False Positive Multiparametric Magnetic Resonance Imaging Phenotypes in the Biopsy-naïve Prostate: Are They Distinct from Significant Cancer-associated Lesions? Lessons from PROMIS

MR Molecular Imaging of Extradomain-B Fibronectin for Assessing Progression and Therapy Resistance of Prostate Cancer

Accurate assessment and characterization of the progression and therapy response of prostate cancer are essential for precision healthcare of patients diagnosed with the disease. MRI is a clinical imaging modality routinely used for diagnostic imaging and treatment planning of prostate cancer. Extradomain B fibronectin (EDB-FN) is an oncofetal subtype of fibronectin highly expressed in the extracellular matrix of aggressive cancers, including prostate cancer. It is a promising molecular target for the detection and risk-stratification of prostate cancer with high-resolution MR molecular imaging (MRMI). In this study, we investigated the effectiveness of MRMI with an EDB-FN specific contrast agent MT218 for assessing the progression and therapy resistance of prostate cancer. Low grade LNCaP prostate cancer cells became an invasive phenotype LNCaP-CXCR2 with elevated EDB-FN expression after acquisition of the C-X-C motif chemokine receptor 2 (CXCR2). MT218-MRMI showed brighter signal enhancement in LNCaP-CXCR2 tumor xenografts with a ∼2-fold contrast-to-noise (CNR) increase than in LNCaP tumors in mice. Enzalutamide-resistant C4-2-DR prostate cancer cells were more invasive, with higher EDB-FN expression than parental C4-2 cells. Brighter signal enhancement with a ∼2-fold CNR increase was observed in the C4-2-DR xenografts compared to that of C4-2 tumors in mice with MT218-MRMI. Interestingly, when invasive PC3 prostate cancer cells developed resistance to paclitaxel, the drug-resistant PC3-DR cells became less invasive with reduced EDB-FN expression than the parental PC3 cells. MT218-MRMI detected reduced brightness in the PC3-DR xenografts with more than 2-fold reduction of CNR compared to PC3 tumors in mice. The signal enhancement in all tumors was supported by the immunohistochemical staining of EDB-FN with the G4 monoclonal antibody. The results indicate that MRMI of EDB-FN with MT218 has promise for detection, risk stratification, and monitoring the progression and therapy response of invasive prostate cancer.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Prediction of false-positive PI-RADS 5 lesions on prostate multiparametric MRI: development and internal validation of a clinical-radiological characteristics based nomogram

BACKGROUND

To develop a risk model including clinical and radiological characteristics to predict false-positive The Prostate Imaging Reporting and Data System (PI-RADS) 5 lesions.

METHODS

Data of 612 biopsy-naïve patients who had undergone multiparametric magnetic resonance imaging (mpMRI) before prostate biopsy were collected. Clinical variables and radiological variables on mpMRI were adopted. Lesions were divided into the training and validation cohort randomly. Stepwise multivariate logistic regression analysis with backward elimination was performed to screen out variables with significant difference. A diagnostic nomogram was developed in the training cohort and further validated in the validation cohort. Calibration curve and receiver operating characteristic (ROC) analysis were also performed.

RESULTS

296 PI-RADS 5 lesions in 294 patients were randomly divided into the training and validation cohort (208 : 88). 132 and 56 lesions were confirmed to be clinically significant prostate cancer in the training and validation cohort respectively. The diagnostic nomogram was developed based on prostate specific antigen density, the maximum diameter of lesion, zonality of lesion, apparent diffusion coefficient minimum value and apparent diffusion coefficient minimum value ratio. The C-index of the model was 0.821 in the training cohort and 0.871 in the validation cohort. The calibration curve showed good agreement between the estimation and observation in the two cohorts. When the optimal cutoff values of ROC were 0.288 in the validation cohort, the sensitivity, specificity, PPV, and NPV were 90.6%, 67.9%, 61.7%, and 92.7% in the validation cohort, potentially avoiding 9.7% unnecessary prostate biopsies.

