Multiparametric 3T prostate magnetic resonance imaging to detect cancer: histopathological correlation using prostatectomy specimens processed in customized magnetic resonance imaging based molds

RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate

Image registration can map the ground truth extent of prostate cancer from histopathology images onto MRI, facilitating the development of machine learning methods for early prostate cancer detection. Here, we present RAdiology PatHology Image Alignment (RAPHIA), an end-to-end pipeline for efficient and accurate registration of MRI and histopathology images. RAPHIA automates several time-consuming manual steps in existing approaches including prostate segmentation, estimation of the rotation angle and horizontal flipping in histopathology images, and estimation of MRI-histopathology slice correspondences. By utilizing deep learning registration networks, RAPHIA substantially reduces computational time. Furthermore, RAPHIA obviates the need for a multimodal image similarity metric by transferring histopathology image representations to MRI image representations and vice versa. With the assistance of RAPHIA, novice users achieved expert-level performance, and their mean error in estimating histopathology rotation angle was reduced by 51% (12 degrees vs 8 degrees), their mean accuracy of estimating histopathology flipping was increased by 5% (95.3% vs 100%), and their mean error in estimating MRI-histopathology slice correspondences was reduced by 45% (1.12 slices vs 0.62 slices). When compared to a recent conventional registration approach and a deep learning registration approach, RAPHIA achieved better mapping of histopathology cancer labels, with an improved mean Dice coefficient of cancer regions outlined on MRI and the deformed histopathology (0.44 vs 0.48 vs 0.50), and a reduced mean per-case processing time (51 vs 11 vs 4.5 min). The improved performance by RAPHIA allows efficient processing of large datasets for the development of machine learning models for prostate cancer detection on MRI. Our code is publicly available at: https://github.com/pimed/RAPHIA.

Diagnostic Utility of Artificial Intelligence-assisted Transperineal Biopsy Planning in Prostate Cancer Suspected Men: A Prospective Cohort Study

BACKGROUND AND OBJECTIVE

Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone.

METHODS

A total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration-cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2-5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group ≥2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test.

KEY FINDINGS AND LIMITATIONS

Overall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS- and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs 1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

PATIENT SUMMARY

The computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

Standard operating procedure to reveal prostate cancer specific volatile organic molecules by infrared spectroscopy

The growing number of prostate cancer cases is a real concern in modern society. Over 1.4 million new cases and about 400 thousand (>26%) deaths were registered worldwide in 2020 due to prostate cancer. The high mortality rate of prostate cancer is due to the lack of reliable early detection of the disease. Till now the most reliable diagnosis of cancer is tissue biopsy, which is an invasive process. A non-invasive or minimally invasive technique could lead to a diagnostic tool that will allow for saving or prolonging the lifespan of millions of lives. Metabolite-based diagnostics may have a better chance of early cancer detection. However, reliable detection techniques need to be developed. Infrared spectroscopy based gaseous-biofluid holds great promise towards the development of non-invasive diagnostics. A pilot study based on breath analysis by infrared spectroscopy showed promising results in distinguishing prostate cancer patients from healthy volunteers. Details of the spectral metabolic analysis are presented.

Combiner and HyperCombiner networks: Rules to combine multimodality MR images for prostate cancer localisation

One of the distinct characteristics of radiologists reading multiparametric prostate MR scans, using reporting systems like PI-RADS v2.1, is to score individual types of MR modalities, including T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer. This work aims to demonstrate that it is feasible for low-dimensional parametric models to model such decision rules in the proposed Combiner networks, without compromising the accuracy of predicting radiologic labels. First, we demonstrate that either a linear mixture model or a nonlinear stacking model is sufficient to model PI-RADS decision rules for localising prostate cancer. Second, parameters of these combining models are proposed as hyperparameters, weighing independent representations of individual image modalities in the Combiner network training, as opposed to end-to-end modality ensemble. A HyperCombiner network is developed to train a single image segmentation network that can be conditioned on these hyperparameters during inference for much-improved efficiency. Experimental results based on 751 cases from 651 patients compare the proposed rule-modelling approaches with other commonly-adopted end-to-end networks, in this downstream application of automating radiologist labelling on multiparametric MR. By acquiring and interpreting the modality combining rules, specifically the linear-weights or odds ratios associated with individual image modalities, three clinical applications are quantitatively presented and contextualised in the prostate cancer segmentation application, including modality availability assessment, importance quantification and rule discovery.

Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI

The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients' management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system. Radiomic features were extracted from regions of positive biopsies and Normally Appearing Tissues (NAT) on T2-weighted and Diffusion-weighted Imaging. Using only clinical information available prior to biopsy, five models for predicting low-risk lesions/patients were evaluated, based on: 1: Clinical variables; 2: Lesion-based radiomic features; 3: Lesion and NAT radiomics; 4: Clinical and lesion-based radiomics; and 5: Clinical, lesion and NAT radiomic features. Eighty-three mpMRI exams from 78 men were analyzed. Models 1 and 2 performed similarly (Area under the receiver operating characteristic curve were 0.835 and 0.838, respectively), but radiomics significantly improved the lesion-based performance of the model in a subset analysis of patients with a negative Digital Rectal Exam (DRE). Adding normal tissue radiomics significantly improved the performance in all cases. Similar patterns were observed on patient-level models. To the best of our knowledge, this is the first study to demonstrate that machine learning radiomics-based models can predict patients' risk using combined clinical-genomic classification.

Evaluation of Multiparametric Magnetic Resonance Imaging and Correlation with Radical Prostatectomy Histopathology Specimen in Prostate Cancer

Magnetic resonance imaging (MRI) has shown a great potential in the evaluation and management of prostate cancer. In this study, we would like to evaluate the benefit of multiparametric MRI in the detection and localization of prostate cancer by comparing it with the gold standard of histopathology from radical prostatectomy. In this single-centre prospective study, 90 consecutive patients underwent radical prostatectomy from November 2016 to May 2018. All patients first underwent multiparametric (mp)-MRI, and all suspicious regions of interest were delineated and recorded on a 5-point scale as defined in prostate imaging reporting and data system version 2 (PI-RADS V2) score. All radical prostatectomy specimens, acquired after robotic radical prostatectomy with extended pelvic lymphadenectomy, were sent for histopathological examination (HPE). The mean age of the 90 patients was 65.3 years, and the mean serum prostate-specific antigen (PSA) was 16.9 ng/ml. The sensitivity and specificity of mp-MRI in the detection of the corresponding region of interest (ROI) on HPE were 67.4% and 89.3% respectively. Positive predictive value (PPV), negative predictive value (NPV), and accuracy of mp-MRI in the detection of corresponding ROI on HPE were 86.3%, 73.3%, and 78.3% respectively. The mp-MRI detected 96.8% solitary lesions and 61.7% multifocal lesions on the corresponding ROI on HPE. Multiparametric MRI has an excellent specificity and reasonable sensitivity for the diagnosis of prostate cancer. It is a good modality for the detection of solitary tumours, higher-grade tumours, detection of seminal vesicle invasion and extracapsular extension and helps in the decision-making process before radical prostatectomy, focal therapy or selecting an appropriate candidate for active surveillance.

Predictors of Extraprostatic Extension in Patients with Prostate Cancer

PURPOSE

To identify effective factors predicting extraprostatic extension (EPE) in patients with prostate cancer (PCa).

METHODS

This retrospective cohort study recruited 898 consecutive patients with PCa treated with robot-assisted laparoscopic radical prostatectomy. The patients were divided into EPE and non-EPE groups based on the analysis of whole-mount histopathologic sections. Histopathological analysis (ISUP biopsy grade group) and magnetic resonance imaging (MRI) (PI-RADS v2.1 scores [1-5] and the Mehralivand EPE grade [0-3]) were used to assess the prediction of EPE. We also assessed the clinical usefulness of the prediction model based on decision-curve analysis.

RESULTS

Of 800 included patients, 235 (29.3%) had EPE, and 565 patients (70.7%) did not (non-EPE). Multivariable logistic regression analysis showed that the biopsy ISUP grade, PI-RADS v2.1 score, and Mehralivand EPE grade were independent risk factors for EPE. In the regression assessment of the models, the best discrimination (area under the curve of 0.879) was obtained using the basic model (age, serum PSA, prostate volume at MRI, positive biopsy core, clinical T stage, and D'Amico risk group) and Mehralivand EPE grade 3. Decision-curve analysis showed that combining Mehralivand EPE grade 3 with the basic model resulted in superior net benefits for predicting EPE.

