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

Development and validation of risk-stratified biopsy decision pathways incorporating MRI and PSA-derived indicators

OBJECTIVES

Develop risk-adapted conditional biopsy pathways utilizing MRI in combination with prostate-specific antigen (PSA) density (PSAD) and the ratio of free to total PSA (f/tPSA), respectively, to enhance the detection of clinically significant prostate cancer (csPCa) while minimizing 'negative' biopsies in low-risk patients.

METHODS

The Prostate Imaging Reporting and Data System (PI-RADS) category, PSAD, f/tPSA and biopsy-pathology of 1018 patients were collected retrospectively. Subsequently, PSAD and f/tPSA were divided into four intervals, which were then combined with the MRI findings to construct two risk stratification matrix tables. Six biopsy decision pathways were established: three clinical pathways based solely on PSAD and f/tPSA, and three MRI-combined pathways incorporating both PI-RADS and PSA-derived indicators. The biopsy and clinically insignificant PCa (ciPCa) avoidance, csPCa detection rate, and 'negative' biopsies proportion were assessed. Decision curve analysis (DCA) was employed to evaluate the net benefit associated with each pathway.

RESULTS

When reporting PI-RADS 1 - 2, PSAD ≥ 0.20 ng/ml/cm3 or f/tPSA ≤ 0.10 were found to be useful for patient stratification. When reporting PI-RADS 3, PSAD ≥ 0.10 - 0.15 ng/ml/cm3 and f/tPSA ≤ 0.16 - 0.25 were helpful in distinguishing the risk of csPCa. The three MRI-combined pathways showed higher csPCa detection rates (94% to 96%) than the three clinical pathways (85% to 91%); 'MRI + PSAD + f/tPSA' demonstrated a high csPCa detection rate of 94% while maintaining the maximum biopsy avoidance and lowest 'negative' biopsy proportion of 40% and 25%, respectively. The DCA showed significantly higher net benefits for three MRI-combined pathways compared to all clinical pathways.

CONCLUSIONS

The integration of MRI and PSA-derived indicators enables effective patient risk stratification, thereby providing valuable decision-making pathways to enhance the management of csPCa while minimizing 'negative' biopsies.

An online national quality assessment survey of prostate MRI reading: interreader variability in prostate volume measurement and PI-RADS classification

Background

High-quality assessment of prostate MRI is fundamental in both clinical practice and screening. There is a lack of national level data on variability in prostate volume measurement and PI-RADS assessment. Methods of quality assurance need to be developed.

Methods

All Swedish radiology departments were invited to participate in an external quality assurance of prostate MRI reading. Ten prostate MRI cases were selected by an expert panel to reflect common findings. Readers measured whole gland volume (ellipsoid formula method) and assigned a PI-RADS score in a web-based PACS with full clinical functionality. Expert consensus was used as reference standard. Descriptive statistics were used to show the distribution of volume measurements and PSA density. Reader agreement was assessed using percentages and kappa scores. A feedback document was sent to all participants upon completion of the quality assurance program.

Results

Forty-three radiologists representing 17 departments read at least 7 out of 10 cases. The median difference in prostate volume assessment compared to the reference volume for the 10 cases ranged from -23 mL to + 6 mL. Per case agreement ranged from 33 % to 86 % for the assigned PI-RADS score and from 35 % to 98 % for PI-RADS 1-3 versus PI-RADS 4-5. Interreader agreement was moderate with a median kappa score of 0.53 (IQR 0.48-0.62).

Conclusion

This online model for national quality assurance programs was feasible. Rather large per-case reader variations in prostate volume assessment and PI-RADS scoring were shown. To reduce variability in clinical practice, systematic interreader comparisons should be encouraged.

Two-parametric prescan calibration of gradient-induced sampling errors for rosette MRI

PURPOSE

The aim of this study was to develop a simple, robust, and easy-to-use calibration procedure for correcting misalignments in rosette MRI k-space sampling, with the objective of producing images with minimal artifacts.

METHODS

Quick automatic calibration scans were proposed for the beginning of the measurement to collect information on the time course of the rosette acquisition trajectory. A two-parameter model was devised to match the measured time-varying readout gradient delays and approximate the actual rosette sampling trajectory. The proposed calibration approach was implemented, and performance assessment was conducted on both phantoms and human subjects.

RESULTS

The fidelity of phantom and in vivo images exhibited significant improvement compared with uncorrected rosette data. The two-parameter calibration approach also demonstrated enhanced precision and reliability, as evidenced by quantitativeT 2 * $$ {\mathrm{T}}_2^{\ast } $$ relaxometry analyses.

CONCLUSION

Adequate correction of data sampling is a crucial step in rosette MRI. The presented experimental results underscore the robustness, ease of implementation, and suitability for routine experimental use of the proposed two-parameter rosette trajectory calibration approach.

Giving the prostate the boost it needs: Spiral diffusion MRI using a high-performance whole-body gradient system for high b-values at short echo times

PURPOSE

To address key issues of low SNR and image distortions in prostate diffusion MRI (dMRI) by means of using strong gradients, single-shot spiral readouts and an expanded encoding model for image reconstruction.

METHODS

Diffusion-weighted spin echo imaging with EPI and spiral readouts is performed on a whole-body system equipped with strong gradients (up to 250 mT/m). An expanded encoding model including static off-resonance, coil sensitivities, and magnetic field dynamics is employed for image reconstruction. The acquisitions are performed on a phantom and in vivo (one healthy volunteer and one patient with prostate cancer). The resulting images are compared to conventional dMRI EPI with navigator-based image reconstruction and assessed in terms of their congruence, SNR, tissue contrast, and quantitative parameters.

RESULTS

Using the expanded encoding model, high-quality images of the prostate gland are obtained across all b-values (up to 3 ms/μm2), clearly outperforming the results obtained with conventional image reconstruction. Compared to EPI, spiral imaging provides an SNR gain up to 45% within the gland and even higher in the lesion. In addition, prostate dMRI with single-shot spirals at submillimeter in-plane resolution (0.85 mm) is accomplished.

CONCLUSION

The combination of strong gradients and an expanded encoding model enables imaging of the prostate with unprecedented image quality. Replacing the commonly used EPI with spirals provides the inherent benefit of shorter echo times and superior readout efficiency and results in higher SNR, which is in particular relevant for considered applications.

Chemical shift-encoded multishot EPI for navigator-free prostate DWI

PURPOSE

DWI is an important contrast for prostate MRI to enable early and accurate detection of cancer. This study introduces a Dixon 3-shot-EPI protocol with structured low-rank reconstruction for navigator-free DWI. The aim is to overcome the limitations of single-shot EPI (ssh-EPI), such as geometric distortions and fat signal interference, while addressing the motion-induced phase variations of multishot EPI and simultaneously allowing water/fat separation.

METHODS

DWI data were acquired from 7 healthy volunteers using both Dixon 3-shot EPI and standard fat-suppressed ssh-EPI with similar scan times for comparison. Two readers evaluated image quality using a 5-point Likert scale regarding different aspects. The ADC values were quantitatively compared between protocols. To show feasibility in a clinical setting, the protocol was applied to two patients.

RESULTS

From the reader scores, Dixon 3-shot EPI significantly reduced geometric distortion compared with ssh-EPI (p < 0.01), with no significant differences in edge definition, SNR, or overall image quality. There was no significant difference in ADC values between the two protocols. However, the Dixon multishot-EPI protocol offered advantages such as self-referenced B0 map-driven distortion correction, greater flexibility in imaging parameters, and superior fat suppression. In the patient data, the lesion could be clearly identified in both protocols and on the associated ADC maps.

CONCLUSION

The proposed Dixon 3-shot-EPI protocol shows promise as an alternative to ssh-EPI for prostate DWI, providing reduced geometric distortions and improved fat suppression. It addresses common DWI issues based on EPI and enhances scanning flexibility, indicating potential for optimized imaging.

Urologic Imaging of the Prostate: Cancer and Mimics

This article provides a comprehensive overview of prostate cancer imaging, including detection of clinically significant cancer and initial staging. The role of multiparametric MRI in detection and local staging is discussed, along with the use of conventional imaging and advanced techniques such as Prostate-Specific Membrane Antigen-Positron Emission Tomography (PSMA-PET) for staging of nodal and distant metastases. The article also highlights the importance of differentiating benign prostatic conditions from prostate cancer on imaging to improve diagnostic accuracy and reduce false-positive interpretations.

Investigation of radiomic features on MRI images to identify extraprostatic extension in prostate cancer

BACKGROUND AND OBJECTIVE

Detection of extraprostatic extension (EPE) preoperatively is of critical importance in the context of prostate cancer (PCa) management and outcomes. This study aimed to characterize the radiomic features of malignant prostate lesions based on multi-paramagnetic magnetic resonance imaging (mpMRI).

METHODS

We analyzed 20 patients who underwent mpMRI followed by radical prostatectomy. Two experienced radiologists manually segmented the 3D lesions using the T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) imaging sequences. A total of 210 radiomic features were extracted from each lesion. We used the Recursive Feature Elimination with Cross-Validation to select key features. Using the selected radiomic features, we developed a Multilayer Perceptron (MLP) neural network to classify the EPE and non-EPE lesions. The pathology results were accepted as gold standard for EPE. We measured the performance of the classifier, calculating the area-under-curve (AUC), sensitivity, and specificity.

RESULTS

A total of 25 lesions were segmented, including 12 lesions with EPE and 13 lesions without EPE, based on the pathology reports. We selected 18 radiomic features (18/210). The MLP classifier using these features provided a good sensitivity (0.75), specificity (0.79), and AUC of 0.82, 95 % CL [0.59 - 0.96] in identifying the EPE lesions.

