Whole-mount Histopathology Research Articles

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

PurposeTo establish the incidence, size, zonal location and Gleason Score(GS)/Gleason Grade Group(GG) of sparse versus dense prostate cancer (PCa) lesions and to identify the imaging characteristics of sparse versus dense cancers on multiparametric MRI (mpMRI). MethodsSeventy-six men with untreated PCa were scanned prior to prostatectomy with endorectal-coil 3 T MRI including T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced MRI. Cancerous regions were outlined and graded on the whole-mount, processed specimens, with tissue compositions estimated. Regions with cancer comprising <50 % and ≥ 50 % of the tissue were considered sparse and dense respectively. Regions of interest (ROI) were manually drawn on T2-weighted MRI. Within each patient, area-weighted ROI averages were calculated for each imaging measure for each tissue type, GS/GG, and sparse/dense composition. ResultsA large number of cancer regions were identified on histopathology (n = 1193: 939 (peripheral zone (PZ)) and 254 (transition zone (TZ))). Thirty-seven percent of these lesions were sparse. Sparse lesions were primarily low-grade with the majority of PZ and 100 % of TZ sparse lesions ≤GS3 + 3/GG1. Dense lesions were significantly larger than sparse lesions in both PZ and TZ, p < 0.0001. On imaging, 246/45 PZ and 109/8 TZ dense/sparse 2D cancerous ROIs were drawn. Sparse GS3 + 3 and sparse ≥GS3 + 4 cancers did not have significantly different MRI intensities to dense GS3 + 3 cancers, while sparse GS3 + 3/GG1 cancers differed from benign, p < 0.05. ConclusionHistopathologically identified prostate cancer lesions were sparse in 37 % of cases. Sparse cancers were entirely low grade in TZ and predominantly low-grade in PZ and generally small, thus likely posing lower risk for spread and progression than dense lesions. Sparse lesions were not distinguishable from dense lesions on mpMRI, but could be distinguished from benign tissues.

Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects

BackgroundWhole-mount histopathology (WMH) has been a powerful tool to investigate the characteristics of prostate cancer. However, the latest advancement of WMH was yet under summarization. In this review, we offer a comprehensive exposition of current research utilizing WMH in diagnosing and treating prostate cancer (PCa), and summarize the clinical advantages of WMH and outlines potential on future prospects.MethodsAn extensive PubMed search was conducted until February 26, 2023, with the search term “prostate”, “whole-mount”, “large format histology”, which was limited to the last 4 years. Publications included were restricted to those in English. Other papers were also cited to contribute a better understanding.ResultsWMH exhibits an enhanced legibility for pathologists, which improved the efficacy of pathologic examination and provide educational value. It simplifies the histopathological registration with medical images, which serves as a convincing reference standard for imaging indicator investigation and medical image-based artificial intelligence (AI). Additionally, WMH provides comprehensive histopathological information for tumor volume estimation, post-treatment evaluation, and provides direct pathological data for AI readers. It also offers complete spatial context for the location estimation of both intraprostatic and extraprostatic cancerous region.ConclusionsWMH provides unique benefits in several aspects of clinical diagnosis and treatment of PCa. The utilization of WMH technique facilitates the development and refinement of various clinical technologies. We believe that WMH will play an important role in future clinical applications.

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.

The presence of intraductal carcinoma of prostate is a risk factor for poor pathologic response in men with high-risk prostate cancer receiving neoadjuvant therapy

ObjectiveTo explore the potential association between the presence of intraductal carcinoma of the prostate (IDC-P) on biopsy and pathologic response of primary tumor to neoadjuvant therapy in patients with high-risk prostate cancer. MethodsEighty-five patients with high-risk localized/locally advanced prostate cancer (CaP) who were given 6-month neoadjuvant therapies of androgen deprivation therapy plus docetaxel or abiraterone prior to radical prostatectomy in 2 prospective trials were included in this study. The presence of IDC-P in biopsy pathology was rereviewed by 2 experienced pathologists. Favorable pathologic response was defined as pathologic complete response or minimal residual disease <5 mm on whole-mount histopathology. Characteristics of clinical and biopsy pathology variables were included in univariate and multivariate logistic regression analyses to identify risk factors for the prediction of favorable pathologic response on final pathology. ResultsIDC-P was identified to be present on biopsy pathology of 35 patients (41.2%) while favorable pathologic responses were confirmed in 25 patients (29.4%). Initial prostate-specific antigen (PSA) (OR 3.592, 95% CI 1.176–10.971, P = 0.025) and the presence of IDC-P on biopsy pathology (OR 3.837, 95% CI 1.234–11.930, P = 0.020) were found to be significantly associated with favorable pathologic response in multivariate logistic regression analysis. ConclusionIDC-P on biopsy pathology was found to be an independent risk factor to predict a poor pathology response of primary CaP to neoadjuvant therapies.

PSMA PET/CT and mpMRI discrepancies in prostate cancer detection with whole-mount histopathology gold standard.

