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

Robustness of magnetic resonance imaging and positron emission tomography radiomic features in prostate cancer: Impact on recurrence prediction after radiation therapy

Background and purpose

Radiomic features from MRI and PET are an emerging tool with potential to improve prostate cancer outcomes. However, feature robustness due to image segmentation variations is currently unknown. Therefore, this study aimed to evaluate the robustness of radiomic features with segmentation variations and their impact on predicting biochemical recurrence (BCR).

Materials and methods

Multi-scanner, pre-radiation therapy imaging from 142 patients with localised prostate cancer was used. Imaging included T2-weighted (T2), apparent diffusion coefficient (ADC) MRI, and prostate-specific membrane antigen (PSMA)-PET. The prostate gland and intraprostatic tumours were manually and automatically segmented, and differences were quantified using Dice Coefficient (DC). Radiomic features including shape, first-order, and texture features were extracted for each segmentation from original and filtered images. Intraclass Correlation Coefficient (ICC) and Mean Absolute Percentage Difference (MAPD) were used to assess feature robustness. Random forest (RF) models were developed for each segmentation using robust features to predict BCR.

Results

Prostate gland segmentations were more consistent (mean DC = 0.78) than tumour segmentations (mean DC = 0.46). 112 (3.6 %) radiomic features demonstrated 'excellent' robustness (ICC > 0.9 and MAPD < 1 %), and 480 features (15.4 %) demonstrated 'good' robustness (ICC > 0.75 and MAPD < 5 %). PET imaging provided more features with excellent robustness than T2 and ADC. RF models showed strong predictive power for BCR with a mean area under the receiver-operator-characteristics curve (AUC) of 0.89 (range 0.85-0.93).

Conclusion

When using radiomic features for predictive modelling, segmentation variability should be considered. To develop BCR predictive models, radiomic features from the entire prostate gland are preferable over tumour segmentation-based features.

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment

ABSTRACT

A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study

PURPOSE

To evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality.

METHOD

A CycleGAN algorithm was used to enhance T2WI quality. 96 patients (192 scans) were identified from patients who underwent multiple axial T2WI due to poor quality on the first attempt (RAD1) and improved quality on re-acquisition (RAD2). CycleGAN algorithm gave DL classifier scores (0-1) for quality quantification and produced enhanced versions of QI1 and QI2 from RAD1 and RAD2, respectively. A subset (n = 20 patients) was selected for a blinded, multi-reader study, where four radiologists rated T2WI on a scale of 1-4 for quality. The multi-reader study presented readers with 60 image pairs (RAD1 vs RAD2, RAD1 vs QI1, and RAD2 vs QI2), allowing for selecting sequence preferences and quantifying the quality changes.

RESULTS

The DL classifier correctly discerned 71.9 % of quality classes, with 90.6 % (96/106) as poor quality and 48.8 % (42/86) as diagnostic in original sequences (RAD1, RAD2). CycleGAN images (QI1, QI2) demonstrated quantitative improvements, with consistently higher DL classifier scores than original scans (p < 0.001). In the multi-reader analysis, CycleGAN demonstrated no qualitative improvements, with diminished overall quality and motion in QI2 in most patients compared to RAD2, with noise levels remaining similar (8/20). No readers preferred QI2 to RAD2 for diagnosis.

CONCLUSION

Despite quantitative enhancements with CycleGAN, there was no qualitative boost in T2WI diagnostic quality, noise, or motion. Expert radiologists didn't favor CycleGAN images over standard scans, highlighting the divide between quantitative and qualitative metrics.

Interpretation of Prostate Magnetic Resonance Imaging Using Prostate Imaging and Data Reporting System Version 2.1: A Primer

Prostate magnetic resonance imaging (MRI) is increasingly being used to diagnose and stage prostate cancer. The Prostate Imaging and Data Reporting System (PI-RADS) version 2.1 is a consensus-based reporting system that provides a standardized and reproducible method for interpreting prostate MRI. This primer provides an overview of the PI-RADS system, focusing on its current role in clinical interpretation. It discusses the appropriate use of PI-RADS and how it should be applied by radiologists in clinical practice to assign and report PI-RADS assessments. We also discuss the changes from prior versions and published validation studies on PI-RADS accuracy and reproducibility.

Contemporary Approach to Prostate Imaging and Data Reporting System Score 3 Lesions

The aim of this article is to review the technical and clinical considerations encountered with PI-RADS 3 lesions, which are equivocal for clinically significant Prostate Cancer (csPCa) with detection rates ranging between 10% and 35%. The number of PI-RADS 3 lesions reported vary according to several factors including MRI quality and radiologist training/expertise among the most influential. PI-RADS v.2.1 updated definitions for scores 2 and 3 in the PZ and scores 1 and 2 in the TZ is reviewed. The role of DWI role is highlighted in the assessment of the TZ with the possibility of upgrading score 2 lesions to score 3 based on DWI score. Given the increased utilization for prostate MRI, biparametric MRI can be considered as an alternative for low-risk patients where there is a need to rule out csPCa acknowledging this technique may increase the number of indeterminate cases going for biopsies. Management of patients with equivocal lesions at mpMRI and factors influencing biopsy decision process remain as an unmet need and additional studies using molecular/imaging markers as well as artificial intelligence tools are needed to further address their role in proper patient selection for biopsy.

Update on Optimization of Prostate MR Imaging Technique and Image Quality

Prostate MR imaging quality has improved dramatically over recent times, driven by advances in hardware, software, and improved functional imaging techniques. MRI now plays a key role in prostate cancer diagnostic work-up, but outcomes of the MRI-directed pathway are heavily dependent on image quality and optimization. MR sequences can be affected by patient-related degradations relating to motion and susceptibility artifacts which may enable only partial mitigation. In this Review, we explore issues relating to prostate MRI acquisition and interpretation, mitigation strategies at a patient and scanner level, PI-QUAL reporting, and future directions in image quality, including artificial intelligence solutions.

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

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

Prostate Cancer Local Staging with Magnetic Resonance Imaging

Accurate determination of the local stage of prostate cancer is crucial for treatment planning and prognosis. The primary objective of local staging is to distinguish between organ-confined and locally advanced disease, with the latter carrying a worse clinical prognosis. The presence of locally advanced disease features of prostate cancer, such as extra-prostatic extension, seminal vesicle invasion, and positive surgical margin, can impact the choice of treatment. Over the past decade, multiparametric MRI (mpMRI) has become the preferred imaging modality for the local staging of prostate cancer and has been shown to provide accurate information on the location and extent of disease. It has demonstrated superior performance compared to staging based on traditional clinical nomograms. Despite being a relatively new technique, mpMRI has garnered considerable attention and ongoing investigations. Therefore, in this review, we will discuss the current use of mpMRI on prostate cancer local staging.

Pitfalls in Prostate MR Imaging Interpretation

Multiparametric MR imaging of the prostate is an essential diagnostic study in the evaluation of prostate cancer. Several entities including normal anatomic structures, benign lesions, and posttreatment changes can mimic prostate cancer. An in depth understanding of the pitfalls is important for accurate interpretation of prostate MR imaging.

Evaluation of Prostate Cancer Recurrence with MR Imaging and Prostate Imaging for Recurrence Reporting Scoring System

Detection of prostate cancer recurrence after whole-gland treatment with curative intent is critical to identify patients who may benefit from local salvage therapy. Among the different imaging modalities used in clinical practice, MR imaging is the most accurate in identifying local prostate cancer recurrence; indeed, it is an excellent technique for local recurrence detection superior to PET/CT, even at low PSA, but provides no information about extra-pelvic lymph nodes or bone metastasis. In 2021, a group of experts developed the Prostate Imaging for local Recurrence Reporting scoring system to standardize acquisition, interpretation, and reporting of prostate cancer recurrence.

Prostate health index can stratify patients with Prostate Imaging Reporting and Data System score 3 lesions on magnetic resonance imaging to reduce prostate biopsies

We aim to evaluate prostate health index as an additional risk-stratification tool in patients with Prostate Imaging Reporting and Data System score 3 lesions on multiparametric magnetic resonance imaging. Men with biochemical or clinical suspicion of having prostate cancer who underwent multiparametric magnetic resonance imaging in two tertiary centers (Queen Mary Hospital and Princess Margaret Hospital, Hong Kong, China) between January 2017 and June 2022 were included. Ultrasound-magnetic resonance imaging fusion biopsies were performed after prostate health index testing. Those who only had Prostate Imaging Reporting and Data System score 3 lesions were further stratified into four prostate health index risk groups and the cancer detection rates were analyzed. Out of the 747 patients, 47.3% had Prostate Imaging Reporting and Data System score 3 lesions only. The detection rate of clinically significant prostate cancer in this group was 15.0%. The cancer detection rates of clinically significant prostate cancer had statistically significant differences 5.3% in prostate health index <25.0, 7.4% in prostate health index 25.0-34.9, 17.9% in prostate health index 35.0-54.9, and 52.6% in prostate health index ≥55.0 (P < 0.01). Among the patients, 26.9% could have avoided a biopsy with a prostate health index <25.0, at the expense of a 5.3% risk of missing clinically significant prostate cancer. Prostate health index could be used as an additional risk stratification tool for patients with Prostate Imaging Reporting and Data System score 3 lesions. Biopsies could be avoided in patients with low prostate health index, with a small risk of missing clinically significant prostate cancer.

Future Imaging of Prostate Cancer: Do We Need More Than PSMA PET/CT?