CONCLUSION

Mehralivand EPE grades and PI-RADS v2.1 scores, in addition to basic clinical and demographic information, are potentially useful for predicting EPE in patients with PCa.

Reliability of whole mount radical prostatectomy histopathology as the ground truth for artificial intelligence assisted prostate imaging

The development of artificial intelligence-based imaging techniques for prostate cancer (PCa) detection and diagnosis requires a reliable ground truth, which is generally based on histopathology from radical prostatectomy specimens. This study proposes a comprehensive protocol for the annotation of prostatectomy pathology slides. To evaluate the reliability of the protocol, interobserver variability was assessed between five pathologists, who annotated ten radical prostatectomy specimens consisting of 74 whole mount pathology slides. Interobserver variability was assessed for both the localization and grading of PCa. The results indicate excellent overall agreement on the localization of PCa (Gleason pattern ≥ 3) and clinically significant PCa (Gleason pattern ≥ 4), with Dice similarity coefficients (DSC) of 0.91 and 0.88, respectively. On a per-slide level, agreement for primary and secondary Gleason pattern was almost perfect and substantial, with Fleiss Kappa of .819 (95% CI .659-.980) and .726 (95% CI .573-.878), respectively. Agreement on International Society of Urological Pathology Grade Group was evaluated for the index lesions and showed agreement in 70% of cases, with a mean DSC of 0.92 for all index lesions. These findings show that a standardized protocol for prostatectomy pathology annotation provides reliable data on PCa localization and grading, with relatively high levels of interobserver agreement. More complicated tissue characterization, such as the presence of cribriform growth and intraductal carcinoma, remains a source of interobserver variability and should be treated with care when used in ground truth datasets.

Toward mechatronic MRI-guided focal laser ablation of the prostate: Robust registration for improved needle delivery

BACKGROUND

Multiparametric MRI (mpMRI) is an effective tool for detecting and staging prostate cancer (PCa), guiding interventional therapy, and monitoring PCa treatment outcomes. MRI-guided focal laser ablation (FLA) therapy is an alternative, minimally invasive treatment method to conventional therapies, which has been demonstrated to control low-grade, localized PCa while preserving patient quality of life. The therapeutic success of FLA depends on the accurate placement of needles for adequate delivery of ablative energy to the target lesion. We previously developed an MR-compatible mechatronic system for prostate FLA needle guidance and validated its performance in open-air and clinical 3T in-bore experiments using virtual targets.

PURPOSE

To develop a robust MRI-to-mechatronic system registration method and evaluate its in-bore MR-guided needle delivery accuracy in tissue-mimicking prostate phantoms.

METHODS

The improved registration multifiducial assembly houses thirty-six aqueous gadolinium-filled spheres distributed over a 7.3 × 7.3 × 5.2 cm volume. MRI-guided needle guidance accuracy was quantified in agar-based tissue-mimicking prostate phantoms on trajectories (N = 44) to virtual targets covering the mechatronic system's range of motion. 3T gradient-echo recalled (GRE) MRI images were acquired after needle insertions to each target, and the air-filled needle tracks were segmented. Needle guidance error was measured as the shortest Euclidean distance between the target point and the segmented needle trajectory, and angular error was measured as the angle between the targeted trajectory and the segmented needle trajectory. These measurements were made using both the previously designed four-sphere registration fiducial assembly on trajectories (N = 7) and compared with the improved multifiducial assembly using a Mann-Whitney U test.

RESULTS

The median needle guidance error of the system using the improved registration fiducial assembly at a depth of 10 cm was 1.02 mm with an interquartile range (IQR) of 0.42-2.94 mm. The upper limit of the one-sided 95% prediction interval of needle guidance error was 4.13 mm. The median (IQR) angular error was 0.0097 rad (0.0057-0.015 rad) with a one-sided 95% prediction interval upper limit of 0.022 rad. The median (IQR) positioning error using the previous four-sphere registration fiducial assembly was 1.87 mm (1.77-2.14 mm). This was found to be significantly different (p = 0.0012) from the median (IQR) positioning error of 0.28 mm (0.14-0.95 mm) using the new registration fiducial assembly on the same trajectories. No significant difference was detected between the medians of the angular errors (p = 0.26).

CONCLUSION

This is the first study presenting an improved registration method and validation in tissue-mimicking phantoms of our remotely actuated MR-compatible mechatronic system for delivery of prostate FLA needles. Accounting for the effects of needle deflection, the system was demonstrated to be capable of needle delivery with an error of 4.13 mm or less in 95% of cases under ideal conditions, which is a statistically significant improvement over the previous method. The system will next be validated in a clinical setting.

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Background

High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours.

Methods

In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process.

Results

Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments.

Conclusions

We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.

A Critical Analysis of the Magnetic Resonance Imaging Lesion Diameter Threshold for Adverse Pathology Features

To investigate the relationship between lesion size determined using multiparametric magnetic resonance imaging (mpMRI) and histopathological findings of specimens obtained after mpMRI fusion biopsy and radical prostatectomy (RP). We retrospectively analysed 290 patients with PCa who underwent an MRI fusion biopsy. We measured the diameter of suspicious tumour lesions on diffusion-weighted mpMRI and stratified the cohort into two groups. Group A included patients with a suspicious tumour lesion 10 mm and Group B included those with a suspicious tumour lesion > 10 mm. In Group B, the PI-RADS score determined in mpMRI was higher than Group A, and there was a statistically significant difference between the two groups in terms of clinical T-stage. The PCa detection rate and the number of positive cores were statistically significantly higher in Group B than in Group A. In addition, there was a statistically significant difference between the two groups in relation to the biopsy, the International Society of Urological Pathology (ISUP) grade values, and the presence of clinically significant PCa. In Group B, pathological T-stage and extraprostatic extension (EPE) and surgical margin (SM) positivity were found to be higher among the patients who underwent RP. In the multivariate analysis, the mpMRI lesion size being > 10 mm was found to be an independent predictive factor for SM and EPE positivity. The clinical results of this study support the modification of the lesion size threshold as 10 mm for use in the differentiation of PI-RADS scores 4 and 5.

Pilot study for generating and assessing nomograms and decision curves analysis to predict clinically significant prostate cancer using only spatially registered multi-parametric MRI

Background

Current prostate cancer evaluation can be inaccurate and burdensome. To help non-invasive prostate tumor assessment, recent algorithms applied to spatially registered multi-parametric (SRMP) MRI extracted novel clinically relevant metrics, namely the tumor's eccentricity (shape), signal-to-clutter ratio (SCR), and volume.

Purpose

Conduct a pilot study to predict the risk of developing clinically significant prostate cancer using nomograms and employing Decision Curves Analysis (DCA) from the SRMP MRI-based features to help clinicians non-invasively manage prostate cancer.

Methods

This study retrospectively analyzed 25 prostate cancer patients. MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced) were resized, translated, and stitched to form SRMP MRI. Target detection algorithm [adaptive cosine estimator (ACE)] applied to SRMP MRI determines tumor's eccentricity, noise reduced SCR (by regularizing or eliminating principal components (PC) from the covariance matrix), and volume. Pathology assessed wholemount prostatectomy for Gleason score (GS). Tumors with GS >=4+3 (<=3+4) were judged as "Clinically Significant" ("Insignificant"). Logistic regression combined eccentricity, SCR, volume to generate probability distribution. Nomograms, DCA used all patients plus training (13 patients) and test (12 patients) sets. Area Under the Curves for (AUC) for Receiver Operator Curves (ROC) and p-values evaluated the performance.

Results

Combining eccentricity (0.45 ACE threshold), SCR (3, 4 PCs), SCR (regularized, modified regularization) with tumor volume (0.65 ACE threshold) improved AUC (>0.70) for ROC curves and p-values (<0.05) for logistic fit. DCA showed greater net benefit from model fit than univariate analysis, treating "all," or "none." Training/test sets achieved comparable AUC but with higher p-values.

Conclusions

Performance of nomograms and DCA based on metrics derived from SRMP-MRI in this pilot study were comparable to those using prostate serum antigen, age, and PI-RADS.

