Multiparametric Magnetic Resonance Imaging for the Detection of Clinically Significant Prostate Cancer: What Urologists Need to Know. Part 2: Interpretation

Molecular and diffusion features for identification of clinically significant prostate cancer in PI-RADS 3 lesions

BACKGROUND

The recommendation to perform biopsy of PIRADS 3 lesions has not been adopted with strength as compared to higher scored lesions on multiparametric MRI. This represents a challenging scenario and an unmet need for clinicians to apply a risk adapted approach in these cases. In the present study, we examined clinical and radiologic characteristics in men with PI-RADS 3 index lesions that can predict csPCa on mpMRI-target biopsy.

METHODS

Revision of a prospective database with patients who underwent targeted and systematic biopsies from 2015 to 2023 for PI-RADS 3 lesions identified on mpMRI. Baseline variables were collected, such as PSA density (PSAd), 4Kscore, prostate size, and the apparent diffusion coefficient (ADC) value of the lesion on mpMRI. Logistic regression, receiver operating characteristic (ROC) and decision curve analyses (DCA) assessing the association between clinic-radiologic factors and csPCa were performed.

RESULTS

Overall, 230 patients were included in the study and the median age was 65 years. The median prostate size and PSA were 50 g and 6.26 ng/mL, respectively. 17.4% of patients had csPCa, while 27.5% had Gleason group 1. In univariable logistic analyses, we found that age, BMI, prostate size, PSAd, ADC, and 4Kscore were significant csPCa predictors (P < 0.05). PSAd showed the best prediction performance in terms of AUC (= 0.679). On multivariable analysis, PSAd and 4Kscore were associated with csPCa. The net benefit of PSAd combined with clinical features was superior to those of other parameters. Within patients with PSAd < 0.15, 4Kscore was a statistically significant predictor of csPCa (OR = 3.25, P = 0.032).

CONCLUSION

PSAd and 4Kscore are better predictors of csPCa in patients with PIRADS 3 lesions compared to ADC. The predictive role of 4Kscore is higher in patients with low PSAd. These results can assist practitioners in the risk stratification of patients with equivocal lesions to determine the need of biopsy.

Multiparametric MRI and 18F-PSMA-1007 PET/CT for the Detection of Clinically Significant Prostate Cancer

Background Multiparametric MRI (mpMRI) is effective for detecting prostate cancer (PCa); however, there is a high rate of equivocal Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions and false-positive findings. Purpose To investigate whether fluorine 18 (18F) prostate-specific membrane antigen (PSMA) 1007 PET/CT after mpMRI can help detect localized clinically significant PCa (csPCa), particularly for equivocal PI-RADS 3 lesions. Materials and Methods This prospective study included participants with elevated prostate-specific antigen (PSA) levels referred for prostate mpMRI between September 2020 and February 2022. 18F-PSMA-1007 PET/CT was performed within 30 days of mpMRI and before biopsy. PI-RADS category and level of suspicion (LOS) were assessed. PI-RADS 3 or higher lesions at mpMRI and/or LOS 3 or higher lesions at 18F-PSMA-1007 PET/CT underwent targeted biopsies. PI-RADS 2 or lower and LOS 2 or lower lesions were considered nonsuspicious and were monitored during a 1-year follow-up by means of PSA testing. Diagnostic accuracy was assessed, with histologic examination serving as the reference standard. International Society of Urological Pathology (ISUP) grade 2 or higher was considered csPCa. Results Seventy-five participants (median age, 67 years [range, 52-77 years]) were assessed, with PI-RADS 1 or 2, PI-RADS 3, and PI-RADS 4 or 5 groups each including 25 participants. A total of 102 lesions were identified, of which 80 were PI-RADS 3 or higher and/or LOS 3 or higher and therefore underwent targeted biopsy. The per-participant sensitivity for the detection of csPCa was 95% and 91% for mpMRI and 18F-PSMA-1007 PET/CT, respectively, with respective specificities of 45% and 62%. 18F-PSMA-1007 PET/CT was used to correctly differentiate 17 of 26 PI-RADS 3 lesions (65%), with a negative and positive predictive value of 93% and 27%, respectively, for ruling out or detecting csPCa. One additional significant and one insignificant PCa lesion (PI-RADS 1 or 2) were found at 18F-PSMA-1007 PET/CT that otherwise would have remained undetected. Two participants had ISUP 2 tumors without PSMA uptake that were missed at PET/CT. Conclusion 18F-PSMA-1007 PET/CT showed good sensitivity and moderate specificity for the detection of csPCa and ruled this out in 93% of participants with PI-RADS 3 lesions. Clinical trial registration no. NCT04487847 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Turkbey in this issue.

Multi-scale statistical deformation based co-registration of prostate MRI and post-surgical whole mount histopathology

BACKGROUND

Accurate delineations of regions of interest (ROIs) on multi-parametric magnetic resonance imaging (mpMRI) are crucial for development of automated, machine learning-based prostate cancer (PCa) detection and segmentation models. However, manual ROI delineations are labor-intensive and susceptible to inter-reader variability. Histopathology images from radical prostatectomy (RP) represent the "gold standard" in terms of the delineation of disease extents, for example, PCa, prostatitis, and benign prostatic hyperplasia (BPH). Co-registering digitized histopathology images onto pre-operative mpMRI enables automated mapping of the ground truth disease extents onto mpMRI, thus enabling the development of machine learning tools for PCa detection and risk stratification. Still, MRI-histopathology co-registration is challenging due to various artifacts and large deformation between in vivo MRI and ex vivo whole-mount histopathology images (WMHs). Furthermore, the artifacts on WMHs, such as tissue loss, may introduce unrealistic deformation during co-registration.

PURPOSE

This study presents a new registration pipeline, MSERgSDM, a multi-scale feature-based registration (MSERg) with a statistical deformation (SDM) constraint, which aims to improve accuracy of MRI-histopathology co-registration.

METHODS

In this study, we collected 85 pairs of MRI and WMHs from 48 patients across three cohorts. Cohort 1 (D1), comprised of a unique set of 3D printed mold data from six patients, facilitated the generation of ground truth deformations between ex vivo WMHs and in vivo MRI. The other two clinically acquired cohorts (D2 and D3) included 42 patients. Affine and nonrigid registrations were employed to minimize the deformation between ex vivo WMH and ex vivo T2-weighted MRI (T2WI) in D1. Subsequently, ground truth deformation between in vivo T2WI and ex vivo WMH was approximated as the deformation between in vivo T2WI and ex vivo T2WI. In D2 and D3, the prostate anatomical annotations, for example, tumor and urethra, were made by a pathologist and a radiologist in collaboration. These annotations included ROI boundary contours and landmark points. Before applying the registration, manual corrections were made for flipping and rotation of WMHs. MSERgSDM comprises two main components: (1) multi-scale representation construction, and (2) SDM construction. For the SDM construction, we collected N = 200 reasonable deformation fields generated using MSERg, verified through visual inspection. Three additional methods, including intensity-based registration, ProsRegNet, and MSERg, were also employed for comparison against MSERgSDM.