CONCLUSIONS

This pilot study presents 18 radiomic features derived from T2-weighted and ADC images and demonstrates their potential in the preoperative prediction of EPE in PCa using an MLP model.

Morphologic Urologic Imaging

Imaging plays an important role in the evaluation of the urologic organs. Radiographs, fluoroscopy, ultrasound, computed tomography, and MRI are all modalities that can be used to answer various clinical questions. In this article we provide an overview of the most common imaging examinations performed using these modalities to assess the urologic structures.

Application of high-density 2D receiver coil arrays for improved SNR in prostate MRI

PURPOSE

To study if adaptive image receive (AIR) receiver coil elements can be configured into a 2D array with high (>45% by diameter) element-to-element overlap, allowing improved SNR at depth (0.7-1.5× element diameter) versus conventional (20%) overlap.

METHODS

An anterior array composed of twenty 10-cm diameter elements with 45% overlap arranged into a 4 × 5 grid and a similar 3 × 7 twenty-one-element posterior array were constructed. SNR and g-factor were measured in a pelvic phantom using the new high-density (HD) arrays (41 total elements) and compared to vendor AIR-based arrays (30 total elements) with conventional overlap. T2-weighted fast-spin-echo (T2SE) images acquired using both arrays were compared in 20 subjects. SNR was estimated in vivo. Results were compared blindly by three uroradiologists using a five-point scale. Images using the HD arrays were also compared to a set of images acquired over a range of acceleration factors (R = 2.0, 2.5, 3.0) with the conventional arrays.

RESULTS

SNR within the phantom was on average 15% higher for R = 1.0, 1.5, and 2.0 using the HD arrays. Across the 20 subjects SNR within the prostate was 11% higher and assessed radiologically as significantly higher (p < 0.001) for the HD versus conventional arrays. At all acceleration factors the new HD arrays outperformed the conventional arrays (p ≤ 0.01), allowing increased R for similar SNR.

CONCLUSION

AIR elements can be configured into 2D arrays with high (45%) element-to-element overlap, consistently providing increased SNR at depth versus arrays with conventional (20%) overlap. The SNR improvement allows increased acceleration in T2SE prostate MRI.

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.

Biparametric MRI-based radiomics for noninvastive discrimination of benign prostatic hyperplasia nodules (BPH) and prostate cancer nodules: a bio-centric retrospective cohort study

To investigate the potential of an MRI-based radiomic model in distinguishing malignant prostate cancer (PCa) nodules from benign prostatic hyperplasia (BPH)-, as well as determining the incremental value of radiomic features to clinical variables, such as prostate-specific antigen (PSA) level and Prostate Imaging Reporting and Data System (PI-RADS) score. A restrospective analysis was performed on a total of 251 patients (training cohort, n = 119; internal validation cohort, n = 52; and external validation cohort, n = 80) with prostatic nodules who underwent biparametric MRI at two hospitals between January 2018 and December 2020. A total of 1130 radiomic features were extracted from each MRI sequence, including shape-based features, gray-level histogram-based features, texture features, and wavelet features. The clinical model was constructed using logistic regression analysis. Radiomic models were created by comparing seven machine learning classifiers. The useful clinical variables and radiomic signature were integrated to develop the combined model. Model performance was assessed by receiver operating characteristic curve, calibration curve, decision curve, and clinical impact curve. The ratio of free PSA to total PSA, PSA density, peripheral zone volume, and PI-RADS score were independent determinants of malignancy. The clinical model based on these factors achieved an AUC of 0.814 (95% CI: 0.763-0.865) and 0.791 (95% CI: 0.742-840) in the internal and external validation cohorts, respectively. The clinical-radiomic nomogram yielded the highest accuracy, with an AUC of 0.925 (95% CI: 0.894-0.956) and 0.872 (95% CI: 0.837-0.907) in the internal and external validation cohorts, respectively. DCA and CIC further confirmed the clinical usefulness of the nomogram. Biparametric MRI-based radiomics has the potential to noninvasively discriminate between-BPH and malignant PCa nodules, which outperforms screening strategies based on PSA and PI-RADS.

Semi-standardized evaluation of extraprostatic extension and seminal vesicle invasion with [18F]PSMA-1007 PET/CT: a comparison to MRI using histopathology as reference

BACKGROUND

Positron emission tomography/computed tomography (PET/CT) with prostate specific membrane antigen ligands (PSMA) is established for use in primary staging of prostate cancer to screen for metastases. It has also shown promise in local tumor staging, including detection of extraprostatic extension (EPE) and seminal vesicle invasion (SVI). Previous studies have shown high heterogeneity in methods and results. Our aim was to compare [18F]PSMA-1007 PET/CT to magnetic resonance imaging (MRI) in evaluation of EPE and SVI, building on a previously described method for standardized evaluation. We retrospectively included 124 patients who had undergone MRI, PSMA PET/CT and prostatectomy. PSMA PET/CT images were evaluated by two nuclear medicine physicians. Using a standardized method, they measured length of capsular contact (LCC) and assessed EPE and SVI visually with the use of 5-point Likert scales. A radiologist evaluated MRI images using criteria based on Prostate Imaging-Reporting and Data System version and incorporating LCC measurement and Likert scales. We evaluated diagnostic performance with histopathology as reference, and the interrater reliability of the PET evaluations.

RESULTS

The sensitivity and specificity for detecting EPE with the quantitative LCC method for PSMA PET/CT was 0.46/0.91, for the visual method 0.28/0.82 and for the combination of the two 0.54/0.76. AUC in ROC analysis for the LCC method was 0.70. For MRI the sensitivity and specificity were 0.80/0.64. For SVI, PET/CT and MRI had sensitivity and specificity of 0.14/1.0 and 0.50/0.92 respectively. The intraclass correlation coefficient for the PET LCC measurement was 0.68, the kappa values for the visual Likert scales for PET were 0.53 for EPE and 0.63 for SVI.

CONCLUSIONS

In this study, we attempted to standardize quantitative and qualitative PSMA PET/CT evaluation of EPE and SVI and compare the method with MRI. MRI had a higher sensitivity for EPE while PSMA had a higher specificity. For SVI, both methods had high specificity. The interrater reliability for the PSMA PET/CT evaluations was moderate to substantial.

Natural history of PIRADS-2 lesions on serial multiparametric magnetic resonance imaging: Real-life data from an Academic Center

INTRODUCTION/BACKGROUND

The natural history of prostate imaging reporting and data system (PIRADS) score 2 lesions on serial mpMRIs is largely unknown. Herein, we aimed to evaluate the patients with PIRADS-2 index lesions by using serial mpMRI scans to reveal the rates of mpMRI upgrade in PIRADS score and prostate cancer (PCa) detection.

METHODS/MATERIALS

All mpMRI scans with a PIRADS-2 index lesion from our mpMRI database were evaluated retrospectively. Data from 214 biopsy-naïve patients with a PIRADS-2 index lesion on the initial mpMRI who then underwent at least 1 follow-up mpMRI were reevaluated by an experienced uroradiologist and only those (n = 172) who had a PIRADS-2 index lesion on the initial mpMRI according to PIRADS v2.1 were included in the study. mpMRI progression was defined as the detection of any PIRADS ≥3 lesion at follow-up mpMRI. Histopathological results were evaluated in patients undergoing biopsy upon mpMRI progression.

RESULTS

A total of 172 patients with a mean age of 60.1 ± 8.6 years were evaluated. The median PSA at baseline mpMRI was 4.7 (IQR; 3.3-6.7) ng/dl. Overall mpMRI progression was detected in 54 patients (31.4%), 37 were upgraded to PIRADS-3, 16 to PIRADS-4, and one to PIRADS-5. Multivariate logistic regression analysis revealed that a PSA increase of ≥25% during follow-up was the only predictor of mpMRI upgrade (P = 0.019, OR: 2.384). 30 out of 54 patients underwent a prostate biopsy and PCa was detected in 15 patients; 5 with ISUP grade 1, 10 with ISUP grade 2.

CONCLUSIONS

Almost half of the patients with a PIRADS-2 index lesion were upgraded to PIRADS ≥3 when evaluated with serial mpMRI when a PSA increase of ≥25% was observed during follow-up. PCa was detected in half of the patients who underwent a biopsy. Serial mpMRI can be recommended when monitoring patients with elevating PSA ≥25%, a prostate biopsy can be considered upon a mpMRI progression.

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.

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.

Multimodal approach to optimize biopsy decision-making for PI-RADS 3 lesions on multiparametric MRI

PURPOSE

To develop and evaluate a multimodal approach including clinical parameters and biparametric MRI-based artificial intelligence (AI) model for determining the necessity of prostate biopsy in patients with PI-RADS 3 lesions.

METHODS

This retrospective study included a prospectively recruited patient cohort with PI-RADS 3 lesions who underwent prostate MRI and MRI/US fusion-guided biopsy between April 2019 and February 2024 in a single institution. The study examined demographic data, PSA and PSA density (PSAD) levels, prostate volumes, prospective PI-RADS v2.1-compliant interpretations of a genitourinary radiologist, lesion characteristics, history of prior biopsies, and AI evaluations, focusing mainly on the detection of clinically significant prostate cancer (csPCa) (International Society of Urological Pathology grade group ≥2) on MRI/US fusion-guided biopsy. The AI model lesion segmentations were compared to manual segmentations and biopsy results. The statistical methods employed included Fisher's exact test and logistic regression.