Assessing and testing anomaly detection for finding prostate cancer in spatially registered multi-parametric MRI

Background

Evaluating and displaying prostate cancer through non-invasive imagery such as Multi-Parametric MRI (MP-MRI) bolsters management of patients. Recent research quantitatively applied supervised target algorithms using vectoral tumor signatures to spatially registered T1, T2, Diffusion, and Dynamic Contrast Enhancement images. This is the first study to apply the Reed-Xiaoli (RX) multi-spectral anomaly detector (unsupervised target detector) to prostate cancer, which searches for voxels that depart from the background normal tissue, and detects aberrant voxels, presumably tumors.

Methods

MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images, or seven components) were prospectively collected from 26 patients and then resized, translated, and stitched to form spatially registered multi-parametric cubes. The covariance matrix (CM) and mean μ were computed from background normal tissue. For RX, noise was reduced for the CM by filtering out principal components (PC), regularization, and elliptical envelope minimization. The RX images were compared to images derived from the threshold Adaptive Cosine Estimator (ACE) and quantitative color analysis. Receiver Operator Characteristic (ROC) curves were used for RX and reference images. To quantitatively assess algorithm performance, the Area Under the Curve (AUC) and the Youden Index (YI) points for the ROC curves were computed.

Results

The patient average for the AUC and [YI] from ROC curves for RX from filtering 3 and 4 PC was 0.734[0.706] and 0.727[0.703], respectively, relative to the ACE images. The AUC[YI] for RX from modified Regularization was 0.638[0.639], Regularization 0.716[0.690], elliptical envelope minimization 0.544[0.597], and unprocessed CM 0.581[0.608] using the ACE images as Reference Image. The AUC[YI] for RX from filtering 3 and 4 PC was 0.742[0.711] and 0.740[0.708], respectively, relative to the quantitative color images. The AUC[YI] for RX from modified Regularization was 0.643[0.648], Regularization 0.722[0.695], elliptical envelope minimization 0.508[0.605], and unprocessed CM 0.569[0.615] using the color images as Reference Image. All standard errors were less than 0.020.

Conclusions

This first study of spatially registered MP-MRI applied anomaly detection using RX, an unsupervised target detection algorithm for prostate cancer. For RX, filtering out PC and applying Regularization achieved higher AUC and YI using ACE and color images as references than unprocessed CM, modified Regularization, and elliptical envelope minimization.

Current Approaches for Image Fusion of Histological Data with Computed Tomography and Magnetic Resonance Imaging

Classical analysis of biological samples requires the destruction of the tissue's integrity by cutting or grinding it down to thin slices for (Immuno)-histochemical staining and microscopic analysis. Despite high specificity, encoded in the stained 2D section of the whole tissue, the structural information, especially 3D information, is limited. Computed tomography (CT) or magnetic resonance imaging (MRI) scans performed prior to sectioning in combination with image registration algorithms provide an opportunity to regain access to morphological characteristics as well as to relate histological findings to the 3D structure of the local tissue environment. This review provides a summary of prevalent literature addressing the problem of multimodal coregistration of hard- and soft-tissue in microscopy and tomography. Grouped according to the complexity of the dimensions, including image-to-volume (2D ⟶ 3D), image-to-image (2D ⟶ 2D), and volume-to-volume (3D ⟶ 3D), selected currently applied approaches are investigated by comparing the method accuracy with respect to the limiting resolution of the tomography. Correlation of multimodal imaging could position itself as a useful tool allowing for precise histological diagnostic and allow the a priori planning of tissue extraction like biopsies.

Biparametric MR signal characteristics can predict histopathological measures of prostate cancer

OBJECTIVES

The aim of this study was to establish a new data-driven metric from MRI signal intensity that can quantify histopathological characteristics of prostate cancer.

METHODS

This retrospective study was conducted on 488 patients who underwent biparametric MRI (bp-MRI), including T2-weighted imaging (T2W) and apparent diffusion coefficient (ADC) of diffusion-weighted imaging, and having biopsy-proven prostate cancer between August 2011 and July 2015. Forty-two of the patients who underwent radical prostatectomy and the rest of 446 patients constitute the labeled and unlabeled datasets, respectively. A deep learning model was built to predict the density of epithelium, epithelial nuclei, stroma, and lumen from bp-MRI, called MR-driven tissue density. On both the labeled validation set and the whole unlabeled dataset, the quality of MR-driven tissue density and its relation to bp-MRI signal intensity were examined with respect to different histopathologic and radiologic conditions using different statistical analyses.

RESULTS

MR-driven tissue density and bp-MRI of 446 patients were evaluated. MR-driven tissue density was significantly related to bp-MRI (p < 0.05). The relationship was generally stronger in cancer regions than in benign regions. Regarding cancer grades, significant differences were found in the intensity of bp-MRI and MR-driven tissue density of epithelium, epithelial nuclei, and stroma (p < 0.05). Comparing MR true-negative to MR false-positive regions, MR-driven lumen density was significantly different, similar to the intensity of bp-MRI (p < 0.001).

CONCLUSIONS

MR-driven tissue density could serve as a reliable histopathological measure of the prostate on bp-MRI, leading to an improved understanding of prostate cancer and cancer progression.

KEY POINTS

• Semi-supervised deep learning enables non-invasive and quantitative histopathology in the prostate from biparametric MRI. • Tissue density derived from biparametric MRI demonstrates similar characteristics to the direct estimation of tissue density from histopathology images. • The analysis of MR-driven tissue density reveals significantly different tissue compositions among different cancer grades as well as between MR-positive and MR-negative benign.

Multi-institutional analysis of clinical and imaging risk factors for detecting clinically significant prostate cancer in men with PI-RADS 3 lesions

BACKGROUND

Most Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions do not contain clinically significant prostate cancer (CSPCa; grade group ≥2). This study was aimed at identifying clinical and magnetic resonance imaging (MRI)-derived risk fac- tors that predict CSPCa in men with PI-RADS 3 lesions.

METHODS

This study analyzed the detection of CSPCa in men who underwent MRI-targeted biopsy for PI-RADS 3 lesions. Multivariable logistic regression models with goodness-of-fit testing were used to identify variables associated with CSPCa. Receiver operating curves and decision curve analyses were used to estimate the clinical utility of a predictive model.

RESULTS

Of the 1784 men reviewed, 1537 were included in the training cohort, and 247 were included in the validation cohort. The 309 men with CSPCa (17.3%) were older, had a higher prostate-specific antigen (PSA) density, and had a greater likelihood of an anteriorly located lesion than men without CSPCa (p < .01). Multivariable analysis revealed that PSA density (odds ratio [OR], 1.36; 95% confidence interval [CI], 1.05-1.85; p < .01), age (OR, 1.05; 95% CI, 1.02-1.07; p < .01), and a biopsy-naive status (OR, 1.83; 95% CI, 1.38-2.44) were independently associated with CSPCa. A prior negative biopsy was negatively associated (OR, 0.35; 95% CI, 0.24-0.50; p < .01). The application of the model to the validation cohort resulted in an area under the curve of 0.78. A predicted risk threshold of 12% could have prevented 25% of biopsies while detecting almost 95% of CSPCas with a sensitivity of 94% and a specificity of 34%.

CONCLUSIONS

For PI-RADS 3 lesions, an elevated PSA density, older age, and a biopsy-naive status were associated with CSPCa, whereas a prior negative biopsy was negatively associated. A predictive model could prevent PI-RADS 3 biopsies while missing few CSPCas.

LAY SUMMARY

Among men with an equivocal lesion (Prostate Imaging-Reporting and Data System 3) on multiparametric magnetic resonance imaging (mpMRI), those who are older, those who have a higher prostate-specific antigen density, and those who have never had a biopsy before are at higher risk for having clinically significant prostate cancer (CSPCa) on subsequent biopsy. However, men with at least one negative biopsy have a lower risk of CSPCa. A new predictive model can greatly reduce the need to biopsy equivocal lesions noted on mpMRI while missing only a few cases of CSPCa.

Combining and analyzing novel multi-parametric magnetic resonance imaging metrics for predicting Gleason score

BACKGROUND

Radiologists currently subjectively examine multi-parametric magnetic resonance imaging (MP-MRI) to determine prostate tumor aggressiveness using the Prostate Imaging Reporting and Data System scoring system (PI-RADS). Recent studies showed that modified signal to clutter ratio (SCR), tumor volume, and eccentricity (elongation or roundness) of prostate tumors correlated with Gleason score (GS). No previous studies have combined the prostate tumor's shape, SCR, tumor volume, in order to predict potential tumor aggressiveness and GS.

