Is MRI ready to replace biopsy during active surveillance?

Active surveillance (AS) is a conservative management option recommended for patients diagnosed with low-risk prostate cancer (PCa) and selected cases with intermediate-risk PCa. The adoption of prostate MRI in the primary diagnostic setting has sparked interest in its application during AS. This review aims to examine the role and performance of multiparametric MRI (mpMRI) across the entire AS pathway, from initial stratification to follow-up, also relative to the utilization of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) criteria. Given the high negative predictive value of mpMRI in detecting clinically significant PCa (csPCa), robust evidence supports its use in patient selection and risk stratification at the time of diagnosis or confirmatory biopsy. However, conflicting results have been observed when using MRI in evaluating disease progression during follow-up. Key areas requiring clarification include addressing the clinical significance of MRI-negative csPCa, optimizing MRI quality, determining the role of biparametric MRI (bpMRI) or mpMRI protocols, and integrating artificial intelligence (AI) for improved performance. CLINICAL RELEVANCE STATEMENT: MRI plays an essential role in the selection, stratification, and follow up of patients in active surveillance (AS) for prostate cancer. However, owing to existing limitations, it cannot fully replace biopsies in the context of AS. KEY POINTS: Multiparametric MRI (mpMRI) has become a crucial tool in active surveillance (AS) for prostate cancer (PCa). Conflicting results have been observed regarding multiparametric MRI efficacy in assessing disease progression. Standardizing MRI-guided protocols will be critical in addressing current limitations in active surveillance for prostate cancer.

Defining the optimal target-to-background ratio to identify positive lymph nodes in prostate cancer patients undergoing robot-assisted [99mTc]Tc-PSMA radioguided surgery: updated results and ad interim analyses of a prospective phase II study.

INTRODUCTION: Prostate-specific membrane antigen radioguided surgery (PSMA-RGS) might identify lymph node invasion (LNI) in prostate cancer (PCa) patients undergoing extended pelvic lymph node dissection (ePLND). The optimal target-to-background (TtB) ratio to define RGS positivity is still unknown. MATERIALS & METHODS: Ad interim analyses which focused on 30 patients with available pathological information were conducted. All patients underwent preoperative PSMA positron emission tomography (PET). 99m-Technetium-PSMA imaging and surgery ([99mTc]Tc-PSMA-I&S) was administered the day before surgery. In vivo measurements were conducted using an intraoperative gamma probe. Performance characteristics and implications associated with different TtB ratios were assessed. RESULTS: Overall, 9 (30%) patients had LNI, with 22 (13%) and 80 (11%) positive regions and lymph nodes, respectively. PSMA-RGS showed uptakes in 12 (40%) vs. 7 (23%) vs. 6 (20%) patients for a TtB ratio ≥ 2 vs. ≥ 3 vs. ≥ 4. At a per-region level, sensitivity, specificity and accuracy for a TtB ratio ≥ 2 vs. ≥ 3 vs. ≥ 4 were 72%, 88% and 87% vs. 54%, 98% and 92% vs. 36%, 99% and 91%. Performing ePLND only in patients with suspicious spots at PSMA PET (n = 7) would have spared 77% ePLNDs at the cost of missing 13% (n = 3) pN1 patients. A TtB ratio ≥ 2 at RGS identified 8 (24%) suspicious areas not detected by PSMA PET, of these 5 (63%) harbored LNI, with one pN1 patient (11%) that would have been missed by PSMA PET. Adoption of a TtB ratio ≥ 2 vs. ≥ 3 vs. ≥ 4, would have allowed to spare 18 (60%) vs. 23 (77%) vs. 24 (80%) ePLNDs missing 2 (11%) vs. 3 (13%) vs. 4 (17%) pN1 patients. CONCLUSIONS: PSMA-RGS using a TtB ratio ≥ 2 to identify suspicious nodes, could allow to spare > 50% ePLNDs and would identify additional pN1 patients compared to PSMA PET and higher TtB ratios.

ESR Essentials: using the right scoring system in prostate MRI-practice recommendations by ESUR.