284 Background: Multiparametric MRI (mpMRI) and PSMA-PET are complementary imaging modalities used in the pre-surgical evaluation of patients with prostate cancer (PCa). The aim of this study was to evaluate the imaging and pathology parameters associated with PSMA-PET and mpMRI disagreement with each other and with gold standard histopathology. Methods: Patients undergoing radical prostatectomy (RP) with pre-surgical PSMA-PET and mpMRI were screened for inclusion in this retrospective analysis. Patients had the imaging scans done &lt;3 months from each other, and whole mount histopathology (WMHP) slides available. Two nuclear medicine physicians and 2 radiologists independently contoured PCa lesions on PSMA-PET and mpMRI, respectively. A consensus read was done with a third reader for each modality, and a majority rule was applied (2:1). Quantitative measures were extracted on a lesion-basis (SUVmean, SUVmax, tumor volume). A PET/MRI fusion was obtained, and agreement/disagreement was assessed visually, based on the overlapping lesion contours. WMHP slides were used to establish the disagreement of imaging-identified lesions with the gold standard pathology. Independent samples t-test and one-way ANOVA were used to assess group differences. Univariable and multivariable logistic regression models were used to assess the association of clinical, pathological, and imaging variables with PSMA-PET and mpMRI agreement/disagreement with each other and with pathology. Results: The cohort included 114 patients, and 175 pathology lesions were identified (ISUP 3, n=22; ISUP&gt;3, n=153). mpMRI and PSMA PET identified 138 and 170 lesions, respectively. Sensitivity for ISUP&gt;3 lesions was 79% and 87% for mpMRI and PSMA-PET, respectively. 115/153 (76%) ISUP&gt; 3 lesions were correctly identified, and 14/153 (9%) were missed by both imaging modalities, respectively. 5/153 (3%) were correctly identified by mpMRI and missed by PSMA-PET, 17/153 (11%) were correctly identified by PSMA-PET and missed by mpMRI. Lesion’s ISUP grade group and size on pathology were significantly lower in mpMRI- compared to mpMRI+ and in PET- compared to PET+ lesions. SUVmax and SUVmean were significantly higher in PET+/MRI+ than PET+/MRI- lesions. Lesion’s ISUP grade group and size were significantly correlated to PSMA-PET and mpMRI agreement, whereas PSMA-PET metrics were not. Logistic regression model showed that lesions with lower ISUP, smaller size, and lack of aggressive prostate cancer features on pathology (cribriform pattern and intraductal carcinoma) have higher likelihood of being missed by PET and mpMRI and of PET/MRI disagreement. Conclusions: Higher SUVmax, SUVmean, and tumor volume on PSMA-PET were associated with a TP finding on PSMA-PET, higher ISUP grade group and size on pathology were associated with a TP finding on both PSMA-PET and mpMRI, and with agreement between PSMA-PET and mpMRI.

Association of aggressive prostate cancer features on whole-mount histopathology and quantitative measures on PSMA PET.

289 Background: The intraductal carcinoma (IDC) and the large cribriform growth pattern (LCP) are two architectural patterns of prostate cancer (PC) associated to more aggressive disease, high Gleason scores, advanced tumor stage, biochemical relapse, and distant metastasis. Predicting the presence of these pathology features could be of significant help in case whole-mount pathology (WMHP) is not available. The goal of this analysis was to assess the distribution of quantitative parameters from 68Ga-PSMA-11 PET/CT (PSMA PET) in lesions with and without LCP/IDC patterns on WMHP. We assessed the ability of pre-surgical PSMA PET imaging to predict the presence of aggressive pathology features. Methods: With IRB approval and HIPAA compliance, we derived a study cohort of PC patients who underwent PSMA PET prior to robotic radical prostatectomy (RALP). WMHP analysis described presence/absence of LCP and IDC patterns in each identified PC lesion. Board-certified physicians contoured all PC lesions on PSMA PET. Only lesions matching with WMHP were included in the analysis. PSMA PET metrics (SUVmax, SUVmean and PSMA-volume) were extracted. All lesions were categorized in sub-cohorts as LCP+/IDC+, LCP+/IDC-, LCP-/IDC-, and LCP-/IDC+. One way-ANOVA assessed significant differences among the imaging parameters in the sub-cohorts. The area under the curve (AUC) from ROC analysis was used to assess the ability of imaging metrics to predict the presence of aggressive PC features on WMHP. Results: The final cohort comprised 155 patients and 177 lesions. The median (IQR) PSA at time of RALP was 9.2 (5.55-15.05). 73/177 lesions (41.2%) were categorized as LCP+/IDC+, 63/177 (35.6%) as LCP-/IDC-, 37/177 (20.9%) as LCP+/IDC-, and 4 (2.3%) as LCP-/IDC-. SUVmax (p=0.007), SUVmean (p=0.048) and lesion size on PSMA PET (p=0.003) were significantly higher in lesions showing LCP, whereas only lesion size was significantly higher in lesions showing IDC (p=0.003). Lesions showing LCP+/IDC- had significantly higher SUVmax (p=0.002) and SUVmean (p=0.001) than lesions showing LCP-/IDC-. SUVmean was significantly higher in LCP+/IDC- than in LCP+/IDC+ (0.043). SUVmax and SUVmean were not significantly different among other sub-cohorts. Lesion size on PSMA PET was significantly higher in LCP+/IDC+ than in LCP-/IDC- (0.009). A logistic regression model analysis showed that PSMA PET quantitative measures predict the presence of LCP and/or IDC with moderate accuracy (AUC= 0.69; p=0.001). Conclusions: Higher SUVmax and SUVmean are found in lesions showing LCP, but not in those showing IDC. A linear regression model including SUVmax, SUVmean and PSMA PET lesion volume was able to predict with moderate accuracy the presence of pathology features of aggressive prostate cancer (AUC: 0.69).