RESULTS

Our results suggest that MSERgSDM performed comparably to the ground truth (p > 0.05). Additionally, MSERgSDM (ROI Dice ratio = 0.61, landmark distance = 3.26 mm) exhibited significant improvement over MSERg (ROI Dice ratio = 0.59, landmark distance = 3.69 mm) and ProsRegNet (ROI Dice ratio = 0.56, landmark distance = 4.00 mm) in local alignment.

CONCLUSIONS

This study presents a novel registration method, MSERgSDM, for mapping ex vivo WMH onto in vivo prostate MRI. Our preliminary results demonstrate that MSERgSDM can serve as a valuable tool to map ground truth disease annotations from histopathology images onto MRI, thereby assisting in the development of machine learning models for PCa detection on MRI.

Look-Locker T1 relaxometry and high-resolution T2 in the evaluation of lung lesions: a single-center prospective study

Objective

To explore the feasibility of two magnetic resonance imaging (MRI) sequences-high-resolution T2-weighted (HR T2) and Look-Locker T1 (LL T1) relaxometry-for the investigation focal lung lesions (FLLs). As a secondary objective, we analyzed the diagnostic accuracy of these sequences.

Materials and Methods

This was a prospective observational study involving 39 subjects with FLLs scanned in a 1.5-T MRI system with LL T1 relaxometry and HR T2 sequences focused on the FLL region, in addition to a conventional protocol. All images were evaluated by two radiologists, working independently, who were blinded to other findings.

Results

Most of the examinations (31 of the LL T1 relaxometry sequences and 36 of the HR T2 sequences) were of adequate diagnostic quality. Nondiagnostic examinations were considered so mainly because of limited coverage of the sequences. Of the FLLs studied, 19 were malignant, 17 were benign, and three were excluded from the accuracy analysis because there was no definitive diagnosis. Although LL T1 relaxometry could not distinguish between benign and malignant lesions, the signal intensity at its first inversion time (160 ms) differed between the two groups. The HR T2 sequence was considered the best sequence for assessing specific morphological characteristics, especially pseudocavities and pleural tags. We found that MRI showed better accuracy than did computed tomography (86% vs. 74%).

Conclusion

Both MRI sequences are feasible for the evaluation of FLLs. Images at 160 ms of the LL T1 relaxometry sequence helped distinguish between benign and malignant lesions, and the HR T2 sequence was considered the best sequence for evaluating specific morphological characteristics.

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.

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

PURPOSE

There is substantial variability in multiparametric MRI (mpMRI) protocols and inter-readers' agreement. We tested the effect of a central mpMRI review on the detection of clinically significant PCa (csPCa) in a tertiary referral center.

METHODS

We retrospectively analyzed a cohort of 364 consecutive men with a positive externally performed mpMRI (PI-RADS ≥ 3) who underwent a targeted biopsy (TBx) plus a systematic biopsy at a single tertiary referral center (2018-2020). Of those mpMRIs, 32% (n = 116) were centrally reviewed. We compared the detection of csPCa between the non-central-reviewed vs reviewed group. Multivariable logistic regression models (MVA) tested the relationship between mpMRI central review and the detection of csPCa at TBx.

RESULTS

The detection of csPCa at TBx in non-central-reviewed vs central-reviewed group was 41 vs 63%, respectively (p = 0.001). The distribution of PI-RADS 2, 3, 4, and 5 at initial assessment vs after mpMRI central review was 0, 37, 47, and 16% vs 39, 9, 35, and 16%, respectively (p < 0.004). Of 43 patients with initial PI-RADS 3 score, respectively 67, 21, and 12, and 0% had a revised PI-RADS score of ≤ 2, 3, 4, and 5. At MVA, mpMRI central review (OR: 1.65, CI 0.85-0.98) was significantly associated with higher csPCa detection at TBx.

CONCLUSIONS

We demonstrated that a central review of external mpMRIs may decrease the overcall of equivocal lesions, namely PI-RADS 3, and should be considered to maximize the clinical benefit of TBx in terms of increasing the detection of csPCa and eventually decreasing the rate of unnecessary biopsies.

Are T2WI PI-RADS sub-scores of transition zone prostate lesions biased by DWI information? A multi-reader, single-center study

PURPOSE

According to PI-RADS v2.1, T2-weighted imaging (T2WI) is the dominant sequence for transition zone (TZ) lesions. This study aimed to assess, whether diffusion-weighted imaging (DWI) information influences the assignment of T2WI scores.

METHOD

Out of 283 prostate MRI examinations with correlated biopsy results, fourty-four patients were selected retrospectively: first, 22 patients with a TZ lesion with T2WI and DWI scores ≥ 4, to represent lesions with unequivocal suspicion on T2WI and DWI. Second, 22 additional patients with TZ lesions of similar T2WI appearance but with corresponding DWI score ≤ 3 were added as control. Four residents and one board-certified radiologist each performed two assessments of the included patients: First, only T2WI was available (T2-only read); second, both T2WI and DWI sequences were available (biparametric read). Lesion scores were assessed using Wilcoxon signed-rank test, inter-reader agreement using weighted kappa and Kendall's W statistics, and sensitivity/specificity using McNemar test.

RESULTS

The T2WI scores were significantly different between the T2-only and biparametric read for 3 out of 4 residents (p ≤ 0.049) but not for the radiologist. The overall PI-RADS scores derived from the two reading sessions differed considerably for 35/220 cases (all readers pooled). Inter-reader agreement was fair for the T2WI and overall PI-RADS scores (mean kappa 0.27-0.30) and moderate for the DWI scores (mean kappa 0.43).

CONCLUSIONS

For inexperienced readers, assessment of T2WI is variable and potentially biased by availability of DWI information, which can lead to changes of overall PI-RADS score and consequently clinical management. Assessment of T2WI should be performed before reviewing DWI to ensure non-biased interpretation of TZ lesions in the dominant sequence.