MRI has gained prominence in the diagnostic workup of prostate cancer (PCa) patients, with the Prostate Imaging Reporting and Data System (PI-RADS) being widely used for cancer detection. Beyond PI-RADS, other MRI-based scoring tools have emerged to address broader aspects within the PCa domain. However, the multitude of available MRI-based grading systems has led to inconsistencies in their application within clinical workflows. The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) assesses the likelihood of clinically significant radiological changes of PCa during active surveillance, and the Prostate Imaging for Local Recurrence Reporting (PI-RR) scoring system evaluates the risk of local recurrence after whole-gland therapies with curative intent. Underlying any system is the requirement to assess image quality using the Prostate Imaging Quality Scoring System (PI-QUAL). This article offers practicing radiologists a comprehensive overview of currently available scoring systems with clinical evidence supporting their use for managing PCa patients to enhance consistency in interpretation and facilitate effective communication with referring clinicians. KEY POINTS: Assessing image quality is essential for all prostate MRI interpretations and the PI-QUAL score represents  the standardized tool for this purpose. Current urological clinical guidelines for prostate cancer diagnosis and localization recommend adhering to the PI-RADS recommendations. The PRECISE and PI-RR scoring systems can be used for assessing radiological changes of prostate cancer during active surveillance and the likelihood of local recurrence after radical treatments respectively.

PI-QUAL version 2: an update of a standardised scoring system for the assessment of image quality of prostate MRI.

Multiparametric MRI is the optimal primary investigation when prostate cancer is suspected, and its ability to rule in and rule out clinically significant disease relies on high-quality anatomical and functional images. Avenues for achieving consistent high-quality acquisitions include meticulous patient preparation, scanner setup, optimised pulse sequences, personnel training, and artificial intelligence systems. The impact of these interventions on the final images needs to be quantified. The prostate imaging quality (PI-QUAL) scoring system was the first standardised quantification method that demonstrated the potential for clinical benefit by relating image quality to cancer detection ability by MRI. We present the updated version of PI-QUAL (PI-QUAL v2) which applies to prostate MRI performed with or without intravenous contrast medium using a simplified 3-point scale focused on critical technical and qualitative image parameters. CLINICAL RELEVANCE STATEMENT: High image quality is crucial for prostate MRI, and the updated version of the PI-QUAL score (PI-QUAL v2) aims to address the limitations of version 1. It is now applicable to both multiparametric MRI and MRI without intravenous contrast medium. KEY POINTS: High-quality images are essential for prostate cancer diagnosis and management using MRI. PI-QUAL v2 simplifies image assessment and expands its applicability to prostate MRI without contrast medium. PI-QUAL v2 focuses on critical technical and qualitative image parameters and emphasises T2-WI and DWI.

Preoperative risk calculator for the probability of completing nephron sparing for kidney cancer.

PURPOSE: In absence of predictive models, preoperative estimation of the probability of completing partial (PN) relative to radical nephrectomy (RN) is invariably inaccurate and subjective. We aimed to develop an evidence-based model to assess objectively the probability of PN completion based on patients' characteristics, tumor's complexity, urologist expertise and surgical approach. DESIGN, SETTING AND PARTICIPANTS: 675 patients treated with PN or RN for cT1-2 cN0 cM0 renal mass by seven surgeons at one single experienced centre from 2000 to 2019. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSES: The outcome of the study was PN completion. We used a multivariable logistic regression (MVA) model to investigate predictors of PN completion. We used SPARE score to assess tumor complexity. We used a bootstrap validation to compute the model's predictive accuracy. We investigated the relationship between the outcomes and specific predictors of interest such as tumor's complexity, approach and experience. RESULTS: Of 675 patients, 360 (53%) were treated with PN vs. 315 (47%) with RN. Smaller tumors [Odds ratio (OR): 0.52, 95%CI 0.44-0.61; P < 0.001], lower SPARE score (OR: 0.67, 95%CI 0.47-0.94; P = 0.02), more experienced surgeons (OR: 1.01, 95%CI 1.00-1.02; P < 0.01), robotic (OR: 10; P < 0.001) and open (OR: 36; P < 0.001) compared to laparoscopic approach resulted associated with higher probability of PN completion. Predictive accuracy of the model was 0.94 (95% CI 0.93-0.95). CONCLUSIONS: The probability of PN completion can be preoperatively assessed, with optimal accuracy relaying on routinely available clinical information. The proposed model might be useful in preoperative decision-making, patient consensus, or during preoperative counselling. PATIENT SUMMARY: In patients with a renal mass the probability of completing a partial nephrectomy varies considerably and without a predictive model is invariably inaccurate and subjective. In this study we build-up a risk calculator based on easily available preoperative variables that can predict with optimal accuracy the probability of not removing the entire kidney.