A deep learning model, NAFNet, predicts adverse pathology and recurrence in prostate cancer using MRIs

We aimed to apply a potent deep learning network, NAFNet, to predict adverse pathology events and biochemical recurrence-free survival (bRFS) based on pre-treatment MRI imaging. 514 prostate cancer patients from six tertiary hospitals throughout China from 2017 and 2021 were included. A total of 367 patients from Fudan University Shanghai Cancer Center with whole-mount histopathology of radical prostatectomy specimens were assigned to the internal set, and cancer lesions were delineated with whole-mount pathology as the reference. The external test set included 147 patients with BCR data from five other institutes. The prediction model (NAFNet-classifier) and integrated nomogram (DL-nomogram) were constructed based on NAFNet. We then compared DL-nomogram with radiology score (PI-RADS), and clinical score (Cancer of the Prostate Risk Assessment score (CAPRA)). After training and validation in the internal set, ROC curves in the external test set showed that NAFNet-classifier alone outperformed ResNet50 in predicting adverse pathology. The DL-nomogram, including the NAFNet-classifier, clinical T stage and biopsy results, showed the highest AUC (0.915, 95% CI: 0.871–0.959) and accuracy (0.850) compared with the PI-RADS and CAPRA scores. Additionally, the DL-nomogram outperformed the CAPRA score with a higher C-index (0.732, P < 0.001) in predicting bRFS. Based on this newly-developed deep learning network, NAFNet, our DL-nomogram could accurately predict adverse pathology and poor prognosis, providing a potential AI tools in medical imaging risk stratification.

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

BackgroundMultiparametric magnetic resonance imaging (mpMRI) and positron emission tomography (PET) are widely used for the management of prostate cancer (PCa). However, how these modalities complement each other in PCa risk stratification is still largely unknown. We aim to provide insights into the potential of mpMRI and PET for PCa risk stratification.MethodsWe analyzed data from 55 consecutive patients with elevated prostate-specific antigen and biopsy-proven PCa enrolled in a prospective study between December 2016 and December 2019. [68Ga]PSMA-11 PET (PSMA-PET), [11C]Acetate PET (Acetate-PET) and mpMRI were co-registered with whole-mount histopathology. Lower- and higher-grade lesions were defined by International Society of Urological Pathology (ISUP) grade groups (IGG). We used PET and mpMRI data to differentiate between grades in two cases: IGG 3 vs. IGG 2 (case 1) and IGG ≥ 3 vs. IGG ≤ 2 (case 2). The performance was evaluated by receiver operating characteristic (ROC) analysis.ResultsWe find that the maximum standardized uptake value (SUVmax) for PSMA-PET achieves the highest area under the ROC curve (AUC), with AUCs of 0.72 (case 1) and 0.79 (case 2). Combining the volume transfer constant, apparent diffusion coefficient and T2-weighted images (each normalized to non-malignant prostatic tissue) results in AUCs of 0.70 (case 1) and 0.70 (case 2). Adding PSMA-SUVmax increases the AUCs by 0.09 (p < 0.01) and 0.12 (p < 0.01), respectively.ConclusionsBy co-registering whole-mount histopathology and in-vivo imaging we show that mpMRI and PET can distinguish between lower- and higher-grade prostate cancer, using partially discriminative cut-off values.