RESULTS

The cohort was comprised of 248 patients with 312 PI-RADS 3 lesions in total (n = 268 non-csPCa, n = 44 csPCa). The AI model's negative predictive value (NPV) was 89.2 % for csPCa in all lesions. In patient-level analysis, the NPV was 91.2 % for patients with a highest PI-RADS score of 3. PSAD was a significant predictor of csPCa (odds ratio = 5.8, p = 0.038). Combining AI and PSAD, where AI correctly mapped a lesion or PSAD ≥0.15 ng/mL2, achieved higher sensitivity (77.8 %) while maintaining a high NPV (93.1 %).

CONCLUSION

Combining AI and PSAD has the potential to enhance biopsy decision-making for PI-RADS 3 lesions by minimizing missed csPCa occurrences and reducing unnecessary biopsies.

A diffusion-prepared reduced FOV sequence for prostate MRI near metallic implants

PURPOSE

To enable diffusion weighted imaging in prostate patients with metallic total hip replacements in clinically feasible scan times for prostate cancer screening, and avoid distortion and dropout artifacts present in the conventionally used Echo Planar Imaging (EPI).

METHODS

A reduced field of view (FOV) diffusion-prepared sequence that is robust to the B 0 $$ {\kern0em }_0 $$ inhomogeneities produced by total hip replacements was achieved using high radiofrequency (RF) bandwidth pulses and manipulation for stimulated echo pathways. The reduced FOV along the A/P direction was obtained using slice-select gradient reversal, and the prepared magnetization was imaged with a three-dimensional RF-spoiled gradient echo readout. The sequence was validated in phantom experiments, in vivo in healthy volunteers with and without total hip replacements, and in vivo in patients undergoing a standard MRI prostate exam.

RESULTS

The proposed sequence is robust to shading and distortion artifacts that are encountered by standard diffusion-weighted EPI in the presence of moderate off-resonance. Apparent diffusion coefficient estimates obtained by the proposed sequence were comparable to those obtained with diffusion-weighted EPI.

CONCLUSION

Acquisition of distortionless diffusion weighted images of the prostate is feasible in patients with total hip replacements on conventional, whole-body 3T MRI, using a b-value of 800s / mm 2 $$ \mathrm{s}/{\mathrm{mm}}^2 $$ and nominal resolution of 1.7× $$ \times $$ 1.7× $$ \times $$ 4 mm3 in scan times of 6 min.

Quantitative 3-T Multiparametric MRI Parameters as Predictors of Aggressive Prostate Cancer

Purpose To determine which quantitative 3-T multiparametric MRI (mpMRI) parameters correlate with and help predict the presence of aggressive large cribriform pattern (LCP) and intraductal carcinoma (IDC) prostate cancer (PCa) at whole-mount histopathology (WMHP). Materials and Methods This retrospective study included 130 patients (mean age ± SD, 62.6 years ± 7.2; 100% male) with 141 PCa lesions who underwent preoperative prostate 3-T mpMRI, radical prostatectomy, and WMHP between January 2019 and December 2022. Lesions at WMHP were matched to 3-T mpMRI lesions with American College of Radiology Prostate Imaging Reporting and Data System version 2.1 scores of at least 3 or higher, and the following parameters were derived: apparent diffusion coefficient (ADC), volume transfer constant, rate constant, and initial area under the curve (iAUC). Each lesion was categorized into three subcohorts with increasing aggressiveness: LCP negative and IDC negative (subcohort 1), LCP positive and IDC negative (subcohort 2), and LCP positive and IDC negative (subcohort 3). Analysis of variance was performed to assess differences, Jonckheere test was performed to establish trends, and a classification and regression tree (CART) was used to establish a prediction model. Results Of the 141 total lesions, there were 41 (29.1%), 49 (34.8%), and 51 (36.2%) lesions in subcohorts 1, 2, and 3, with mean ADCs of 892 × 10-6 mm2/sec ± 20, 826 × 10-6 mm2/sec ± 209, and 763 × 10-6 mm2/sec ± 163 (P = .007) and mean iAUCs of 5.4 mmol/L/sec ± 2.5, 6.7 mmol/L/sec ± 3.0, and 6.9 mmol/L/sec ± 3.5 (P = .04), respectively. ADC was negatively correlated (P = .004), and rate constant and iAUC were positively correlated (P = .048 and P = .04, respectively) with increasing histologic PCa aggressiveness. The CART model correctly allocated 39.0%, 24.5%, and 84.3% of PCa lesions to subcohorts 1, 2, and 3, respectively. Conclusion Quantitative 3-T mpMRI parameters significantly correlated with and helped predict aggressive LCP and IDC PCa at WMHP. Keywords: Prostate, MRI, Pathology © RSNA, 2025.

Adaptive window adjustment with boundary DoU loss for cascade segmentation of anatomy and lesions in prostate cancer using bpMRI

Accurate segmentation of prostate anatomy and lesions using biparametric magnetic resonance imaging (bpMRI) is crucial for the diagnosis and treatment of prostate cancer with the aid of artificial intelligence. In prostate bpMRI, different tissues and pathologies are best visualized within specific and narrow ranges for each sequence, which have varying requirements for image window settings. Currently, adjustments to window settings rely on experience, lacking an efficient method for universal automated adjustment. Hence, we propose an Adaptive Window Adjustment (AWA) module capable of adjusting window settings to accommodate different image modalities, sample data, and downstream tasks. Moreover, given the pivotal role that loss functions play in optimizing model performance, we investigate the performance of different loss functions in segmenting prostate anatomy and lesions. Our study validates the superiority of the Boundary Difference over Union (DoU) Loss in these tasks and extends its applicability to 3D medical imaging. Finally, we propose a cascaded segmentation approach tailored for prostate anatomy and lesions. This approach leverages anatomical structure information to enhance lesion segmentation accuracy. Experimental results on the Prostate158, ProstateX, and PI-CAI datasets confirm the effectiveness of the proposed methods. Our code of methods is available at https://github.com/WenHao-L/AWA_BoundaryDoULoss.

Minimizing prostate diffusion weighted MRI examination time through deep learning reconstruction

PURPOSE

To study the diagnostic image quality of high b-value diffusion weighted images (DWI) derived from standard and variably reduced datasets reconstructed with a commercially available deep learning reconstruction (DLR) algorithm.

MATERIALS AND METHODS

This was a retrospective study of 52 patients undergoing conventional prostate MRI with raw image data reconstructed using both conventional 2D Cartesian and DLR algorithms. Simulated shortened DWI acquisition times were performed by reconstructing images using DLR datasets harboring a reduced number of excitations (NEX). Two radiologists independently evaluated the image quality using a 4-point Likert scale. Signal-to-noise ratio (SNR) analysis was performed for the entire cohort and a subset of patients identified as having clinically significant prostate cancer identified at MRI, and later confirmed by pathology.

RESULTS

Radiologists perceived less image noise for both restricted and large field of view (FOV) standard NEX dataset DLR diffusion images compared to conventionally reconstructed images with good interreader agreement. Diagnostic image quality was maintained for all DLR images using variably reduced NEX compared to conventionally reconstructed images employing the standard NEX. Improved signal to noise was observed for the restricted FOV DLR images compared to conventional reconstruction using standard NEX. DLR diffusion images derived from reduced NEX datasets translated to time reductions of up to 68 % and 39 % for the restricted and large FOV series acquisitions, respectively.

CONCLUSION

DLR of diffusion weighted images can reduce image noise at standard NEX and potentially reduce prostate MRI exam time when utilizing reduced NEX datasets without sacrificing diagnostic image quality.

High-resolution Diffusion-weighted Imaging of the Prostate Using Multiplexed Sensitivity-encoding: Comparison with the Conventional and Reduced Field-of-view Techniques

PURPOSE

To compare objective and subjective image quality, lesion conspicuity, and apparent diffusion coefficient (ADC) of high-resolution multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) with conventional DWI (c-DWI) and reduced FOV DWI (rFOV-DWI) in prostate MRI.

METHODS

Forty-seven patients who underwent prostate MRI, including c-DWI, rFOV-DWI, and MUSE-DWI, were retrospectively evaluated. SNR and ADC of normal prostate tissue and contrast-to-noise ratio (CNR) and ADC of prostate cancer (PCa) were measured and compared between the three sequences. Image quality and lesion conspicuity were independently graded by two radiologists using a 5-point scale and compared between the three sequences.

RESULTS

The SNR of normal prostate tissue was significantly higher with rFOV-DWI than with the other two DWI techniques (P ≤ 0.01). The CNR of the PCa was significantly higher with rFOV-DWI than with MUSE-DWI (P < 0.05). The ADC of normal prostate tissue measured by rFOV-DWI was lower than that measured by MUSE-DWI and c-DWI (P < 0.01), while there was no difference in the ADC of cancers. In the qualitative analysis, MUSE-DWI showed significantly higher scores than rFOV-DWI and c-DWI for visibility of anatomy and overall image quality in both readers, and significantly higher scores for distortion in one of the two readers (P < 0.001). There was no difference in lesion conspicuity between the three sequences.

CONCLUSION

High-resolution MUSE-DWI showed higher image quality and reduced distortion compared to c-DWI, while maintaining a wide FOV and similar ADC quantification, although no difference in lesion conspicuity was observed.