CONCLUSIONS

We developed and validated a diagnostic nomogram by including 5 factors. False positive PI-RADS 5 lesions could be distinguished from clinically significant ones, thus avoiding unnecessary prostate biopsy.

An Update on the Role of mpMRI and 68Ga-PSMA PET Imaging in Primary and Recurrent Prostate Cancer

The objective of this work was to review comparisons of the efficacy of 68Ga-PSMA-11 (prostate-specific membrane antigen) PET/CT and multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer among patients undergoing initial staging prior to radical prostatectomy or experiencing recurrent prostate cancer, based on histopathological data. A comprehensive search was conducted in PubMed and Web of Science, and relevant articles were analyzed with various parameters, including year of publication, study design, patient count, age, PSA (prostate-specific antigen) value, Gleason score, standardized uptake value (SUVmax), detection rate, treatment history, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and PI-RADS (prostate imaging reporting and data system) scores. Only studies directly comparing PSMA-PET and mpMRI were considered, while those examining combined accuracy or focusing on either modality alone were excluded. In total, 24 studies comprising 1717 patients were analyzed, with the most common indication for screening being staging, followed by relapse. The findings indicated that 68Ga-PSMA-PET/CT effectively diagnosed prostate cancer in patients with suspected or confirmed disease, and both methods exhibited comparable efficacy in identifying lesion-specific information. However, notable heterogeneity was observed, highlighting the necessity for standardization of imaging and histopathology systems to mitigate inter-study variability. Future research should prioritize evaluating the combined diagnostic performance of both modalities to enhance sensitivity and reduce unnecessary biopsies. Overall, the utilization of PSMA-PET and mpMRI in combination holds substantial potential for significantly advancing the diagnosis and management of prostate cancer.

Creating a novel multiparametric magnetic resonance imaging-based biopsy strategy for reducing unnecessary prostate biopsies: a retrospective cohort study

Background

The overdiagnosis of prostate cancer (PCa) caused by unnecessary prostate biopsy has become a worldwide problem that urgently requires a solution. We aimed to reduce the unnecessary prostate biopsies and increase the detection rate of clinically significant PCa (csPCa) by creating a novel multiparametric magnetic resonance imaging (mpMRI)-based strategy.

Methods

A total of 1,194 eligible patients who underwent transperineal prostate biopsies from January 2018 to December 2022 were included in this retrospective study. Of these patients, 1,080 who received prostate biopsies from January 2018 to July 2022 were regarded as cohort 1 for primary analysis, and 114 patients who received prostate biopsies from August 2022 to December 2022 were collected in cohort 2 for validation. All the mpMRI images were quantitatively evaluated by the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v. 2.1). The diagnostic performances were assessed through the receiver operating characteristic (ROC) curve and area under the curve (AUC) and were compared with the DeLong test. Cancer diagnosis-free survival analysis was performed using the Kaplan-Meier method and log-rank test. The primary endpoint of this study was clinically significant PCa with an International Society of Urological Pathology (ISUP) grade ≥2.

Results

In cohort 1, the results of ROC curves demonstrated that the PI-RADS score had a higher diagnostic accuracy (AUC =0.898 for any-grade PCa; AUC =0.917 for csPCa) than did the other clinical variables (P<0.001). Under the novel mpMRI-based biopsy strategy, all patients with PI-RADS 1 can safely avoid prostate biopsy. For patients with PI-RADS 2, prostate biopsy should be considered for patients with prostate-specific antigen density (PSAD) ≥0.3 ng/mL2 and prostate volume <65 mL. As for patients with PI-RADS 3, structured surveillance programs can be a viable option if PSAD <0.3 ng/mL2 and prostate volume ≥65 mL. Finally, patients with a PI-RADS score of 4 and 5 should undergo prostate biopsy due to the high probability of clinically significant PCa. In the validation analysis of cohort 2, 48 patients were placed into a biopsy-spared group with no csPCa cases, while 66 patients were placed in a biopsy-needed group, with an csPCa detection rate of 50.0%. Overall, the novel strategy demonstrated a sensitivity, specificity, positive predictive value, and negative predictive value of 98.9%, 57.5%, 50.5%, and 99.2%, respectively, for diagnosing csPCa.