New insights into molecular signaling pathways and current advancements in prostate cancer diagnostics & therapeutics

Prostate adenocarcinoma accounts for more than 20% of deaths among males due to cancer. It is the fifth-leading cancer diagnosed in males across the globe. The mortality rate is quite high due to prostate cancer. Despite the fact that advancements in diagnostics and therapeutics have been made, there is a lack of effective drugs. Metabolic pathways are altered due to the triggering of androgen receptor (AR) signaling pathways, and elevated levels of dihydrotestosterone are produced due to defects in AR signaling that accelerate the growth of prostate cancer cells. Further, PI3K/AKT/mTOR pathways interact with AR signaling pathway and act as precursors to promote prostate cancer. Prostate cancer therapy has been classified into luminal A, luminal B, and basal subtypes. Therapeutic drugs inhibiting dihydrotestosterone and PI3K have shown to give promising results to combat prostate cancer. Many second-generation Androgen receptor signaling antagonists are given either as single agent or with the combination of other drugs. In order to develop a cure for metastasized prostate cancer cells, Androgen deprivation therapy (ADT) is applied by using surgical or chemical methods. In many cases, Prostatectomy or local radiotherapy are used to control metastasized prostate cancer. However, it has been observed that after 1.5 years to 2 years of Prostatectomy or castration, there is reoccurrence of prostate cancer and high incidence of castration resistant prostate cancer is seen in population undergone ADT. It has been observed that Androgen derivation therapy combined with drugs like abiraterone acetate or docetaxel improve overall survival rate in metastatic hormone sensitive prostate cancer (mHSPC) patients. Scientific investigations have revealed that drugs inhibiting poly ADP Ribose polymerase (PARP) are showing promising results in clinical trials in the prostate cancer population with mCRPC and DNA repair abnormalities. Recently, RISUG adv (reversible inhibition of sperm under guidance) has shown significant results against prostate cancer cell lines and MTT assay has validated substantial effects of this drug against PC3 cell lines. Current review paper highlights the advancements in prostate cancer therapeutics and new drug molecules against prostate cancer. It will provide detailed insights on the signaling pathways which need to be targeted to combat metastasized prostate cancer and castration resistant prostate cancer.

Use of artificial intelligence in discerning the need for prostate biopsy and readiness for clinical practice: a systematic review protocol

BACKGROUND

Variability and inaccuracies in the diagnosis of prostate cancer, and the risk of complications from invasive tests, have been extensively reported in the research literature. To address this, the use of artificial intelligence (AI) has been attracting increased interest in recent years to improve the diagnostic accuracy and objectivity. Although AI literature has reported promising results, further research is needed on the identification of evidence gaps that limit the potential adoption in prostate cancer screening practice.

METHODS

A systematic electronic search strategy will be used to identify peer-reviewed articles published from inception to the date of searches and indexed in CINAHL, IEEE Xplore, MEDLINE, Scopus, and Web of Science Core Collection databases. Registries including Cochrane Central Register of Controlled Trials, ClinicalTrials.gov and International Clinical Trials Registry Platform (ICTRP) will be searched for unpublished studies, and experts were invited to provide suitable references. The research and reporting will be based on Cochrane recommendations and PRISMA guidelines, respectively. The screening and quality assessment of the articles will be conducted by two of the authors independently, and conflicts will be resolved by a third author.

DISCUSSION

This systematic review will summarise the use of AI techniques to predict the need for prostate biopsy based on clinical and demographic indicators, including its diagnostic accuracy and readiness for adoption in clinical practice.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022336540.

Impact of Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography on the Therapeutic Decision of Prostate Carcinoma Primary Staging: A Retrospective Analysis at the Brazilian National Public Health System

BACKGROUND

Prostate cancer (PCa) is the most common malignant tumor in males and conventional imaging does not provide accurate primary staging. Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) presents superior performance and strongly affects therapeutic choice.

OBJECTIVE

The aim of this study was to evaluate the impact of PSMA PET, compared with conventional imaging methods, on the therapeutic approach in primary staging scenarios in patients with PCa treated at the Brazilian National Public Health System.

METHODS

Overall, 35 patients diagnosed with PCa were evaluated using PSMA after conventional staging imaging with multiparametric magnetic resonance (MMR) and/or total abdominal computed tomography (CT) scan and bone scintigraphy (BS). The PCa extension identified by PET was compared with conventional imaging; staging changes and the management impact were then determined. PET comparison with conventional imaging, staging, and decision-making changes was analyzed using descriptive statistics.

RESULTS

PET revealed local disease (LD) in 15 (42.9%) patients, seminal vesicle invasion (SVI) in 5 (14.3%) patients, pelvic nodal impairment (PNI) in 7 (20%) patients, pelvic and distant nodes in 3 (8.6%) patients, pelvic nodes and bone metastasis in 4 (11.4%) patients, and pelvic and distant nodes and bone metastasis in 1 (2.8%) patient. Staging changes were observed in 60% of patients, with downstaging predominance (76.2%). Volume increase was identified in 11 (31.4%) patients (only 4 related to upstaging, 36.4%). The board changed management decisions for 60% of the patients. The main limitations of this study were the sample size and its retrospective nature.

CONCLUSIONS

PSMA findings changed the management decisions in more than half of the patients, which made the majority eligible for locoregional treatment and avoided unnecessary procedures in the systemic disease scenario.

Prostate imaging-reporting and data system version 2 has improved biopsy tumor grade accuracy: a single, tertiary institutional experience

PURPOSE

To investigate change in prostate biopsy accuracy regarding tumor grade before and after the release of Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) in a single tertiary institution.

METHODS

We retrospectively evaluated 1191 patients with biopsy-proven prostate cancer (PCa) who had undergone prostate magnetic resonance imaging (MRI) and surgery before (2013 cohort, n = 394) and 5 years after PI-RADSv2 release (2020 cohort, n = 797). The highest tumor grade of each biopsy and surgical specimen was recorded, respectively. We compared concordant, underestimated, and overestimated biopsy rates regarding tumor grade to surgery between two cohorts, respectively. For patients who underwent both prostate MRI and biopsy at our institution, we investigated proportion of pre-biopsy MRI, age, and prostate-specific antigen of patients, and performed logistic regression to analyze which parameters are associated with concordant biopsy.

RESULTS

Concordant and underestimated biopsy rates were significantly different between two cohorts: Concordance and underestimation rates were 47.2% and 46.3% in 2013 and 54.5% and 36.4% in 2020 (p = .019; p = .003), respectively. Overestimated biopsy rates were similar (p = .993). Proportion of pre-biopsy MRI was significantly higher in 2020 than in 2013 (80.9% versus 4.9%; p < .001), and was independently associated with concordant biopsy results in multivariate analysis (odds ratio = 1.486; 95% confidence interval, 1.057-2.089; p = .022).

CONCLUSIONS

There was a significant change in proportion of pre-biopsy MRI before and after the release of PI-RADSv2 in patients who underwent surgery for PCa. This change appears to have improved biopsy accuracy regarding tumor grade by reducing underestimation.

A prospective study of the prostate health index density and multiparametric magnetic resonance imaging in diagnosing clinically significant prostate cancer

PURPOSE

To evaluate the predictive performance of the prostate health index (PHI) and PHI density (PHID), for clinically significant prostate cancer (csPCa) in patients with a PI-RADS score ≤3.

MATERIALS AND METHODS

Patients tested for total prostate-specific antigen (tPSA, ≤100 ng/mL), free PSA (fPSA), and p2PSA at Peking University First Hospital were prospectively enrolled. Possible predictive factors of csPCa were analyzed using the receiver operating characteristic (ROC) curve. Results were expressed as area under the curve (AUC) with 95% confidence intervals (CI). The cutoff values of PHI and PHID were determined.