Diagnostic accuracy of fully hybrid [68Ga]Ga-PSMA-11 PET/MRI and [68Ga]Ga-RM2 PET/MRI in patients with biochemically recurrent prostate cancer: a prospective single-center phase II clinical trial.

PURPOSE: To compare the diagnostic accuracy and detection rates of PET/MRI with [68Ga]Ga-PSMA-11 and [68Ga]Ga-M2 in patients with biochemical recurrence of prostate cancer (PCa). METHODS: Sixty patients were enrolled in this prospective single-center phase II clinical trial from June 2020 to October 2022. Forty-four/60 completed all study examinations and were available at follow-up (median: 22.8 months, range: 6-31.5 months). Two nuclear medicine physicians analyzed PET images and two radiologists interpreted MRI; images were then re-examined to produce an integrated PET/MRI report for both [68Ga]Ga-PSMA-11 and [68Ga]Ga-RM2 examinations. A composite reference standard including histological specimens, response to treatment, and conventional imaging gathered during follow-up was used to validate imaging findings. Detection rates, accuracy, sensitivity, specificity, positive, and negative predictive value were assessed. McNemar's test was used to compare sensitivity and specificity on a per-patient base and detection rate on a per-region base. Prostate bed, locoregional lymph nodes, non-skeletal distant metastases, and bone metastases were considered. p-value significance was defined below the 0.05 level after correction for multiple testing. RESULTS: Patients' median age was 69.8 years (interquartile range (IQR): 61.8-75.1) and median PSA level at time of imaging was 0.53 ng/mL (IQR: 0.33-2.04). During follow-up, evidence of recurrence was observed in 31/44 patients. Combining MRI with [68Ga]Ga-PSMA-11 PET and [68Ga]Ga-RM2 PET resulted in sensitivity = 100% and 93.5% and specificity of 69.2% and 69.2%, respectively. When considering the individual imaging modalities, [68Ga]Ga-RM2 PET showed lower sensitivity compared to [68Ga]Ga-PSMA-11 PET and MRI (61.3% vs 83.9% and 87.1%, p = 0.046 and 0.043, respectively), while specificity was comparable among the imaging modalities (100% vs 84.6% and 69.2%, p = 0.479 and 0.134, respectively). CONCLUSION: This study brings further evidence on the utility of fully hybrid PET/MRI for disease characterization in patients with biochemically recurrent PCa. Imaging with [68Ga]Ga-PSMA-11 PET showed high sensitivity, while the utility of [68Ga]Ga-RM2 PET in absence of a simultaneous whole-body/multiparametric MRI remains to be determined.

Vesical Imaging-Reporting and Data System use predicting the outcome of neoadjuvant pembrolizumab in muscle-invasive bladder cancer.

OBJECTIVE: To evaluate the predictive capability of the pre- and post-pembrolizumab Vesical Imaging-Reporting and Data System (VI-RADS) to identify ypT0N0 or ypT≤1N0 response in muscle-invasive bladder cancer (MIBC) within the PURE-01 trial (ClinicalTrials.gov identifier: NCT02736266). PATIENTS AND METHODS: Patients were staged with bladder multiparametric magnetic resonance imaging (mpMRI) before and after treatment (three cycles of pembrolizumab) prior to radical cystectomy (RC). Logistic regression models were used to analyse the pre- and post- pembrolizumab VI-RADS against ypT≤1N0 and ypT0N0 response. The VI-RADS scores were dichotomised between 0 and 3 (0 = no evidence of disease) and 4-5. Event-free survival (EFS) and overall survival (OS) analyses were performed. Comprehensive genomic profiling and transcriptome-wide expression profiling data were matched with the VI-RADS scores. RESULTS: In total, 110 patients underwent centrally reviewed scans (N = 220 mpMRI), treated between February 2017 and July 2020. Both pre- and post-pembrolizumab VI-RADS 0-3 scores were the only significant covariates that predicted the ypT≤1N0 endpoint in multivariable analyses, and the strongest effect was seen with post-pembrolizumab VI-RADS 0-3 predicting the ypT≤1N0 response (P < 0.001). The area under the curve for this model was 0.90. Post-pembrolizumab VI-RADS 0-3 also predicted a longer EFS (P < 0.001) and OS (P = 0.044). The scores of several gene signatures from baseline tumours differed between the pre-pembrolizumab VI-RADS 0-3 and 4-5 categories. CONCLUSION: Post-pembrolizumab VI-RADS scores are strongly associated with pathological downstaging and survival. VI-RADS scores were also characterised by distinct biomarker features. These results indicate that the VI-RADS is emerging as an important tool for designing next-generation trials for MIBC.

