patient history and peer consultation). Histopathology and at least 3 years (median 5 [IQR 4-6] years) of follow-up were used to establish the reference standard. The statistical analysis plan was prespecified with a primary hypothesis of non-inferiority (considering a margin of 0·05) and a secondary hypothesis of superiority towards the AI system, if non-inferiority was confirmed. This study was registered at ClinicalTrials.gov, NCT05489341.

FINDINGS

Of the 10 207 examinations included from Jan 1, 2012, through Dec 31, 2021, 2440 cases had histologically confirmed Gleason grade group 2 or greater prostate cancer. In the subset of 400 testing cases in which the AI system was compared with the radiologists participating in the reader study, the AI system showed a statistically superior and non-inferior AUROC of 0·91 (95% CI 0·87-0·94; p<0·0001), in comparison to the pool of 62 radiologists with an AUROC of 0·86 (0·83-0·89), with a lower boundary of the two-sided 95% Wald CI for the difference in AUROC of 0·02. At the mean PI-RADS 3 or greater operating point of all readers, the AI system detected 6·8% more cases with Gleason grade group 2 or greater cancers at the same specificity (57·7%, 95% CI 51·6-63·3), or 50·4% fewer false-positive results and 20·0% fewer cases with Gleason grade group 1 cancers at the same sensitivity (89·4%, 95% CI 85·3-92·9). In all 1000 testing cases where the AI system was compared with the radiology readings made during multidisciplinary practice, non-inferiority was not confirmed, as the AI system showed lower specificity (68·9% [95% CI 65·3-72·4] vs 69·0% [65·5-72·5]) at the same sensitivity (96·1%, 94·0-98·2) as the PI-RADS 3 or greater operating point. The lower boundary of the two-sided 95% Wald CI for the difference in specificity (-0·04) was greater than the non-inferiority margin (-0·05) and a p value below the significance threshold was reached (p<0·001).

INTERPRETATION

An AI system was superior to radiologists using PI-RADS (2.1), on average, at detecting clinically significant prostate cancer and comparable to the standard of care. Such a system shows the potential to be a supportive tool within a primary diagnostic setting, with several associated benefits for patients and radiologists. Prospective validation is needed to test clinical applicability of this system.

FUNDING

Health~Holland and EU Horizon 2020.