Conclusions

An mpMRI-based biopsy strategy can effectively avoid about 40% of prostate biopsies and maintain a high detection rate for clinically significant PCa. It can further provide valuable guidance for patients and physicians in considering the necessity of prostate biopsy.

Magnetic Resonance Imaging Follow-up of Targeted Biopsy-negative Prostate Lesions

BACKGROUND

The optimal radiological follow-up of prostate lesions negative on magnetic resonance imaging (MRI)-targeted biopsy (MRI-TB) is yet to be optimised.

OBJECTIVE

To present medium-term radiological and clinical follow-up of biopsy-negative lesions.

DESIGN, SETTING, AND PARTICIPANTS

The records for men who underwent multiparametric MRI at the UCLH one-stop clinic for suspected prostate cancer between September 2017 and March 2020 were reviewed (n = 1199). Patients with Likert 4 or 5 lesions were considered (n = 495), and those with a subsequent negative MRI-TB comprised the final study population (n = 91).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Baseline and follow-up MRI and biopsy data (including prostate-specific antigen [PSA], prostate volume, radiological scores, and presence of any noncancerous pathology) were extracted from reports. The last follow-up date was the date of the last test or review in clinic.

RESULTS AND LIMITATIONS

Median follow-up was 1.8 yr (656 d, interquartile range [IQR] 359-1008). At baseline, the median age was 65.4 yr (IQR 60.7-70.0), median PSA was 7.1 ng/ml (IQR 4.7-10.0), median prostate volume was 54 ml (IQR 39.5-75.0), and median PSA density (PSAD) was 0.13 ng/ml2 (IQR 0.09-0.18). Eighty-six men (95%) had Likert 4 lesions, while the remaining five (5%) had Likert 5 lesions. Only 21 men (23%) had a single lesion; most had at least two. Atrophy was the most prevalent pathology on MRI-TB, present in 64 men (74%), and followed by acute inflammation in 42 (46%), prostatic intraepithelial neoplasia in 33 (36%), chronic inflammation in 18 (20%), atypia in 13 (14%), and granulomatous inflammation in three (3%). Fifty-eight men had a second MRI study (median 376 d, IQR 361-412). At the second MRI, median PSAD decreased to 0.11 ng/ml2 (IQR 0.08-0.18). A Likert 4 or 5 score persisted only in five men (9%); 40 men (69%) were scored Likert 3, while the remaining 13 (22%) were scored Likert 2 (no lesion). Of 45 men with a Likert ≥3 score, most only had one lesion at the second MRI (28 men; 62%). Of six men with repeat MRI-TB during the study period, two were subsequently diagnosed with prostate cancer and both had persistent Likert 4 scores (at baseline and at least one follow-up MRI).

CONCLUSIONS

Most biopsy-negative MRI lesions in the prostate resolve over time, but any persistent lesions should be closely monitored.

PATIENT SUMMARY

Lesions in the prostate detected via magnetic resonance imaging (MRI) scans that are negative for cancer on biopsy usually resolve. Repeat MRI can indicate persistent lesions that might need a second biopsy.

Multivariable stratification of PI-RADS version 2.1 categories for the risk of false-positive target biopsy: Impact on prostate biopsy decisions

PURPOSE

To identify clinical and multiparametric magnetic resonance imaging (mpMRI) factors predicting false positive target biopsy (FP-TB) of prostate imaging reporting and data system version 2.1 (PI-RADSv2.1) ≥ 3 findings.