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

Accurate delineations of regions of interest (ROIs) on multi-parametric magnetic resonance imaging (mpMRI) are crucial for development of automated, machine learning-based prostate cancer (PCa) detection and segmentation models. However, manual ROI delineations are labor-intensive and susceptible to inter-reader variability. Histopathology images from radical prostatectomy (RP) represent the "gold standard" in terms of the delineation of disease extents, for example, PCa, prostatitis, and benign prostatic hyperplasia (BPH). Co-registering digitized histopathology images onto pre-operative mpMRI enables automated mapping of the ground truth disease extents onto mpMRI, thus enabling the development of machine learning tools for PCa detection and risk stratification. Still, MRI-histopathology co-registration is challenging due to various artifacts and large deformation between in vivo MRI and ex vivo whole-mount histopathology images (WMHs). Furthermore, the artifacts on WMHs, such as tissue loss, may introduce unrealistic deformation during co-registration. This study presents a new registration pipeline, MSERgSDM, a multi-scale feature-based registration (MSERg) with a statistical deformation (SDM) constraint, which aims to improve accuracy of MRI-histopathology co-registration. In this study, we collected 85 pairs of MRI and WMHs from 48 patients across three cohorts. Cohort 1 (D1), comprised of a unique set of 3D printed mold data from six patients, facilitated the generation of ground truth deformations between ex vivo WMHs and in vivo MRI. The other two clinically acquired cohorts (D2 and D3) included 42 patients. Affine and nonrigid registrations were employed to minimize the deformation between ex vivo WMH and ex vivo T2-weighted MRI (T2WI) in D1. Subsequently, ground truth deformation between in vivo T2WI and ex vivo WMH was approximated as the deformation between in vivo T2WI and ex vivo T2WI. In D2 and D3, the prostate anatomical annotations, for example, tumor and urethra, were made by a pathologist and a radiologist in collaboration. These annotations included ROI boundary contours and landmark points. Before applying the registration, manual corrections were made for flipping and rotation of WMHs. MSERgSDM comprises two main components: (1) multi-scale representation construction, and (2) SDM construction. For the SDM construction, we collected N=200 reasonable deformation fields generated using MSERg, verified through visual inspection. Three additional methods, including intensity-based registration, ProsRegNet, and MSERg, were also employed for comparison against MSERgSDM. Our results suggest that MSERgSDM performed comparably to the ground truth (p>0.05). Additionally, MSERgSDM (ROI Dice ratio=0.61, landmark distance=3.26mm) exhibited significant improvement over MSERg (ROI Dice ratio=0.59, landmark distance=3.69mm) and ProsRegNet (ROI Dice ratio=0.56, landmark distance=4.00mm) in local alignment. This study presents a novel registration method, MSERgSDM, for mapping ex vivo WMH onto in vivo prostate MRI. Our preliminary results demonstrate that MSERgSDM can serve as a valuable tool to map ground truth disease annotations from histopathology images onto MRI, thereby assisting in the development of machine learning models for PCa detection on MRI.

Immunohistochemical and histopathological validation of 18 F-PSMA-1007 PET/CT for intraprostatic cancerous lesions.

Prostate-specific membrane antigen (PSMA) is overexpressed in prostate cancer (PCa). In this study, we aim to immunohistochemically and histopathological validate the fluorine-18 (18 F)-PSMA-1007 positron emission tomography/computed tomography (PET/CT) for intraprostatic PCa lesions. Between February 2019 and October 2020, patients with biopsy-proven, treatment-naïve intermediate-to-high-risk PCa undergoing an 18 F-PSMA-1007 PET/CT before robot-assisted radical prostatectomy (RARP) were prospectively enrolled. For all PCa lesions found on whole-mount histopathology, location, size, International Society of Urological Pathology (ISUP) grade group (GG), and immune reactive score (IRS) were assessed after PSMA staining. ISUP GG ≥ 3 PCa was defined as clinically significant (cs) PCa. All lesions were matched on PSMA PET/CT and the maximum standardized uptake value (SUVmax) was measured. A total of 125 lesions were analyzed in the 80 RARP specimens, of which 49 (40%) were csPCa and 76 (60%) non-csPCa. Linear multivariable regressions showed that an increase in SUVmax significantly correlated with a higher ISUP GG (p values between 0.021 and 0.001) and a higher IRS (p = 0.017). Logistic multivariable regression showed that csPCa significantly correlated with a higher SUVmax (odds ratio, OR: 1.17 [95% confidence interval, CI: 1.04-1.21, p = 0.005]), an increase in tumor length (OR: 1.05 [95% CI 1.01-1.10, p = 0.020]) and a higher IRS (OR; 1.24 [95% CI 1.07-1.47, p = 0.006]). A SUVmax threshold of 4 would have resulted in one (2%) missed lesion with csPCa. This prospective study revealed that 18 F-PSMA-1007 PET/CT SUVmax is correlated with the ISUP GG and IRS, and thereby could be a tool to characterize intraprostatic PCa lesions.

Prediction and Mapping of Intraprostatic Tumor Extent with Artificial Intelligence