METHODS

MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were obtained, resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm [adaptive cosine estimator (ACE)]. Pixel-based blobbing, and labeling were applied to the threshold ACE images. Eccentricity calculation used moments of inertia from the blobs. Tumor volume was computed by counting pixels within multi parametric MRI blobs and tumor outlines based on pathologist assessment of whole mount histology. Pathology assessment of GS was performed on whole mount prostatectomy. The covariance matrix and mean of normal tissue background was computed from normal prostate. Using signatures and normal tissue statistics, the z-score, noise corrected SCR [principal component (PC), modified regularization] from each patient was computed. Eccentricity, tumor volume, and SCR were fitted to GS. Analysis of variance assesses the relationship among the variables.

RESULTS

A multivariate analysis generated correlation coefficient (0.60 to 0.784) and P value (0.00741 to <0.0001) from fitting two sets of independent variates, namely, tumor eccentricity (the eccentricity for the largest blob, weighted average for the eccentricity) and SCR (removing 3 PCs, removing 4 PCs, modified regularization, and z-score) to GS. The eccentricity t-statistic exceeded the SCR t-statistic. The three-variable fit to GS using tumor volume (histology, MRI) yielded correlation coefficients ranging from 0.724 to 0.819 (P value <<0.05). Tumor volumes generated from histology yielded higher correlation coefficients than MRI volumes. Adding volume to eccentricity and SCR adds little improvement for fitting GS due to higher correlation coefficients among independent variables and little additional, independent information.

CONCLUSIONS

Combining prostate tumors eccentricity with SCR relatively highly correlates with GS.

Analysis of the cause of missed diagnosis in mpMRI/TRUS fusion-guided targeted prostate biopsy

Objectives

To investigate the causes of missed diagnosis in mpMRI/TRUS fusion-guided targeted prostate biopsy.

Methods

The clinical data of 759 patients who underwent transperineal prostate biopsy from March 2021 to June 2021 at Nanjing DrumTower Hospital were retrospectively analyzed. Twenty-one patients had MRI contraindications. Ultimately, 738 patients completed mpMRI/TRUS fusion-guided targeted prostate biopsy + 12-core transperineal systematic biopsy after mpMRI and PI-RADS scoring. The pathological diagnoses from targeted and systematic biopsy were compared to evaluate and analyze the reasons for missed diagnoses in targeted biopsy.

Results

A total of 388 prostate cancer patients were identified, including 37 (9%) missed diagnoses with targeted biopsy and 44 (11.34%) with systematic biopsy. Between the target biopsy missed diagnosis group and not missed diagnosis group, there was no significant difference in age (71.08 ± 7.11 vs. 71.80 ± 7.94), but PSA (13.63 ± 12.41 vs. 54.54 ± 177.25 ng/ml), prostate volume (61.82 ± 40.64 vs. 44.34 ± 25.07 cm³), PSAD (0.27 ± 0.28 vs. 1.07 ± 2.91), and ISUP grade [1(1) vs. 3(2)] were significantly different. The pathological results of the 37 targeted biopsy missed diagnoses were recompared with MRI: 21 prostate cancers were normal on MRI; 9 cancer areas were abnormal on MRI; and 7 cancer areas on MRI were PI-RADS 3.

Conclusions

Early prostate cancer, large prostate, effect of local anesthesia, doctor–patient cooperation, MRI diagnosis, and operator technology were possible factors for missed diagnosis in targeted biopsy. Improvements imaging technology, greater experience, and personalized biopsy may lead to an accurate pathological diagnosis.

The role of novel imaging in prostate cancer focal therapy: treatment and follow-up

PURPOSE OF REVIEW

Multiparametric magnetic resonance imaging (mpMRI) has fundamentally changed how intraprostatic lesions are visualized, serving as a highly sensitive means for detecting clinically significant prostate cancer (csPCa) via image-targeted biopsy. However, limitations associated with mpMRI have led to the development of new imaging technologies with the goal of better characterizing intraprostatic disease burden to more accurately guide treatment planning and surveillance for prostate cancer focal therapy. Herein, we review several novel imaging modalities with an emphasis on clinical data reported within the past two years.

RECENT FINDINGS

7T MRI, artificial intelligence applied to mpMRI, positron emission tomography combined with either computerized tomography or MRI, contrast-enhanced ultrasound, and micro-ultrasound are novel imaging modalities with the potential to further improve intraprostatic lesion localization for applications in focal therapy for prostate cancer. Many of these technologies have demonstrated equivalent or favorable diagnostic accuracy compared to contemporary mpMRI for identifying csPCa and some have even shown improved capabilities to define lesion borders, to provide volumetric estimates of lesions, and to assess the adequacy of focal ablation of planned treatment zones.

SUMMARY

Novel imaging modalities with capabilities to better characterize intraprostatic lesions have the potential to improve accuracy in treatment planning, real-time assessment of the ablation zone, and posttreatment surveillance; however, many of these technologies require further validation to determine their clinical utility.

Biosensors for Caspase-3: From chemical methodologies to biomedical applications

Caspase-3 plays irreplaceable roles in apoptosis and related diseases. An imbalance in the measured levels of Caspase-3 is implicated in irreversible apoptosis. Therefore, the detection of Caspase-3 is of great significance for apoptosis imaging and the evaluation effect of early tumor treatment and other diseases. Herein, advances in the recent innovations of Caspase-3 response fluorescence biosensors, including molecular probes and nanoprobes, are systematically summarized in sections corresponding. The performances of various luminescence probes in Caspase-3 detection are discussed intensively in the design strategy of chemical structure, response mechanism and biological application. Finally, the current challenges and prospects of the design of new Caspase-3 responsive fluorescence probes for apoptosis imaging, or similar molecular event are proposed.

[Modern tomography imaging techniques in urological diseases]

BACKGROUND

Radiologic imaging is important for the detection, staging and follow-up of urological tumors. Basic therapy decisions for both oncological (surgical vs. systemic therapy, e.g. in testicular cancer) and non-oncological pathologies (interventional vs. conservative therapy, e.g. for ureteral stones) depend largely on the tomographic imaging performed. Due to its almost ubiquitous availability, speed and cost-effectiveness, computed tomography (CT) plays an important role not only in the clarification of abdominal trauma and non-traumatic emergencies, but also in staging and follow-up of oncological patients. However, the level of radiation exposure, impaired renal function and allergies to iodinated contrast media limit the use of CT. Magnetic resonance imaging (MRI) can be a good alternative for many areas of application in oncological and non-oncological imaging due to its high soft tissue differentiation and functional-specific protocols but without the use of ionizing radiation.

AIM

In the following, the main indications of abdominal and pelvic CT and MRI in urology and their limitations are summarized.

RESULTS

The areas of application between CT and MRI are increasingly overlapping, since the latest developments in CT continue to further reduce radiation exposure and increase contrast information, while the speed and robustness of MRI are significantly improving at the same time.

Development and testing quantitative metrics from multi-parametric magnetic resonance imaging that predict Gleason score for prostate tumors

BACKGROUND

Radiologists currently subjectively examine multi-parametric magnetic resonance imaging (MRI) to detect possible clinically significant lesions using the Prostate Imaging Reporting and Data System (PI-RADS) protocol. The assessment of imaging, however, relies on the experience and judgement of radiologists creating opportunity for inter-reader variability. Quantitative metrics, such as z-score and signal to clutter ratio (SCR), are therefore needed.

METHODS

Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resampled, rescaled, translated, and stitched to form spatially registered multi-parametric cubes for patients undergoing radical prostatectomy. Multi-parametric signatures that characterize prostate tumors were inserted into z-score and SCR. The multispectral covariance matrix was computed for the outlined normal prostate. The z-score from each MRI image was computed and summed. To reduce noise in the covariance matrix, following matrix decomposition, the noisy eigenvectors were removed. Also, regularization and modified regularization was applied to the covariance matrix by minimizing the discrimination score. The filtered and regularized covariance matrices were inserted into the SCR calculation. The z-score and SCR were quantitatively compared to Gleason scores from clinical pathology assessment of the histology of sectioned wholemount prostates.