METHOD

We retrospectively included 221 men with and without previous negative prostate biopsy who underwent 3.0 T/1.5 T mpMRI for suspicious clinically significant prostate cancer (csPCa) between April 2019-July 2021. A study coordinator revised mpMRI reports provided by one of two radiologists (experience of > 1500/>500 mpMRI examinations, respectively) and matched them with the results of transperineal systematic biopsy plus fusion target biopsy (TB) of PI-RADSv2.1 ≥ 3 lesions or PI-RADSv2.1 ≤ 2 men with higher clinical risk. A multivariable model was built to identify features predicting FP-TB of index lesions, defined as the absence of csPCa (International Society of Urogenital Pathology [ISUP] ≥ 2). The model was internally validated with the bootstrap technique, receiving operating characteristics (ROC) analysis, and decision analysis.

RESULTS

Features significantly associated with FP-TB were age < 65 years (odds ratio [OR] 2.77), prostate-specific antigen density (PSAD) < 0.15 ng/mL/mL (OR 2.45), PI-RADS 4/5 category vs. category 3 (OR 0.15/0.07), and multifocality (OR 0.46), with a 0.815 area under the curve (AUC) in assessing FP-TB. When adjusting PI-RADSv2.1 categorization for the model, mpMRI showed 87.5% sensitivity and 79.9% specificity for csPCa, with a greater net benefit in triggering biopsy compared to unadjusted categorization or adjustment for PSAD only at decision analysis, from threshold probability ≥ 15%.

CONCLUSION

Adjusting PI-RADSv2.1 categories for a multivariable risk of FP-TB is potentially more effective in triggering TB of index lesions than unadjusted PI-RADS categorization or adjustment for PSAD alone.

Advances in Urological Cancer in 2022, from Basic Approaches to Clinical Management

This Special Issue includes 12 articles and 3 reviews dealing with several basic and clinical aspects of prostate, renal, and urinary tract cancer published during 2022 in Cancers, and intends to serve as a multidisciplinary chance to share the last advances in urological neoplasms [...].

Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions

The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal−Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68−0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.

Regional Histopathology and Prostate MRI Positivity: A Secondary Analysis of the PROMIS Trial

Background The effects of regional histopathologic changes on prostate MRI scans have not been accurately quantified in men with an elevated prostate-specific antigen (PSA) level and no previous biopsy. Purpose To assess how Gleason grade, maximum cancer core length (MCCL), inflammation, prostatic intraepithelial neoplasia (PIN), or atypical small acinar proliferation within a Barzell zone affects the odds of MRI visibility. Materials and Methods In this secondary analysis of the Prostate MRI Imaging Study (PROMIS; May 2012 to November 2015), consecutive participants who underwent multiparametric MRI followed by a combined biopsy, including 5-mm transperineal mapping (TPM), were evaluated. TPM pathologic findings were reported at the whole-prostate level and for each of 20 Barzell zones per prostate. An expert panel blinded to the pathologic findings reviewed MRI scans and declared which Barzell areas spanned Likert score 3-5 lesions. The relationship of Gleason grade and MCCL to zonal MRI outcome (visible vs nonvisible) was assessed using generalized linear mixed-effects models with random intercepts for individual participants. Inflammation, PIN, and atypical small acinar proliferation were similarly assessed in men who had negative TPM results. Results Overall, 161 men (median age, 62 years [IQR, 11 years]) were evaluated and 3179 Barzell zones were assigned MRI status. Compared with benign areas, the odds of MRI visibility were higher when a zone contained cancer with a Gleason score of 3+4 (odds ratio [OR], 3.1; 95% CI: 1.9, 4.9; P < .001) or Gleason score greater than or equal to 4+3 (OR, 8.7; 95% CI: 4.5, 17.0; P < .001). MCCL also determined visibility (OR, 1.24 per millimeter increase; 95% CI: 1.15, 1.33; P < .001), but odds were lower with each prostate volume doubling (OR, 0.7; 95% CI: 0.5, 0.9). In men who were TPM-negative, the presence of PIN increased the odds of zonal visibility (OR, 3.7; 95% CI: 1.5, 9.1; P = .004). Conclusion An incremental relationship between cancer burden and prostate MRI visibility was observed. Prostatic intraepithelial neoplasia contributed to false-positive MRI findings. ClinicalTrials.gov registration no. NCT01292291 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Harmath in this issue.