BackgroundMagnetic resonance imaging (MRI) underestimation of prostate cancer extent complicates the definition of focal treatment margins. ObjectiveTo validate focal treatment margins produced by an artificial intelligence (AI) model. Design, setting, and participantsTesting was conducted retrospectively in an independent dataset of 50 consecutive patients who had radical prostatectomy for intermediate-risk cancer. An AI deep learning model incorporated multimodal imaging and biopsy data to produce three-dimensional cancer estimation maps and margins. AI margins were compared with conventional MRI regions of interest (ROIs), 10-mm margins around ROIs, and hemigland margins. The AI model also furnished predictions of negative surgical margin probability, which were assessed for accuracy. Outcome measurements and statistical analysisComparing AI with conventional margins, sensitivity was evaluated using Wilcoxon signed-rank tests and negative margin rates using chi-square tests. Predicted versus observed negative margin probability was assessed using linear regression. Clinically significant prostate cancer (International Society of Urological Pathology grade ≥2) delineated on whole-mount histopathology served as ground truth. Results and limitationsThe mean sensitivity for cancer-bearing voxels was higher for AI margins (97%) than for conventional ROIs (37%, p < 0.001), 10-mm ROI margins (93%, p = 0.24), and hemigland margins (94%, p < 0.001). For index lesions, AI margins were more often negative (90%) than conventional ROIs (0%, p < 0.001), 10-mm ROI margins (82%, p = 0.24), and hemigland margins (66%, p = 0.004). Predicted and observed negative margin probabilities were strongly correlated (R2 = 0.98, median error = 4%). Limitations include a validation dataset derived from a single institution's prostatectomy population. ConclusionsThe AI model was accurate and effective in an independent test set. This approach could improve and standardize treatment margin definition, potentially reducing cancer recurrence rates. Furthermore, an accurate assessment of negative margin probability could facilitate informed decision-making for patients and physicians. Patient summaryArtificial intelligence was used to predict the extent of tumors in surgically removed prostate specimens. It predicted tumor margins more accurately than conventional methods.

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

BackgroundAccurate local staging is critical for treatment planning and prognosis in prostate cancer (PCa). Although multiparametric magnetic resonance imaging (mpMRI) has high specificity for detection of extraprostatic extension (EPE) and seminal vesicle invasion (SVI), its sensitivity remains limited. 18F-PSMA-1007 positron emission tomography/computed tomography (PET/CT) may be more accurate in determining T stage. ObjectiveTo assess the diagnostic performance of 18F-PSMA-1007 PET/CT in comparison to mpMRI for intraprostatic tumour localisation and detection of EPE and SVI in men with primary PCa undergoing robot-assisted radical prostatectomy (RARP). Design, setting, and participantsBetween February 2019 and October 2020, 105 treatment-naïve patients with biopsy-proven intermediate- or high-risk PCa undergoing mpMRI and 18F-PSMA-1007 PET/CT before RARP were prospectively enrolled. Outcome measurements and statistical analysisThe diagnostic accuracy of 18F-PSMA-1007 PET/CT and mpMRI for intraprostatic tumour localisation and detection of EPE and SVI was assessed via histopathological examination of whole-mount RP specimens. The sensitivity, specificity, negative predictive value, positive predictive value, and accuracy were calculated. The McNemar test was used to compare outcomes between imaging modalities. Results and limitationsIn 80 RP specimens, 129 PCa lesions were found, of which 96 were clinically significant PCa (csPCa). Per-lesion sensitivity for localisation of overall PCa was 85% (95% confidence interval [CI] 77–90%) with PSMA PET/CT and 62% (95% CI 53–70%) with mpMRI (p < 0.001). Per-lesion sensitivity for csPCa was 95% (95% CI 88–98%) with PSMA PET/CT and 73% (95% CI 63–81%) with mpMRI (p < 0.001). The diagnostic accuracy of PSMA PET/CT and mpMRI for detection of EPE per lesion did not significantly differ (sensitivity 45%, 95% CI 31–60% vs 55%, 95% CI 40–69%; p = 0.3; specificity 85%, 95% CI 75–92% vs 90%, 95% CI 81–86%; p = 0.5). The sensitivity and specificity of PSMA PET/CT and mpMRI for detection of SVI did not significantly differ (sensitivity 47%, 95% CI 21–73% vs 33%, 95% CI 12–62; p = 0.6; specificity 94%, 95% CI 88–98% vs 96%, 95% CI 90–99%; p = 0.8). Conclusions18F-PSMA-1007 is a promising imaging modality for localising intraprostatic csPCa but did not show additional value in assessing EPE and SVI in comparison to mpMRI. Patient summaryA new imaging technique called PET/CT (positron emission tomography/computed tomography) with the radioactive tracer 18F-PSMA-1007 shows promise in identifying the location of clinically significant prostate cancer. However, it does not seem to be of additional value over magnetic resonance imaging (MRI) for determining the local tumour stage.

Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy

BackgroundProstate-Specific Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are well-established modalities for identifying intra-prostatic lesions (IPLs) in localised prostate cancer. This study aimed to investigate the use of PSMA PET/CT and mpMRI for biologically targeted radiation therapy treatment planning by: (1) analysing the relationship between imaging parameters at a voxel-wise level and (2) assessing the performance of radiomic-based machine learning models to predict tumour location and grade.MethodsPSMA PET/CT and mpMRI data from 19 prostate cancer patients were co-registered with whole-mount histopathology using an established registration framework. Apparent Diffusion Coefficient (ADC) maps were computed from DWI and semi-quantitative and quantitative parameters from DCE MRI. Voxel-wise correlation analysis was conducted between mpMRI parameters and PET Standardised Uptake Value (SUV) for all tumour voxels. Classification models were built using radiomic and clinical features to predict IPLs at a voxel level and then classified further into high-grade or low-grade voxels.ResultsPerfusion parameters from DCE MRI were more highly correlated with PET SUV than ADC or T2w. IPLs were best detected with a Random Forest Classifier using radiomic features from PET and mpMRI rather than either modality alone (sensitivity, specificity and area under the curve of 0.842, 0.804 and 0.890, respectively). The tumour grading model had an overall accuracy ranging from 0.671 to 0.992.ConclusionsMachine learning classifiers using radiomic features from PSMA PET and mpMRI show promise for predicting IPLs and differentiating between high-grade and low-grade disease, which could be used to inform biologically targeted radiation therapy planning.