Explainable artificial intelligence-driven prostate cancer screening using exosomal multi-marker based dual-gate FET biosensor

Prostate Imaging Reporting and Data System (PI-RADS) score, a reporting system of prostate MRI cases, has become a standard prostate cancer (PCa) screening method due to exceptional diagnosis performance. However, PI-RADS 3 lesions are an unmet medical need because PI-RADS provides diagnosis accuracy of only 30-40% at most, accompanied by a high false-positive rate. Here, we propose an explainable artificial intelligence (XAI) based PCa screening system integrating a highly sensitive dual-gate field-effect transistor (DGFET) based multi-marker biosensor for ambiguous lesions identification. This system produces interpretable results by analyzing sensing patterns of three urinary exosomal biomarkers, providing a possibility of an evidence-based prediction from clinicians. In our results, XAI-based PCa screening system showed a high accuracy with an AUC of 0.93 using 102 blinded samples with the non-invasive method. Remarkably, the PCa diagnosis accuracy of patients with PI-RADS 3 was more than twice that of conventional PI-RADS scoring. Our system also provided a reasonable explanation of its decision that TMEM256 biomarker is the leading factor for screening those with PI-RADS 3. Our study implies that XAI can facilitate informed decisions, guided by insights into the significance of visualized multi-biomarkers and clinical factors. The XAI-based sensor system can assist healthcare professionals in providing practical and evidence-based PCa diagnoses.

MRI-based Radiomics for Predicting Prostate Cancer Grade Groups: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies

RATIONALE AND OBJECTIVES

Prostate cancer (PCa) is the second most common cancer among men and a leading cause of cancer-related mortalities. Radiomics has shown promising performances in the classification of PCa grade group (GG) in several studies. Here, we aimed to systematically review and meta-analyze the performance of radiomics in predicting GG in PCa.

MATERIALS AND METHODS

Adhering to PRISMA-DTA guidelines, we included studies employing magnetic resonance imaging-derived radiomics for predicting GG, with histopathologic evaluations as the reference standard. Databases searched included Web of Sciences, PubMed, Scopus, and Embase. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and METhodological RadiomICs Score (METRICS) tools were used for quality assessment. Pooled estimates for sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the curve (AUC) were calculated. Cochran's Q and I-squared tests assessed heterogeneity, while meta-regression, subgroup analysis, and sensitivity analysis addressed potential sources. Publication bias was evaluated using Deek's funnel plot, while clinical applicability was assessed with Fagan nomograms and likelihood ratio scattergrams.

RESULTS

Data were extracted from 43 studies involving 9983 patients. Radiomics models demonstrated high accuracy in predicting GG. Patient-based analyses yielded AUCs of 0.93 for GG≥2, 0.91 for GG≥3, and 0.93 for GG≥4. Lesion-based analyses showed AUCs of 0.84 for GG≥2 and 0.89 for GG≥3. Significant heterogeneity was observed, and meta-regression identified sources of heterogeneity. Radiomics model showed moderate power to exclude and confirm the GG.

CONCLUSION

Radiomics appears to be an accurate noninvasive tool for predicting PCa GG. It improves the performance of standard diagnostic methods, enhancing clinical decision-making.

Three-dimensional spatial localization and volume estimation of prostate tumors using 18F-PSMA-1007 PET/CT versus multiparametric MRI

PURPOSE

Fluorine-18 prostate-specific membrane antigen-1007 positron emission tomography/computed tomography (18F-PSMA-1007 PET/CT) has been shown to be superior to multiparametric magnetic resonance imaging (MRI) for the locoregional staging of intermediate-risk and high-risk prostate tumors. This study aims to evaluate whether it is also superior in estimating tumor parameters, such as three-dimensional spatial localization and volume.

METHODS

134 participants underwent 18F-PSMA-1007 PET/CT and MRI prior to radical prostatectomy as part of the validating paired-cohort Next Generation Trial (NCT05141760). MRI, 18F-PSMA-1007 PET/CT, and final pathology were independently assessed by blinded radiologists, nuclear medicine physicians, and pathologists, respectively. Individual tumor nodules were measured in three dimensions and cognitively registered to 38 segment prostate diagrams as per PI-RADSv2.1. Correct spatial localization was compared using McNemar test and estimation of tumor volumes were compared using linear regression and partial F-test.

RESULTS

286 tumor nodules were identified by final histopathology. 18F-PSMA-1007 PET/CT was superior to MRI for correct localization (186 [65.0%] vs 134 [46.9%], p < 0.001) and tumor volume estimation (R2 = 0.545 vs 0.431, p < 0.001). Larger tumors and higher Gleason Grade Group (GGG) were associated with correct localization by 18F-PSMA-1007 PET/CT (OR = 2.05, p < 0.001 for tumor volume and OR = 4.92, p < 0.01 for ≥ GGG3) and MRI (OR = 1.81, p < 0.001 for tumor volume and OR = 11.67, p < 0.001 for ≥ GGG3).

CONCLUSION

18F-PSMA-1007 PET/CT outperforms MRI for determination of three-dimensional spatial localization and volume of prostate tumors. These findings support the use of 18F-PSMA-1007 PET/CT prior to definitive treatment of localized prostate cancers.

Visibility of mpMRI region of interest on ultrasound during cognitive fusion targeted biopsy predicts prostate cancer detection: a prospective single-center study

PURPOSE

The purpose of this study was to evaluate the nature of ultrasound characteristics during mpMRI/TRUS cognitive fusion targeted biopsy (cTB).

METHODS

From 2023 to 2024, data from 502 lesions in 426 men who underwent targeted combined systematic biopsy were analyzed. All lesions had a Prostate Imaging Reporting and Data System (PI-RADS) score of ≥ 3. The primary endpoint was the detection rate of prostate cancer (PCa) according to the PI-RADS score/ultrasound characteristics, categorized as benign or invisible (Bi), hypoechoic only (Ho), and hypoechoic with microcalcification (Hm), assessed through cross-stratification. The secondary endpoints included the distribution of ultrasound characteristics across PI-RADS scores, prostate zones, and histological types. Finally, associations between ultrasound characteristics and clinically significant PCa (csPCa) were assessed using multivariate logistic regression analysis (MVA).

RESULTS

Among lesions, 233 (46%) were Bi, 210 (42%) Ho, and 59 (12%) Hm. First, Bi lesions had a 64% (103/161) non-cancer rate in PI-RADS 3, while Ho + Hm lesions showed the highest csPCa rate in PI-RADS 5 at 82% (102/124). Additionally, Ho + Hm lesions were predominantly observed in PI-RADS 5 (92% [114/124]) and in the peripheral zone (64% [179/278]). Notably, Hm lesions had a significantly higher percentage of cribriform morphology than Ho lesions (32% vs. 14%, P = 0.001). Finally, MVA confirmed Ho ([Ref Bi] OR 4.95, P < 0.001) and Hm ([Ref Bi] OR 27.7, P < 0.001) as independent predictors of csPCa.

CONCLUSION

In cTB, the identification of Ho and Hm lesions on TRUS enhances the diagnostic yield of csPCa by facilitating more precise localization compared to Bi lesions.

CLINICAL TRIAL REGISTRATION

No. 2023-272-002, July 14, 2023.

PI-RADS in Predicting csPCa: A Comparison Between Academic and Nonacademic Centers

INTRODUCTION

The introduction of multiparametric prostate magnetic resonance imaging (mpMRI) has revolutionized prostate cancer (PCa) diagnosis, enhancing the localization of clinically significant prostate cancer (csPCa) and guiding targeted biopsies. However, significant disparities in the execution, interpretation, and reporting of prostate MRI examinations across centers necessitate greater standardization and accuracy. This study compares the diagnostic efficacy of mpMRI from academic and nonacademic centers in detecting csPCa and identifies factors associated with csPCa detection.

MATERIALS AND METHODS

Between July 2018 and October 2023, we prospectively followed 810 men at SS. Annunziata Hospital of Chieti who underwent MRI/US fusion biopsies due to elevated prostate-specific antigen (PSA) and/or abnormal digital rectal examination (DRE). Patients with mpMRI-documented suspicious lesions classified as PI-RADS ≥ 3 were included. Patients were divided into two groups based on the source of their mpMRI (academic or nonacademic centers). All biopsies were conducted using the MRI/US fusion technique. Clinical, mpMRI, and pathological data were collected and analyzed. Statistical analyses were performed using R software.

RESULTS

The cohort included 354 patients from academic centers and 456 from nonacademic centers. There were no significant differences in patient demographics, such as age and PSA levels, between the groups. Patients at academic centers were more likely to receive a higher number of elevated PI-RADS scores compared to those at nonacademic centers (PI-RADS > 3: 72.6% vs. 62.3%, p = 0.003). Histopathological analysis revealed no significant differences in the ISUP grade distribution between groups. Increased age, PSA levels, and positive DRE were significantly associated with higher odds of detecting csPCa. Median PSA density was significantly higher in patients with csPCa compared to those without csPCa (0.14 vs. 0.11 ng/mL/cm³, p < 0.001). Academic centers exhibited a higher odds ratio for csPCa detection in patients with PI-RADS scores > 3 compared to nonacademic centers.

CONCLUSION

Our study highlights significant variability in PI-RADS score assignments between academic and nonacademic centers, affecting csPCa detection rates. This variability underscores the need for greater standardization in PI-RADS scoring to reduce disparities and improve diagnostic uniformity across centers.

Is ipsilateral systematic biopsy combined with targeted biopsy the optimal substitute for bilateral systematic biopsy combined with targeted biopsy: A systematic review and meta-analysis

BACKGROUND

The current standard prostate biopsy method, which combine systematic biopsy (SB) with targeted biopsy (TB), has shortcomings such as overdiagnosis and overtreatment. To evaluate the effectiveness of ipsilateral systematic biopsy (ips-SB) combined with targeted biopsy (ips-SB+TB) and contralateral SB (con-SB) combined with TB (con-SB+TB) as potential alternatives to SB+TB.