Variability of mpMRI diagnostic performance according to the upfront individual patient risk of having clinically significant prostate cancer.

BACKGROUND: To assess the variation of multiparametric magnetic resonance imaging (mpMRI) positive predictive value (PPV) according to each patient's risk of clinically significant prostate cancer (csPCa) based exclusively on clinical factors. METHODS: We evaluated 999 patients with positive mpMRI (PI-RADS ≥ 3) receiving targeted (TBx) plus systematic prostate biopsy. We built a multivariable logistic regression analysis (MVA) using clinical risk factors to calculate the individual patients' risk of harboring csPCa at TBx. A second MVA tested the association between individual patients' clinical risk and mpMRI PPV accounting for the PI-RADS score. Finally, we plotted the PPV of each PI-RADS score by the individual patient pretest probability of csPCa using a LOWESS approach. RESULTS: Overall, TBx found csPCa in 21%, 51%, and 80% of patients with PI-RADS 3, 4, and 5 lesions, respectively. At MVA, age, PSA, digital rectal examination (DRE), and prostate volume were significantly associated with the risk of csPCa at biopsy. DRE yielded the highest odds ratio (OR: 2.88; p < 0.001). The individual patient's clinical risk was significantly associated with mpMRI PPV (OR: 2.49; p < 0.001) using MVA. Plotting the mpMRI PPV according to the predicted clinical risks, we observed that for patients with clinical risk close to 0 versus patients with risk higher than 90%, the mpMRI PPV of PI-RADS 3, 4, and 5 ranged from 0% to 75%, from 0% to 96%, and from 45% to 100%, respectively. CONCLUSION: mpMRI PPV varies according to the individual pretest patient's risk based on clinical factors. These findings should be considered in the decision-making process for patients with suspect MRI findings referred for a prostate biopsy. Moreover, our data support the need for further studies to create an individualized risk prediction tool.

First-in-human in-vivo depiction of paraganglioma metabolism by hyperpolarised 13C-magnetic resonance.

Phaeochromocytomas (PCC) and paragangliomas (PGL), cumulatively referred to as PPGLs, are neuroendocrine tumours arising from neural crest-derived cells in the sympathetic and parasympathetic nervous systems. Predicting future tumour behaviour and the likelihood of metastatic disease remains problematic as genotype-phenotype correlations are limited, the disease has variable penetrance and, to date, no reliable molecular, cellular or histological markers have emerged. Tumour metabolism quantification can be considered as a method to delineating tumour aggressiveness by utilising hyperpolarised 13 C-MR (HP-MR). The technique may provide an opportunity to non-invasively characterise disease behaviour. Here, we present the first instance of the analysis of PPGL metabolism via HP-MR in a single case.

Added Value of Prostate-specific Antigen Density in Selecting Prostate Biopsy Candidates Among Men with Elevated Prostate-specific Antigen and PI-RADS ≥3 Lesions on Multiparametric Magnetic Resonance Imaging of the Prostate: A Systematic Assessment by PI-RADS Score.