Diagnosis and Pathologic Reporting of Prostate Cancer in the Era of MRI-Targeted Prostate Biopsy

Historically, the detection of prostate cancer relied upon a systematic yet random sampling of the prostate by transrectal ultrasound guided biopsy. This approach was a nontargeted technique that led to the under detection of cancers at biopsy and the upgrading of cancers at radical prostatectomy. Multiparametric MRI-targeted prostate biopsy allows for an image-directed approach to the identification of prostate cancer. MRI-targeted biopsy of the prostate is superior for the detection of clinically significant prostate cancer. As this technique has become more prevalent among urologists, pathologists need to recognize how this development impacts cancer diagnosis and reporting.

Avoiding Unnecessary Biopsy after Multiparametric Prostate MRI with VERDICT Analysis: The INNOVATE Study

Background In men suspected of having prostate cancer (PCa), up to 50% of men with positive multiparametric MRI (mpMRI) findings (Prostate Imaging Reporting and Data System [PI-RADS] or Likert score of 3 or higher) have no clinically significant (Gleason score ≤3+3, benign) biopsy findings. Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumor (VERDICT) MRI analysis could improve the stratification of positive mpMRI findings. Purpose To evaluate VERDICT MRI, mpMRI-derived apparent diffusion coefficient (ADC), and prostate-specific antigen density (PSAD) as determinants of clinically significant PCa (csPCa). Materials and Methods Between April 2016 and December 2019, men suspected of having PCa were prospectively recruited from two centers and underwent VERDICT MRI and mpMRI at one center before undergoing targeted biopsy. Biopsied lesion ADC, lesion-derived fractional intracellular volume (FIC), and PSAD were compared between men with csPCa and those without csPCa, using nonparametric tests subdivided by Likert scores. Area under the receiver operating characteristic curve (AUC) was calculated to test diagnostic performance. Results Among 303 biopsy-naive men, 165 study participants (mean age, 65 years ± 7 [SD]) underwent targeted biopsy; of these, 73 had csPCa. Median lesion FIC was higher in men with csPCa (FIC, 0.53) than in those without csPCa (FIC, 0.18) for Likert 3 (P = .002) and Likert 4 (0.60 vs 0.28, P < .001) lesions. Median lesion ADC was lower for Likert 4 lesions with csPCa (0.86 × 10-3 mm2/sec) compared with lesions without csPCa (1.12 × 10-3 mm2/sec, P = .03), but there was no evidence of a difference for Likert 3 lesions (0.97 × 10-3 mm2/sec vs 1.20 × 10-3 mm2/sec, P = .09). PSAD also showed no difference for Likert 3 (0.17 ng/mL2 vs 0.12 ng/mL2, P = .07) or Likert 4 (0.14 ng/mL2 vs 0.12 ng/mL2, P = .47) lesions. The diagnostic performance of FIC (AUC, 0.96; 95% CI: 0.93, 1.00) was higher (P = .02) than that of ADC (AUC, 0.85; 95% CI: 0.79, 0.91) and PSAD (AUC, 0.74; 95% CI: 0.66, 0.82) for the presence of csPCa in biopsied lesions. Conclusion Lesion fractional intracellular volume enabled better classification of clinically significant prostate cancer than did apparent diffusion coefficient and prostate-specific antigen density. Clinical trial registration no. NCT02689271 © RSNA, 2022 Online supplemental material is available for this article.