RESULTS

We enrolled 222 patients in this study. The prevalence of csPCa in the PI-RADS ≤3 subgroup (n=89) was 22.47% (20/89). Age, tPSA, F/T, prostate volume, PSA density, PHI, PHID, and PI-RADS score were significantly associated with csPCa. PHID (AUC: 0.829 [95% CI: 0.717-0.941]) was the best predictor of csPCa. PHID >0.956 was set as the threshold of suspicious csPCa with a sensitivity of 85.00% and a specificity of 73.91%, avoiding 94.44% of unnecessary biopsies but missing 15.00% csPCa. A threshold of PHI ≥52.83 showed the same sensitivity but a rather lower specificity of 65.22% that avoided 93.75% of unnecessary biopsies.

CONCLUSIONS

PHI and PHID have the best predictive performance of csPCa in patients with PI-RADS score ≤3. A threshold value of PHID ≥0.956 may be used as the criterion for biopsy in these patients.

Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer

Background

around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─).

Objective

To quantify the differences between MR visible (MRI+) and MRI^─ CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI).

Methods

This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI^─ CsPCa referred to lesions with PI-RADS v2 score < 3 but ISUP grade group > 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (D_v) comprised 52 patients from a single institution, the remaining 112 patients were used for training (D_t). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI.Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on D_t to identify radiomic features associated with MRI^─ and MRI⁺ CsPCa to generate corresponding risk scores R^MRI─ and R^MRI+. R_bpMRI was further generated by integrating R^MRI─ and R^MRI+. Statistical significance was determined using the Wilcoxon signed-rank test.

Results

Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI^─ CsPCa (p < 0.05). Intra-lesional ADC Haralick and CoLlAGe radiomic features were significantly different among MRI^─ and MRI⁺ CsPCa (p < 0.05). R_bpMRI yielded the highest AUC of 0.82 (95 % CI 0.72-0.91) compared to AUCs of R^MRI+ 0.76 (95 % CI 0.63-0.89), and PI-RADS 0.58 (95 % CI 0.50-0.72) on D_v. R_bpMRI correctly reclassified 10 out of 14 MRI^─ CsPCa on D_v.

Conclusion

Our preliminary results demonstrated that both intra-lesional and peri-lesional bpMRI radiomic features were significantly associated with MRI^─ CsPCa. These features could assist in CsPCa identification on bpMRI.

Modern Tools for Modern Brachytherapy

This review aims to showcase the brachytherapy tools and technologies that have emerged during the last 10 years. Soft-tissue contrast using magnetic resonance and ultrasound imaging has seen enormous growth in use to plan all forms of brachytherapy. The era of image-guided brachytherapy has encouraged the development of advanced applicators and given rise to the growth of individualised 3D printing to achieve reproducible and predictable implants. These advances increase the quality of implants to better direct radiation to target volumes while sparing normal tissue. Applicator reconstruction has moved beyond manual digitising, to drag and drop of three-dimensional applicator models with embedded pre-defined source pathways, ready for auto-recognition and automation. The simplified TG-43 dose calculation formalism directly linked to reference air kerma rate of high-energy sources in the medium water remains clinically robust. Model-based dose calculation algorithms accounting for tissue heterogeneity and applicator material will advance the field of brachytherapy dosimetry to become more clinically accurate. Improved dose-optimising toolkits contribute to the real-time and adaptive planning portfolio that harmonises and expedites the entire image-guided brachytherapy process. Traditional planning strategies remain relevant to validate emerging technologies and should continue to be incorporated in practice, particularly for cervical cancer. Overall, technological developments need commissioning and validation to make the best use of the advanced features by understanding their strengths and limitations. Brachytherapy has become high-tech and modern by respecting tradition and remaining accessible to all.

A Contemporary Report of Low-Dose-Rate Brachytherapy for Prostate Cancer Using MRI for Risk Stratification: Disease Outcomes and Patient-Reported Quality of Life

PURPOSE

We examined a prospective consecutive cohort of low dose rate (LDR) brachytherapy for prostate cancer to evaluate the efficacy of monotherapy for unfavorable-intermediate risk (UIR) disease, and explore factors associated with toxicity and quality of life (QOL).

METHODS

149 men with prostate cancer, including 114 staged with MRI, received Iodine-125 brachytherapy alone (144-145 Gy) or following external beam radiation therapy (110 Gy; EBRT). Patient-reported QOL was assessed by the Expanded Prostate Index Composite (EPIC) survey, and genitourinary (GU) and gastrointestinal (GI) toxicity were prospectively recorded (CTC v4.0). Global QOL scores were assessed for decline greater than the minimum clinically important difference (MCID). Univariate analysis (UVA) was performed, with 30-day post-implant dosimetry covariates stratified into quartiles. Median follow-up was 63 mo.

RESULTS

Men with NCCN low (n = 42) or favorable-intermediate risk (n = 37) disease were treated with brachytherapy alone, while most with high-risk disease had combined EBRT (n = 17 of 18). Men with UIR disease (n = 52) were selected for monotherapy (n = 42) based on clinical factors and MRI findings. Freedom from biochemical failure-7 yr was 98%. Of 37 men with MRI treated with monotherapy for UIR disease, all 36 men without extraprostatic extension were controlled. Late Grade 2+/3+ toxicity occurred in 55/3% for GU and 8/2% for GI, respectively. Fifty men were sexually active at baseline and had 2 yr sexual data; 37 (74%) remained active at 2 yr. Global scores for urinary incontinence (UC), urinary irritation/obstruction (UIO), bowel function, and sexual function (SF) showed decreases greater than the MCID (p < 0.05) in UC at 2 mo, UIO at 2 and 6 mo, and SF at 2-24 mo, and >5 yr. Analysis did not reveal any significant associations with any examined rectal or urethral dosimetry for late toxicity or QOL.

CONCLUSION

Disease outcomes and patient-reported QOL support LDR brachytherapy, including monotherapy for UIR disease.

CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI

Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices. This difficulty can be overcome by leveraging important multi-scale image-based information from adjacent slices, but current methods do not fully learn and exploit such cross-slice information. In this paper, we propose a novel cross-slice attention mechanism, which we use in a Transformer module to systematically learn cross-slice information at multiple scales. The module can be utilized in any existing deep-learning-based segmentation framework with skip connections. Experiments show that our cross-slice attention is able to capture cross-slice information significant for prostate zonal segmentation in order to improve the performance of current state-of-the-art methods. Cross-slice attention improves segmentation accuracy in the peripheral zones, such that segmentation results are consistent across all the prostate slices (apex, mid-gland, and base). The code for the proposed model is available at https://bit.ly/CAT-Net.