In the setting of prostate cancer (PCa), many different imaging modalities are available to correctly assess staging, restaging, treatment response and radio-ligand therapy recruitment. The introduction of fluoride or gallium-labelled prostate specific membrane antigen (PSMA) made a revolution in PCa management, also due to its possible theragnostic use. Nowadays PSMA-PET/CT is a fundamental tool for staging and restaging PCa. This review discusses the latest findings in PSMA imaging in PCa patients and the impact of PSMA imaging on the patients' management in primary staging, biochemical recurrence and in advanced prostate cancer, always keeping in mind the important theragnostic role of PSMA. This review tries also to assess the current role of other radiopharmaceuticals as Choline, FACBC or other radiotracers like gastrin-releasing peptide receptor targeting tracers and FAPI in different PCa settings.

mpMRI-targeted biopsy of the prostate in men ≥ 75 years. 7-year report from a high-volume referral center

OBJECTIVE

Multiparametric magnetic resonance imaging (mpMRI) -Ultrasound- fusion guided biopsy of the prostate (FBx) is the new gold standard for the detection of prostate cancer. Hallmark studies showing superior detection rates of FBx over randomized biopsies routinely excluded patients≥75 years and information on outcome of FBx on this patient cohort is sparse. As a large referral center, we have performed FBx on a substantial number of patients this age. By evaluating outcome of FBx of patients over the age of 75 years we wanted to close the gap of knowledge on this patient cohort.

MATERIALS AND METHODS

Between 2015 -2022, 1577 patients underwent FBx at our department and were considered for analysis. Clinical and histopathological parameters were recorded. Clinical data comprised age at FBx, serum level of Prostate-specific antigen (PSA), prostate volume, PSA-density, history of previous biopsies of the prostate, result of the digital rectal examination (DRE) and assessment of the indexlesion of mpMRI according to the Prostate Imaging and Reporting Data System (PI-RADS). Univariate analysis and multivariable logistic regression was used to identify age barrier of 75 years as a potential risk factor of detection of clinically significant prostate cancer by FBx.

RESULTS

379/1577 patients (24%) were≥75 years and 1198/1577 (76%) patients were < 75 years, respectively. Preoperative PSA was significantly higher in patients≥75 years compared to patients < 75 years (9.54 vs. 7.8, p < 0.001). Patients≥75 years presented significantly more often with mpMRI target lesions classified as PI-RADS 5 compared to patients < 75 years (45% vs. 29%, p < 0.001). Detection rate of clinically significant prostate cancer was significantly higher in patients≥75 years compared to patients < 75 years (63% vs. 43%, p < 0.001). Aggressive prostate cancer grade ISUP 5 was significantly more often detected in patients≥75 years compared to patients < 75 years (13% vs. 8%, p = 0.03). On multivariable logistic regression model adjusted for PSA and PI-RADS score, age barrier of 75 years was identified as a significant risk factor for the detection of clinically significant prostate cancer by FBx (OR: 1.77, 95% CI: 1.36 -2.31, p < 0.001).

CONCLUSION

After evaluation of a large patient cohort, we show that age≥75 years represents a significant risk factor for the detection of clinically significant prostate cancer. Further studies on mid- and long term outcome are necessary to draw conclusions for clinical decision making in this patient cohort.

Germline DNA Damage Response Gene Mutations in Localized Prostate Cancer

Background and Objectives: Germline DNA damage response (DDR) gene mutations correlate with increased prostate cancer (PCa) risk and a more aggressive form of the disease. DDR mutation testing is recommended for metastatic PCa cases, while eligible information about the mutations' burden in the early-stage localized PCa is still limited. This study is aimed at the prospective detection of DDR pathway mutations in cases with localized PCa and correlation with clinical, histopathological, and radiological data. A comparison to the previously assessed cohort of the advanced PCa was performed. Materials and Methods: Germline DDR gene mutations were assessed prospectively in DNA samples from 139 patients, using a five-gene panel (BRCA1, BRCA2, ATM, CHEK2, and NBN) targeted next-generation sequencing. Results: This study revealed an almost three-fold higher risk of localized PCa among mutation carriers as compared to non-carriers (OR 2.84 and 95% CI: 0.75-20.23, p = 0.16). The prevalence of germline DDR gene mutations in PCa cases was 16.8% (18/107) and they were detected only in cases with PI-RADS 4/5 lesions. BRCA1/BRCA2/ATM mutation carriers were 2.6 times more likely to have a higher (>1) cISUP grade group compared to those with a CHEK2 mutation (p = 0.27). However, the number of cISUP > 1-grade patients with a CHEK2 mutation was significantly higher in advanced PCa than in localized PCa: 66.67% vs. 23.08% (p = 0.047). Conclusions: The results of our study suggest the potential of genetic screening for selected DDR gene mutations for early identification of cases at risk of aggressive PCa.

Use of PIRADS 2.1 to predict capsular invasion in patients with radiologic T3a prostate cancer

Objective

Using multi-parametric magnetic resonance imaging (mpMRI) to identify patients with clinical T3a (cT3a) who were overestimated on mpMRI with final pathological T2 (pT2). To suggest that the neurovascular bundle (NVB) can be preserved by evaluating the characteristics of patients according to their pathological grade among cT3a prostate cancer (PCa) patients using mpMRI.

Materials and methods

Patients who underwent robot-assisted laparoscopic radical prostatectomy (RALP) were retrospectively analyzed and those patients with clinical T3aN0M0 were enrolled. These enrolled patients were divided into a localized cancer group with pT2 PCa and a locally advanced group with pT3a or higher. Factors affecting the diagnosis of localized PCa after RALP in patients with cT3a PCa were evaluated.

Results

Among the preoperative parameters of patients with cT3a PCa, the prostate specific antigen density (PSAD) (OR: 3.76, 95% CI: 1.85-7.64, p<0.001), international society of urological pathology (ISUP) grade (p<0.05), and index lesion size (OR: 1.44, 95% CI: 1.85-7.64, p<0.001) were significantly associated with pathological locally advanced PCa. Optimal cut-off values for prediction of pT3a or higher were 0.36 ng/mL2 for PSAD (sensitivity: 55.7%, specificity: 70.8%), 1.77 cm for index lesion size (sensitivity: 54.3%, specificity: 66.0%), and 2.5 for ISUP grading (sensitivity: 67.6%, specificity: 53.2%). For prediction of pT3a or higher among patients with cT3a PCa, a nomogram was developed using ISUP grade, index lesion size, and PSAD on prostate biopsy (area under the curve: 0.71, 95% CI: 0.670-0.754, p<0.001). PSAD less than 0.36 index lesion size less than 1.77 cm, and biopsy ISUP grade 1-2 are highly likely to indicate that there is no actual extracapsular extension in cT3a PCa patients.

Conclusions

PSAD, ISUP, and index lesion size showed significant associations with the classification of pathologic localized and locally advanced PCa in patients with cT3a PCa. A nomogram including these features can predict the diagnosis of locally advanced PCa in patients with cT3a PCa.

Predictors of prostate cancer detection in MRI PI-RADS 3 lesions - Reality of a tertiary center

INTRODUCTION AND OBJECTIVES

The Prostate Imaging Reporting and Data System (PI-RADS) score reports the likelihood of a clinically significant prostate cancer (CsPCa) based on various multiparametric prostate magnetic resonance imaging (mpMRI) characteristics. The PI-RADS category 3 is an intermediate status, with an equivocal risk of malignancy. The PSA density (PSAD) has been proposed as a tool to facilitate biopsy decisions on PI-RADS category 3 lesions. The objective of this study is to determine the frequency of CsPCa, assess the diagnostic value of targeted biopsy and identify clinical predictors to improve the CsPCa detection rate in PI-RADS category 3 lesions.

METHODS

Between 1st January 2017 and 31st December 2022, a total of 1661 men underwent a prostate biopsy at our institution. Clinical and mpMRI data of men with PI-RADS 3 lesions was reviewed. The study population was divided into two groups: target group, including those submitted to systematic plus targeted biopsy versus non-target group when only systematic or saturation biopsy were performed. Patients with PI-RADS 3 lesions were divided into three categories based on pathological biopsy results: benign, clinically insignificant disease (score Gleason = 6 or International Society of Urologic Pathologic (ISUP) 1) and clinically significant cancer (score Gleason ≥ 7 (3+4) or ISUP ≥ 2) according to target and non-target group. Univariate and multivariate analyses were performed to identify clinical predictors to improve the CsPCa detection rate in PI-RADS category 3 lesions.

RESULTS

A total of 130 men with PIRADS 3 index lesions were identified. Pathologic results were benign in 77 lesions (59.2%), 19 (14.6%) were clinically insignificant (Gleason score 6) and 34 (26.2%) were clinically significant (Gleason score 7 or higher). Eighty-seven of the patients were included in the target group (66.9%) and 43 in the non-target group (33.1%). The CsPCa detection was higher in the non-target group (32.6%, n = 14 vs 23.0%, n = 20 respectively). When systematic and target biopsies were jointly performed, if the results of systematic biopsies are not considered and only the results of target biopsies are taken into account, a CsPCa diagnosis would be missed on 9 patients. The differences of insignificant cancer and CsPCa rates among the target or non-target group were not statistically significant (p = 0.50 and p = 0.24, respectively). on multivariate analysis, the abnormal DRE and lesions localized in Peripheral zone (PZ) were significantly associated with a presence of CsPCa in PI-RADS 3 lesions (oR = 3.61, 95% CI [1.22,10.72], p = 0.02 and oR = 3.31, 95% CI [1.35, 8.11], p = 0.01, respectively). A higher median PSAD significantly predisposed for CsPCa on univariate analyses (p = 0.05), however, was not significant in the multivariate analysis (p = 0.76). In our population, using 0.10 ng/ml/ml as a cut-off to perform biopsy, 41 patients would have avoided biopsy (31.5%), but 5 cases of CsPCa would not have been detected (3.4%). We could not identify any statistical significance between other clinical and imagiological variables and CsPCa detection.