METHODS

A comprehensive literature search was conducted in Cochrane, Embase, Ovid, and PubMed databases until September 2024. 2,732 references were identified, and 11 records were included.

MAIN FINDINGS

The study included a total of 5,249 patients and revealed that ips-SB+TB detected slightly less PCa than SB+TB with a relative risk (RR) of 0.95 (95% CI 0.91, 1.00), P = 0.05. In terms of csPCa detection, ips-SB+TB showed a comparable detection rate with SB+TB (RR 0.98 [95% CI 0.94, 1.01], P = 0.60). There was a statistically significant difference in csPCa detection between con-SB+TB and SB+TB (RR 0.92 [95% CI 0.86, 0.99], P = 0.02). The detection rates of clinically insignificant PCa (ciPCa) were comparable between con-SB+TB vs. SB+TB (con-SB+TB vs. SB+TB: RR 0.90 [95% CI 0.79, 1.04], P = 0.15). However, fewer ciPCa cases were detected in ips-SB+TB compared to SB+TB (RR 0.86 [95% CI 0.75, 0.99], P = 0.04).

CONCLUSIONS

In this review, our analysis highlights ips-SB+TB has the comparable detection efficiency of PCa and csPCa compared to SB+TB, and its potential to be the substitute of the SB+TB with less cores and less detection of ciPCa.

Is there a comparable Mp-MRI for incidental prostate uptake on 18 F-FDG PET/CT?

PURPOSE

Although 18 F-FDG-PET/CT is helpful in defining many types of cancer, localized prostate cancer should not be treated with this technique. This study describes the use of multi-parametric MRI (mpMRI) to characterize incidental 18 F-FDG uptake in the prostate.

METHODS AND MATERIALS

While 18 F-FDG-PET/CT is useful for characterizing a variety of cancers, it is not advised for prostate cancer that is localized. This work investigates the use of mpMRI to describe incidental 18 F-FDG uptake in the prostate.mpMRI included T2-weighted (T2W), dynamic contrast enhancement (DCE), and apparent diffusion coefficient (ADC) sequences. Patients were classified according to PI-RADS (Prostate Imaging Reporting and Data System) version 2.1 by an experienced uroradiologist, and 18 F-FDG-PET was evaluated to determine whether the area of involvement on CT had a counterpart in mpMRI. A biopsy was performed on 30 of the 92 patients. These patients' maximum standardized uptake values (SUVmax) 6 < and ≥ 6, PS(PSA) density 0.15 < and ≥ 0.15, PSA level, uptake pattern (focal involvement/diffuse involvement), and PI-RADS scores were compared. P < .05 was considered statistically significant. Logistic regression was used to analyze PI-RADS score groups age, PSA, PSA density and SUVmax.

RESULTS

In the study, 92 patients with incidental 18 F-FDG-PET/CT prostate uptake were examined. Median age was 66, PSA median was 3.6 ng/ml (range: 0-3198 ng/ml). Notably, in 70.6% of cases, PET/CT uptake didn't correlate with mp-MRI findings. Among PI-RADS 3-4-5 patients (29.3%), there was a correlation. Biopsies in 30 patients revealed 43.3% benign, 56.7% malignant. Significant differences between benign and malignant cases were observed in PSA density, PI-RADS scores, and PSA levels (p < .05), while SUVmax and uptake pattern were not significant. In multivariate logistic regression analysis, PI-RADS score groups were found to be independent risk factors for predicting malignancy.

CONCLUSIONS

Our study showed that incidental 18 F-FDG-PET/CT prostate uptake was detected and that high PSA density values, PI-RADS scores, and PSA values, such as in routine patients, and not PET-CT findings such as SUVmax and uptake pattern, were more predictive of malignancy.

A two-stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach

INTRODUCTION

Accurate identification and grading of clinically significant prostate cancer (csPCa, Gleason Score ≥ 7) without invasive procedures remains a significant clinical challenge. This study aims to develop and evaluate a two-stage model designed for precise Gleason grading. The model initially uses radiomics-based multiparametric MRI to identify csPCa and then refines the Gleason grading by integrating clinical indicators and radiomics features.

METHODS

We retrospectively analysed 399 patients with PI-RADS ≥ 3 lesions, categorising them into non-significant prostate cancer (nsPCa, 263 cases) and csPCa (136 cases, subdivided by GGs). Regions of interest (ROIs) for the prostate and lesions were manually delineated on T2-weighted and apparent diffusion coefficient (ADC) images, followed by the extraction of radiomics features. A two-stage model was developed: the first stage identifies csPCa using radiomics-based MRI, and the second integrates clinical indicators for Gleason grading. Model efficacy was evaluated by sensitivity, specificity, accuracy and area under the curve (AUC), with external validation on 100 patients.

RESULTS

The first-stage model demonstrated excellent diagnostic accuracy for csPCa, achieving AUCs of 0.989, 0.982 and 0.976 in the training, testing and external validation cohorts, respectively. The second-stage model exhibited commendable Gleason grading capabilities, with AUCs of 0.82, 0.844 and 0.83 across the same cohorts. Decision curve analysis supported the clinical applicability of both models.

CONCLUSIONS

This study validated the potential of T2W and ADC image radiomics features as biomarkers in distinguishing csPCa. Combining these features with clinical indicators for csPCa Gleason grading provides superior predictive performance and significant clinical benefit.

The accuracy of fluorine 18-labelled prostate-specific membrane antigen PET/CT and MRI for diagnosis of prostate cancer in PSA grey zone

BACKGROUND

The diagnostic utility of prostate biopsy is limited for prostate cancer (PCa) in the prostate-specific antigen (PSA) grey zone. This study aims to evaluate the diagnostic performance of multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) for PSA grey zone PCa and clinically significant PCa (csPCa).

METHODS

A total of 82 patients with PSA levels ranging from 4 to 10 ng/mL who underwent 18F-PSMA-1007 PET/CT, mpMRI, and prostate biopsy were prospectively enrolled. For 18F-PSMA-1007 PET/CT and mpMRI in detecting PCa and csPCa, sensitivity, specificity, and area under the curve (AUC) were assessed using biopsy histology as the standard.

RESULTS

18F-PSMA-1007 PET/CT demonstrated better diagnostic performance for PCa than mpMRI (AUC 0.81 vs. 0.63, P = 0.02). 11.0% of patients with PI-RADS 3-5 had no PCa on biopsy, of whom 77.8% were correctly differentiated by 18F-PSMA-1007 PET/CT. Combined 18F-PSMA-1007 PET/CT + mpMRI improved sensitivity (92.5% vs. 73.6%) and negative predictive value (NPV, 78.9% vs. 53.3%) compared with mpMRI alone.

CONCLUSIONS

18F-PSMA-1007 PET/CT outperformed mpMRI for detecting PCa in the grey zone level of PSA. 18F-PSMA-1007 PET/CT in combination with mpMRI has additional improvement in sensitivity and NPV for csPCa detection.

CLINICAL TRIAL REGISTRATION

NCT05958004, 2024-07.

Evaluation of a deep learning prostate cancer detection system on biparametric MRI against radiological reading

OBJECTIVES

This study aims to evaluate a deep learning pipeline for detecting clinically significant prostate cancer (csPCa), defined as Gleason Grade Group (GGG) ≥ 2, using biparametric MRI (bpMRI) and compare its performance with radiological reading.

MATERIALS AND METHODS

The training dataset included 4381 bpMRI cases (3800 positive and 581 negative) across three continents, with 80% annotated using PI-RADS and 20% with Gleason Scores. The testing set comprised 328 cases from the PROSTATEx dataset, including 34% positive (GGG ≥ 2) and 66% negative cases. A 3D nnU-Net was trained on bpMRI for lesion detection, evaluated using histopathology-based annotations, and assessed with patient- and lesion-level metrics, along with lesion volume, and GGG. The algorithm was compared to non-expert radiologists using multi-parametric MRI (mpMRI).

RESULTS

The model achieved an AUC of 0.83 (95% CI: 0.80, 0.87). Lesion-level sensitivity was 0.85 (95% CI: 0.82, 0.94) at 0.5 False Positives per volume (FP/volume) and 0.88 (95% CI: 0.79, 0.92) at 1 FP/volume. Average Precision was 0.55 (95% CI: 0.46, 0.64). The model showed over 0.90 sensitivity for lesions larger than 650 mm³ and exceeded 0.85 across GGGs. It had higher true positive rates (TPRs) than radiologists equivalent FP rates, achieving TPRs of 0.93 and 0.79 compared to radiologists' 0.87 and 0.68 for PI-RADS ≥ 3 and PI-RADS ≥ 4 lesions (p ≤ 0.05).

CONCLUSION

The DL model showed strong performance in detecting csPCa on an independent test cohort, surpassing radiological interpretation and demonstrating AI's potential to improve diagnostic accuracy for non-expert radiologists. However, detecting small lesions remains challenging.

KEY POINTS

Question Current prostate cancer detection methods often do not involve non-expert radiologists, highlighting the need for more accurate deep learning approaches using biparametric MRI. Findings Our model outperforms radiologists significantly, showing consistent performance across Gleason Grade Groups and for medium to large lesions. Clinical relevance This AI model improves prostate detection accuracy in prostate imaging, serves as a benchmark with reference performance on a public dataset, and offers public PI-RADS annotations, enhancing transparency and facilitating further research and development.