RESULTS

Twenty-six consecutive patients were enrolled in this retrospective study. Median patient age was 60 years (range, 49 to 75 years), median prostate-specific antigen (PSA) was 5.8 ng/mL (range, 2.3 to 23.7 ng/mL), and median Gleason score was 7 (range, 6 to 9). A linear fit of the summed z-score against Gleason score found a correlation of R=0.48 and a P value of 0.015. A linear fit of the SCR from regularizing covariance matrix against Gleason score found a correlation of R=0.39 and a P value of 0.058. The SCR employing the modified regularizing covariance matrix against Gleason score found a correlation of R=0.52 and a P value of 0.007. A linear fit of the SCR from filtering out 3 and 4 eigenvectors from the covariance matrix against Gleason score found correlations of R=0.50 and 0.44, respectively, and P values of 0.011 and 0.027, respectively.

CONCLUSIONS

Z-score and SCR using filtered and regularized covariance matrices derived from spatially registered multi-parametric MRI correlates with Gleason score with highly significant P values.

Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume

Background

Prostate tumor volume predicts biochemical recurrence, metastases, and tumor proliferation. A recent study showed that prostate tumor eccentricity (elongation or roundness) correlated with Gleason score. No studies examined the relationship among the prostate tumor's shape, volume, and potential aggressiveness.

Methods

Of the 26 patients that were analyzed, 18 had volumes >1 cc for the histology-based study, and 25 took up contrast material for the MRI portion of this study. This retrospective study quantitatively compared tumor eccentricity and volume measurements from pathology assessment sectioned wholemount prostates and multi-parametric MRI to Gleason scores. Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Pixel-based blobbing, and labeling were applied to digitized pathology slides and threshold ACE images. Tumor volumes were measured by counting voxels within the blob. Eccentricity calculation used moments of inertia from the blobs.

Results

From wholemount prostatectomy slides, fitting two sets of independent variables, prostate tumor eccentricity (largest blob eccentricity, weighted eccentricity, filtered weighted eccentricity) and tumor volume (largest blob volume, average blob volume, filtered average blob volume) to Gleason score in a multivariate analysis, yields correlation coefficient R=0.798 to 0.879 with P<0.01. The eccentricity t-statistic exceeded the volume t-statistic. Fitting histology-based total prostate tumor volume against Gleason score yields R=0.498, P=0.0098. From multi-parametric MRI, the correlation coefficient R between the Gleason score and the largest blob eccentricity for varying thresholds (0.30 to 0.55) ranged from -0.51 to -0.672 (P<0.01). For varying thresholds (0.60 to 0.80) for MRI detection, the R between the largest blob volume eccentricity against the Gleason score ranged from 0.46 to 0.50 (P<0.03). Combining tumor eccentricity and tumor volume in multivariate analysis failed to increase Gleason score prediction.

Conclusions

Prostate tumor eccentricity, determined by histology or MRI, more accurately predicted Gleason score than prostate tumor volume. Combining tumor eccentricity with volume from histology-based analysis enhanced Gleason score prediction, unlike MRI.

Prostate zones and cancer: lost in transition?

Localized prostate cancer shows great clinical, genetic and environmental heterogeneity; however, prostate cancer treatment is currently guided solely by clinical staging, serum PSA levels and histology. Increasingly, the roles of differential genomics, multifocality and spatial distribution in tumorigenesis are being considered to further personalize treatment. The human prostate is divided into three zones based on its histological features: the peripheral zone (PZ), the transition zone (TZ) and the central zone (CZ). Each zone has variable prostate cancer incidence, prognosis and outcomes, with TZ prostate tumours having better clinical outcomes than PZ and CZ tumours. Molecular and cell biological studies can improve understanding of the unique molecular, genomic and zonal cell type features that underlie the differences in tumour progression and aggression between the zones. The unique biology of each zonal tumour type could help to guide individualized treatment and patient risk stratification.

A review of artificial intelligence in prostate cancer detection on imaging

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

Integration of magnetic resonance imaging into prostate cancer nomograms

The decision whether to undergo prostate biopsy must be carefully weighed. Nomograms have widely been utilized as risk calculators to improve the identification of prostate cancer by weighing several clinical factors. The recent inclusion of multiparametric magnetic resonance imaging (mpMRI) findings into nomograms has drastically improved their nomogram's accuracy at identifying clinically significant prostate cancer. Several novel nomograms have incorporated mpMRI to aid in the decision-making process in proceeding with a prostate biopsy in patients who are biopsy-naïve, have a prior negative biopsy, or are on active surveillance. Furthermore, novel nomograms have incorporated mpMRI to aid in treatment planning of definitive therapy. This literature review highlights how the inclusion of mpMRI into prostate cancer nomograms has improved upon their performance, potentially reduce unnecessary procedures, and enhance the individual risk assessment by improving confidence in clinical decision-making by both patients and their care providers.

Recent Advancements in CT and MR Imaging of Prostate Cancer

CT and MRI are both commonly used in prostate cancer (PCa) management, which includes a large spectrum from screening positive pre-diagnosis phase to metastatic disease. CT and MRI have continually evolved to meet the changing demands for PCa management. For CT, novel techniques such as dual energy CT and photon counting CT show promising results for tissue characterization and quantification. For MRI, the detection, staging, and management of prostate cancer has been significantly improved by the development of multiparametric, biparametric, and whole-body MRI techniques. Additionally, research on ultrasmall superparamagnetic particles of iron oxide contrast-enhanced MRI has revealed promising results for nodal staging of PCa. In this manuscript we aim to outline the current status and recent advancements of CT and MRI in PCa imaging.

Evaluation of multiparametric prostate magnetic resonance imaging findings in patients with a Gleason score of 6 in transrectal ultrasonography-guided biopsy

Purpose

We aimed to evaluate prostate multiparametric magnetic resonance imaging (mpMRI) findings of patients with a Gleason score (GS) of 6 and effectiveness of MRI based on the final pathology result in patients undergoing radical prostatectomy (RP).

Material and methods

mpMRI findings of 80 patients who had a GS of 3 + 3 and who underwent mpMRI were evaluated retrospectively. The mpMRI were scored according to the PIRADS v2.1 guidelines. The patients were divided into those with a high probability of clinically significant cancer (CSC) (PI-RADS 4-5) and those with a low probability of CSC (PI-RADS 2-3).

Results

Of the 80 patients, 33.8% had PI-RADS 2-3, and 66.2% had PI-RADS 4-5 lesions. There was a significant difference between the groups in prostate specific antigen (PSA) value, PSA density, patient age, and tumour percentage on biopsy. When the pathology results were taken as the gold standard in the group that underwent RP, sensitivity, specificity, and accuracy of mpMRI were calculated as 94.74%, 100%, and 96.3%, respectively, an increase in the final GS was found in 9 (33.3%) of the 27 patients, and 70.35% of patients were identified as having CSC.

Conclusions

PI-RADS 4-5 scores have high sensitivity and negative predictive value in the diagnosis of CSC. mpMRI is a reliable and non-invasive diagnostic method that can complement biopsy results in decision-making in patients who are initially evaluated as low risk.

Correlation of in-vivo imaging with histopathology: A review

Despite tremendous advancements in in vivo imaging modalities, there remains substantial uncertainty with respect to tumor delineation on in these images. Histopathology remains the gold standard for determining the extent of malignancy, with in vivo imaging to histopathologic correlation enabling spatial comparisons. In this review, the steps necessary for successful imaging to histopathologic correlation are described, including in vivo imaging, resection, fixation, specimen sectioning (sectioning technique, securing technique, orientation matching, slice matching), microtome sectioning and staining, correlation (including image registration) and performance evaluation. The techniques used for each of these steps are also discussed. Hundreds of publications from the past 20 years were surveyed, and 62 selected for detailed analysis. For these 62 publications, each stage of the correlative pathology process (and the sub-steps of specimen sectioning) are listed. A statistical analysis was conducted based on 19 studies that reported target registration error as their performance metric. While some methods promise greater accuracy, they may be expensive. Due to the complexity of the processes involved, correlative pathology studies generally include a small number of subjects, which hinders advanced developments in this field.

Focal Prostate Stereotactic Body Radiation Therapy With Correlative Pathological and Radiographic-Based Treatment Planning

Introduction

Advances in multiparametric MRI (mpMRI) combining anatomic and functional imaging can accurately identify foci of adenocarcinoma within the prostate, offering the possibility of partial gland therapy. We performed tandem prospective pilot trials to investigate the feasibility of focal prostate SBRT (f-SBRT) based on correlating diagnostic mpMRI and biopsies with confirmatory pathology in treatment planning.