Are Urologists Ready for Interpretation of Multiparametric MRI Findings? A Prospective Multicentric Evaluation

Aim: To assess urologists’ proficiency in the interpretation of multiparametric magnetic resonance imaging (mpMRI). Materials and Methods: Twelve mpMRIs were shown to 73 urologists from seven Italian institutions. Responders were asked to identify the site of the suspicious nodule (SN) but not to assign a PIRADS score. We set an a priori cut-off of 75% correct identification of SN as a threshold for proficiency in mpMRI reading. Data were analyzed according to urologists’ hierarchy (UH; resident vs. consultant) and previous experience in fusion prostate biopsies (E-fPB, defined as <125 vs. ≥125). Additionally, we tested for differences between non-proficient vs. proficient mpMRI readers. Multivariable logistic regression analyses (MVLRA) tested potential predictors of proficiency in mpMRI reading. Results: The median (IQR) number of correct identifications was 8 (6−8). Anterior nodules (number 3, 4 and 6) represented the most likely prone to misinterpretation. Overall, 34 (47%) participants achieved the 75% cut-off. When comparing consultants vs. residents, we found no differences in terms of E-fPB (p = 0.9) or in correct identification rates (p = 0.6). We recorded higher identification rates in urologists with E-fBP vs. their no E-fBP counterparts (75% vs. 67%, p = 0.004). At MVLRA, only E- fPB reached the status of independent predictor of proficiency in mpMRI reading (OR: 3.4, 95% CI 1.2−9.9, p = 0.02) after adjusting for UH and type of institution. Conclusions: Despite urologists becoming more familiar with interpretation of mpMRI, their results are still far from proficient. E-fPB enhances the proficiency in mpMRI interpretation.

Computer-aided diagnosis of prostate cancer based on deep neural networks from multi-parametric magnetic resonance imaging

Objectives: To evaluate a new deep neural network (DNN)–based computer-aided diagnosis (CAD) method, namely, a prostate cancer localization network and an integrated multi-modal classification network, to automatically localize prostate cancer on multi-parametric magnetic resonance imaging (mp-MRI) and classify prostate cancer and non-cancerous tissues.

Materials and methods: The PROSTAREx database consists of a “training set” (330 suspected lesions from 204 cases) and a “test set” (208 suspected lesions from 104 cases). Sequences include T2-weighted, diffusion-weighted, Ktrans, and apparent diffusion coefficient (ADC) images. For the task of abnormal localization, inspired by V-net, we designed a prostate cancer localization network with mp-MRI data as input to achieve automatic localization of prostate cancer. Combining the concepts of multi-modal learning and ensemble learning, the integrated multi-modal classification network is based on the combination of mp-MRI data as input to distinguish prostate cancer from non-cancerous tissues through a series of operations such as convolution and pooling. The performance of each network in predicting prostate cancer was examined using the receiver operating curve (ROC), and the area under the ROC curve (AUC), sensitivity (TPR), specificity (TNR), accuracy, and Dice similarity coefficient (DSC) were calculated.

Results: The prostate cancer localization network exhibited excellent performance in localizing prostate cancer, with an average error of only 1.64 mm compared to the labeled results, an error of about 6%. On the test dataset, the network had a sensitivity of 0.92, specificity of 0.90, PPV of 0.91, NPV of 0.93, and DSC of 0.84. Compared with multi-modal classification networks, the performance of single-modal classification networks is slightly inadequate. The integrated multi-modal classification network performed best in classifying prostate cancer and non-cancerous tissues with a TPR of 0.95, TNR of 0.82, F1-Score of 0.8920, AUC of 0.912, and accuracy of 0.885, which fully confirmed the feasibility of the ensemble learning approach.

Conclusion: The proposed DNN-based prostate cancer localization network and integrated multi-modal classification network yielded high performance in experiments, demonstrating that the prostate cancer localization network and integrated multi-modal classification network can be used for computer-aided diagnosis (CAD) of prostate cancer localization and classification.

AI-assisted biparametric MRI surveillance of prostate cancer: feasibility study

OBJECTIVES

To evaluate the feasibility of automatic longitudinal analysis of consecutive biparametric MRI (bpMRI) scans to detect clinically significant (cs) prostate cancer (PCa).

METHODS

This retrospective study included a multi-center dataset of 1513 patients who underwent bpMRI (T2 + DWI) between 2014 and 2020, of whom 73 patients underwent at least two consecutive bpMRI scans and repeat biopsies. A deep learning PCa detection model was developed to produce a heatmap of all PIRADS ≥ 2 lesions across prior and current studies. The heatmaps for each patient's prior and current examination were used to extract differential volumetric and likelihood features reflecting explainable changes between examinations. A machine learning classifier was trained to predict from these features csPCa (ISUP > 1) at the current examination according to biopsy. A classifier trained on the current study only was developed for comparison. An extended classifier was developed to incorporate clinical parameters (PSA, PSA density, and age). The cross-validated diagnostic accuracies were compared using ROC analysis. The diagnostic performance of the best model was compared to the radiologist scores.

RESULTS

The model including prior and current study (AUC 0.81, CI: 0.69, 0.91) resulted in a higher (p = 0.04) diagnostic accuracy than the current only model (AUC 0.73, CI: 0.61, 0.84). Adding clinical variables further improved diagnostic performance (AUC 0.86, CI: 0.77, 0.93). The diagnostic performance of the surveillance AI model was significantly better (p = 0.02) than of radiologists (AUC 0.69, CI: 0.54, 0.81).

CONCLUSIONS

Our proposed AI-assisted surveillance of prostate MRI can pick up explainable, diagnostically relevant changes with promising diagnostic accuracy.

KEY POINTS

• Sequential prostate MRI scans can be automatically evaluated using a hybrid deep learning and machine learning approach. • The diagnostic accuracy of our csPCa detection AI model improved by including clinical parameters.

More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis—A Systematic Review

(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords “prostate cancer”, “MRI”, “radiomics”, “textural analysis”, “artificial intelligence”, “computer assisted diagnosis”, out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.

Single center analysis of an advisable control interval for follow-up of patients with PI-RADS category 3 in multiparametric MRI of the prostate

To evaluate if follow-up mpMRI scans of patients in PI-RADS category 3 are safe enough to omit or delay prostate biopsy in the future and to determine an optimal control interval. This retrospective single center study includes consecutive PI-RADS category 3 patients with one or more follow-up mpMRI (T2WI, DWI, DCE) and subsequent MRI-targeted and systematic TRUS-guided biopsy between 2012 and 2018. Primary study objective was the verification of a significant PI-RADS category upgrade in follow-up mpMRI in patients with subsequent PCA positive biopsy versus patients with negative biopsy. Further objectives were development of the PI-RADS category and clinical parameters between initial and follow-up mpMRI in the context of histopathologic results and time interval. Eighty-nine patients (median PSA 6.6 ng/ml; PSAD 0.13 ng/ml/ml) were finally included (follow-up period 31 ± 18 months). 19 cases had PCA (median PSA 7.8 ng/ml; PSAD 0.14 ng/ml/ml). 4 cases had csPCA (median PSA 5.4 ng/ml; PSAD 0.13 ng/ml/ml) for which there was a significant PI-RADS upgrade after 12-24 months (mean 3.75; p = 0.01) compared to patients without PCA (mean 2.74). Without PCA the mean PI-RADS category decreased after 25-36 months (mean 2.74; p = 0.02). Clinical parameters did not change significantly except a PSAD increase for PCA patients after 24 months. Patients within PI-RADS category 3 may not need prompt biopsy since those with PCA reliably demonstrate a PI-RADS category upgrade in follow-up mpMRI after 12-24 months. PI-RADS 3 patients with negative biopsy do not benefit from follow-up mpMRI earlier than 24 months.

