Prostate Needle Core Biopsies Research Articles

Clinical significance of location of perineural cancer invasion detected on prostate needle core biopsy.

The clinical impact of site-specific perineural invasion (PNI) in prostate cancer remains poorly understood. We compared radical prostatectomy findings and oncologic outcomes in 434 patients with single-site PNI on systematic sextant biopsy. PNI was present in the right apex (n = 62; 14%), right mid (n = 70; 16%), right base (n = 89; 21%), left apex (n = 64; 15%), left mid (n = 58; 13%), and left base (n = 91; 21%). There were no significant differences in biopsy or prostatectomy findings, when comparing apex vs. mid vs. base PNI. Univariate analysis revealed that apex-localized PNI was associated with a significantly higher risk of progression, compared with base (P = 0.037) or mid/base (P = 0.024) PNI. Multivariable analysis showed that apex-localized PNI was an independent risk factor for progression (hazard ratio 2.049, P = 0.002). Among biopsies demonstrating PNI at one sextant site, apex-localized PNI is independently associated with poorer prognosis, though not worse histopathologic features on prostatectomy, compared with mid or base PNI.

Binary semantic segmentation for detection of prostate adenocarcinoma using an ensemble with attention and residual U-Net architectures.

An accurate determination of the Gleason Score (GS) or Gleason Pattern (GP) is crucial in the diagnosis of prostate cancer (PCa) because it is one of the criterion used to guide treatment decisions for prognostic-risk groups. However, the manually designation of GP by a pathologist using a microscope is prone to error and subject to significant inter-observer variability. Deep learning has been used to automatically differentiate GP on digitized slides, aiding pathologists and reducing inter-observer variability, especially in the early GP of cancer. This article presents a binary semantic segmentation for the GP of prostate adenocarcinoma. The segmentation separates benign and malignant tissues, with the malignant class consisting of adenocarcinoma GP3 and GP4 tissues annotated from 50 unique digitized whole slide images (WSIs) of prostate needle core biopsy specimens stained with hematoxylin and eosin. The pyramidal digitized WSIs were extracted into image patches with a size of 256 × 256 pixels at a magnification of 20×. An ensemble approach is proposed combining U-Net-based architectures, including traditional U-Net, attention-based U-Net, and residual attention-based U-Net. This work initially considers a PCa tissue analysis using a combination of attention gate units with residual convolution units. The performance evaluation revealed a mean Intersection-over-Union of 0.79 for the two classes, 0.88 for the benign class, and 0.70 for the malignant class. The proposed method was then used to produce pixel-level segmentation maps of PCa adenocarcinoma tissue slides in the testing set. We developed a screening tool to discriminate between benign and malignant prostate tissue in digitized images of needle biopsy samples using an AI approach. We aimed to identify malignant adenocarcinoma tissues from our own collected, annotated, and organized dataset. Our approach returned the performance which was accepted by the pathologists.

A Rare Case of Incidental Primary Follicular Lymphoma Co-Existing With Grade Group 5 Prostatic Adenocarcinoma

Abstract Introduction/Objective Primary lymphoma of the prostate is an exceedingly rare entity comprising less than 0.1% of all prostatic cancers with follicular lymphoma making up only 12% of primary prostatic lymphomas. There is very limited literature owing to their rarity and underdiagnosis. To the best of our knowledge, prostatic adenocarcinoma with an incidental follicular lymphoma presenting as a prostate imaging reporting and data system 4 (PI-RADS 4) lesion on multiparametric magnetic resonance imaging (mpMRI) with mildly elevated serum prostate specific antigen (PSA) has not been previously described. Methods/Case Report A 73 year old male presented to Danbury Hospital for follow up on benign prostatic hyperplasia (BPH) diagnosed in 2020. The patient was experiencing nocturia and had a serum PSA of 4.8. Multiparametric MRI revealed an enlarged, 5.5 x 4.1 x 3.3 cm, prostate gland (estimate weight of 45.5 grams) with a 1.2 cm PI-RADS 4 lesion in the right anterior mid gland. There was no evidence of metastatic disease on his most recent body imaging. He was subsequently scheduled for prostate needle biopsy. Histopathology showed a high grade prostatic adenocarcinoma, with a Gleason score 4 + 5=9, along with a focally prominent dense lymphoid infiltrate in four of four cores from the same area of abnormality that was detected on mpMRI. The latter comprised of ill defined follicles and sheets of small cleaved atypical lymphoid cells consistent with centrocytes and very few centroblasts. By immunohistochemical staining, the atypical cells in lymphoid follicles and interfollicular areas were positive for CD20, CD10 and BCL2 and negative for cyclin D1. CD21 stain highlighted the follicular dendritic cell meshwork in the germinal centers with a low Ki67 proliferation index. A final diagnosis of low grade follicular lymphoma (grade1-2) coexisting with the prostatic adenocarcinoma was rendered. The patient is currently undergoing lymphoma staging as the treatment for adenocarcinoma is planned. Results (if a Case Study enter NA) NA Conclusion Primary lymphoma of the prostate usually presents with non specific obstructive voiding symptoms and normal or near normal PSA levels which makes it challenging for physicians to suspect lymphomas. The lymphocytic infiltrate, originally thought to be prostatitis, underwent further evaluation with immunohistochemistry due to its monotonous appearance, which clinched the final diagnosis. Careful evaluation of prostate core needle biopsies is necessary even after spotting carcinoma in order to rule out low grade lymphomas.

Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection.

Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking. To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance. Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads. Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase. This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.

Risk Stratification by Quantification of Perineural Cancer Invasion on Prostate Needle Core Biopsy: Should It Be Counted?

We assessed the prognostic significance of quantification of perineural invasion on prostate biopsy. We quantified actual perineural invasion foci in the entire prostate biopsy specimens from 724 patients and compared corresponding radical prostatectomy findings and long-term oncologic outcomes. No perineural invasion was detected in 524 (72.4%) prostate biopsies, whereas 1 (≥129; 17.8%), 2 (n=40; 5.5%), 3 (n=18; 2.5%), 4 (n=7; 1.0%), and 5-10 (n=6; 0.8%) perineural invasion foci were present in other cases. We confirmed a higher risk of recurrence after radical prostatectomy in patients with perineural invasion on prostate biopsy than in those with no perineural invasion (P < .001). Remarkably, recurrence-free survival was comparable between those with 0 vs 1 perineural invasion (P = .9) or 2 vs ≥3 perineural invasions (P = .3). Nonetheless, multifocal perineural invasion per prostate biopsy (vs single perineural invasion; P < .001) and >1 perineural invasion per 10-mm tumor (vs ≤1 perineural invasion; P = .008) were associated with worse outcomes. Interestingly, in a subgroup outcome analysis of single vs multifocal perineural invasions per prostate biopsy, there was a significant difference in patients showing perineural invasion involving only 1 of the sextant sites. In multivariable analysis, both multifocal perineural invasions/case (HR=5.48, P < .001) and >1 perineural invasion/10-mm tumor (HR=3.96, P < .001) showed significance for recurrence. Meanwhile, compared with CAPRA (Cancer of the Prostate Risk Assessment) score alone (0.687/0.685), Harrell's C index/AUC for predicting 5-year recurrence-free survival was gradually increased when 1 (0.722/0.740), 2 (0.747/0.773), or 3 (0.760/0.792) point(s) were additionally assigned to multifocal perineural invasion. Multifocal perineural invasion and >1 perineural invasion per 10-mm tumor on each prostate biopsy were thus found to be associated with poorer prognosis, as independent predictors, in men with prostate cancer undergoing radical prostatectomy.

Artificial intelligence–assisted cancer diagnosis improves the efficiency of pathologists in prostatic biopsies

Paige Prostate is a clinical-grade artificial intelligence tool designed to assist the pathologist in detecting, grading, and quantifying prostate cancer. In this work, a cohort of 105 prostate core needle biopsies (CNBs) was evaluated through digital pathology. Then, we compared the diagnostic performance of four pathologists diagnosing prostatic CNB unaided and, in a second phase, assisted by Paige Prostate. In phase 1, pathologists had a diagnostic accuracy for prostate cancer of 95.00%, maintaining their performance in phase 2 (93.81%), with an intraobserver concordance rate between phases of 98.81%. In phase 2, pathologists reported atypical small acinar proliferation (ASAP) less often (about 30% less). Additionally, they requested significantly fewer immunohistochemistry (IHC) studies (about 20% less) and second opinions (about 40% less). The median time required for reading and reporting each slide was about 20% lower in phase 2, in both negative and cancer cases. Lastly, the average total agreement with the software performance was observed in about 70% of the cases, being significantly higher in negative cases (about 90%) than in cancer cases (about 30%). Most of the diagnostic discordances occurred in distinguishing negative cases with ASAP from small foci of well-differentiated (less than 1.5 mm) acinar adenocarcinoma. In conclusion, the synergic usage of Paige Prostate contributes to a significant decrease in IHC studies, second opinion requests, and time for reporting while maintaining highly accurate diagnostic standards.

Development of machine learning–powered models for prostate cancer HRD prediction.