Materials and Methods

Patients with pathologic focal Gleason 6–7 disease and a corresponding PIRADS 4–5 lesion on mpMRI underwent targeted and comprehensive biopsies using MRI/ultrasound fusion under electromagnetic sensor navigation. After rigorous analysis for imaging biopsy concordance, five of 18 patients were eligible to proceed to f-SBRT. Chi-squared test was used for differences from expected outcomes, and concordance was estimated with binomial distribution theory and Wilson’s method.

Results

Six patients had Gleason 6 and 12 had Gleason 3 + 4 disease (mean PSA: 5.8 ng/ml, range: 2.2–8.4). Absolute concordance was 43.8% (95% CI: 0.20, 0.64). Patterns of discordance included additional sites of ipsilateral disease, bilateral disease, and negative target. Five were upstaged to a new NCCN risk category necessitating treatment escalation. The five patients with concordant pathology completed three-fraction f-SBRT with sparing of the surrounding normal structures (including contralateral neurovascular bundle), with no reported grade 2+ toxicities and favorable PSA responses (mean: 41% decrease).

Conclusions

On our pilot trials of f-SBRT planning using rigorous imaging and pathology concordance, image-guided confirmatory biopsies frequently revealed additional disease, suggesting the need for caution in partial-gland therapy. For truly focal disease, f-SBRT provided excellent dosimetry, minimal toxicity, and encouraging biochemical response. Clinical Trial Registration: www.clinicaltrials.gov, NCT02681614; NCT02163317.

Correlation of prostate tumor eccentricity and Gleason scoring from prostatectomy and multi-parametric-magnetic resonance imaging

Background

Proliferating cancer cells interacting with their microenvironment affects a tumor's spatial shape. Elongation or roundness (eccentricity) of lung, skin, and breast cancers indicates the cancer's relative aggressiveness. Non-invasive determination of the prostate tumor's shape should provide meaningful input for prognostication and clinical management. There are currently few studies of prostate tumor shape, therefore this study examines the relationship between a prostate tumor's eccentricity, derived from spatially registered multi-parametric MRI and histology slides, and Gleason scores.

Methods

A total of 26 consecutive patients were enrolled in the study. Median patient age was 60 years (range, 49 to 75 years), median PSA was 5.8 ng/mL (range, 2.3 to 23.7 ng/mL, and median Gleason score was 7 (range, 6 to 9). Multi-parametric MRI (T1, T2, Diffusion, Dynamic Contrast Enhanced) were resampled, rescaled, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Also, tumor shape was computed from the histology slides. Blobbing, labeling, and calculation of eccentricity using moments of inertia were applied to the multi-parametric MRI and histology slides. The eccentricity measurements were compared to the Gleason scores from 25 patients.

Results

From histology slides analysis: the correlation coefficient between the eccentricity for the largest blob and a weighted average eccentricity against the Gleason score ranged from -0.67 to -0.78 for all 18 patients whose tumor volume exceeded 1.0 cc. From multi-parametric MRI analysis: the correlation coefficient between the eccentricity for the largest blob for varying thresholds against the Gleason score ranged from -0.60 to -0.66 for all 25 patients showing contrast uptake in the Dynamic Contrast Enhancement (DCE) MRI.

Conclusions

Spherical shape prostate adenocarcinoma shows a propensity for higher Gleason score. This novel finding follows lung and breast adenocarcinomas but depart from other primary tumor types. Analysis of multi-parametric MRI can non-invasively determine the prostate tumor's morphology and add critical information for prognostication and disease management. Eccentricity of smaller tumors (<1.0 cc) from MP-MRI correlates well with Gleason score, unlike eccentricity measured using histology of wholemount prostatectomy.

Technical Note: Histological validation of anatomical imaging for breast modeling using a novel cryo-microtome

PURPOSE

Precise correlation between three-dimensional (3D) imaging and histology can aid biomechanical modeling of the breast. We develop a framework to register ex vivo images to histology using a novel cryo-fluorescence tomography (CFT) device.

METHODS

A formalin-fixed cadaveric breast specimen, including chest wall, was subjected to high-resolution magnetic resonance (MR) imaging. The specimen was then frozen and embedded in an optimal cutting temperature (OCT) compound. The OCT block was placed in a CFT device with an overhead camera and 50 μm thick slices were successively shaved off the block. After each shaving, the block-face was photographed. At select locations including connective/adipose tissue, muscle, skin, and fibroglandular tissue, 20 μm sections were transferred onto cryogenic tape for manual hematoxylin and eosin staining, histological assessment, and image capture. A 3D white-light image was automatically reconstructed from the photographs by aligning fiducial markers embedded in the OCT block. The 3D MR image, 3D white-light image, and photomicrographs were rigidly registered. Target registration errors (TREs) were computed based on 10 pairs of points marked at fibroglandular intersections. The overall MR-histology registration was used to compare the MR intensities at tissue extraction sites with a one-way analysis of variance.

RESULTS

The MR image to CFT-captured white-light image registration achieved a mean TRE of 0.73 ± 0.25 mm (less than the 1 mm MR slice resolution). The block-face white-light image and block-face photomicrograph registration showed visually indistinguishable alignment of anatomical structures and tissue boundaries. The MR intensities at the four tissue sites identified from histology differed significantly (p < 0.01). Each tissue pair, except the skin-connective/adipose tissue pair, also had significantly different MR intensities (p < 0.01).

CONCLUSIONS

Fine sectioning in a highly controlled imaging/sectioning environment enables accurate registration between the MR image and histology. Statistically significant differences in MR signal intensities between histological tissues are indicators for the specificity of correlation between MRI and histology.

Cost-effectiveness of MRI targeted biopsy strategies for diagnosing prostate cancer in Singapore

BACKGROUND

To evaluate the cost-effectiveness of six diagnostic strategies involving magnetic resonance imaging (MRI) targeted biopsy for diagnosing prostate cancer in initial and repeat biopsy settings from the Singapore healthcare system perspective.

METHODS

A combined decision tree and Markov model was developed. The starting model population was men with mean age of 65 years referred for a first prostate biopsy due to clinical suspicion of prostate cancer. The six diagnostic strategies were selected for their relevance to local clinical practice. They comprised MRI targeted biopsy following a positive pre-biopsy multiparametric MRI (mpMRI) [Prostate Imaging - Reporting and Data System (PI-RADS) score ≥ 3], systematic biopsy, or saturation biopsy employed in different testing combinations and sequences. Deterministic base case analyses with sensitivity analyses were performed using costs from the healthcare system perspective and quality-adjusted life years (QALY) gained as the outcome measure to yield incremental cost-effectiveness ratios (ICERs).

RESULTS

Deterministic base case analyses showed that Strategy 1 (MRI targeted biopsy alone), Strategy 2 (MRI targeted biopsy ➔ systematic biopsy), and Strategy 4 (MRI targeted biopsy ➔ systematic biopsy ➔ saturation biopsy) were cost-effective options at a willingness-to-pay (WTP) threshold of US$20,000, with ICERs ranging from US$18,975 to US$19,458. Strategies involving MRI targeted biopsy in the repeat biopsy setting were dominated. Sensitivity analyses found the ICERs were affected mostly by changes to the annual discounting rate and prevalence of prostate cancer in men referred for first biopsy, ranging between US$15,755 to US$23,022. Probabilistic sensitivity analyses confirmed Strategy 1 to be the least costly, and Strategies 2 and 4 being the preferred strategies when WTP thresholds were US$20,000 and US$30,000, respectively.

LIMITATIONS AND CONCLUSIONS

This study found MRI targeted biopsy to be cost-effective in diagnosing prostate cancer in the biopsy-naïve setting in Singapore.