MRI as a screening tool for prostate cancer: current evidence and future challenges

Purpose

Prostate cancer (PCa) screening, which relies on prostate-specific antigen (PSA) testing, is a contentious topic that received negative attention due to the low sensitivity and specificity of PSA to detect clinically significant PCa. In this context, due to the higher sensitivity and specificity of magnetic resonance imaging (MRI), several trials investigate the feasibility of “MRI-only” screening approaches, and question if PSA testing may be replaced within prostate cancer screening programs.

Methods

This narrative review discusses the current literature and the outlook on the potential of MRI-based PCa screening.

Results

Several prospective randomized population-based trials are ongoing. Preliminary study results appear to favor the “MRI-only” approach. However, MRI-based PCa screening programs face a variety of obstacles that have yet to be fully addressed. These include the increased cost of MRI, lack of broad availability, differences in MRI acquisition and interpretation protocols, and lack of long-term impact on cancer-specific mortality. Partly, these issues are being addressed by shorter and simpler MRI approaches (5–20 min bi-parametric MRI), novel quality indicators (PI-QUAL) and the implementation of radiomics (deep learning, machine learning).

Conclusion

Although promising preliminary results were reported, MRI-based PCa screening still lack long-term data on crucial endpoints such as the impact of MRI screening on mortality. Furthermore, the issues of availability, cost-effectiveness, and differences in MRI acquisition and interpretation still need to be addressed.

Multiparametric Magnetic Resonance Imaging for the Detection of Clinically Significant Prostate Cancer: What Urologists Need to Know. Part 4: Transperineal Magnetic Resonance-Ultrasound Fusion Guided Biopsy Using Local Anesthesia

BACKGROUND

Transperineal magnetic resonance imaging-transrectal ultrasound fusion guided biopsy (MFGB) is an increasingly popular technique due to increasing rates of biopsy-related infections. However, its widespread implementation has been hampered by the supposed necessity of epidural or general anesthesia.

OBJECTIVE

To demonstrate the technique, feasibility, and results of transperineal MFGB under local anesthesia, in an ambulatory setting without the administration of prophylactic antibiotics.

DESIGN, SETTING, AND PARTICIPANTS

This single-center study enrolled consecutive biopsy-naïve men with a clinical suspicion of prostate cancer into a prospective database between November 2015 and November 2020. Men with Prostate Imaging Reporting and Data System (PI-RADS) version 2 scores 3-5 underwent transperineal MFGB.

SURGICAL PROCEDURE

Transperineal MFGB was performed in an ambulatory setting under local anesthesia by a single operator.

MEASUREMENTS

Procedure-associated adverse events were recorded. Patient discomfort during both the local anesthesia and the biopsy procedure was determined using a visual analogic scale (0-10). Detection rates of grade group (GG) ≥2 prostate cancer and the proportion of men with GG 1 cancer were assessed.

RESULTS AND LIMITATIONS

A total of 1097 eligible men underwent transperineal MFGB. The complication rate was 0.73% (8/1097); complications comprised five (0.46%) urinary tract infections including one hospitalization and three (0.27%) urinary retentions. In 735 men, the median pain scores were 2 (interquartile range [IQR] 2-3) for the local anesthesia procedure and 1 (IQR 0-2) for the biopsy. Prostate cancer was detected in 84% (926/1097) of men; 66% (723/1097) had GG ≥2 and 19% (203/1097) GG 1.

CONCLUSIONS

Transperineal MFGB can safely be performed as an outpatient procedure under local anesthesia in an ambulatory setting. The detection rate of clinically significant prostate cancer is high, and biopsy is well tolerated. Although no antibiotic prophylaxis was used, the rate of infectious complications is practicably negligible.

PATIENT SUMMARY

This article shows how tissue samples (biopsies) can accurately be obtained from suspicious regions seen on prostate magnetic resonance imaging via needles inserted in the perineum (skin between the scrotum and the anus) in men with suspected prostate cancer. This technique appears to be very well tolerated under local anesthesia and has a lower risk of infection without antibiotic prophylaxis than the more common biopsy route through the rectum, with antibiotics.

Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge

Objectives

To assess Prostate Imaging Reporting and Data System (PI-RADS)–trained deep learning (DL) algorithm performance and to investigate the effect of data size and prior knowledge on the detection of clinically significant prostate cancer (csPCa) in biopsy-naïve men with a suspicion of PCa.

Methods

Multi-institution data included 2734 consecutive biopsy-naïve men with elevated PSA levels (≥ 3 ng/mL) that underwent multi-parametric MRI (mpMRI). mpMRI exams were prospectively reported using PI-RADS v2 by expert radiologists. A DL framework was designed and trained on center 1 data (n = 1952) to predict PI-RADS ≥ 4 (n = 1092) lesions from bi-parametric MRI (bpMRI). Experiments included varying the number of cases and the use of automatic zonal segmentation as a DL prior. Independent center 2 cases (n = 296) that included pathology outcome (systematic and MRI targeted biopsy) were used to compute performance for radiologists and DL. The performance of detecting PI-RADS 4–5 and Gleason > 6 lesions was assessed on 782 unseen cases (486 center 1, 296 center 2) using free-response ROC (FROC) and ROC analysis.

Results

The DL sensitivity for detecting PI-RADS ≥ 4 lesions was 87% (193/223, 95% CI: 82–91) at an average of 1 false positive (FP) per patient, and an AUC of 0.88 (95% CI: 0.84–0.91). The DL sensitivity for the detection of Gleason > 6 lesions was 85% (79/93, 95% CI: 77–83) @ 1 FP compared to 91% (85/93, 95% CI: 84–96) @ 0.3 FP for a consensus panel of expert radiologists. Data size and prior zonal knowledge significantly affected performance (4%, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.05$$\end{document}p<0.05).

Conclusion

PI-RADS-trained DL can accurately detect and localize Gleason > 6 lesions. DL could reach expert performance using substantially more than 2000 training cases, and DL zonal segmentation.

Key Points

• AI for prostate MRI analysis depends strongly on data size and prior zonal knowledge.

• AI needs substantially more than 2000 training cases to achieve expert performance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08320-y.

Prostate cancer: diagnostic yield of modified transrectal ultrasound-guided twelve-core combined biopsy (targeted plus systematic biopsies) using prebiopsy magnetic resonance imaging

PURPOSE

This study aimed to analyze the diagnostic yield of modified transrectal ultrasound (TRUS)-guided 12-core combined biopsy (CB) using prebiopsy magnetic resonance imaging (MRI) for detecting clinically significant prostate cancer (csPCa).