BACKGROUND: A significant proportion of patients with positive multiparametric magnetic resonance imaging (mpMRI; Prostate Imaging-Reporting and Data System [PI-RADS] scores of 3-5) have negative biopsy results. OBJECTIVE: To systematically assess all prostate-specific antigen density (PSAD) values and identify an appropriate cutoff for identification of patients with positive mpMRI who could potentially avoid biopsy on the basis of their PI-RADS score. DESIGN, SETTING, AND PARTICIPANTS: The study included a cohort of 1341 patients with positive mpMRI who underwent combined targeted and systematic biopsies. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Multivariable logistic regression analysis (MVA) was used to assess the association between PSAD and the risk of clinically significant prostate cancer (csPCa, grade group ≥2) after adjusting for confounders. We used locally weighted scatterplot smoothing to explore csPCa risk according to PSAD and PI-RADS scores. PSAD utility was observed only for patients with PI-RADS 3 lesions, so we plotted the effect of each PSAD value as a cutoff for this subgroup in terms of biopsies saved, csPCa cases missed, and clinically insignificant PCa (ciPCa, grade group 1) cases not detected. RESULTS AND LIMITATIONS: Overall, 667 (50%) csPCa cases were identified. On MVA, PSAD independently predicted csPCa (odds ratio 1.57; p < 0.001). For PI-RADS ≥4 lesions, the csPCa risk was ≥40% regardless of PSAD. Conversely, among patients with PI-RADS 3 lesions, csPCa risk ranged from 0% to 60% according to PSAD values, and a PSAD cutoff of 0.10 ng/ml/cm3 corresponded to a threshold probability of 10% for csPCa. Using this PSAD cutoff for patients with PI-RADS 3 lesions would have saved 32% of biopsies, missed 7% of csPCa cases, and avoided detection of 34% of ciPCa cases. Limitations include selection bias and the high experience of the radiologists and urologists involved. CONCLUSIONS: Patients with PI-RADS ≥4 lesions should undergo prostate biopsy regardless of their PSAD, while PSAD should be used to stratify patients with PI-RADS 3 lesions. Using a threshold probability of 10% for csPCa, our data suggest that the appropriate strategy is to avoid biopsy in patients with PI-RADS 3 lesions and PSAD <0.10 ng/ml/cm3. Our results also provide information to help in tailoring an appropriate strategy for every patient with positive mpMRI findings. PATIENT SUMMARY: We investigated whether a cutoff value for PSAD (prostate-specific antigen density) could identify patients with suspicious prostate lesions on MRI (magnetic resonance imaging) who could avoid biopsy according to the PI-RADS score for their scan. We found that patients with PI-RADS ≥4 should undergo prostate biopsy regardless of their PSAD. A PSAD cutoff of 0.10 should be used to stratify patients with PI-RADS 3.

Dissemination of endometrial cancer MRI staging guidelines among young radiologists: an ESUR Junior Network survey.

OBJECTIVES: Imaging guidelines could play an important role in the training of radiologists, but the extent of their adoption in residency programs is unclear. With this survey, the European Society of Urogenital Radiology (ESUR) Junior Network aimed to assess the dissemination of the ESUR guidelines on endometrial cancer MRI staging (EC-ESUR guidelines) among young radiologists. METHODS: An online questionnaire targeted to last year radiology residents and radiologists in the first year of their career was designed. It included 24 questions, structured in 4 sections (i.e., background, general, acquisition protocol, interpretation, and reporting). The survey was active between April and May 2022, accepting answers worldwide. Answers were solicited with a social media campaign and with the support of national scientific societies. Subgroup analysis was performed based on variables such as subspecialty of interest and number of EC-ESUR guidelines consultations using the Wilcoxon rank sum test. RESULTS: In total, 118 participants completed the questionnaire, of which 94 (80%) were from Europe and 46 (39%) with a special interest in urogenital radiology. Overall, 68 (58%) stated that the guidelines were not part of their residency teaching programs while 32 (27%) had never even consulted the guidelines. Interest in urogenital radiology as a subspecialty and EC-ESUR guidelines consultations were associated with greater confidence in supervising scan acquisition, interpreting, and reporting EC MRI staging exams. CONCLUSION: Four years after publication, the adoption of EC-ESUR guidelines in residency programs is heterogeneously low. Despite a possible selection bias, our findings indicate that active promotion of EC-ESUR guidelines is required. KEY POINTS: • The adoption of ESUR guidelines on endometrial cancer in radiology residency programs is heterogeneous. • Almost one third of respondents stated they had never even consulted the guidelines. • Confidence toward guidelines was higher in those who were exposed to more endometrial cancer MRI staging scans. • Reading the guidelines was associated with a greater confidence in protocol acquisition, interpretation, and reporting. • Active efforts to promote their dissemination are required.