Developmental trends and knowledge frameworks in the application of radiomics in prostate cancer: a bibliometric analysis from 2000 to 2024

BACKGROUND

This research utilized the bibliometrics method to analyze the published literature related to prostate cancer (PCa) imaging. Furthermore, current knowledge and research hotspots of radiomics in PCa diagnosis and treatment were comprehensively reviewed, as well as progress and emerging trends in field were explored.

METHODS

In this investigation, the relevant literature on radiomics, and PCa was retrieved from Web of Science Core Collection (WoSCC) databases from 2000 and 2024. Furthermore, a comprehensive bibliometric analysis was carried out using advanced tools like CiteSpace6.2, VOS viewer, and the 'bibliometrix' package of R software to visualize the annual distribution of publications across various aspects such as authors, countries, journals, institutions, and keywords.

RESULTS

This analysis included 593 from 58 countries including China and the United States. Chinese Academy of Sciences and Frontiers in Oncology were the institutions and journals that publish the most relevant articles, -while Radiology journal had the greatest number of co-cited publications. Furthermore, 3,621 authors published on this topic, of which Madabhushi Anant and Stoyanova Radka had the highest contributions. Moreover, Lambin, P. had the most co-citations. In addition, the diagnostic characteristics of radiomics in PCa imaging and treatment strategies are the current research focal points. The establishment of multi-functional imaging techniques and independent factor models warrants future investigation.

CONCLUSIONS

In summary, this analysis revealed that the research on PCa imaging is developing vigorously, focusing on the diagnostic methods and intervention measures of imaging in PCa diagnosis and treatment. In the future, there is an urgent need for improved collaboration and communication among countries and institutions.

Radical prostatectomy without prostate biopsy based on a noninvasive diagnostic strategy: a prospective single-center study

BACKGROUND

Prostate biopsy is the most common approach for diagnosing prostate cancer (PCa); however, it has inherent limitations, such as the invasive procedure, postoperative complications, and false negative results. We aimed to provide a noninvasive diagnostic strategy for patients with highly suspected PCa and to evaluate the feasibility of performing biopsy-spared radical prostatectomy.

METHODS

This prospective study included a total of 57 patients between November 10, 2022, and December 1, 2023. All 57 patients underwent radical prostatectomy without prior prostate biopsy based on a noninvasive diagnostic strategy consisting of a diagnostic prediction model [comprised of the prostate imaging-reporting and data system (PI-RADS) score and prostate-specific antigen density (PSAD)] and the 18F-prostate-specific membrane antigen (PSMA)-1007 positron emission tomography (PET)/computed tomography (CT) examination. The primary endpoint was the positive predictive value (PPV) of clinically significant PCa [the International Society of Urological Pathology (ISUP) grade ≥2, Gleason score ≥3 + 4]. The secondary endpoints were a PPV of any-grade PCa (ISUP grade ≥ 1, Gleason score ≥3 + 3) and high-grade PCa (ISUP grade ≥3, Gleason score ≥4 + 3), and the false positive rate of the diagnostic strategy.

RESULTS

Of the 371 screened patients with clinically suspected PCa, 57 patients fulfilled the criteria and consented to participate in this study. The median PSAD level was 0.56 (0.42-0.82) ng/mL2; 13 (22.8%) patients were identified as having a PI-RADS score of 4, and 44 (77.2%) patients with a PI-RADS score of 5. The median SUVmax of 18F-PSMA-1007 PET/CT was 21.6 (15.8-33.0). For the 57 enrolled patients who received radical prostatectomy directly, the PPV of clinically significant PCa was 98.2% [56/57, 95% confidence interval (CI): 90.6-100%]. Only 1.8% (1/57, 95% CI: 0.0-9.4%) of patients were diagnosed with clinically insignificant PCa (ISUP grade = 1, Gleason score = 3 + 3). The PPV of any-grade PCa and high-grade PCa were 100% and 73.7% (42/57, 95% CI: 60.3-84.5%), respectively. No one had a false positive result.

CONCLUSIONS

We proposed a noninvasive diagnostic strategy consisting sequentially of a diagnostic prediction model and the 18F-PSMA-1007 PET/CT examination for diagnosing PCa. Despite some limitations, our initial findings suggest the potential feasibility of radical prostatectomy without prior prostate biopsy.

Differentiating clinically significant prostate cancer from clinically insignificant prostate cancer using qualitative and semi-quantitative indices of dynamic contrast-enhanced MRI

PURPOSE

To investigate the utility of qualitative and semi-quantitative evaluation of DCE-MRI for detecting clinically significant prostate cancer (csPC).

METHODS

This retrospective study analyzed 307 lesions in 231 patients who underwent 3.0T MRI. Experienced radiologists assessed PI-RADS v 2.1 assessment category, qualitative contrast enhancement (QCE), contrast enhancement pattern (CEP: type 1, 2, 3), tumor contrast ratio, and tumor size of PC lesions in consensus. Mean and 0-10th-percentile ADC value of the lesion (ADCmean and ADC0-10) were calculated. Specimens obtained from MRI-ultrasound fusion-guided prostate biopsy were used as the pathological reference standard.

RESULTS

In assessment of tumor aggressiveness, PI-RADS assessment category, QCE, tumor size, and ratio of CEP 2 + 3 were significantly higher in PC with Gleason score (GS) ≥ 3 + 4 (n = 256) than in PC with GS = 6 (n = 51) (P ≤ 0.001). Tumor ADCmean and tumor ADC0-10 were comparable between PC with GS ≥ 3 + 4 and PC with GS = 6 (P = 0.164 to 0.504). Regarding diagnostic performance of csPC in 45 PI-RADS 3 transition zone lesions, only ratio of CEP 2 + 3 was significantly higher in PC with GS ≥ 3 + 4 (n = 31) than in PC with GS = 6 (n = 14) (P = 0.008).

CONCLUSION

Qualitative DCE-MRI indices may contribute to PC aggressiveness and improve detection of csPC in PI-RADS assessment category 3 lesions.

Adaptive fusion of dual-view for grading prostate cancer

Accurate preoperative grading of prostate cancer is crucial for assisted diagnosis. Multi-parametric magnetic resonance imaging (MRI) is a commonly used non-invasive approach, however, the interpretation of MRI images is still subject to significant subjectivity due to variations in physicians' expertise and experience. To achieve accurate, non-invasive, and efficient grading of prostate cancer, this paper proposes a deep learning method that adaptively fuses dual-view MRI images. Specifically, a dual-view adaptive fusion model is designed. The model employs encoders to extract embedded features from two MRI sequences: T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC). The model reconstructs the original input images using the embedded features and adopts a cross-embedding fusion module to adaptively fuse the embedded features from the two views. Adaptive fusion refers to dynamically adjusting the fusion weights of the features from the two views according to different input samples, thereby fully utilizing complementary information. Furthermore, the model adaptively weights the prediction results from the two views based on uncertainty estimation, further enhancing the grading performance. To verify the importance of effective multi-view fusion for prostate cancer grading, extensive experiments are designed. The experiments evaluate the performance of single-view models, dual-view models, and state-of-the-art multi-view fusion algorithms. The results demonstrate that the proposed dual-view adaptive fusion method achieves the best grading performance, confirming its effectiveness for assisted grading diagnosis of prostate cancer. This study provides a novel deep learning solution for preoperative grading of prostate cancer, which has the potential to assist clinical physicians in making more accurate diagnostic decisions and has significant clinical application value.

Impact of software-assisted structured reporting on radiology residents approaching prostate MRI

PURPOSE

To evaluate the potential advantages of software-assisted structured reporting for radiology residents approaching multiparametric prostate MRI (mpMRI).

METHODS

MpMRI scans from 100 patients, performed for prostate cancer (PCa) detection or staging, were anonymized, and reviewed by six second-year radiology residents without previous experience in prostate MRI, following 6 h of intensive training. The dataset was split into two subsets of 50 cases each. All residents were asked to report scans from the first subset using a basic text processor (narrative reports -NR-). For the second subset, one group used a dedicated software to produce structured reports (SR) while the other continued with NR. Report completeness was assessed using a PI-RADS-based checklist, and statistical analyses, including Wilcoxon rank sum and Pearson's Chi-squared tests, were performed to compare word count, reporting time, and concordance with an expert radiologist's findings.

RESULTS

All readers adopting SR in the second batch demonstrated a significant increase in word count and a decrease in reporting time compared to the first batch. Image quality and final impressions were missing from all NR, while gland size, lesion description, and PI-RADS score were consistently included in nearly all reports (96-100 %). One of the three residents using SR showed a statistically significant improvement in concordance with the expert radiologist on index lesion location and clinically significant PCa presence (p = 0.001), while the other two exhibited positive trends (p = 0.061-0.078).

CONCLUSIONS

The adoption of SR allowed radiology residents to decrease their reporting time and improve the comprehensiveness of their reports, while increasing concordance with an expert radiologist.

The number of involved regions by prostate adenocarcinoma predicts histopathology concordance between radical prostatectomy specimens and MRI/ultrasound-fusion targeted prostate biopsy

Introduction

The prostate biopsy (PB) results should be concordant with prostatectomy histopathology to avoid overestimating or underestimating the disease, leading to inappropriate or undertreatment of prostate cancer (PCa) patients. Since the introduction of multiparametric Magnetic Resonance Imaging (mpMRI) in the diagnostic pathway of PCa, most studies have shown that MRI/Ultrasound fusion-guided (MRI-fusion) PB improves concordance with histopathology of radical prostatectomy specimens. This study aimed to evaluate the improvement in concordance of prostatectomy specimens with PB histopathology obtained using the MRI-fusion approach compared with the 12-core TRUS-Bx and to identify the variables influencing this.