Dual-Tracer PET-MRI-Derived Imaging Biomarkers for Prediction of Clinically Significant Prostate Cancer.

To investigate if imaging biomarkers derived from 3-Tesla dual-tracer [(18)F]fluoromethylcholine (FMC) and [68Ga]Ga-PSMAHBED-CC conjugate 11 (PSMA)-positron emission tomography can adequately predict clinically significant prostate cancer (csPC). We assessed 77 biopsy-proven PC patients who underwent 3T dual-tracer PET/mpMRI followed by radical prostatectomy (RP) between 2014 and 2017. We performed a retrospective lesion-based analysis of all cancer foci and compared it to whole-mount histopathology of the RP specimen. The primary aim was to investigate the pretherapeutic role of the imaging biomarkers FMC- and PSMA-maximum standardized uptake values (SUVmax) for the prediction of csPC and to compare it to the mpMRI-methods and PI-RADS score. Overall, we identified 104 cancer foci, 69 were clinically significant (66.3%) and 35 were clinically insignificant (33.7%). We found that the combined FMC+PSMA SUVmax were the only significant parameters (p < 0.001 and p = 0.049) for the prediction of csPC. ROC analysis showed an AUC for the prediction of csPC of 0.695 for PI-RADS scoring (95% CI 0.591 to 0.786), 0.792 for FMC SUVmax (95% CI 0.696 to 0.869), 0.852 for FMC+PSMA SUVmax (95% CI 0.764 to 0.917), and 0.852 for the multivariable CHAID model (95% CI 0.763 to 0.916). Comparing the AUCs, we found that FMC+PSMA SUVmax and the multivariable model were significantly more accurate for the prediction of csPC compared to PI-RADS scoring (p = 0.0123, p = 0.0253, respectively). Combined FMC+PSMA SUVmax seems to be a reliable parameter for the prediction of csPC and might overcome the limitations of PI-RADS scoring. Further prospective studies are necessary to confirm these promising preliminary results.

Comparison of Diffusion Kurtosis Imaging and Standard Mono-Exponential Apparent Diffusion Coefficient in Diagnosis of Significant Prostate Cancer-A Correlation with Gleason Score Assessed on Whole-Mount Histopathology Specimens.

The study was undertaken to compare the diagnostic performance of diffusion kurtosis imaging (DKI) with the standard monoexponential (ME) apparent diffusion coefficient (ADC) model in the detection of significant prostate cancer (PCa), using whole-mount histopathology of radical prostatectomy specimens as a reference standard. 155 patients with prostate cancer had undergone multiparametric magnetic resonance imaging (mpMRI) at 3T before prostatectomy. Quantitative diffusion parameters-the apparent diffusion coefficient corrected for non-Gaussian behavior (Dapp), kurtosis (K), ADC1200, and ADC2000 were correlated with Gleason score and compared between cancerous and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors. The mean values of all diffusion parameters (Dapp, K, ADC1200, ADC2000) were significantly different both between malignant and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors. Although the kurtosis model was better fitted to DWI data, the diagnostic performance in receiver operating characteristic (ROC) analysis of DKI and the standard ADC model in the detection of significant PCa was similar in the peripheral zone (PZ) and in peripheral and transitional zones (TZ) together. In conclusion, our study was not able to demonstrate a clear superiority of the kurtosis model over standard ADC in the diagnosis of significant PCa in PZ and in both zones combined.

Radiomics based on biparametric MRI for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard.

We aimed to study radiomics approach based on biparametric magnetic resonance imaging (MRI) for determining significant residual cancer after androgen deprivation therapy (ADT). Ninety-two post-ADT prostate cancer patients underwent MRI before prostatectomy (62 with significant residual disease and 30 with complete response or minimum residual disease [CR/MRD]). Totally, 100 significant residual, 52 CR/MRD lesions, and 70 benign tissues were selected according to pathology. First, 381 radiomics features were extracted from T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient (ADC) maps. Optimal features were selected using a support vector machine with a recursive feature elimination algorithm (SVM-RFE). Then, ADC values of significant residual, CR/MRD lesions, and benign tissues were compared by one-way analysis of variance. Logistic regression was used to construct models with SVM features to differentiate between each pair of tissues. Third, the efficiencies of ADC value and radiomics models for differentiating the three tissues were assessed by area under receiver operating characteristic curve (AUC). The ADC value (mean ± standard deviation [s.d.]) of significant residual lesions ([1.10 ± 0.02] × 10-3 mm2 s-1) was significantly lower than that of CR/MRD ([1.17 ± 0.02] × 10-3 mm2 s-1), which was significantly lower than that of benign tissues ([1.30 ± 0.02] × 10-3 mm2 s-1; both P < 0.05). The SVM feature models were comparable to ADC value in distinguishing CR/MRD from benign tissue (AUC: 0.766 vs 0.792) and distinguishing residual from benign tissue (AUC: 0.825 vs 0.835) (both P > 0.05), but superior to ADC value in differentiating significant residual from CR/MRD (AUC: 0.748 vs 0.558; P = 0.041). Radiomics approach with biparametric MRI could promote the detection of significant residual prostate cancer after ADT.