A rapid review of influential factors and appraised solutions on organ delineation uncertainties reduction in radiotherapy

Background and purpose.Accurate volume delineation plays an essential role in radiotherapy. Contouring is a potential source of uncertainties in radiotherapy treatment planning that could affect treatment outcomes. Therefore, reducing the degree of contouring uncertainties is crucial. The role of utilized imaging modality in the organ delineation uncertainties has been investigated. This systematic review explores the influential factors on inter-and intra-observer uncertainties of target volume and organs at risk (OARs) delineation focusing on the used imaging modality for these uncertainties reduction and the reported subsequent histopathology and follow-up assessment.Methods and materials.An inclusive search strategy has been conducted to query the available online databases (Scopus, Google Scholar, PubMed, and Medline). 'Organ at risk', 'target', 'delineation', 'uncertainties', 'radiotherapy' and their relevant terms were utilized using every database searching syntax. Final article extraction was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Included studies were limited to the ones published in English between 1995 and 2020 and that just deal with computed tomography (CT) and magnetic resonance imaging (MRI) modalities.Results.A total of 923 studies were screened and 78 were included of which 31 related to the prostate 20 to the breast, 18 to the head and neck, and 9 to the brain tumor site. 98% of the extracted studies performed volumetric analysis. Only 24% of the publications reported the dose deviations resulted from variation in volume delineation Also, heterogeneity in studied populations and reported geometric and volumetric parameters were identified such that quantitative synthesis was not appropriate.Conclusion.This review highlightes the inter- and intra-observer variations that could lead to contouring uncertainties and impede tumor control in radiotherapy. For improving volume delineation and reducing inter-observer variability, the implementation of well structured training programs, homogeneity in following consensus and guidelines, reliable ground truth selection, and proper imaging modality utilization could be clinically beneficial.

A 25-year perspective on evaluation and understanding of biomarkers in urologic cancers

The past 25 years have witnessed an explosion of investigative attempts to identify clinically useful biomarkers which can have meaningful impacts for patients with urologic cancers. However, in spite of the enormous amount of research aiming to identify markers with the hope of impacting patient care, only a handful have proven to have true clinical utility. Improvements in targeted imaging, pan-omics evaluation, and genetic sequencing at the tissue and single-cell levels have yielded many potential targets for continued biomarker investigation. This article, as one in this series for the 25th Anniversary Issue of Urologic Oncology: Seminars and Original Investigations, serves to give a perspective on our progress and failures over the past quarter-century in our highest volume urologic cancers: prostate, bladder, and kidney cancers.

Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

Background

Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel ¹H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading.

Methods

Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient.

Results

The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01).

Conclusion

The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

The role of pre-biopsy mpMRI in lymph node staging for prostate cancer

INTRODUCTION

Prostate cancer (PCa) staging is an integral part in the management of prostate cancer. The gold standard for diagnosing lymph node invasion is a surgical lymphadenectomy, with no superior imaging modality available at the clinician's disposal. Our aim in this study is to identify if a pre-biopsy multiparametric MRI (mpMRI) can provide enough information about pelvic lymph nodes in intermediate and high risk PCa patients, and whether it can substitute further cross sectional imaging (CSI) modalities of the abdomen and pelvis in these risk categories.

METHODS

Patients with intermediate and high risk prostate cancer were collected between January 2015 and June 2019, while excluding patients who did not undergo a pre-biopsy mpMRI or a CSI. Date regarding biopsy result, PSA, MRI results, CSI imaging results were collected. Using Statistical Package for the Social Sciences (SPSS) version 24.0, statistical analysis was conducted using the Cohen's Kappa agreement for comparison of mpMRI with CSI. McNemar's test and receiver operator curve (ROC) curve were used for comparison of sensitivity of both tests when comparing to the gold standard of lymphadenectomy.

RESULTS

A total of 143 patients fit the inclusion criteria. We further stratified our patients into according to PSA level and Gleason score. Overall, agreement between mpMRI and all CSI was 0.857. When stratifying patients based on Gleason score and PSA, the higher the grade or PSA, the higher agreement between mpMRI and CSI. The sensitivity of mpMRI (73.7%) is similar to CSI (68.4%). When comparing CSI sensitivity to that of mpMRI, no significant difference was present by utilizing the McNemar test and very similar receiver operating characteristic curve.

CONCLUSION

A pre-biopsy mpMRI can potentially substitute further cross sectional imaging in our cohort of patients. However, larger prospective studies are needed to confirm our findings.

MRI-guided focal laser ablation of prostate cancer: a prospective single-arm, single-center trial with 3 years of follow-up

PURPOSE

We aimed to assess post-interventional and 36-month follow-up results of a single-center, single-arm, in-bore phase I trial of focal laser ablation (FLA) guided by multiparametric magnetic resonance imaging (mpMRI).

METHODS

FLA procedures were done in-bore MRI using a transperineal approach. Primary endpoints were feasibility and safety expressed as lack of grade 3 complications. Secondary endpoints were changes in international prostate symptom score (IPSS), sexual health inventory for men (SHIM), quality of life (QoL) scores, and serum prostate specific antigen (PSA) levels. Treatment outcomes were assessed by combined mpMRI-ultrasound fusion-guided and extended sextant systematic biopsy after 12, 24, and optionally after 36 months.

RESULTS

Fifteen participants were included. Seven patients (46.67%) had Gleason 3+3 and 8 patients (53.33%) had Gleason 3+4 cancer. All patients tolerated the procedure well, and no grade 3/4 complications occurred. All grade 1 and 2 complications were transient and resolved completely. There was no significant change in mean IPSS from baseline (-1, p = 0.460) and QoL (0, p = 0.441) scores following FLA but there was a significant drop in mean SHIM scores (-2, p = 0.010) compared to pretreatment baselines. Mean PSA significantly decreased after FLA (-2.5, p < 0.001). Seven out of 15 patients (46.67%) had residual cancer in, adjacent, or in close proximity to the treatment area (1 × 4+3=7, 1 × 3+4=7, and 5 × 3+3=6). Four out of 15 patients (26.67%) underwent salvage therapy (2 repeat FLA, 2 radical prostatectomy).

CONCLUSION

After 3 years of follow-up we conclude focal laser ablation is safe and feasible without significant complications.

Current State of Personalized Genitourinary Cancer Radiotherapy in the Era of Precision Medicine

Radiation therapy plays a crucial role for the management of genitourinary malignancies, with technological advancements that have led to improvements in outcomes and decrease in treatment toxicities. However, better risk-stratification and identification of patients for appropriate treatments is necessary. Recent advancements in imaging and novel genomic techniques can provide additional individualized tumor and patient information to further inform and guide treatment decisions for genitourinary cancer patients. In addition, the development and use of targeted molecular therapies based on tumor biology can result in individualized treatment recommendations. In this review, we discuss the advances in precision oncology techniques along with current applications for personalized genitourinary cancer management. We also highlight the opportunities and challenges when applying precision medicine principles to the field of radiation oncology. The identification, development and validation of biomarkers has the potential to personalize radiation therapy for genitourinary malignancies so that we may improve treatment outcomes, decrease radiation-specific toxicities, and lead to better long-term quality of life for GU cancer survivors.

Registration of histopathology to magnetic resonance imaging of prostate cancer

BACKGROUND AND PURPOSE

The diagnostic accuracy of new imaging techniques requires validation, preferably by histopathological verification. The aim of this study was to develop and present a registration procedure between histopathology and in-vivo magnetic resonance imaging (MRI) of the prostate, to estimate its uncertainty and to evaluate the benefit of adding a contour-correcting registration.

MATERIALS AND METHODS

For twenty-five prostate cancer patients, planned for radical prostatectomy, a 3D-printed prostate mold based on in-vivo MRI was created and an ex-vivo MRI of the specimen, placed inside the mold, was performed. Each histopathology slice was registered to its corresponding ex-vivo MRI slice using a 2D-affine registration. The ex-vivo MRI was rigidly registered to the in-vivo MRI and the resulting transform was applied to the histopathology stack. A 2D deformable registration was used to correct for specimen distortion concerning the specimen's fit inside the mold. We estimated the spatial uncertainty by comparing positions of landmarks in the in-vivo MRI and the corresponding registered histopathology stack.

RESULTS

Eighty-four landmarks were identified, located in the urethra (62%), prostatic cysts (33%), and the ejaculatory ducts (5%). The median number of landmarks was 3 per patient. We showed a median in-plane error of 1.8 mm before and 1.7 mm after the contour-correcting deformable registration. In patients with extraprostatic margins, the median in-plane error improved from 2.1 mm to 1.8 mm after the contour-correcting deformable registration.

CONCLUSIONS

Our registration procedure accurately registers histopathology to in-vivo MRI, with low uncertainty. The contour-correcting registration was beneficial in patients with extraprostatic surgical margins.

Assessment of magnetic resonance imaging (MRI)-fusion prostate biopsy with concurrent standard systematic ultrasound-guided biopsy among men requiring repeat biopsy

INTRODUCTION

The role of magnetic resonance imaging (MRI)-fusion biopsy (FB) remains unclear in men with prior negative prostate biopsies. This study aimed to compare the diagnostic accuracy of FB with concurrent systematic biopsy (SB) in patients requiring repeat prostate biopsies.