METHODS

This retrospective study included 130 consecutive patients who underwent modified TRUS-guided 12-core CB using cognitive fusion for lesions of Prostate Imaging-Reporting and Data System (PI-RADS) category ≥ 3. The 12-core CB comprised 3-6-core targeted biopsy (TB) and systematic biopsy (SB). For SB, tissue sampling in TB regions was omitted, and 3-core sampling (i.e., apex, mid, and base) in the contralateral peripheral zone of TB was mandatory. csPCa was defined as International Society of Urological Pathology (ISUP) grade ≥ 2 cancer. The per-patient cancer detection rates (CDRs) according to biopsy type or PI-RADS category were investigated.

RESULTS

The CDRs of TB, SB, and CB for csPCa were 47.7% (62/130 patients), 29.2% (38/130), and 52.3% (68/130), respectively. For csPCa, the CDRs of TB and CB according to PI-RADS categories of 3, 4, or 5 were 25.0% (8/32) and 31.3% (10/32), 41.2% (28/68) and 45.6% (31/68), or 86.7% (26/30) and 90.0% (27/30), respectively. In 6 (4.6%) patients, csPCa was detected only by SB. In 18 (13.8%) patients, SB detected PCa of a higher ISUP grade than TB. In 11 (8.5%) patients, SB detected csPCa at contralateral peripheral zone of TB.

CONCLUSION

Modified TRUS-guided 12-core CB using prebiopsy MRI seems to be feasible. It may reduce total biopsy cores in patients who are suitable for CB based on prebiopsy MRI findings.

Clinical implementation of pre-biopsy magnetic resonance imaging pathways for the diagnosis of prostate cancer

Objective

To assess the outcomes of pre‐biopsy magnetic resonance imaging (MRI) pathways, as a tool in biopsy‐naïve men with suspicion of prostate cancer, in routine clinical practice. Secondary outcomes included a comparison of transrectal MRI‐directed biopsy (TR‐MRDB) and transperineal (TP)‐MRDB in men with suspicious MRI.

Patients and Methods

We retrospectively assessed a two‐centre cohort of consecutive biopsy‐naïve men with suspicion of prostate cancer who underwent a Prostate Imaging‐Reporting and Data System version 2 (PI‐RADS v2) compliant pre‐biopsy MRI in a single, high‐volume centre between 2015 and 2019 (Centre 1). Men with suspicious MRI scans underwent TR‐MRDB in Centre 1 and TP‐MRDB with additional random biopsies (RB) in Centre 2. The MRI and histopathology were assessed in the same institution (Centre 1). Outcomes included: (i) overall detection rates of Grade Group (GG) 1, GG ≥2, and GG ≥3 cancer in men with suspicious MRI; (ii) Biopsy‐avoidance due to non‐suspicious MRI; and (iii) Cancer detection rates and biopsy‐related complications between TR‐ and TP‐MRDB. To reduce confounding bias for MRDB comparisons, inverse probability weighting (IPW) was performed for age, digital rectal examination, prostate‐specific antigen (PSA), prostate volume, PSA density, and PI‐RADS category.

Results

Of the 2597 men included, the overall GG 1, GG ≥2, and GG ≥3 prevalence was 8% (210/2597), 27% (697/2597), and 15% (396/2597), respectively. Biopsy was avoided in 57% (1488/2597) of men. After IPW, the GG 1, GG ≥2 and GG ≥3 detection rates after TR‐ and TP‐MRDB were comparable at 24%, 57%, and 32%; and 18%, 64%, and 38%, respectively; with mean differences of −5.7% (95% confidence interval [CI] −13% to 1.4%), 6.1% (95% CI −2.1% to 14%), and 5.7% (95% CI −1.7% to 13%). Complications were similar in TR‐MRDB (0.50%) and TP‐MRDB with RB (0.62%; mean difference 0.11%, 95% CI −0.87% to 1.1%).

Conclusion

This high‐volume, two‐centre study shows pre‐biopsy MRI as a decision tool is implementable in daily clinical practice. Compared to recent trials, a substantially higher biopsy avoidance rate was achieved without compromising GG ≥2/GG ≥3 detection and coinciding with lower over detection rates of GG 1 cancer. Prostate cancer detection and complication rates were comparable for TR‐ and TP‐MRDB.

Biparametric Magnetic Resonance Imaging-Derived Nomogram to Detect Clinically Significant Prostate Cancer by Targeted Biopsy for Index Lesion

BACKGROUND

Currently, it is necessary to investigate how to combine biparametric magnetic resonance imaging (bpMRI) with various clinical parameters for the detection of clinically significant prostate cancer (csPCa).

PURPOSE

To develop a multivariate prebiopsy nomogram using clinical and bpMRI parameters for estimating the probability of csPCa.

STUDY TYPE

Retrospective, single-center study.

SUBJECTS

Two hundred and twenty-six patients who underwent targeted biopsy (TBx) for the MRI-suspected index lesion because of clinical suspicions of PCa.

FIELD STRENGTH/SEQUENCE

A 3 T MRI including turbo spin-echo T₂ -weighted and diffusion-weighted single-shot echo-planar imaging sequences.

ASSESSMENT

Prebiopsy clinical and bpMRI parameters were patient age, biopsy history (biopsy-naïve or repeated biopsy status), prostate-specific antigen density (PSAD), Prostate Imaging-Reporting and Data System version 2.1 (PI-RADSv2.1), and apparent diffusion coefficient ratio (ADCR). ADCR was defined as mean ADC of the index lesion divided by mean ADC of the contralateral prostatic region. A multivariate prebiopsy nomogram for csPCa (i.e. Gleason sum ≥7) was developed. Area under the curve (AUC) of each parameter and prebiopsy nomogram was assessed. Five-fold cross-validation was performed for robust estimation of performance of the prebiopsy nomogram.

STATISTICAL TESTS

Logistic regression, receiver-operating curve, and 5-fold cross-validation. P-value < 0.05 was considered statistically significant.

RESULTS

Proportion of csPCa was 31.9% (72/226). The AUCs of age, biopsy-naïve status, PSAD, PI-RADSv2.1, ADCR, and prebiopsy nomogram were 0.657 (95% confidence interval [CI], 0.580-0.733), 0.593 (95% CI, 0.525-0.660), 0.762 (95% CI, 0.697-0.826), 0.824 (95% CI, 0.770-0.878), 0.829 (95% CI, 0.769-0.888), and 0.906 (95% CI, 0.863-0.948), respectively: AUC of nomogram was significantly different than that of individual parameter. In the 5-fold cross-validation, the mean AUC of the prebiopsy nomogram for csPCa was 0.888 (95% CI, 0.786-0.983).