CONCLUSIONS

PI-RADS 3 lesions were associated with a low likelihood of CsPCa detection. A systematic biopsy associated or not with target biopsy is essential in PI-RADS 3 lesions, and targeted biopsy did not demonstrate to be superior in the detection of CsPCa. The presence of abnormal DRE and lesions localized in PZ potentially predict the presence of CsPCa in biopsied PI-RADS 3 lesions.

Quantification and significance of extraprostatic findings on prostate MRI: a retrospective analysis and three-tier classification

OBJECTIVES

To quantify extraprostatic findings (EPFs) on prostate MRI, estimate the proportion of reported and unreported EPFs, assess their clinical importance, and propose standardized reporting of EPFs.

MATERIALS AND METHODS

Prostate 3-T MRI studies, reports, and clinical data from 623 patients (age 67.9 ± 8.2 years) were retrospectively analyzed and re-evaluated for the presence of EPFs and their clinical significance: E1-no finding or findings that have no clinical significance; E2-potentially significant findings; and E3-significant findings.

RESULTS

Secondary reading identified 1236 EPFs in 593 patients (1.98 ± 1.13 EPFs per patient, no EPFs in 30 patients), from which 468 (37.8%) were mentioned in the original report. The most common findings included diverticulosis (44% of patients), hydrocele (34%), inguinal fat hernia (16%), and bladder wall trabecular hypertrophy (15%). There were 80 (6.5%) E2 EPFs and 30 (2.4%) E3 EPFs. From E3 EPFs, 10 (33%) were not originally reported. A workup was suggested in 35 (52%) of the 67 originally reported E2 and E3 findings with follow-up and performed in 20 (30%). Fourteen (21%) EPFs in 11 patients influenced their management. Four experienced radiologists originally reported 1.8 to 2.5 findings per patient (p < 0.0001).

CONCLUSIONS

EPFs on prostate MRI are frequent, but only 2.4% are clinically significant (E3), and 33% of these are not reported. Only 30% of E2 and E3 findings are further explored, and 21% influence patient management. We suggest that an "E" category should be attached to the PI-RADS system to identify the presence of EPFs that require further workup.

CRITICAL RELEVANCE STATEMENT

Extraprostatic findings on prostate MRI are frequent, but only 2.4% are clinically significant (E3), and 33% of these are not reported. We advocate standardized reporting of extraprostatic findings indicating their clinical significance.

KEY POINTS

• Extraprostatic findings on prostate MRI are frequent with an average of two findings per patient. • 2.4% of extraprostatic findings are significant, and 33% of these are not reported. • There is a significant variability among experienced radiologists in reporting extraprostatic findings.

A radiomics based method for prediction of prostate cancer Gleason score using enlarged region of interest

BACKGROUND

Prostate cancer (PCa) is one of the most common cancers in men worldwide, and its timely diagnosis and treatment are becoming increasingly important. MRI is in increasing use to diagnose cancer and to distinguish between non-clinically significant and clinically significant PCa, leading to more precise diagnosis and treatment. The purpose of this study is to present a radiomics-based method for determining the Gleason score (GS) for PCa using tumour heterogeneity on multiparametric MRI (mp-MRI).

METHODS

Twenty-six patients with biopsy-proven PCa were included in this study. The quantitative T2 values, apparent diffusion coefficient (ADC) and signal enhancement rates (α) were calculated using multi-echo T2 images, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), for the annotated region of interests (ROI). After texture feature analysis, ROI range expansion and feature filtering was performed. Then obtained data were put into support vector machine (SVM), K-Nearest Neighbor (KNN) and other classifiers for binary classification.

RESULTS

The highest classification accuracy was 73.96% for distinguishing between clinically significant (Gleason 3 + 4 and above) and non-significant cancers (Gleason 3 + 3) and 83.72% for distinguishing between Gleason 3 + 4 from Gleason 4 + 3 and above, which was achieved using initial ROIs drawn by the radiologists. The accuracy improved when using expanded ROIs to 80.67% using SVM and 88.42% using Bayesian classification for distinguishing between clinically significant and non-significant cancers and Gleason 3 + 4 from Gleason 4 + 3 and above, respectively.

CONCLUSIONS

Our results indicate the research significance and value of this study for determining the GS for prostate cancer using the expansion of the ROI region.

Deep-learning prostate cancer detection and segmentation on biparametric versus multiparametric magnetic resonance imaging: Added value of dynamic contrast-enhanced imaging

OBJECTIVES

To develop diagnostic algorithms of multisequence prostate magnetic resonance imaging for cancer detection and segmentation using deep learning and explore values of dynamic contrast-enhanced imaging in multiparametric imaging, compared with biparametric imaging.

METHODS

We collected 3227 multiparametric imaging sets from 332 patients, including 218 cancer patients (291 biopsy-proven foci) and 114 noncancer patients. Diagnostic algorithms of T2-weighted, T2-weighted plus dynamic contrast-enhanced, biparametric, and multiparametric imaging were built using 2578 sets, and their performance for clinically significant cancer was evaluated using 649 sets.

RESULTS

Biparametric and multiparametric imaging had following region-based performance: sensitivity of 71.9% and 74.8% (p = 0.394) and positive predictive value of 61.3% and 74.8% (p = 0.013), respectively. In side-specific analyses of cancer images, the specificity was 72.6% and 89.5% (p < 0.001) and the negative predictive value was 78.9% and 83.5% (p = 0.364), respectively. False-negative cancer on multiparametric imaging was smaller (p = 0.002) and more dominant with grade group ≤2 (p = 0.028) than true positive foci. In the peripheral zone, false-positive regions on biparametric imaging turned out to be true negative on multiparametric imaging more frequently compared with the transition zone (78.3% vs. 47.2%, p = 0.018). In contrast, T2-weighted plus dynamic contrast-enhanced imaging had lower specificity than T2-weighted imaging (41.1% vs. 51.6%, p = 0.042).

CONCLUSIONS

When using deep learning, multiparametric imaging provides superior performance to biparametric imaging in the specificity and positive predictive value, especially in the peripheral zone. Dynamic contrast-enhanced imaging helps reduce overdiagnosis in multiparametric imaging.

Value of the newly developed pelvic dimension index/prostate volume ratio in predicting positive surgical margin in prostate cancer

PURPOSE

To evaluate the diagnostic performance of pelvimetric measurements, in particular the pelvic dimension index (PDI)/prostate volume (PV) ratio (PDI/PV), in predicting positive surgical margin (PSM) in prostate cancer (PC).

MATERIALS AND METHODS

127 patients who had pre-operative pelvic imaging were included in this study. Demographic and clinical data were recorded. Apical depth (AD), interspinous distance (ISD), intertuberous distance (ITD), bony femoral width (BFW), soft-tissue width (SW), symphysis angle (SA), anteroposterior diameter of the pelvic inlet (API), anteroposterior diameter of the pelvic mid-plane (APM), anteroposterior diameter of the pelvic outlet (APO), pelvic depth (PD), bony width index (BWI), soft tissue width index (SWI), pelvic cavity index (PCI), PDI and PV were measured on MRI or CT. Using PDI and PV, we developed a new parameter of "PDI to PV ratio" (PDI/PV). Logistic regression analysis was used to determine the predictive potential of variables in detection of PSM.

RESULTS

The AD, PV, SA and total prostate specific antigen (PSA) were significantly higher in PSM( +), while PDI, BWI, SWI, API, PDI/PV and PD were significantly lower in PSM( +) (p < 0.05). In multivariate analysis, PDI/PV ratio and clinical stage were all significant predictor of PSM, where PDI/PV ratio was the strongest predictor, followed by clinical stage.

CONCLUSION

Pelvimetric measurements indicating deep location of the prostatic apex rather than pelvic width are more effective in predicting PSM. Prediction of PSM with pelvimetric measurements, in particular PDI/PV ratio, may be helpful for surgical planning in preoperative period.

Artificial intelligence as diagnostic aiding tool in cases of Prostate Imaging Reporting and Data System category 3: the results of retrospective multi-center cohort study

PURPOSE

To study the effect of artificial intelligence (AI) on the diagnostic performance of radiologists in interpreting prostate mpMRI images of the PI-RADS 3 category.

METHODS

In this multicenter study, 16 radiologists were invited to interpret prostate mpMRI cases with and without AI. The study included a total of 87 cases initially diagnosed as PI-RADS 3 by radiologists without AI, with 28 cases being clinically significant cancers (csPCa) and 59 cases being non-csPCa. The study compared the diagnostic efficacy between readings without and with AI, the reading time, and confidence levels.

RESULTS

AI changed the diagnosis in 65 out of 87 cases. Among the 59 non-csPCa cases, 41 were correctly downgraded to PI-RADS 1-2, and 9 were incorrectly upgraded to PI-RADS 4-5. For the 28 csPCa cases, 20 were correctly upgraded to PI-RADS 4-5, and 5 were incorrectly downgraded to PI-RADS 1-2. Radiologists assisted by AI achieved higher diagnostic specificity and accuracy than those without AI [0.695 vs 0.000 and 0.736 vs 0.322, both P < 0.001]. Sensitivity with AI was not significantly different from that without AI [0.821 vs 1.000, P = 1.000]. AI reduced reading time significantly compared to without AI (mean: 351 seconds, P < 0.001). The diagnostic confidence score with AI was significantly higher than that without AI (Cohen Kappa: -0.016).

CONCLUSION

With the help of AI, there was an improvement in the diagnostic accuracy of PI-RADS category 3 cases by radiologists. There is also an increase in diagnostic efficiency and diagnostic confidence.

Impact of operator expertise on transperineal free-hand mpMRI-fusion-targeted biopsies under local anaesthesia for prostate cancer diagnosis: a multicenter prospective learning curve

PURPOSE

Transperineal mpMRI-targeted fusion prostate biopsies (TPFBx) are recommended for prostate cancer diagnosis, but little is known about their learning curve (LC), especially when performed under local anaesthesia (LA). We investigated how operators' and institutions' experience might affect biopsy results.