18 F-DCFPyL positron emissiontomography/magnetic resonanceimaging-guided ultrasound fusion biopsy is an identical pathway in prostate cancer diagnosis.

Prostate biopsy is still unavoidable in patients with a rising prostate-specific antigeneven thoughmultiparametric magnetic resonance imaging (MRI)is widely used. 18 F-DCFPyL positron emission tomography (PET)/MRIwas proved to be promising both in sensitivity and specificity. But its guiding fusion biopsy and the advantages in the diagnosis of prostate disease is seldom reported. This study aimed to verify the feasibility and advantage of 18 F-DCFPyLPET/MRI-guided fusion targeted biopsy (TB) over whole-mount histopathology (WMH) for prostate cancer diagnosis. A prospective study of 94 biopsy-naïve patients were conducted using 18 F-DCFPyL PET/MRI scans and scored on a scale of 1-4. Systematic biopsy was performed for all patients. Patients with suspicious lesions also underwent PET/MRI/transrectal ultrasound-guided fusion biopsy. Patients with pathologically confirmed cancer underwent surgery and WMHsections. Systematic biopsy was compared with TBfor the detection of index tumors (ITs). Significant cancer was defined as Grade group (GG) 2 or higher no matter the length of the cancer core. 18 F-DCFPyL PET/MRIdetected 30/94 (32%) patients with a score of 4, all of whom were verified to have prostate cancer. While it detected 10patients with a score of 1 (10.6%), they were shown to have no cancer. The sensitivity and specificity of 18 F-DCFPyLPET/MRIwere 94.4% and 75%, respectively, if images with a score of 3 are defined as positive. Systematic biopsy detected 18% (203/1128) samples as prostate cancer; conversely, TBdetected 113 samples out of 259 scores (43.6%). A statistically significant difference was seen between the PCa detection rates by TB and SB (p < 0.001). All targeted lesions were pathologically proven to be the ITon WMH. In biopsy-naïve patients, the ultrasound fusion biopsy targeted by 18 F-DCFPyLPET/MRIis an identical pathway for the detection of prostate cancer.

Preoperative Multiparametric Prostate Magnetic Resonance Imaging Structured Report Informs Risk for Positive Apical Surgical Margins During Radical Prostatectomy.

The prostatic apex is the most frequent location of positive surgical margin (PSM) after surgery. Data regarding the ability of multiparametric magnetic resonance imaging (mpMRI) to prospectively identify men at risk for apical PSMs (aPSMs) using a structured report are lacking. The aims of the study are to determine and to compare the rate of aPSM in men with versus without prospectively flagged at-risk prostate lesions during clinical mpMRI interpretation using whole-mount histopathology as the reference standard. This single-center, retrospective study of prospectively collected data included treatment-naive men with abnormal 3T mpMRI (PI-RADS v2 score ≥3) between January 2016 and December 2018 followed by surgery. During routine clinical interpretation, radiologists flagged prostate lesions abutting the apical most gland and/or encircling the distal most prostatic urethra using standardized language available as a "pick list" option in the structured report. Logistic regression was used to compare the rate of PSM in 2 groups (flagged vs nonflagged men). Propensity score covariate adjustment corrected for potential selection bias according to age, prostate-specific antigen (PSA), PSA density, grade group, and pT stage. The estimate was further adjusted by including surgeon as a covariate. A total of 428 men were included. A statistically significant higher proportion of aPSMs was noted in flagged (56% [51/91]) compared with nonflagged apical lesions (31% [105/337]; adjusted odds ratio, 2.5; 95% confidence interval, 1.6-4.1; P < 0.01). The difference in aPSM between both groups also varied according to the surgeon performing the RP. Prostate-specific antigen, PSA density, lesion size, apical location, Prostate Imaging Reporting & Data System score, grade group, pT stage, and surgeon's experience were associated with higher PSM rate. Biochemical recurrence, defined as PSA greater than 0.2 ng/mL on 2 measurements after RP, was significantly associated with PSM status (propensity score adjusted odds ratio, 3.1; 95% confidence interval, 1.8-5.3; P < 0.0001); however, patients flagged by radiologists did not have a significant difference in biochemical recurrence rates as compared with nonflagged patients ( P = 0.11). Standard language built into structured reports for mpMRI of the prostate helps identify preoperatively patients at risk for aPSM. Multiparametric MRI is able to identify patients at increased risk for aPSM, and this information can be conveyed in a structured report to urologists, facilitating patient counseling and treatment decisions.