Comparison of Multiparametric Magnetic Resonance Imaging with Prostate-Specific Membrane Antigen Positron-Emission Tomography Imaging in Primary Prostate Cancer Diagnosis: A Systematic Review and Meta-Analysis

Multiparametric magnetic-resonance imaging (mpMRI) has proven utility in diagnosing primary prostate cancer. However, the diagnostic potential of prostate-specific membrane antigen positron-emission tomography (PSMA PET) has yet to be established. This study aims to systematically review the current literature comparing the diagnostic performance of mpMRI and PSMA PET imaging to diagnose primary prostate cancer. A systematic literature search was performed up to December 2021. Quality analyses were conducted using the QUADAS-2 tool. The reference standard was whole-mount prostatectomy or prostate biopsy. Statistical analysis involved the pooling of the reported diagnostic performances of each modality, and differences in per-patient and per-lesion analysis were compared using a Fisher’s exact test. Ten articles were included in the meta-analysis. At a per-patient level, the pooled values of sensitivity, specificity, and area under the curve (AUC) for mpMRI and PSMA PET/CT were 0.87 (95% CI: 0.83−0.91) vs. 0.93 (95% CI: 0.90−0.96, p < 0.01); 0.47 (95% CI: 0.23−0.71) vs. 0.54 (95% CI: 0.23−0.84, p > 0.05); and 0.84 vs. 0.91, respectively. At a per-lesion level, the pooled sensitivity, specificity, and AUC value for mpMRI and PSMA PET/CT were lower, at 0.63 (95% CI: 0.52−0.74) vs. 0.79 (95% CI: 0.62−0.92, p < 0.001); 0.88 (95% CI: 0.81−0.95) vs. 0.71 (95% CI: 0.47−0.90, p < 0.05); and 0.83 vs. 0.84, respectively. High heterogeneity was observed between studies. PSMA PET/CT may better confirm the presence of prostate cancer than mpMRI. However, both modalities appear comparable in determining the localisation of the lesions.

Differentiating False Positive Lesions from Clinically Significant Cancer and Normal Prostate Tissue Using VERDICT MRI and Other Diffusion Models

False positives on multiparametric MRIs (mp-MRIs) result in many unnecessary invasive biopsies in men with clinically insignificant diseases. This study investigated whether quantitative diffusion MRI could differentiate between false positives, true positives and normal tissue non-invasively. Thirty-eight patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI, followed by transperineal biopsy. The patients were categorized into two groups following biopsy: (1) significant cancer—true positive, 19 patients; (2) atrophy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN)—false positive, 19 patients. The clinical apparent diffusion coefficient (ADC) values were obtained, and the intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted via deep learning. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) (p < 0.0001) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular−extravascular volume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were found for the VERDICT fIC (p = 0.004) and IVIM D. These results demonstrate that model-based diffusion MRI could reduce unnecessary biopsies occurring due to false positive prostate lesions and shows promising sensitivity to benign diseases.

Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies

Computer-aided diagnosis (CAD) of prostate cancer on multiparametric magnetic resonance imaging (mpMRI), using artificial intelligence (AI), may reduce missed cancers and unnecessary biopsies, increase inter-observer agreement between radiologists, and alleviate pressures caused by rising case incidence and a shortage of specialist radiologists to read prostate mpMRI. However, well-designed evaluation studies are required to prove efficacy above current clinical practice. A systematic search of the MEDLINE, EMBASE, and arXiv electronic databases was conducted for studies that compared CAD for prostate cancer detection or classification on MRI against radiologist interpretation and a histopathological reference standard, in treatment-naïve men with a clinical suspicion of prostate cancer. Twenty-seven studies were included in the final analysis. Due to substantial heterogeneities in the included studies, a narrative synthesis is presented. Several studies reported superior diagnostic accuracy for CAD over radiologist interpretation on small, internal patient datasets, though this was not observed in the few studies that performed evaluation using external patient data. Our review found insufficient evidence to suggest the clinical deployment of artificial intelligence algorithms at present. Further work is needed to develop and enforce methodological standards, promote access to large diverse datasets, and conduct prospective evaluations before clinical adoption can be considered.