METHODS

Baseline, procedure and pathology data of consecutive TPFBx under LA were prospectively collected at two academic Institutions, from Sep 2016 to May 2019. Main inclusion criterion was a positive MRI. Endpoints were biopsy duration, clinically significant prostate cancer detection rate on targeted cores (csCDR-T), complications, pain and urinary function. Data were analysed per-centre and per-operator (with ≥ 50 procedures), comparing groups of consecutive patient, and subsequently through regression and CUSUM analyses. Learning curves were plotted using an adjusted lowess smoothing function.

RESULTS

We included 1014 patients, with 27.3% csCDR-T and a median duration was 15 min (IQR 12-18). A LC for biopsy duration was detected, with the steeper phase ending after around 50 procedures, in most operators. No reproducible evidence in favour of an impact of experience on csPCa detection was found at operator's level, whilst a possible gentle LC of limited clinical relevance emerged at Institutional level; complications, pain and IPSS variations were not related to operator experience.

CONCLUSION

The implementation of TPFBx under LA was feasible, safe and efficient since early phases with a relatively short learning curve for procedure time.

Current magnetic resonance imaging-based diagnostic strategies for prostate cancer

Recent developments in multiparametric MRI and MRI-targeted biopsy have made it possible to detect clinically significant cancers more accurately and efficiently than ever before. Furthermore, software that enables easy MRI/US image fusion has been developed and is already available on the market, and this has provided a tailwind for the spread of MRI-based prostate cancer diagnostic strategies. Such precise diagnosis of prostate cancer localization is essential for highly accurate focal therapy. In addition, a recent large-scale study applying MRI to community screening for prostate cancer has reported its usefulness. By contrast, concerns about overdiagnosis and overtreatment, the existence of inter-reader variability in MRI diagnosis, and issues with current MRI-targeted biopsy have emerged. In this article, we review the development of multiparametric MRI and MRI-targeted biopsy to date and the current issues and discuss future directions.

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

PURPOSE

To develop a prediction model based on patient-related characteristics for detecting prostate cancer (PCa) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 4-5 in multiparametric magnetic resonance imaging (mp-MRI), aiming to optimize pre-biopsy risk stratification in MRI.

MATERIALS AND METHODS

The patient-related characteristics including the lesion location, age, prostate-specific antigen (PSA), free prostate-specific antigen (fPSA), fPSA/PSA, prostate-specific antigen density (PSAD) and body mass index (BMI) were collected for patients who underwent mp-MRI and prostate biopsy between February 2014 and October 2022. Univariate and multivariate logistic regression analyses were conducted to select independent predictors of PCa and further create a prediction model. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) were also calculated.

RESULTS

A total of 833 patients were included in this study. In the subgroup PI-RADS 4, the independent characteristics of lesion location, age, fPSA/PSA and PSAD were selected to create the prediction model with an AUC of 0.748 (95% CI 0.694-0.803), sensitivity of 61.88%, specificity of 85.32%, PPV of 92.52%, and NPV of 43.26%. Besides, the prediction model in PI-RADS 5 was created using PSA and PSAD with an AUC of 0.893 (95% CI 0.844-0.941), sensitivity of 81.40%, specificity of 84.85%, PPV of 98.37% and NPV of 28.87%.

CONCLUSION

The patient-related clinical characteristics were significant predictors of PCa and the prediction model based on selected characteristics could achieve a medium risk prediction of PCa in PI-RADS 4-5.

Parameterized Gompertz-Guided Morphological AutoEncoder for Predicting Pulmonary Nodule Growth

The growth rate of pulmonary nodules is a critical clue to the cancerous diagnosis. It is essential to monitor their dynamic progressions during pulmonary nodule management. To facilitate the prosperity of research on nodule growth prediction, we organized and published a temporal dataset called NLSTt with consecutive computed tomography (CT) scans. Based on the self-built dataset, we develop a visual learner to predict the growth for the following CT scan qualitatively and further propose a model to predict the growth rate of pulmonary nodules quantitatively, so that better diagnosis can be achieved with the help of our predicted results. To this end, in this work, we propose a parameterized Gempertz-guided morphological autoencoder (GM-AE) to generate any future-time-span high-quality visual appearances of pulmonary nodules from the baseline CT scan. Specifically, we parameterize a popular mathematical model for tumor growth kinetics, Gompertz, to predict future masses and volumes of pulmonary nodules. Then, we exploit the expected growth rate on the mass and volume to guide decoders generating future shape and texture of pulmonary nodules. We introduce two branches in an autoencoder to encourage shape-aware and textural-aware representation learning and integrate the generated shape into the textural-aware branch to simulate the future morphology of pulmonary nodules. We conduct extensive experiments on the self-built NLSTt dataset to demonstrate the superiority of our GM-AE to its competitive counterparts. Experiment results also reveal the learnable Gompertz function enjoys promising descriptive power in accounting for inter-subject variability of the growth rate for pulmonary nodules. Besides, we evaluate our GM-AE model on an in-house dataset to validate its generalizability and practicality. We make its code publicly available along with the published NLSTt dataset.

Prostate Imaging after Focal Ablation (PI-FAB): A Proposal for a Scoring System for Multiparametric MRI of the Prostate After Focal Therapy

At present there is no standardised system for scoring the appearance of the prostate on multiparametric magnetic resonance imaging (MRI) after focal ablation for localised prostate cancer. We propose a novel scoring system, the Prostate Imaging after Focal Ablation (PI-FAB) score, to fill this gap. PI-FAB involves a 3-point scale for rating MRI sequences in sequential order: (1) dynamic contrast-enhanced sequences; (2) diffusion-weighted imaging, split into assessment of the high-b-value sequence first and then the apparent diffusion coefficient map; and (3) T2-weighted imaging. It is essential that the pretreatment scan is also available to help with this assessment. We designed PI-FAB using our experience of reading postablation scans over the past 15 years and include details for four representative patients initially treated with high-intensity focus ultrasound at our institution to demonstrate the scoring system. We propose PI-FAB as a standardised method for evaluating prostate MRI scans after treatment with focal ablation. The next step is to evaluate its performance across multiple experienced readers of MRI after focal therapy in a clinical data set. PATIENT SUMMARY: We propose a scoring system called PI-FAB for assessing the appearance of magnetic resonance imaging scans of the prostate after focal treatment for localised prostate cancer. This will help clinicians in deciding on further follow-up.

Head-to-Head Comparison of 18F-PSMA-1007 Positron Emission Tomography/Computed Tomography and Multiparametric Magnetic Resonance Imaging with Whole-mount Histopathology as Reference in Localisation and Staging of Primary Prostate Cancer

BACKGROUND

Accurate local staging is critical for treatment planning and prognosis in prostate cancer (PCa). Although multiparametric magnetic resonance imaging (mpMRI) has high specificity for detection of extraprostatic extension (EPE) and seminal vesicle invasion (SVI), its sensitivity remains limited. 18F-PSMA-1007 positron emission tomography/computed tomography (PET/CT) may be more accurate in determining T stage.

OBJECTIVE

To assess the diagnostic performance of 18F-PSMA-1007 PET/CT in comparison to mpMRI for intraprostatic tumour localisation and detection of EPE and SVI in men with primary PCa undergoing robot-assisted radical prostatectomy (RARP).

DESIGN, SETTING, AND PARTICIPANTS

Between February 2019 and October 2020, 105 treatment-naïve patients with biopsy-proven intermediate- or high-risk PCa undergoing mpMRI and 18F-PSMA-1007 PET/CT before RARP were prospectively enrolled.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The diagnostic accuracy of 18F-PSMA-1007 PET/CT and mpMRI for intraprostatic tumour localisation and detection of EPE and SVI was assessed via histopathological examination of whole-mount RP specimens. The sensitivity, specificity, negative predictive value, positive predictive value, and accuracy were calculated. The McNemar test was used to compare outcomes between imaging modalities.

RESULTS AND LIMITATIONS

In 80 RP specimens, 129 PCa lesions were found, of which 96 were clinically significant PCa (csPCa). Per-lesion sensitivity for localisation of overall PCa was 85% (95% confidence interval [CI] 77-90%) with PSMA PET/CT and 62% (95% CI 53-70%) with mpMRI (p < 0.001). Per-lesion sensitivity for csPCa was 95% (95% CI 88-98%) with PSMA PET/CT and 73% (95% CI 63-81%) with mpMRI (p < 0.001). The diagnostic accuracy of PSMA PET/CT and mpMRI for detection of EPE per lesion did not significantly differ (sensitivity 45%, 95% CI 31-60% vs 55%, 95% CI 40-69%; p = 0.3; specificity 85%, 95% CI 75-92% vs 90%, 95% CI 81-86%; p = 0.5). The sensitivity and specificity of PSMA PET/CT and mpMRI for detection of SVI did not significantly differ (sensitivity 47%, 95% CI 21-73% vs 33%, 95% CI 12-62; p = 0.6; specificity 94%, 95% CI 88-98% vs 96%, 95% CI 90-99%; p = 0.8).

CONCLUSIONS

18F-PSMA-1007 is a promising imaging modality for localising intraprostatic csPCa but did not show additional value in assessing EPE and SVI in comparison to mpMRI.

PATIENT SUMMARY

A new imaging technique called PET/CT (positron emission tomography/computed tomography) with the radioactive tracer 18F-PSMA-1007 shows promise in identifying the location of clinically significant prostate cancer. However, it does not seem to be of additional value over magnetic resonance imaging (MRI) for determining the local tumour stage.

Is prostatic adenocarcinoma with cribriform architecture more difficult to detect on prostate MRI?