[68Ga]Ga-PSMA and [68Ga]Ga-RM2 PET/MRI vs. Histopathological Images in Prostate Cancer: A New Workflow for Spatial Co-Registration.

This study proposed a new workflow for co-registering prostate PET images from a dual-tracer PET/MRI study with histopathological images of resected prostate specimens. The method aims to establish an accurate correspondence between PET/MRI findings and histology, facilitating a deeper understanding of PET tracer distribution and enabling advanced analyses like radiomics. To achieve this, images derived by three patients who underwent both [68Ga]Ga-PSMA and [68Ga]Ga-RM2 PET/MRI before radical prostatectomy were selected. After surgery, in the resected fresh specimens, fiducial markers visible on both histology and MR images were inserted. An ex vivo MRI of the prostate served as an intermediate step for co-registration between histological specimens and in vivo MRI examinations. The co-registration workflow involved five steps, ensuring alignment between histopathological images and PET/MRI data. The target registration error (TRE) was calculated to assess the precision of the co-registration. Furthermore, the DICE score was computed between the dominant intraprostatic tumor lesions delineated by the pathologist and the nuclear medicine physician. The TRE for the co-registration of histopathology and in vivo images was 1.59 mm, while the DICE score related to the site of increased intraprostatic uptake on [68Ga]Ga-PSMA and [68Ga]Ga-RM2 PET images was 0.54 and 0.75, respectively. This work shows an accurate co-registration method for histopathological and in vivo PET/MRI prostate examinations that allows the quantitative assessment of dual-tracer PET/MRI diagnostic accuracy at a millimetric scale. This approach may unveil radiotracer uptake mechanisms and identify new PET/MRI biomarkers, thus establishing the basis for precision medicine and future analyses, such as radiomics.

Extraprostatic incidental findings on prostate mpMRI: A pictorial review from the ESUR junior network.

The role of multiparametric MRI (mpMRI) in prostate cancer setting is increasingly consolidated and, as a result, its usage in clinical practice is in exponential growth. However, beyond the prostate gland, several key structures are included in the field of view of mpMRI scans. Consequently, various extra-prostatic incidental findings (IFs) belonging to different anatomical systems can be accidentally recognized. Therefore, it is mandatory for a radiologist to be familiar with the wide range of pathologies potentially encountered, to guide management and avoid patient anxiety and costs due to additional work-up prompted by clinically insignificant extra-prostatic findings. With this pictorial review, we aim to illustrate a wide range of IFs that can be detected when performing mpMRI of the prostate, focusing on their imaging characteristics, differential diagnosis, and clinical relevance. Additionally, we propose the CheckDEEP, the Checklist for DEtection of ExtraProstatic findings, to be used for a thorough evaluation of target areas within each anatomical system.

Impact of prostate imaging quality (PI-QUAL) score on the detection of clinically significant prostate cancer at biopsy.

PURPOSE: To investigate the impact of Prostate Imaging Quality (PI-QUAL) scores on the diagnostic performance of multiparametric MRI (mpMRI) in a targeted biopsy cohort. PATIENTS AND METHODS: 300 patients who underwent both mpMRI and biopsy were included. PI-QUAL scores were retrospectively assigned by two radiologists in consensus and were correlated to pre-biopsy PI-RADS scores and biopsy outcomes. Clinically significant prostate cancer (csPCa) was defined as ISUP grade ≥ 2. RESULTS: Image quality was optimal (PI-QUAL ≥ 4) in 249/300 (83%) and suboptimal (PI-QUAL < 4) in 51/300 (17%). The proportion of PI-RADS 3 scores referred for biopsy was higher in scans of suboptimal vs optimal quality (51% vs 33%). For PI-QUAL < 4 scans, the positive predictive value (PPV) was lower compared to PI-QUAL ≥ 4 (35% [95%CI: 22, 48] vs 48% [95%CI: 41, 55]; difference -13% [95%CI: -27, 2]; p 0.090), as was the detection rate of csPCa in both PI-RADS 3 and PI-RADS 4-5 (15% vs 23% and 56 vs 63%, respectively). The overall MRI quality increased over time. CONCLUSIONS: Scan quality may affect the diagnostic performance of prostate mpMRI in patients undergoing MRI-guided biopsy. Scans of suboptimal quality (PI-QUAL < 4) were associated with lower PPV for csPCa.