DATA CONCLUSIONS

This multivariate prebiopsy nomogram using clinical and bpMRI parameters may help estimate the probability of csPCa in patients undergoing TBx. ADCR seems to enhance the role of bpMRI in detecting csPCa.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction

We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model² for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imaging (bpMRI). Deep attention mechanisms drive its detection network, targeting salient structures and highly discriminative feature dimensions across multiple resolutions. Its goal is to accurately identify csPCa lesions from indolent cancer and the wide range of benign pathology that can afflict the prostate gland. Simultaneously, a decoupled residual classifier is used to achieve consistent false positive reduction, without sacrificing high sensitivity or computational efficiency. In order to guide model generalization with domain-specific clinical knowledge, a probabilistic anatomical prior is used to encode the spatial prevalence and zonal distinction of csPCa. Using a large dataset of 1950 prostate bpMRI paired with radiologically-estimated annotations, we hypothesize that such CNN-based models can be trained to detect biopsy-confirmed malignancies in an independent cohort. For 486 institutional testing scans, the 3D CAD system achieves 83.69±5.22% and 93.19±2.96% detection sensitivity at 0.50 and 1.46 false positive(s) per patient, respectively, with 0.882±0.030 AUROC in patient-based diagnosis -significantly outperforming four state-of-the-art baseline architectures (U-SEResNet, UNet++, nnU-Net, Attention U-Net) from recent literature. For 296 external biopsy-confirmed testing scans, the ensembled CAD system shares moderate agreement with a consensus of expert radiologists (76.69%; kappa = 0.51±0.04) and independent pathologists (81.08%; kappa = 0.56±0.06); demonstrating strong generalization to histologically-confirmed csPCa diagnosis.

Prostate Imaging Reporting and Data System (PI-RADS): What the radiologists need to know?

We aim to review the new modifications in MR imaging technique, image interpretation, lexicon, and scoring system of the last version of Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) in a simple and practical way. This last version of PI-RADS v2.1 describes the new technical modifications in the protocol of Multiparametric MRI (MpMRI) including T2, diffusion-weighted imaging (DWI), and dynamic contrast enhancement (DCE) parameters. It includes also; new guidelines in the image interpretation specifications in new locations (lesions located in the central zone and anterior fibromuscular stroma), clarification of T2 scoring of lesions of the transition zone, the distinction between DWI score 2 and 3 lesions in the transition zone and peripheral zone, as well as between positive and negative enhancement in DCE. Biparametric MRI (BpMRI) along with simplified PI-RADS is gaining more acceptances in the assessment of clinically significant prostatic cancer.

Diagnostic pathway of the biopsy-naïve patient suspected for prostate cancer: Real-life scenario when multiparametric Magnetic Resonance Imaging is not centralized

INTRODUCTION

We aimed to compare the pathway including multi-parametric Magnetic Resonance Imaging (mpMRI) versus the one without mpMRI in detection of prostate cancer (PCa) when mpMRI is not centralized.

MATERIALS

January 2019-March 2020: prospective data collection of trans-perineal prostate biopsies. Group A: biopsy-naïve patients who underwent mpMRI (at any institution) versus Group B: patients who did not. Within Group A, patients were stratified into those with negative mpMRI (mpMRI-, PIRADS v2.1=1-3, with PSA density <0.15 if PIRADS 3) who underwent standard biopsy (SB), versus those with positive mpMRI (mpMRI+, when PIRADS 3-5, with PSA density>0.15 if PIRADS 3) who underwent cognitive fusion biopsy.

RESULTS

Two hundred and eighty one biopsies were analyzed. 153 patients underwent mpMRI (Group A). 98 mpMRI+ underwent fusion biopsy; 55 mpMRI- underwent SB. 128 Group B patients underwent SB. Overall PCa detection rate was 52.3% vs. 48.4% (Group A vs. B, P=0.5). Non-clinically-significant PCa was detected in 7.8 vs. 13.3% (Group A vs. B, P=0.1). Among the 98 mpMRI+ Group A patients only 2 had non clinically-significant disease. In 55 mpMRI- patients who underwent SB, 10 (18.2%) had clinically-significant PCa. Prostate volume predicted detection of PCa. In Group B, age and PSA predicted PCa. Sensitivity of mpMRI was 75.0% for all PCa, 85.3% for clinically-significant PCa.

CONCLUSION

Higher detection of PCa and lower detection of non-clinically-significant PCa favored mpMRI pathway. A consistent number of clinically-significant PCa was diagnosed after a mpMRI-. Thus, in real-life scenario, mpMRI- does not obviate indication to biopsy when mpMRI is not centralized.

LEVEL OF EVIDENCE

3.

PI-RADS version 2.1 scoring system is superior in detecting transition zone prostate cancer: a diagnostic study

PURPOSE

The studies comparing the versions 2 vs. 2.1 of the Prostate Imaging Reporting and Data System (PI-RADS) are rare. This study aimed to evaluate whether PI-RADS version 2.1 is superior in detecting transition zone prostate cancer in comparison with PI-RADS version 2.

METHODS

This was a diagnostic study of patients with prostate diseases who visited the Urology Department of The Second Affiliated Hospital of Soochow University and underwent a magnetic resonance imaging (MRI) examination between 03-01-2016 and 10-31-2018. The images originally analyzed using PI-RADS version 2 were retrospectively re-analyzed and scored in 2019 according to the updated PI-RADS version 2.1. The kappa and receiver operating characteristic (ROC) curves were used.

RESULTS

For Reader 1, compared with PI-RADS version 2, version 2.1 had higher sensitivity (85% vs. 79%, P = 0.03), lower specificity (65% vs. 83%, P < 0.001), and lower area under the curve (AUC) (0.749 vs. 0.809, P < 0.001). For Reader 2 (first attempt), compared with PI-RADS version 2, version 2.1 had lower specificity (67% vs. 91%, P < 0.001) and lower AUC (0.702 vs. 0.844, P < 0.001). For Reader 2 (second attempt), compared with PI-RADS version 2, version 2.1 had higher sensitivity (88% vs. 78%, P < 0.001) and lower specificity (77% vs. 91%, P < 0.001). The kappa between the two attempts for Reader 2 was 0.321.

CONCLUSION

These results suggest that PI-RADS version 2.1 might improve the detection of prostate cancers in the transition zone compared with PI-RADS version 2 but that it might results in higher numbers of biopsies because of lower specificity.

Prostate MRI: practical guidelines for interpreting and reporting according to PI-RADS version 2.1

The increasing precision of multiparametric magnetic resonance imaging of the prostate, together with greater experience and standardization in its interpretation, has given this technique an important role in the management of prostate cancer, the most prevalent non-cutaneous cancer in men. This article reviews the concepts in PI-RADS version 2.1 for estimating the probability and zonal location of significant tumors of the prostate, using a practical approach that includes current considerations about the prerequisites for carrying out the test and recommendations for interpreting the findings. It emphasizes benign findings that can lead to confusion and the criteria for evaluating the probability of local spread, which must be included in the structured report.

The role of radiomics in prostate cancer radiotherapy

"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.