BACKGROUND

Cribriform (CBFM) pattern on prostate biopsy has been implicated as a predictor for high-risk features, potentially leading to adverse outcomes after definitive treatment. This study aims to investigate whether the CBFM pattern containing prostate cancers (PCa) were associated with false negative magnetic resonance imaging (MRI) and determine the association between MRI and histopathological disease burden.

METHODS

Patients who underwent multiparametric magnetic resonance imaging (mpMRI), combined 12-core transrectal ultrasound (TRUS) guided systematic (SB) and MRI/US fusion-guided biopsy were retrospectively queried for the presence of CBFM pattern at biopsy. Biopsy cores and lesions were categorized as follows: C0 = benign, C1 = PCa with no CBFM pattern, C2 = PCa with CBFM pattern. Correlation between cancer core length (CCL) and measured MRI lesion dimension were assessed using a modified Pearson correlation test for clustered data. Differences between the biopsy core groups were assessed with the Wilcoxon-signed rank test with clustering.

RESULTS

Between 2015 and 2022, a total of 131 consecutive patients with CBFM pattern on prostate biopsy and pre-biopsy mpMRI were included. Clinical feature analysis included 1572 systematic biopsy cores (1149 C0, 272 C1, 151 C2) and 736 MRI-targeted biopsy cores (253 C0, 272 C1, 211 C2). Of the 131 patients with confirmed CBFM pathology, targeted biopsy (TBx) alone identified CBFM in 76.3% (100/131) of patients and detected PCa in 97.7% (128/131) patients. SBx biopsy alone detected CBFM in 61.1% (80/131) of patients and PCa in 90.8% (119/131) patients. TBx and SBx had equivalent detection in patients with smaller prostates (p = 0.045). For both PCa lesion groups there was a positive and significant correlation between maximum MRI lesion dimension and CCL (C1 lesions: p < 0.01, C2 lesions: p < 0.001). There was a significant difference in CCL between C1 and C2 lesions for T2 scores of 3 and 5 (p ≤ 0.01, p ≤ 0.01, respectively) and PI-RADS 5 lesions (p ≤ 0.01), with C2 lesions having larger CCL, despite no significant difference in MRI lesion dimension.

CONCLUSIONS

The extent of disease for CBFM-containing tumors is difficult to capture on mpMRI. When comparing MRI lesions of similar dimensions and PIRADS scores, CBFM-containing tumors appear to have larger cancer yield on biopsy. Proper staging and planning of therapeutic interventions is reliant on accurate mpMRI estimation. Special considerations should be taken for patients with CBFM pattern on prostate biopsy.

A radiomics approach based on MR imaging for classification of deficiency and excess syndrome of traditional Chinese medicine in prostate cancer

Objective

To explore the potential imaging biomarkers for predicting Traditional Chinese medicine (TCM) deficiency and excess syndrome in prostate cancer (PCa) patients by radiomics approach based on MR imaging.

Methods

A total of 121 PCa patients from 2 centers were divided into 1 training cohort with 84 PCa patients and 1 validation cohort with 37 PCa patients. The PCa patients were divided into deficiency and excess syndrome group according to TCM syndrome differentiation. Radiomic features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging and apparent diffusion coefficient images originated from diffusion-weighted imaging. A radiomic signature was constructed after reduction of dimension in training group by the minimum redundancy maximum relevance and the least absolute shrinkage and selection operator. The performance of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve.

Results

The radiomic scores of PCa with TCM excess syndrome group were statistically higher than those of PCa with TCM deficiency syndrome group among T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models. The area under ROC curves for T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models were 0.824, 0.824, 0.847 in the training cohort and 0.759, 0.750, 0.809 in the validation cohort, respectively. The apparent diffusion coefficient imaging model had the best discrimination in separating patients with TCM excess syndrome and deficiency syndrome, and its accuracy was 0.788, 0.778 in the training and validation cohort, respectively. The calibration curve demonstrated that there was a high consistency between the prediction of radiomic scores and the actual classification of TCM's deficiency and excess syndrome in PCa.

Conclusion

The radiomic signature based on MR imaging can be performed as a non-invasive, potential approach to discriminate TCM deficiency syndrome from excess syndrome in PCa, in which apparent diffusion coefficient imaging model has the best diagnostic efficiency.

Bi-parametric prostate MRI with a recall system for contrast enhanced imaging: Improving accessibility while maintaining quality

PURPOSE

To review the efficacy of a recall system for bi-parametric non-contrast prostate MRI (bp-MRI).

METHODS

A bi-parametric protocol was instituted in July 2020 for all patients who had a prostate MRI requested, excluding those after treatment of prostate cancer, patients with hip prosthesis or pacemaker, and those who lived out-of-town. The protocol consisted of tri-planar T2-weighted and diffusion weighted images (DWI) (b = 50, 800 s/mm2 for ADC map; b = 1,500 s/mm2 acquired separately) in accordance with the Prostate Imaging Reporting & Data system (PI-RADS) v2.1 guidelines. After interpretation of bp-MRI exams, patients with equivocal (PI-RADS 3) lesions in peripheral zone (PZ) or any technical limitations were recalled for contrast administration.

RESULTS

Out of 909 bp-MRI scans performed from July 2020 to April 2021, only 52 (5.7 %) were recalled, of which 46 (88.5 %) attended. Amongst these, 41/52 (78.8 %) were recalled for PZ PI-RADS 3 lesions, while the rest of 11 (21.2 %) cases were recalled for technical reasons. Mean time to subsequent recall scan was 11.6 days. On assessment of post-contrast imaging, 29/46 (63 %) cases were upgraded to PI-RADS 4 while 17/46 (37 %) remained PI-RADS 3. This system avoided contrast-agent use in 857 patients, with contrast cost savings of €64,620 (US$68,560) and 214 hours 15 minutes of scanner time was saved. This allowed 255 additional bp-MRI scans to be performed, reducing the waitlist from 1 year to 2-3 weeks.

CONCLUSION

A bi-parametric prostate MRI protocol with a robust recall system for contrast administration not only saved time eliminating the marked backlog but was also more cost efficient without compromising the quality of patient care.

Multiparametric MRI characteristics of prostatitis and atrophy in the peripheral zone in men without prostate cancer

PURPOSE

To analyse multiparametric magnetic resonance imaging (mpMRI) characteristics and appearance of histopathologically proven non-cancerous intraprostatic findings focussing on quantity of prostatitis and atrophy in the peripheral zone.

METHOD

In this retrospective analysis consecutive patients with mpMRI followed by MRI/TRUS-fusion biopsy comprising targeted (TB) and systematic biopsy (SB) cores without prostate cancer (PC) at histopathology were included. Subgroup analysis was performed in younger men (≤50 years). The proportions of prostatitis and atrophy were quantified for each biopsy core based on histopathology. MRI findings in the peripheral zone (PZ) and index lesions (IL, most suspicious/representative lesion) were characterized regarding changes in T2w, ADC value, and enhancement of dynamic contrast enhancement (DCE) and correlated with quantity of prostatitis and atrophy.

RESULTS

Seventy-two patients were analysed. The median baseline characteristics were PSA 5.4 ng/ml (4.0-7.9), PI-RADS classification 3 (2-4), prostate volume 43 ml (33-57), and PSA density 0.13 ng/ml2 (0.10-0.19). Prostatitis was found in 44 % (n = 32) and atrophy in 65 % (n = 47) of cases. The quantity of atrophy demonstrated a significant correlation to T2w changes, ADC increase and DCE enhancement (p = 0.05, p = 0.05, p = 0.01), whereas quantity of prostatitis did not show any significant correlation to the MRI changes (p = 0.68, p = 0.58, p = 0.95). Quantity of prostatitis and atrophy increased with PI-RADS classification. Younger men had lower PSA (4.4 vs. 7.8 ml/ng; p < 0.001), smaller prostate volume (40 vs. 59 ml; p = 0.001), and lower PI-RADS classification (2-3 vs. 3-4; p = 0.005) and prostatitis and atrophy were less frequently observed (p ≤ 0.01, p = 0.03).

CONCLUSIONS

Quantity of atrophy and prostatitis had different influence on MRI characteristics and increased within higher PI-RADS classification. Younger men had diffuse hypointense changes at T2w images, but less quantity of prostatitis and atrophy.

Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology

Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.

A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI

BACKGROUND

Deep learning has been used to detect or characterize prostate cancer (PCa) on medical images. The present study was designed to develop an integrated transfer learning nomogram (TLN) for the prediction of PCa and benign conditions (BCs) on magnetic resonance imaging (MRI).

METHODS

In this retrospective study, a total of 709 patients with pathologically confirmed PCa and BCs from two institutions were included and divided into training (n = 309), internal validation (n = 200), and external validation (n = 200) cohorts. A transfer learning signature (TLS) that was pretrained with the whole slide images of PCa and fine-tuned on prebiopsy MRI images was constructed. A TLN that integrated the TLS, the Prostate Imaging-Reporting and Data System (PI-RADS) score, and the clinical factor was developed by multivariate logistic regression. The performance of the TLS, clinical model (CM), and TLN were evaluated in the validation cohorts using the receiver operating characteristic (ROC) curve, the Delong test, the integrated discrimination improvement (IDI), and decision curve analysis.

RESULTS

TLS, PI-RADS score, and age were selected for TLN construction. The TLN yielded areas under the curve of 0.9757 (95% CI, 0.9613-0.9902), 0.9255 (95% CI, 0.8873-0.9638), and 0.8766 (95% CI, 0.8267-0.9264) in the training, internal validation, and external validation cohorts, respectively, for the discrimination of PCa and BCs. The TLN outperformed the TLS and the CM in both the internal and external validation cohorts. The decision curve showed that the TLN added more net benefit than the CM.

CONCLUSIONS

The proposed TLN has the potential to be used as a noninvasive tool for PCa and BCs differentiation.