External validation of a convolutional neural network for the automatic segmentation of intraprostatic tumor lesions on 68Ga-PSMA PET images.

Introduction: State of the art artificial intelligence (AI) models have the potential to become a "one-stop shop" to improve diagnosis and prognosis in several oncological settings. The external validation of AI models on independent cohorts is essential to evaluate their generalization ability, hence their potential utility in clinical practice. In this study we tested on a large, separate cohort a recently proposed state-of-the-art convolutional neural network for the automatic segmentation of intraprostatic cancer lesions on PSMA PET images. Methods: Eighty-five biopsy proven prostate cancer patients who underwent 68Ga PSMA PET for staging purposes were enrolled in this study. Images were acquired with either fully hybrid PET/MRI (N = 46) or PET/CT (N = 39); all participants showed at least one intraprostatic pathological finding on PET images that was independently segmented by two Nuclear Medicine physicians. The trained model was available at https://gitlab.com/dejankostyszyn/prostate-gtv-segmentation and data processing has been done in agreement with the reference work. Results: When compared to the manual contouring, the AI model yielded a median dice score = 0.74, therefore showing a moderately good performance. Results were robust to the modality used to acquire images (PET/CT or PET/MRI) and to the ground truth labels (no significant difference between the model's performance when compared to reader 1 or reader 2 manual contouring). Discussion: In conclusion, this AI model could be used to automatically segment intraprostatic cancer lesions for research purposes, as instance to define the volume of interest for radiomics or deep learning analysis. However, more robust performance is needed for the generation of AI-based decision support technologies to be proposed in clinical practice.

Role of [68Ga]Ga-PSMA-11 PET radiomics to predict post-surgical ISUP grade in primary prostate cancer.

PURPOSE: The aim of this study is to investigate the role of [68Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology (PSISUP) grade in primary prostate cancer (PCa). METHODS: This retrospective study included 47 PCa patients who underwent [68Ga]Ga-PSMA-11 PET at IRCCS San Raffaele Scientific Institute before radical prostatectomy. The whole prostate was manually contoured on PET images and 103 image biomarker standardization initiative (IBSI)-compliant radiomic features (RFs) were extracted. Features were then selected using the minimum redundancy maximum relevance algorithm and a combination of the 4 most relevant RFs was used to train 12 radiomics machine learning models for the prediction of PSISUP grade: ISUP ≥ 4 vs ISUP < 4. Machine learning models were validated by means of fivefold repeated cross-validation, and two control models were generated to assess that our findings were not surrogates of spurious associations. Balanced accuracy (bACC) was collected for all generated models and compared with Kruskal-Wallis and Mann-Whitney tests. Sensitivity, specificity, and positive and negative predictive values were also reported to provide a complete overview of models' performance. The predictions of the best performing model were compared against ISUP grade at biopsy. RESULTS: ISUP grade at biopsy was upgraded in 9/47 patients after prostatectomy, resulting in a bACC = 85.9%, SN = 71.9%, SP = 100%, PPV = 100%, and NPV = 62.5%, while the best-performing radiomic model yielded a bACC = 87.6%, SN = 88.6%, SP = 86.7%, PPV = 94%, and NPV = 82.5%. All radiomic models trained with at least 2 RFs (GLSZM-Zone Entropy and Shape-Least Axis Length) outperformed the control models. Conversely, no significant differences were found for radiomic models trained with 2 or more RFs (Mann-Whitney p > 0.05). CONCLUSION: These findings support the role of [68Ga]Ga-PSMA-11 PET radiomics for the accurate and non-invasive prediction of PSISUP grade.

Impact of prostate MRI central review over the diagnostic performance of MRI-targeted biopsy: should we routinely ask for an expert second opinion?