Patients and methods

The study included 218 men who were diagnosed with PCa by PB and underwent radical prostatectomy between 2016 and 2023. The patients were grouped based on the biopsy method: 115 underwent TRUS-Bx, and 103 underwent MRI-fusion PB. The histopathological grading of these biopsy approaches was compared with that of radical prostatectomy specimens. Multivariate logistic regression analyses were conducted to evaluate the impact of various criteria on histopathological concordance.

Results

In patients with unfavorable intermediate- and high-risk PCa, MRI-fusion PB showed significantly better concordance with prostatectomy histopathology than TRUS-Bx (73.1% vs. 42.9%, p = 0.018). MRI-fusion PB had a significantly lower downgrading of prostatectomy histopathology than TRUS-Bx in all grade categories. The number of cancer-involved regions of the prostate is an independent predictor for concordance (OR = 1.24, 95%CI = 1.04-1.52, p = 0.02) and downgrading (OR = 0.46, 95%CI = 0.24-0.83, p = 0.01).

Conclusions

Using an MRI-fusion PB improves histopathological concordance in patients with unfavorable intermediate and high-risk PCa. It reduces the downgrading rate of prostatectomy histopathology compared with TRUS-Bx in all grade categories. The number of cancer-involved regions is an independent predictor of the concordance between biopsy and final histopathology after prostatectomy and post-prostatectomy histopathology downgrading. Our findings could assist in selecting PCa patients for AS and focal treatment based on the histopathology obtained from the MRI-fusion PB.

Enhancing thin slice 3D T2-weighted prostate MRI with super-resolution deep learning reconstruction: Impact on image quality and PI-RADS assessment

PURPOSES

This study aimed to assess the effectiveness of Super-Resolution Deep Learning Reconstruction (SR-DLR) -a deep learning-based technique that enhances image resolution and quality during MRI reconstruction- in improving the image quality of thin-slice 3D T2-weighted imaging (T2WI) and Prostate Imaging-Reporting and Data System (PI-RADS) assessment in prostate Magnetic Resonance Imaging (MRI).

METHODS

This retrospective study included 33 patients who underwent prostate MRI with SR-DLR between November 2022 and April 2023. Thin-slice 3D-T2WI of the prostate was obtained and reconstructed with and without SR-DLR (matrix: 720 × 720 and 240 × 240, respectively). We calculated the contrast and contrast-to-noise ratio (CNR) between the internal and external glands of the prostate, as well as the slope of pelvic bone and adipose tissue. Two radiologists evaluated qualitative image quality and assessed PI-RADS scores of each reconstruction.

RESULTS

The final analysis included 28 male patients (age range: 47-88 years; mean age: 70.8 years). The CNR with SR-DLR was significantly higher than without SR-DLR (1.93 [IQR: 0.79, 3.83] vs. 1.88 [IQR: 0.63, 3.82], p = 0.002). No significant difference in contrast was observed between images with and without SR-DLR (p = 0.864). The slope with SR-DLR was significantly higher than without SR-DLR (0.21 [IQR: 0.15, 0.25] vs. 0.15 [IQR: 0.12, 0.19], p < 0.01). Qualitative scores for contrast, sharpness, artifacts, and overall image quality were significantly higher with SR-DLR than without SR-DLR (p < 0.05 for all). The kappa values for 2D-T2WI and 3D-T2WI increased from 0.694 and 0.640 to 0.870 and 0.827 with SR-DLR for both readers.

CONCLUSIONS

SR-DLR has the potential to improve image quality and the ability to assess PI-RADS scores in thin-slice 3D-T2WI of the prostate without extending MRI acquisition time.

SUMMARY

Super-Resolution Deep Learning Reconstruction (SR-DLR) significantly improved image quality of thin-slice 3D T2-weighted imaging (T2WI) without extending the acquisition time. Additionally, the PI-RADS scores from 3D-T2WI with SR-DLR demonstrated higher agreement with those from 2D-T2WI.

Synthetic diffusion-weighted imaging in prostate cancer diagnosis: a comparison study with different B-value combinations

AIM

To evaluate the impact of different b-value combinations on synthetic diffusion-weighted imaging (sDWI) and determine the sDWI with an optimal b-value combination for prostatic cancer (PCa) diagnosis.

MATERIAL AND METHODS

A retrospective analysis of 68 patients with abnormal prostate-specific antigen (PSA) was conducted. The sDWI images with b value of 1500 s/mm2 were separately reconstructed by the following five b-value combinations: b=0, 200s/mm2 (sDWI0-200); b=600, 800s/mm2 (sDWI600-800); b=0, 600s/mm2 (sDWI0-600); b=200, 800s/mm2 sDWI200-800); b=0, 800s/mm2 (sDWI0-800). Quantitative analysis was performed on the acquired DWI (aDWI) images with b=1500s/mm2 (aDWI1500) and all sDWI images. These six image groups were scored in five aspects for image quality and further reviewed by two radiologists via six protocols: Protocol Ⅰ, T2WI+sDWI0-200; Protocol Ⅱ, T2WI+sDWI600-800; Protocol Ⅲ, T2WI+sDWI0-600; Protocol Ⅳ, T2WI+sDWI200-800; Protocol Ⅴ, T2WI+sDWI0-800; Protocol Ⅵ, T2WI+aDWI1500. The corresponding diagnostic efficacies for PCa were evaluated using receiver operating characteristic (ROC) curves.

RESULTS

Contrast ratio values of all sDWI images were higher than those of aDWI1500 images. Contrast-to-noise ratio values of sDWI0-200 and sDWI600-800 images were lower than those of the rest sDWI images. All subjective quality scores of sDWI0-600, sDWI200-800, and sDWI0-800 were significantly higher than other groups except for background signal suppression. The area under the curve (AUC) of Protocol Ⅲ, Ⅳ, Ⅴ, and Ⅵ was significantly larger than those of other protocols.

CONCLUSION

Different b-value combinations impact the image quality and diagnostic accuracy of sDWI for PCa detection. The combination of b≤200s/mm2 and b≥600s/mm2 revealed to be optimal.

Comparison of tracer kinetic models in differentiating malignant from normal prostate tissue using dynamic contrast-enhanced MRI

Purpose

The aim of this study was to evaluate the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived kinetic parameters with high spatiotemporal resolution in discriminating malignant from normal prostate tissues.

Methods

Fifty patients with suspicious of malignant diseases in prostate were included in this study. Regions of interest (ROI) were manually delineated by experienced radiologists. Voxel-wise kinetic parameters were produced with the following tracer kinetic models (TKMs): Tofts model, extended Tofts model (ETM), Brix's conventional two-compartment model (Brix), adiabatic tissue homogeneity model (ATH), and distributed parameter model (DP). The initial area under the signal-time curve (IAUC) with an uptake integral approach was also included. Mann-Whitney U test and receiver operating characteristic (ROC) curves were used to evaluate the capability of distinguishing tumor lesions from normal tissues. A p-value of 0.05 or less is considered statistically significant. ROI based parameters correlation analysis between DP and ETM were performed.

Results

624 lesions and 269 normal tissue ROIs were obtained. Thirty parameters were derived from the six kinetic models. Except for PS from Brix, statistically significant differences between lesions and normal tissues (P<0.05) were observed in other parameters.Ve from DP, ATH and Brix and PS from ATH have AUC values less than 0.6 in the ROC analysis. MTT, Vp and PS from DP, Ktrans from ETM and Tofts, E and PS from ATH, IAUC parameters and F from Brix have AUC values larger than 0.8. Ve and Vp from DP and ETM are correlated (r> 0.65). The correlation coefficient between Ktrans from ETM and PS from DP is 0.751.

Conclusion

MTT, Vp and PS from DP, Ktrans from ETM and Tofts, E and PS from ATH, F from Brix and IAUC parameters can be used to differentiate malignant lesions from normal tissues in the prostate.

Standardization and advancements efforts in breast diffusion-weighted imaging

Recent advancements in breast magnetic resonance imaging (MRI) have significantly enhanced breast cancer detection and characterization. Breast MRI offers superior sensitivity, particularly valuable for high-risk screening and assessing disease extent. Abbreviated protocols have emerged, providing efficient cancer detection while reducing scan time and cost. Diffusion-weighted imaging (DWI), a non-contrast technique, has shown promise in differentiating malignant from benign lesions. It offers shorter scanning times and eliminates contrast agent risks. Apparent diffusion coefficient (ADC) values provide quantitative measures for lesion characterization, potentially reducing unnecessary biopsies. Studies have revealed some correlations between ADC values and hormone receptor status in breast cancers, although substantial variability exists among studies. However, standardization remains challenging. Initiatives such as European Society of Breast Imaging (EUSOBI), Diffusion-Weighted Imaging Screening Trial (DWIST), Quantitative Imaging Biomarkers Alliance (QIBA) have proposed guidelines to ensure consistency in imaging protocols and equipment specifications, addressing variability in ADC measurements across different sites and vendors. Advanced techniques like Intravoxel incoherent motion (IVIM) and non-Gaussian DWI offer insights into tissue microvasculature and microstructure. Despite ongoing challenges, the integration of these advanced MRI techniques shows great promise for improving breast cancer diagnosis, characterization, and treatment planning. Continued research and standardization efforts are crucial for maximizing the potential of breast DWI in enhancing patient care and outcomes.

The Evaluation of Radiolabeled Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography for Initial Staging in Intermediate-Risk Prostate Cancer Patients: A Retrospective Multicenter Analysis

OBJECTIVES

The aim of the present study was to assess the performance of radiolabeled-PSMA PET/CT in a cohort of intermediate-risk prostate cancer (PCa) patients for initial staging.