METHODS

Patients with previous negative prostate biopsies requiring repeat biopsies were included. Those without suspicious lesions (≥Prostate Imaging-Reporting and Data System [PI-RADS] 3) on MRI were excluded. All patients underwent FB followed by SB. The primary outcome was the sensitivity for clinically significant prostate cancer (Gleason score ≥7). The secondary objective was identification of potential predictive factors of biopsy performance.

RESULTS

A total of 53 patients were included; 41 (77%) patients were found to have clinically significant prostate cancer. FB had a higher detection rate of significant cancer compared to SB (85% vs. 76%, respectively, p=0.20) and lower diagnosis of indolent (Gleason score 3+3=6) cancer (10% vs. 27%, respectively, p=0.05). FB alone missed six (15%) clinically significant cancers, compared to 10 (24%) with SB. SB performance was significantly impaired in patients with anterior lesions and high prostate volumes (p<0.05). There was high degree of pathological discordance between the two approaches, with concordance seen in only 34% of patients.

CONCLUSIONS

In patients with prior negative biopsies and ongoing suspicion for prostate cancer, a combined approach of FB with SB is needed for optimal detection and risk classification of clinically significant disease. Anterior tumors and large prostates were significant predictors of poor SB performance and an MRI-fusion alone approach in these settings could be considered.

Comparison of initial and second opinion reads of multiparametric magnetic resonance imaging of the prostate for transperineal template-guided biopsies with MRI-Ultrasound fusion

PURPOSE

To investigate the value of second-opinion evaluation of multiparametric prostate magnetic resonance imaging (MRI) by subspecialised uroradiologists for the detection of significant cancer in transperineal fusion prostate biopsy.

METHODS

The evaluated data included age, PSA (ng/ml), PSA density, Gleason score, digital rectal examination (DRE), prostate volume of 149 patients. Twenty-seven patients (18%) had no previous prostate biopsy, 114 patients (77%) had a previous negative biopsy, and 8 patients (5%) were on active surveillance. Using PI-RADS v2 scores for mpMRI a second report was performed by a specialist uroradiologist. In all cases a subsequent transperineal biopsy was performed with at least 2 cores per target and additional background systemic cores. Initial and second-opinion radiology reports were evaluated for detection of any cancer and Gleason score (GS) 7-10 cancer, including positive predictive value and negative (NPV) and compared by Fisher's exact test.

RESULTS

At transperineal biopsy, 51 % (76/149) of patients had a GS 6-10 prostate cancer (PCa), 27 % (40/149) of patients had a GS 3 + 3 PCa and 12 % (18/149) a GS 3 + 4 and 12 % (18/149) had a GS ≥4 + 3 PCa. Agreement between initial and second-opinion reads was observed in 57.7% (86/149; kappa value = 0.32). The detection of clinically significant cancers with second-opinion reads was significantly higher (0.61; 17/28) compared to initial reads (0.35; 17/49); P = 0.034.

CONCLUSIONS

Second reading of prostate mpMRIs by subspecialised uroradiologists significantly improved the positive predictive value for detection of clinically significant prostate cancer and showed a trend towards improved NPV for MRI-negative cases where biopsy could be safely avoided. Urologists should be aware that the experience of the reporter will affect the report when making decisions if and how to obtain biopsies. Reporter experience may help to reduce overcalling and avoid over-targeting of lesions.

ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate

Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed clinically significant cancers, overdiagnosed low-risk cancers, and frequent false positives. Interpretation of MRI could be greatly improved by providing radiologists with an answer key that clearly shows cancer locations on MRI. Registration of histopathology images from patients who had radical prostatectomy to pre-operative MRI allows such mapping of ground truth cancer labels onto MRI. However, traditional MRI-histopathology registration approaches are computationally expensive and require careful choices of the cost function and registration hyperparameters. This paper presents ProsRegNet, a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of image preprocessing, estimation of affine and deformable transformations by deep neural networks, and mapping cancer labels from histopathology images onto MRI using estimated transformations. We trained our neural network using MR and histopathology images of 99 patients from our internal cohort (Cohort 1) and evaluated its performance using 53 patients from three different cohorts (an additional 12 from Cohort 1 and 41 from two public cohorts). Results show that our deep learning pipeline has achieved more accurate registration results and is at least 20 times faster than a state-of-the-art registration algorithm. This important advance will provide radiologists with highly accurate prostate MRI answer keys, thereby facilitating improvements in the detection of prostate cancer on MRI. Our code is freely available at https://github.com/pimed//ProsRegNet.

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Simple Summary

The increasing interest in implementing artificial intelligence in radiomic models has occurred alongside advancement in the tools used for computer-aided diagnosis. Such tools typically apply both statistical and machine learning methodologies to assess the various modalities used in medical image analysis. Specific to prostate cancer, the radiomics pipeline has multiple facets that are amenable to improvement. This review discusses the steps of a magnetic resonance imaging based radiomics pipeline. Present successes, existing opportunities for refinement, and the most pertinent pending steps leading to clinical validation are highlighted.

Abstract

The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.

3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction

The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However, identifying the presence and extent of cancer on MRI remains challenging, leading to high variability in detection even among expert radiologists. Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI. To date, such improvement has been limited by the lack of accurately labeled MRI datasets. Data from patients who underwent radical prostatectomy enables the spatial alignment of digitized histopathology images of the resected prostate with corresponding pre-surgical MRI. This alignment facilitates the delineation of detailed cancer labels on MRI via the projection of cancer from histopathology images onto MRI. We introduce a framework that performs 3D registration of whole-mount histopathology images to pre-surgical MRI in three steps. First, we developed a novel multi-image super-resolution generative adversarial network (miSRGAN), which learns information useful for 3D registration by producing a reconstructed 3D MRI. Second, we trained the network to learn information between histopathology slices to facilitate the application of 3D registration methods. Third, we registered the reconstructed 3D histopathology volumes to the reconstructed 3D MRI, mapping the extent of cancer from histopathology images onto MRI without the need for slice-to-slice correspondence. When compared to interpolation methods, our super-resolution reconstruction resulted in the highest PSNR relative to clinical 3D MRI (32.15 dB vs 30.16 dB for BSpline interpolation). Moreover, the registration of 3D volumes reconstructed via super-resolution for both MRI and histopathology images showed the best alignment of cancer regions when compared to (1) the state-of-the-art RAPSODI approach, (2) volumes that were not reconstructed, or (3) volumes that were reconstructed using nearest neighbor, linear, or BSpline interpolations. The improved 3D alignment of histopathology images and MRI facilitates the projection of accurate cancer labels on MRI, allowing for the development of improved MRI interpretation schemes and machine learning models to automatically detect cancer on MRI.

Tumor location on MRI determines outcomes of patients with prostate cancer after total prostate cryoablation

We investigated the association between tumor location on multiparametric magnetic resonance imaging (mpMRI) and outcomes of prostate cancer patients after primary total prostate cryoablation (PTPC). Between March 2010 and December 2012, consecutive 192 prostate cancer patients receiving PTPC were enrolled. Tumor locations were determined and classified as anterior apex (AA), anterior midgland (AM), anterior base (AB), posterior apex (PA), posterior midgland (PM) and posterior base (PB) using mpMRI. Midline location, central location, seminal vesicle invasion, extraprostatic extension, multiple tumors, and tumor volume were also identified. Prostate local recurrence and biochemical failure were considered as primary and secondary endpoints, respectively. Tumors on mpMRI were identified in 148 (77.1%) patients. Tumor locations were most frequently noted in PM (89, 46.4%), followed by AM (55, 28.6%), PB (53, 27.6%), PA (46, 24%), AA (35, 18.2%) and AB (31, 16.1%). Midline and central tumors were observed in 34 (17.7%) and 14 (7.3%) patients, respectively. During a median follow-up duration of 81 months (range, 2-114 months), 71 (37.0%) and 29 (40.8%) patients experienced biochemical failure and local recurrence, respectively. Multivariable analyses revealed only AA tumors increased the risk of local recurrences (HR = 2.98, 95% CI. 1.36-6.49). None of location-related parameters was associated with biochemical failure. Tumor location on mpMRI has a significant association with local tumor recurrence in patients receiving PTPC. Physicians should be cautious when conducting cryoablation for prostate tumors in AA location.