PURPOSE: There is substantial variability in multiparametric MRI (mpMRI) protocols and inter-readers' agreement. We tested the effect of a central mpMRI review on the detection of clinically significant PCa (csPCa) in a tertiary referral center. METHODS: We retrospectively analyzed a cohort of 364 consecutive men with a positive externally performed mpMRI (PI-RADS ≥ 3) who underwent a targeted biopsy (TBx) plus a systematic biopsy at a single tertiary referral center (2018-2020). Of those mpMRIs, 32% (n = 116) were centrally reviewed. We compared the detection of csPCa between the non-central-reviewed vs reviewed group. Multivariable logistic regression models (MVA) tested the relationship between mpMRI central review and the detection of csPCa at TBx. RESULTS: The detection of csPCa at TBx in non-central-reviewed vs central-reviewed group was 41 vs 63%, respectively (p = 0.001). The distribution of PI-RADS 2, 3, 4, and 5 at initial assessment vs after mpMRI central review was 0, 37, 47, and 16% vs 39, 9, 35, and 16%, respectively (p < 0.004). Of 43 patients with initial PI-RADS 3 score, respectively 67, 21, and 12, and 0% had a revised PI-RADS score of ≤ 2, 3, 4, and 5. At MVA, mpMRI central review (OR: 1.65, CI 0.85-0.98) was significantly associated with higher csPCa detection at TBx. CONCLUSIONS: We demonstrated that a central review of external mpMRIs may decrease the overcall of equivocal lesions, namely PI-RADS 3, and should be considered to maximize the clinical benefit of TBx in terms of increasing the detection of csPCa and eventually decreasing the rate of unnecessary biopsies.

Beyond diagnosis: is there a role for radiomics in prostate cancer management?

The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.

Quantification of Prostate Cancer Metabolism Using 3D Multiecho bSSFP and Hyperpolarized [1-13 C] Pyruvate: Metabolism Differs Between Tumors of the Same Gleason Grade.

BACKGROUND: Three-dimensional (3D) multiecho balanced steady-state free precession (ME-bSSFP) has previously been demonstrated in preclinical hyperpolarized (HP) 13 C-MRI in vivo experiments, and it may be suitable for clinical metabolic imaging of prostate cancer (PCa). PURPOSE: To validate a signal simulation framework for the use of sequence parameter optimization. To demonstrate the feasibility of ME-bSSFP for HP 13 C-MRI in patients. To evaluate the metabolism in PCa measured by ME-bSSFP. STUDY TYPE: Retrospective single-center cohort study. PHANTOMS/POPULATION: Phantoms containing aqueous solutions of [1-13 C] lactate (2.3 M) and [13 C] urea (8 M). Eight patients (mean age 67 ± 6 years) with biopsy-confirmed Gleason 3 + 4 (n = 7) and 4 + 3 (n = 1) PCa. FIELD STRENGTH/SEQUENCES: 1 H MRI at 3 T with T2 -weighted turbo spin-echo sequence used for spatial localization and spoiled dual gradient-echo sequence used for B0 -field measurement. ME-bSSFP sequence for 13 C MR spectroscopic imaging with retrospective multipoint IDEAL metabolite separation. ASSESSMENT: The primary endpoint was the analysis of pyruvate-to-lactate conversion in PCa and healthy prostate regions of interest (ROIs) using model-free area under the curve (AUC) ratios and a one-directional kinetic model (kP ). The secondary objectives were to investigate the correlation between simulated and experimental ME-bSSFP metabolite signals for HP 13 C-MRI parameter optimization. STATISTICAL TESTS: Pearson correlation coefficients with 95% confidence intervals and paired t-tests. The level of statistical significance was set at P < 0.05. RESULTS: Strong correlations between simulated and empirical ME-bSSFP signals were found (r > 0.96). Therefore, the simulation framework was used for sequence optimization. Whole prostate metabolic HP 13 C-MRI, observing the conversion of pyruvate into lactate, with a temporal resolution of 6 seconds was demonstrated using ME-bSSFP. Both assessed metrics resulted in significant differences between PCa (mean ± SD) (AUC = 0.33 ± 012, kP  = 0.038 ± 0.014) and healthy (AUC = 0.15 ± 0.10, kP  = 0.011 ± 0.007) ROIs. DATA CONCLUSION: Metabolic HP 13 C-MRI in the prostate using ME-bSSFP allows for differentiation between aggressive PCa and healthy tissue. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.