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

OBJECTIVE

To assess the effect of preoperative MRI with standardized Prostate Imaging-Reporting and Data System (PI-RADS) assessment on pathological outcomes in prostate cancer (PCa) patients who underwent radical prostatectomy (RP).

PATIENTS AND METHODS

This retrospective cohort study included patients who had undergone prostate MRI and subsequent RP for PCa between January 2017 and December 2022. The patients were divided into the PI-RADS group and the non-PI-RADS group according to evaluation scheme of presurgery MRI. The preoperative characteristics and postoperative outcomes were retrieved and analyzed. The pathological outcomes included pathological T stage (pT2 vs. pT3-4) and positive surgical margins (PSMs). Patients were further stratified according to statistically significant preoperative variables to assess the difference in pathological outcomes. A propensity score matching based on the above preoperative characteristics was additionally performed.

RESULTS

A total of 380 patients were included in this study, with 201 patients in the PI-RADS group and 179 in the non-PI-RADS group. The two groups had similar preoperative characteristics, except for clinical T stage (cT). As for pathological outcomes, the PI-RADS group showed a significantly lower percentage of pT3-4 (21.4% vs. 48.0%, p < 0.001), a lower percentage of PSMs (31.3% vs. 40.9%, p = 0.055), and a higher concordance between the cT and pT (79.1% vs. 64.8%, p = 0.003). The PI-RADS group also showed a lower proportion of pT3-4 (p < 0.001) in the cT1-2 subgroup and the cohort after propensity score matching. The PSM rate of cT3 patients was reduced by 39.2% in the PI-RADS group but without statistical significance (p = 0.089).

CONCLUSIONS

Preoperative MRI with standardized PI-RADS assessment could benefit the decision-making of patients by reducing the rate of pathologically confirmed non-organ-confined PCa after RP and slightly reducing the PSM rate compared with non-PI-RADS assessment.

Diagnostic accuracy of multiparametric ultrasound in the diagnosis of prostate cancer: systematic review and meta-analysis

OBJECTIVES

Ultrasound (US) technology has recently made advances that have led to the development of modalities including elastography and contrast-enhanced ultrasound. The use of different US modalities in combination may increase the accuracy of PCa diagnosis. This study aims to assess the diagnostic accuracy of multiparametric ultrasound (mpUS) in the PCa diagnosis.

METHODS

Through September 2023, we searched through Cochrane CENTRAL, PubMed, Embase, Scopus, Web of Science, ClinicalTrial.gov, and Google Scholar for relevant studies. We used standard methods recommended for meta-analyses of diagnostic evaluation. We plot the SROC curve, which stands for summary receiver operating characteristic. To determine how confounding factors affected the results, meta-regression analysis was used.

RESULTS

Finally, 1004 patients from 8 studies that were included in this research were examined. The diagnostic odds ratio for PCa was 20 (95% confidence interval (CI), 8-49) and the pooled estimates of mpUS for diagnosis were as follows: sensitivity, 0.88 (95% CI, 0.81-0.93); specificity, 0.72 (95% CI, 0.59-0.83); positive predictive value, 0.75 (95% CI, 0.63-0.87); and negative predictive value, 0.82 (95% CI, 0.71-0.93). The area under the SROC curve was 0.89 (95% CI, 0.86-0.92). There was a significant heterogeneity among the studies (p < 0.01). According to meta-regression, both the sensitivity and specificity of mpUS in the diagnosis of clinically significant PCa (csPCa) were inferior to any PCa.

CONCLUSION

The diagnostic accuracy of mpUS in the diagnosis of PCa is moderate, but the accuracy in the diagnosis of csPCa is significantly lower than any PCa. More relevant research is needed in the future.

CRITICAL RELEVANCE STATEMENT

This study provides urologists and sonographers with useful data by summarizing the accuracy of multiparametric ultrasound in the detection of prostate cancer.

KEY POINTS

• Recent studies focused on the role of multiparametric ultrasound in the diagnosis of prostate cancer. • This meta-analysis revealed that multiparametric ultrasound has moderate diagnostic accuracy for prostate cancer. • The diagnostic accuracy of multiparametric ultrasound in the diagnosis of clinically significant prostate cancer is significantly lower than any prostate cancer.

A novel clinically significant prostate cancer prediction system with multiparametric MRI and PSA: P.Z.A. score

PURPOSE

This study aims to establish and validate a new diagnosis model called P.Z.A. score for clinically significant prostate cancer (csPCa).

METHODS

The demographic and clinical characteristics of 956 patients were recorded. Age, prostate-specific antigen (PSA), free/total PSA (f/tPSA), PSA density (PSAD), peripheral zone volume ratio (PZ-ratio), and adjusted PSAD of PZ (aPSADPZ) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The nomogram was established, and discrimination abilities of the new nomogram were verified with a calibration curve and area under the ROC curve (AUC). The clinical benefits of P.Z.A. score were evaluated by decision curve analysis and clinical impact curves. External validation of the model using the validation set was also performed.

RESULTS

The AUCs of aPSADPZ, age, PSA, f/tPSA, PSAD and PZ-ratio were 0.824, 0.672, 0.684, 0.715, 0.792 and 0.717, respectively. The optimal threshold of P.Z.A. score was 0.41. The nomogram displayed excellent net benefit and better overall calibration for predicting the occurrence of csPCa. In addition, the number of patients with csPCa predicted by P.Z.A. score was in good agreement with the actual number of patients with csPCa in the high-risk threshold. The validation set provided better validation of the model.

CONCLUSION

P.Z.A. score (including PIRADS(P), aPSADPZ(Z) and age(A)) can increase the detection rate of csPCa, which may decrease the risk of misdiagnosis and reduce the number of unnecessary biopsies. P.Z.A. score contains data that is easy to obtain and is worthy of clinical replication.

Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review

The aim of this review is to summarise recent scientific literature regarding the clinical use of DCE-MRI as a component of multiparametric resonance imaging of the prostate. This review presents the principles of DCE-MRI acquisition and analysis, the current role of DCE-MRI in clinical practice with special regard to its role in presently available categorisation systems, and an overview of the advantages and disadvantages of DCE-MRI described in the current literature. DCE-MRI is an important functional sequence that requires intravenous administration of a gadolinium-based contrast agent and gives information regarding the vascularity and capillary permeability of the lesion. Although numerous studies have confirmed that DCE-MRI has great potential in the diagnosis and monitoring of prostate cancer, its role is still inadequate in the PI-RADS categorisation. Moreover, there have been numerous scientific discussions about abandoning the intravenous application of gadolinium-based contrast as a routine part of MRI examination of the prostate. In this review, we summarised the recent literature on the advantages and disadvantages of DCE-MRI, focusing on an overview of currently available data on bpMRI and mpMRI, as well as on studies providing information on the potential better usability of DCE-MRI in improving the sensitivity and specificity of mpMRI examinations of the prostate.

Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer

Prostate cancer (PCa) diagnosis on multi-parametric magnetic resonance images (MRI) requires radiologists with a high level of expertise. Misalignments between the MRI sequences can be caused by patient movement, elastic soft-tissue deformations, and imaging artifacts. They further increase the complexity of the task prompting radiologists to interpret the images. Recently, computer-aided diagnosis (CAD) tools have demonstrated potential for PCa diagnosis typically relying on complex co-registration of the input modalities. However, there is no consensus among research groups on whether CAD systems profit from using registration. Furthermore, alternative strategies to handle multi-modal misalignments have not been explored so far. Our study introduces and compares different strategies to cope with image misalignments and evaluates them regarding to their direct effect on diagnostic accuracy of PCa. In addition to established registration algorithms, we propose 'misalignment augmentation' as a concept to increase CAD robustness. As the results demonstrate, misalignment augmentations can not only compensate for a complete lack of registration, but if used in conjunction with registration, also improve the overall performance on an independent test set.

The grade of individual prostate cancer lesions predicted by magnetic resonance imaging and positron emission tomography

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) and positron emission tomography (PET) are widely used for the management of prostate cancer (PCa). However, how these modalities complement each other in PCa risk stratification is still largely unknown. We aim to provide insights into the potential of mpMRI and PET for PCa risk stratification.

METHODS

We analyzed data from 55 consecutive patients with elevated prostate-specific antigen and biopsy-proven PCa enrolled in a prospective study between December 2016 and December 2019. [68Ga]PSMA-11 PET (PSMA-PET), [11C]Acetate PET (Acetate-PET) and mpMRI were co-registered with whole-mount histopathology. Lower- and higher-grade lesions were defined by International Society of Urological Pathology (ISUP) grade groups (IGG). We used PET and mpMRI data to differentiate between grades in two cases: IGG 3 vs. IGG 2 (case 1) and IGG ≥ 3 vs. IGG ≤ 2 (case 2). The performance was evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

We find that the maximum standardized uptake value (SUVmax) for PSMA-PET achieves the highest area under the ROC curve (AUC), with AUCs of 0.72 (case 1) and 0.79 (case 2). Combining the volume transfer constant, apparent diffusion coefficient and T2-weighted images (each normalized to non-malignant prostatic tissue) results in AUCs of 0.70 (case 1) and 0.70 (case 2). Adding PSMA-SUVmax increases the AUCs by 0.09 (p < 0.01) and 0.12 (p < 0.01), respectively.

CONCLUSIONS

By co-registering whole-mount histopathology and in-vivo imaging we show that mpMRI and PET can distinguish between lower- and higher-grade prostate cancer, using partially discriminative cut-off values.

Danish Prostate Cancer Consortium Study 1 (DPCC-1) protocol: Multicentre prospective validation of the urine-based three-microRNA biomarker model uCaP

INTRODUCTION

The primary objective of the Danish Prostate Cancer Consortium Study 1 (DPCC-1) is to provide validation for a novel urine-based microRNA biomarker, called uCaP, for a diagnosis of prostate cancer.