METHODS

This is a retrospective, multicenter analysis of patients diagnosed with intermediate-risk PCa who were staged using radiolabeled PSMA PET/CT to evaluate the extent of the disease before initiating appropriate treatment. The study included patients from the Nuclear Medicine Units of the Humanitas group between 2021 and 2024. The change in management due to the PSMA PET/CT examination was assessed.

RESULTS

A total of 181 patients were enrolled across all three centers. Histopathological assessment from biopsy revealed that 51.4% of patients had favorable PCa, while 48.6% had unfavorable disease. PET/CT was positive for the primary lesions in all patients, but it revealed a positivity rate in 23 (12.7%) patients for nodes and distant organs, with a positivity rate of 0.21 in the unfavorable group and 0.05 in the favorable group (p < 0.005). Based on follow-up data, diagnostic accuracy was higher than 90% in both the favorable and unfavorable groups for lymph node and distant metastases. The inclusion of PSMA PET/CT in the diagnostic algorithm for patients with intermediate-risk PCa impacted patient management in 24 (13.3%) cases, based on the multidisciplinary team decision.

CONCLUSIONS

PSMA PET/CT can affect the management of patients with intermediate-risk PCa in up to 13% of cases, mainly for unfavorable diseases. New imaging techniques as a first-line imaging procedure can help to plan the correct therapeutic approach in the intermediate-risk PCa group.

How Prostate Cancer Growth Patterns Impact Detection and Interreader Agreement on Multiparametric MRI

RATIONALE AND OBJECTIVES

Multiparametric MRI (mpMRI) substantially improves the detection of significant prostate carcinoma (PCa) compared to systematic biopsy. Nevertheless, mpMRI can overlook aggressive forms of PCa. Recent studies showed, that infiltrative growth (INF) has less restricted diffusion. This study aims to explore the impact of growth patterns on the detection of lesions.

MATERIALS AND METHODS

This retrospective study analyzed 52 patients who underwent radical prostatectomy, with preoperative mpMRI. For each patient, one dominant lesion was identified on one whole-mount prostatectomy section. Two pathologists (P1, P2) independently classified the growth pattern whether as expansive (EXP) being defined with at least three 5mm² regions of interest consisting entirely of carcinoma without benign glands or else as infiltrative (INF). Two radiologists (R1, R2) independently classified selected lesions according to PI-RADSv2.1. based on pathological localization. Apparent diffusion coefficient (ADC) values were measured in correlation with matched histopathology slides. Interreader-agreement was evaluated using weighted Cohen's Kappa. The relationship between PI-RADS scores and pathological diagnoses was analyzed using logistic regression.

RESULTS

Pathologic lesion characterization regarding growth patterns achieved almost perfect agreement (κ = 0.88), so did PI-RADS classification of mpMRI (κ = 0.90). PI-RADS scores correlated significantly with EXP growth patterns. Average ADC values were lower for EXP lesions (0.83×10-3 mm2/s, CI: 0.72-0.94×10-3 mm2/s) compared to INF lesions (0.97×10-3 mm2/s, CI: 0.86-1.07×10-3 mm2/s; p = 0.08). On T2 images, 8 of 28 (29%) INF lesions and 1 of 24 (4%) EXP lesions were not visible.

CONCLUSION

PCa missed on mpMRI more frequently demonstrate INF growth patterns. Lesions with EXP growth patterns show lower ADC values and have higher PI-RADS scores.

Bimodal imaging: Detection rate of clinically significant prostate cancer is higher in MRI lesions visible to transrectal ultrasound

BACKGROUND

To explore the detection rates of clinically significant prostate cancer (csPCa; ISUP ≥2) in patients with a single MRI lesion that is visible or invisible on transrectal ultrasound (TRUS) during biopsy.

METHODS

Retrospective analyses of patients who underwent targeted and systematic biopsy of the prostate for one MRI-visible lesion (PI-RADS score ≥ 3) between 2017 and 2022. TRUS-visibility, PI-RADS score, and clinical parameters were recorded prospectively. Univariable and multivariable logistic regression models were used to identify predictors of csPCa.

RESULTS

277 consecutive patients with one MRI-visible lesion were identified. A correlating lesion on TRUS was present in 147/277 (53%). The median age, PSA level, and prostate volume were 68.0 years (IQR: 62.0-73.0), 7.3 ng/ml (IQR: 5.4-10.8) and 45.0 cc (IQR: 32.0-68.0), respectively. Baseline parameters were not significantly different between the two groups. CsPCa was detected in 59/130 (45%) without and in 102/147 (69%) patients with a corresponding TRUS lesion. In multivariable logistic regression analysis predicting csPCa, TRUS-visibility (OR: 2.13, CI: 1.14-4.03, p = 0.02) and PI-RADS score (PI-RADS 4: OR: 7.28, CI: 3.33-17.19; PI-RADS 5: OR: 13.39, CI: 5.27-36.83, p < 0.001) achieved independent predictor status.

CONCLUSIONS

Bimodal-visible lesions more often harbored csPCa and were easier to target. TRUS-visibility of MRI lesions is an independent predictor of csPCa. Therefore, education in both modalities is essential. Despite MRI, the ultrasound should still be diligently examined.

The impact of benign tissue within cancerous regions in the prostate: Characterizing sparse and dense prostate cancers on whole-mount histopathology and on multiparametric MRI

PURPOSE

To establish the incidence, size, zonal location and Gleason Score(GS)/Gleason Grade Group(GG) of sparse versus dense prostate cancer (PCa) lesions and to identify the imaging characteristics of sparse versus dense cancers on multiparametric MRI (mpMRI).

METHODS

Seventy-six men with untreated PCa were scanned prior to prostatectomy with endorectal-coil 3 T MRI including T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced MRI. Cancerous regions were outlined and graded on the whole-mount, processed specimens, with tissue compositions estimated. Regions with cancer comprising <50 % and ≥ 50 % of the tissue were considered sparse and dense respectively. Regions of interest (ROI) were manually drawn on T2-weighted MRI. Within each patient, area-weighted ROI averages were calculated for each imaging measure for each tissue type, GS/GG, and sparse/dense composition.

RESULTS

A large number of cancer regions were identified on histopathology (n = 1193: 939 (peripheral zone (PZ)) and 254 (transition zone (TZ))). Thirty-seven percent of these lesions were sparse. Sparse lesions were primarily low-grade with the majority of PZ and 100 % of TZ sparse lesions ≤GS3 + 3/GG1. Dense lesions were significantly larger than sparse lesions in both PZ and TZ, p < 0.0001. On imaging, 246/45 PZ and 109/8 TZ dense/sparse 2D cancerous ROIs were drawn. Sparse GS3 + 3 and sparse ≥GS3 + 4 cancers did not have significantly different MRI intensities to dense GS3 + 3 cancers, while sparse GS3 + 3/GG1 cancers differed from benign, p < 0.05.

CONCLUSION

Histopathologically identified prostate cancer lesions were sparse in 37 % of cases. Sparse cancers were entirely low grade in TZ and predominantly low-grade in PZ and generally small, thus likely posing lower risk for spread and progression than dense lesions. Sparse lesions were not distinguishable from dense lesions on mpMRI, but could be distinguished from benign tissues.

Risk Calculator Strategy Before Magnetic Resonance Imaging Stratification for Biopsy-naïve Men with Suspicion for Prostate Cancer: A Cost-effectiveness Analysis

Background and objective

Current guidelines on prostate cancer (PCa) diagnosis recommend risk stratification before prostate biopsy, using either a risk calculator (RC) or magnetic resonance imaging (MRI). The aim of our study was to assess the effectiveness and cost effectiveness of an RC strategy and a direct MRI (dMRI) strategy.

Methods

Data for biopsy-naïve men suspected of having PCa on the basis of elevated prostate specific antigen (PSA) and/or abnormal digital rectal examination (DRE) were retrospectively collected from two large teaching hospitals. The RC and dMRI strategies were evaluated for PCa detection, effectiveness, and costs. The RC strategy used the Rotterdam prostate cancer risk calculator 3/4 and MRI for stratification, while the dMRI strategy directly used MRI findings. Clinically significant (cs)PCa was defined as a Gleason score ≥3 + 4.

Key findings and limitations

In total, 1458 men were included for analysis, of whom 944 were in the RC group and 514 were in the dMRI group. The RC strategy significantly reduced MRI use by 47.8% (52.2% vs 99.8%; p < 0.001) and reduced costs by 14.3% (€422.45 vs €492.77; p < 0.001) in comparison to the dMRI strategy. The number of patients who underwent prostate biopsy (36.5% vs. 40.9%; p = 0.11) and the csPCa detection rate (43.5% vs 45.2%; p = 0.69) were similar between the groups. The study is limited by its retrospective nature, so the findings should be interpreted with caution.

Conclusions and clinical implications

Both the RC strategy and the dMRI strategy are viable options for PCa diagnosis, with the former significantly reducing MRI use and overall diagnostic costs per person. Therefore, the RC strategy might be preferred over dMRI, particularly in contexts aiming for sustainable health care practices that optimize resource allocation and cost effectiveness.

Patient summary

We compared two different approaches for men with a suspicion of prostate cancer. One uses a risk calculator to decide on whether to perform an MRI (magnetic resonance imaging) scan, and the other proceeds directly to MRI. In both cases, prostate biopsy is performed in cases with positive MRI findings. The number of patients who needed a biopsy and the cancer detection rate were similar for the two approaches.