Correlation of 68Ga-RM2 PET with Postsurgery Histopathology Findings in Patients with Newly Diagnosed Intermediate- or High-Risk Prostate Cancer.

68Ga-RM2 targets gastrin-releasing peptide receptors (GRPRs), which are overexpressed in prostate cancer (PC). Here, we compared preoperative 68Ga-RM2 PET to postsurgery histopathology in patients with newly diagnosed intermediate- or high-risk PC. Methods: Forty-one men, 64.0 ± 6.7 y old, were prospectively enrolled. PET images were acquired 42-72 min (median ± SD, 52.5 ± 6.5 min) after injection of 118.4-247.9 MBq (median ± SD, 138.0 ± 22.2 MBq) of 68Ga-RM2. PET findings were compared with preoperative multiparametric MRI (mpMRI) (n = 36) and 68Ga-PSMA11 PET (n = 17) and correlated to postprostatectomy whole-mount histopathology (n = 32) and time to biochemical recurrence. Nine participants decided to undergo radiation therapy after study enrollment. Results: All participants had intermediate- (n = 17) or high-risk (n = 24) PC and were scheduled for prostatectomy. Prostate-specific antigen was 8.8 ± 77.4 (range, 2.5-504) and 7.6 ± 5.3 ng/mL (range, 2.5-28.0 ng/mL) when participants who ultimately underwent radiation treatment were excluded. Preoperative 68Ga-RM2 PET identified 70 intraprostatic foci of uptake in 40 of 41 patients. Postprostatectomy histopathology was available in 32 patients in which 68Ga-RM2 PET identified 50 of 54 intraprostatic lesions (detection rate = 93%). 68Ga-RM2 uptake was recorded in 19 nonenlarged pelvic lymph nodes in 6 patients. Pathology confirmed lymph node metastases in 16 lesions, and follow-up imaging confirmed nodal metastases in 2 lesions. 68Ga-PSMA11 and 68Ga-RM2 PET identified 27 and 26 intraprostatic lesions, respectively, and 5 pelvic lymph nodes each in 17 patients. Concordance between 68Ga-RM2 and 68Ga-PSMA11 PET was found in 18 prostatic lesions in 11 patients and 4 lymph nodes in 2 patients. Noncongruent findings were observed in 6 patients (intraprostatic lesions in 4 patients and nodal lesions in 2 patients). Sensitivity and accuracy rates for 68Ga-RM2 and 68Ga-PSMA11 (98% and 89% for 68Ga-RM2 and 95% and 89% for 68Ga-PSMA11) were higher than those for mpMRI (77% and 77%, respectively). Specificity was highest for mpMRI with 75% followed by 68Ga-PSMA11 (67%) and 68Ga-RM2 (65%). Conclusion: 68Ga-RM2 PET accurately detects intermediate- and high-risk primary PC, with a detection rate of 93%. In addition, 68Ga-RM2 PET showed significantly higher specificity and accuracy than mpMRI and a performance similar to 68Ga-PSMA11 PET. These findings need to be confirmed in larger studies to identify which patients will benefit from one or the other or both radiopharmaceuticals.

Diagnostic Accuracy of Transperineal MRI-Ultrasound Fusion Biopsy at the Introduction Period

Magnetic resonance imaging (MRI) ultrasound fusion biopsy is becoming popular owing to the better detection rate of clinically significant prostate cancer (csPCa). We retrospectively evaluated the accuracy of MRI-targeted biopsy during the period of introduction at a single academic center by comparing findings of its specimen and whole-mount histopathology. Between June 2018 and January 2021, 106 transperineal MRI-ultrasound fusion biopsies using BioJet software were performed. Among the cases, 15 subsequently underwent robotic-assisted laparoscopic radical prostatectomy and were eligible for analysis. This study included all regions of interest (ROIs) with a Prostate Imaging Reporting and Data System v2 category of 3 or greater on pre-biopsy MRI．For each lesion, grade group of MRI-targeted biopsy specimens and prostatectomy specimens were compared. From a total of 25 ROIs identified among 15 males, csPCa was found in 21 (84%) of the concordant locations of prostatectomy specimens. However, MRI-targeted biopsy could diagnose csPCa in only 12 (48%) of them. In the csPCa undetected group, the ROI volume was significantly smaller (median volume 0.23 ml vs 0.40 ml, p＝0.03). We also found that in cases where PCa was not detected through MRI-targeted biopsy, the biopsy sample length was significantly shorter (median length 9 mm vs 17 mm, p＝0.01). Our data suggest that failure of detecting PCa in MRI-targeted biopsy could be due to technical errors at the introduction period of the technique. A sufficient sampling length of 10 mm or more is desirable, especially for small lesions.