METHODS AND ANALYSIS

Eligible participants are biopsy naïve men aged ≥18 years with prostate-specific antigen (PSA) levels ≥3 ng/mL, who are referred to prostate MRI due to suspicion of PC at one of the following three major urology/uroradiology centers: Aarhus University Hospital, Herlev & Gentofte University Hospital, or Odense University Hospital, where MRI and targeted biopsy are implemented in clinical use. Exclusion criteria include previous diagnosis of urogenital cancer, contraindication to MRI, gender reassignment treatment or PSA level >20 ng/mL. The participants will be asked to donate a urine sample in connection with their MRI. The study is observational, uses a diagnostic accuracy testing setup and will integrate into the current diagnostic pathway.We will measure the levels of the three microRNAs in the uCaP model (miR-222-3 p, miR-24-3 p and miR-30c-5p) in extracellular vesicle-enriched cell-free urine samples, to assess if uCaP can improve specificity and retain sensitivity for International Society of Urological Pathology Grade Group ≥2 PC, when used as a reflex test to PSA ≥3 ng/mL. We hypothesise that uCaP can improve selection for prostate MRI and reduce the number of unnecessary scans and biopsies.

ETHICS AND DISSEMINATION

This study is approved by the Central Denmark Region Committee on Health Research Ethics (reference number: 1-10-72-85-22). All participants will provide written informed consent. Study results will be published in peer-reviewed journals and presented in scientific meetings.

TRIAL REGISTRATION NUMBER

NCT05767307 at clinicaltrials.gov.

Multiparametric Magnetic Resonance Imaging of Penile Cancer: A Pictorial Review

The role of multiparametric magnetic resonance imaging (mpMRI) in assessing penile cancer is not well defined. However, this modality may be successfully applied for preoperative staging and patient selection; postoperative local and regional surveillance; and assessments of treatment response after oncological therapies. Previous studies have been mostly limited to a few small series evaluating the accuracy of MRI for the preoperative staging of penile cancer. This review discusses the principles of non-erectile mpMRI, including functional techniques and their applications in evaluating the male genital region, along with clinical protocols and technical considerations. The latest clinical classifications and guidelines are reviewed, focusing on imaging recommendations and discussing potential gaps and disadvantages. The development of functional MRI techniques and the extraction of quantitative parameters from these sequences enables the noninvasive assessment of phenotypic and genotypic tumor characteristics. The applications of advanced techniques in penile MRI are yet to be defined. There is a need for prospective trials and feasible multicenter trials due to the rarity of the disease, highlighting the importance of minimum technical requirements for MRI protocols, particularly image resolution, and finally determining the role of mpMRI in the assessment of penile cancer.

Prostate zones and tumor morphological parameters on magnetic resonance imaging for predicting the tumor-stage diagnosis of prostate cancer

PURPOSE

To determine whether the morphological parameters of prostate zones and tumors on magnetic resonance imaging (MRI) can predict the tumor-stage (T-stage) of prostate cancer (PCa) and establish an optimal T-stage diagnosis protocol based on three-dimensional reconstruction and quantization after image segmentation.

METHODS

A dataset of the prostate MRI scans and clinical data of 175 patients who underwent biopsy and had pathologically proven PCa from January 2018 to November 2020 was retrospectively analyzed. The authors manually segmented and measured the volume, major axis, and cross-sectional area of the peripheral zone (PZ), transition zone, central zone (CZ), anterior fibromuscular stroma, and tumor. The differences were evaluated by the One-Way analysis of variance, Pearson's chi-squared test, or independent samples t-test. Spearman's correlation coefficient and receiver operating characteristic curve analyses were also performed. The cut-off values of the T-stage diagnosis were generated using Youden's J index.

RESULTS

The prostate volume (PV), PZ volume (PZV), CZ volume, tumor's major axis (TA), tumor volume (TV), and volume ratio of the TV and PV were significantly different among stages T1 to T4. The cut-off values of the PV, PZV, CZV, TA, TV, and the ratio of TV/PV for the discrimination of the T1 and T2 stages were 53.63 cm3, 11.60 cm3, 1.97 cm3, 2.30 mm, 0.90 cm3, and 0.03 [area under the curves (AUCs): 0.628, 0.658, 0.610, 0.689, 0.724, and 0.764], respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T2 and T3 stages were 2.80 mm, 8.29 cm3, and 0.12 (AUCs: 0.769, 0.702, and 0.688), respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T3 and T4 stages were 4.17 mm, 18.71 cm3, and 0.22 (AUCs: 0.674, 0.709, and 0.729), respectively.

CONCLUSION

The morphological parameters of the prostate zones and tumors on the MRIs are simple and valuable diagnostic factors for predicting the T-stage of patients with PCa, which can help make accurate diagnoses and lateral treatment decisions.

Diagnosis of prostate cancer with magnetic resonance imaging in men treated with 5-alpha-reductase inhibitors

PURPOSE

The primary aim of this study was to evaluate if exposure to 5-alpha-reductase inhibitors (5-ARIs) modifies the effect of MRI for the diagnosis of clinically significant Prostate Cancer (csPCa) (ISUP Gleason grade ≥ 2).

METHODS

This study is a multicenter cohort study including patients undergoing prostate biopsy and MRI at 24 institutions between 2013 and 2022. Multivariable analysis predicting csPCa with an interaction term between 5-ARIs and PIRADS score was performed. Sensitivity, specificity, and negative (NPV) and positive (PPV) predictive values of MRI were compared in treated and untreated patients.

RESULTS

705 patients (9%) were treated with 5-ARIs [median age 69 years, Interquartile range (IQR): 65, 73; median PSA 6.3 ng/ml, IQR 4.0, 9.0; median prostate volume 53 ml, IQR 40, 72] and 6913 were 5-ARIs naïve (age 66 years, IQR 60, 71; PSA 6.5 ng/ml, IQR 4.8, 9.0; prostate volume 50 ml, IQR 37, 65). MRI showed PIRADS 1-2, 3, 4, and 5 lesions in 141 (20%), 158 (22%), 258 (37%), and 148 (21%) patients treated with 5-ARIs, and 878 (13%), 1764 (25%), 2948 (43%), and 1323 (19%) of untreated patients (p < 0.0001). No difference was found in csPCa detection rates, but diagnosis of high-grade PCa (ISUP GG ≥ 3) was higher in treated patients (23% vs 19%, p = 0.013). We did not find any evidence of interaction between PIRADS score and 5-ARIs exposure in predicting csPCa. Sensitivity, specificity, PPV, and NPV of PIRADS ≥ 3 were 94%, 29%, 46%, and 88% in treated patients and 96%, 18%, 43%, and 88% in untreated patients, respectively.

CONCLUSIONS

Exposure to 5-ARIs does not affect the association of PIRADS score with csPCa. Higher rates of high-grade PCa were detected in treated patients, but most were clearly visible on MRI as PIRADS 4 and 5 lesions.

TRIAL REGISTRATION

The present study was registered at ClinicalTrials.gov number: NCT05078359.

Long-Term Outcomes of a Prospective Study on Highly Hypofractionated Intensity Modulated Radiation Therapy for Localized Prostate Cancer for 3 Weeks

PURPOSE

Reports of radiation therapy for prostate cancer using dose fractions between moderate hypofractionation and ultrahypofractionation are limited. This pilot study involved the application of highly hypofractionated intensity modulated radiation therapy (IMRT) in 15 fractions for 3 weeks and the number of fractions was intermediate between the 2 previously mentioned dose fractions. The long-term outcomes are reported.

METHODS AND MATERIALS

From April 2014 to September 2015, patients with low- to intermediate-risk prostate cancer received 54 Gy in 15 fractions (3.6 Gy per fraction) for 3 weeks using IMRT without intraprostatic fiducial markers or a rectal hydrogel spacer. Neoadjuvant hormone therapy (HT) was administered for 4 to 8 months. Adjuvant HT was not administered to any patients. Rates of biochemical relapse-free survival, clinical relapse-free survival, overall survival, and the cumulative incidence of late grade ≥2 toxicities were analyzed.

RESULTS

Twenty-five patients were enrolled in this prospective study; 24 of them were treated with highly hypofractionated IMRT (17% had low-risk and 83% had intermediate-risk disease). The median neoadjuvant HT duration was 5.3 months. The median follow-up period was 77 months (range, 57-87 months). Biochemical relapse-free survival, clinical relapse-free survival, and overall survival rates were 91.7%, 95.8%, and 95.8% at 5 years, and 87.5%, 86.3%, and 95.8% at 7 years, respectively. Neither grade ≥2 late gastrointestinal toxicity nor grade ≥3 late genitourinary toxicity was observed. The cumulative incidence rates of grade 2 genitourinary toxicity were 8.5% and 18.3% at 5 and 7 years, respectively.

CONCLUSIONS

Highly hypofractionated IMRT delivering 54 Gy in 15 fractions for 3 weeks for prostate cancer without intraprostatic fiducial markers facilitated favorable oncological outcomes without severe complications. This treatment approach may be a possible alternative to moderate hypofractionation, but further validation is needed.

The PRECISE Recommendations for Prostate MRI in Patients on Active Surveillance for Prostate Cancer: A Critical Review

The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations were published in 2016 to standardize the reporting of MRI examinations performed to assess for disease progression in patients on active surveillance for prostate cancer. Although a limited number of studies have reported outcomes from use of PRECISE in clinical practice, the available studies have demonstrated PRECISE to have high pooled NPV but low pooled PPV for predicting progression. Our experience in using PRECISE in clinical practice at two teaching hospitals has highlighted issues with its application and areas requiring clarification. This Clinical Perspective critically appraises PRECISE on the basis of this experience, focusing on the system's key advantages and disadvantages and exploring potential changes to improve the system's utility. These changes include consideration of image quality when applying PRECISE scoring, incorporation of quantitative thresholds for disease progression, adoption of a PRECISE 3F sub-category for progression not qualifying as substantial, and comparisons with both the baseline and most recent prior examinations. Items requiring clarification include derivation of a patient-level score in patients with multiple lesions, intended application of PRECISE score 5 (i.e., if requiring development of disease that is no longer organ-confined), and categorization of new lesions in patients with prior MRI-invisible disease.

Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability

OBJECTIVES

To evaluate a fully automatic deep learning system to detect and segment clinically significant prostate cancer (csPCa) on same-vendor prostate MRI from two different institutions not contributing to training of the system.

MATERIALS AND METHODS

In this retrospective study, a previously bi-institutionally validated deep learning system (UNETM) was applied to bi-parametric prostate MRI data from one external institution (A), a PI-RADS distribution-matched internal cohort (B), and a csPCa stratified subset of single-institution external public challenge data (C). csPCa was defined as ISUP Grade Group ≥ 2 determined from combined targeted and extended systematic MRI/transrectal US-fusion biopsy. Performance of UNETM was evaluated by comparing ROC AUC and specificity at typical PI-RADS sensitivity levels. Lesion-level analysis between UNETM segmentations and radiologist-delineated segmentations was performed using Dice coefficient, free-response operating characteristic (FROC), and weighted alternative (waFROC). The influence of using different diffusion sequences was analyzed in cohort A.

RESULTS

In 250/250/140 exams in cohorts A/B/C, differences in ROC AUC were insignificant with 0.80 (95% CI: 0.74-0.85)/0.87 (95% CI: 0.83-0.92)/0.82 (95% CI: 0.75-0.89). At sensitivities of 95% and 90%, UNETM achieved specificity of 30%/50% in A, 44%/71% in B, and 43%/49% in C, respectively. Dice coefficient of UNETM and radiologist-delineated lesions was 0.36 in A and 0.49 in B. The waFROC AUC was 0.67 (95% CI: 0.60-0.83) in A and 0.7 (95% CI: 0.64-0.78) in B. UNETM performed marginally better on readout-segmented than on single-shot echo-planar-imaging.

CONCLUSION

For same-vendor examinations, deep learning provided comparable discrimination of csPCa and non-csPCa lesions and examinations between local and two independent external data sets, demonstrating the applicability of the system to institutions not participating in model training.

CLINICAL RELEVANCE STATEMENT

A previously bi-institutionally validated fully automatic deep learning system maintained acceptable exam-level diagnostic performance in two independent external data sets, indicating the potential of deploying AI models without retraining or fine-tuning, and corroborating evidence that AI models extract a substantial amount of transferable domain knowledge about MRI-based prostate cancer assessment.

KEY POINTS

• A previously bi-institutionally validated fully automatic deep learning system maintained acceptable exam-level diagnostic performance in two independent external data sets. • Lesion detection performance and segmentation congruence was similar on the institutional and an external data set, as measured by the weighted alternative FROC AUC and Dice coefficient. • Although the system generalized to two external institutions without re-training, achieving expected sensitivity and specificity levels using the deep learning system requires probability thresholds to be adjusted, underlining the importance of institution-specific calibration and quality control.

Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence

BACKGROUND

To investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy.

METHODS

Clinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction.

RESULTS

RadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models achieved an accuracy of 82.6% and 78.4% in staging T3 and LN+. The iBCR-Net, when expressed as a presurgical model by integrating RadS, AI-diagnosed T3 stage and PSA, can match a state-of-the-art histopathological model (C-index, 0.81 to 0.83 vs 0.79 to 0.81, p > 0.05); and has maximally 5.16-fold, 12.8-fold, and 2.09-fold (p < 0.05) benefit to conventional D'Amico score, the Cancer of the Prostate Risk Assessment (CAPRA) score and the CAPRA Postsurgical score.

CONCLUSIONS

AI-aided iBCR-Net using high-throughput MRI can predict PCa BCR accurately and thus may provide an alternative to the conventional method for PCa risk stratification.

Use of Imaging and Biopsy in Prostate Cancer Diagnosis: A Survey From the Asian Prostate Imaging Working Group

OBJECTIVE

To elucidate the use of radiological studies, including nuclear medicine, and biopsy for the diagnosis and staging of prostate cancer (PCA) in clinical practice and understand the current status of PCA in Asian countries via an international survey.

MATERIALS AND METHODS

The Asian Prostate Imaging Working Group designed a survey questionnaire with four domains focused on prostate magnetic resonance imaging (MRI), other prostate imaging, prostate biopsy, and PCA backgrounds. The questionnaire was sent to 111 members of professional affiliations in Korea, Japan, Singapore, and Taiwan who were representatives of their working hospitals, and their responses were analyzed.

RESULTS

This survey had a response rate of 97.3% (108/111). The rates of using 3T scanners, antispasmodic agents, laxative drugs, and prostate imaging-reporting and data system reporting for prostate MRI were 21.6%-78.9%, 22.2%-84.2%, 2.3%-26.3%, and 59.5%-100%, respectively. Respondents reported using the highest b-values of 800-2000 sec/mm² and fields of view of 9-30 cm. The prostate MRI examinations per month ranged from 1 to 600, and they were most commonly indicated for biopsy-naïve patients suspected of PCA in Japan and Singapore and staging of proven PCA in Korea and Taiwan. The most commonly used radiotracers for prostate positron emission tomography are prostate-specific membrane antigen in Singapore and fluorodeoxyglucose in three other countries. The most common timing for prostate MRI was before biopsy (29.9%). Prostate-targeted biopsies were performed in 63.8% of hospitals, usually by MRI-ultrasound fusion approach. The most common presentation was localized PCA in all four countries, and it was usually treated with radical prostatectomy.

CONCLUSION

This survey showed the diverse technical details and the availability of imaging and biopsy in the evaluation of PCA. This suggests the need for an educational program for Asian radiologists to promote standardized evidence-based imaging approaches for the diagnosis and staging of PCA.

Utility of accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction in female pelvic MRI: a multi-reader study

OBJECTIVES

To determine the clinical feasibility of T2-weighted turbo spin-echo (T2-TSE) imaging with deep learning reconstruction (DLR) in female pelvic MRI compared with conventional T2 TSE in terms of image quality and scan time.

METHODS

Between May 2021 and September 2021, 52 women (mean age, 44 years ± 12) who underwent 3-T pelvic MRI with additional T2-TSE using a DLR algorithm were included in this single-center prospective study with patient's informed consents. Conventional, DLR, and DLR T2-TSE images with reduced scan times were independently assessed and compared by four radiologists. The overall image quality, differentiation of anatomic details, lesion conspicuity, and artifacts were evaluated using a 5-point scale. Inter-observer agreement of the qualitative scores was compared and reader protocol preferences were then evaluated.

RESULTS

In the qualitative analysis of all readers, fast DLR T2-TSE showed significantly better overall image quality, differentiation of anatomic regions, lesion conspicuity, and lesser artifacts than conventional T2-TSE and DLR T2-TSE, despite approximately 50% reduction in scan time (all p < 0.05). The inter-reader agreement for the qualitative analysis was moderate to good. All readers preferred DLR over conventional T2-TSE regardless of scan time and preferred fast DLR T2-TSE (57.7-78.8%), except for one who preferred DLR over fast DLR T2-TSE (53.8% vs. 46.1%).

CONCLUSION

In female pelvic MRI, image quality and accelerated image acquisition for T2-TSE can be significantly improved by using DLR compared to conventional T2-TSE. Fast DLR T2-TSE was non-inferior to DLR T2-TSE in terms of reader preference and image quality.

CLINICAL RELEVANCE STATEMENT

DLR of T2-TSE in female pelvic MRI enables fast imaging along with maintaining optimal image quality compared with parallel imaging-based conventional T2-TSE.

KEY POINTS

• Conventional T2 turbo spin-echo based on parallel imaging has limitations for accelerated image acquisition while maintaining good image quality. • Deep learning image reconstruction showed better image quality in both images obtained using the same or accelerated image acquisition parameters compared with conventional T2 turbo spin-echo in female pelvic MRI. • Deep learning image reconstruction enables accelerated image acquisition while maintaining good image quality in the T2-TSE of female pelvic MRI.

A Novel Magnetic Resonance Imaging/Ultrasound Fusion Prostate Biopsy Technique Using Transperineal Ultrasound: An Initial Experience

OBJECTIVE

To report an initial experience with a novel, "fully" transperineal (TP) prostate fusion biopsy using an unconstrained ultrasound transducer placed on the perineal skin to guide biopsy needles inserted via a TP approach.

METHODS

Conventional TP prostate biopsies for detection of prostate cancer have been performed with transrectal ultrasound, requiring specialized hardware, imposing limitations on needle trajectory, and contributing to patient discomfort. Seventy-six patients with known or suspected prostate cancer underwent 78 TP biopsy sessions in an academic center between June 2018 and April 2022 and were included in this study. These patients underwent TP prostate fusion biopsy using a grid or freehand device with transrectal ultrasound as well as TP prostate fusion biopsy using TP ultrasound in the same session. Per-session and per-lesion cancer detection rates were compared for conventional and fully TP biopsies using Fisher exact and McNemar's tests.

RESULTS

After a refinement period in 30 patients, 92 MRI-visible prostate lesions were sampled in 46 subsequent patients, along with repeat biopsies in 2 of the 30 patients from the refinement period. Grade group ≥2 cancer was diagnosed in 24/92 lesions (26%) on conventional TP biopsy (17 lesions with grid, 7 with freehand device), and in 25/92 lesions (27%) on fully TP biopsy (P = 1.00), with a 73/92 (79%) rate of agreement for grade group ≥2 cancer between the two methods.

CONCLUSION

Fully TP biopsy is feasible and may detect prostate cancer with detection rates comparable to conventional TP biopsy.