206 Background: In prostate cancer, homologous recombination deficiency (HRD) is associated with poor prognosis, and sensitivity to DNA damaging agents and DNA damage repair (DDR) inhibitors. As new classes of DDR inhibitors become available, identifying patients with HRD will be critical for treatment selection. Here, we present machine learning (ML)-based models trained to predict HRD status directly from hematoxylin and eosin (H&amp;E) whole slide images (WSI). Methods: ML models were trained to predict and segment cells and tissue regions within the tumor microenvironment (TME) using annotated (N=91,021 annotations) WSI of H&amp;E-stained resections from the cancer genome atlas prostate adenocarcinoma (TCGA PRAD) dataset (N=401) and needle core biopsies from a proprietary dataset (N=1,000). Quantified Human Interpretable Features (HIFs) that describe the TME composition were extracted. Three models were trained to predict HRD status using 373 WSI with known HRD score (TCGA PRAD; train N=259, validation N=76, and test N=38). Two models used input from the TME model: An HIF multivariate logistic regression model, and a graph neural network (GNN) where predictions are based on the complex spatial relationships within the TME. An end-to-end (E2E) multiple instance learning model predicted directly from the WSI. Two cutoffs for HRD were defined using Gaussian Mixture Models, resulting in 99 WSI (train N=72, validation N=18, and test N=9) positive for the Genomic Instability (&gt;16 events) cutoff, and 58 WSI (train N=44, validation N=10, test N=4) positive for the Genomic Instability (&gt;22 events) cutoff. An independent validation set of 45 biopsies and 16 resections from a biobank of metastatic castration resistant prostate cancer with HRD status determined by whole-exome sequencing was compared to ML model H&amp;E-based HRD prediction. Results: In the TCGA test set of resection samples, all three models moderately or strongly predicted HRD status, with the HIF model showing the best performance (AUROC 0.87, sensitivity 0.88, specificity 0.62). The same HIF model performed equally well (AUROC 0.85, Sensitivity 0.93, specificity 0.67) in the resection samples from the independent validation set. However, the model performance went down (AUROC 0.69, sensitivity 0.91, specificity: 0.3) when both resection and needle biopsy samples were included, highlighting the importance of a representative training set to achieve robust performance in a real world setting. Further model training and validation with a more diverse dataset is required to accurately assess the performance of the model on needle biopsies. Conclusions: ML models trained on resection prostate cancer samples performed well in predicting HRD status when applied to the same sample type, demonstrating the potential of ML models to predict genomic biomarkers status in surgical specimens for treatment decision.

Stimulated Raman Scattering Microscopy Enables Gleason Scoring of Prostate Core Needle Biopsy by a Convolutional Neural Network.

A platform combining stimulated Raman scattering microscopy and a convolutional neural network provides rapid histopathology and automated Gleason scoring on fresh prostate core needle biopsies without complex tissue processing.

Real-time diagnosis and Gleason grading of prostate core needle biopsies using nonlinear microscopy

Rapid histologic assessment of fresh prostate biopsies may reduce patient anxiety, aid in biopsy sampling, and enable specimen triaging for molecular/genomic analyses and research that could benefit from fresh tissue analysis. Nonlinear microscopy (NLM) is a fluorescence microscopy technique that can produce high-resolution images of freshly excised tissue resembling formalin-fixed paraffin-embedded (FFPE) H&E. NLM enables evaluation of tissue up to ~100 µm below the surface, analogous to serial sectioning, but without requiring microtome sectioning. One hundred and seventy biopsies were collected from 63 patients who underwent in-bore MRI or MRI/ultrasound fusion biopsy procedures. Biopsies were stained in acridine orange and sulforhodamine 101, a nuclear and cytoplasmic/stromal fluorescent dye, for 45 s. Genitourinary pathologists evaluated the biopsies using NLM by translating the biopsies in real time to areas of interest and NLM images were recorded. After NLM evaluation, the biopsies were processed for standard FFPE H&E and similarities and differences between NLM and FFPE H&E were investigated. Accuracies of NLM diagnoses and Gleason scores were calculated using FFPE histology as the gold standard. Pathologists achieved a 92.4% sensitivity (85.0–96.9%, 95% confidence intervals) and 100.0% specificity (94.3–100.0%) for detecting carcinoma compared to FFPE histology. The agreement between the Grade Group determined by NLM versus FFPE histology had an unweighted Cohen's Kappa of 0.588. The average NLM evaluation time was 2.10 min per biopsy (3.08 min for the first 20 patients, decreasing to 1.54 min in subsequent patients). Further studies with larger patient populations, larger number of pathologists, and multiple institutions are warranted. NLM is a promising method for future rapid evaluation of prostate needle core biopsies.

Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery.

Mass spectrometry is an effective imaging tool for evaluating biological tissue to detect cancer. With the assistance of deep learning, this technology can be used as a perioperative tissue assessment tool that will facilitate informed surgical decisions. To achieve such a system requires the development of a database of mass spectrometry signals and their corresponding pathology labels. Assigning correct labels, in turn, necessitates precise spatial registration of histopathology and mass spectrometry data. This is a challenging task due to the domain differences and noisy nature of images. In this study, we create a registration framework for mass spectrometry and pathology images as a contribution to the development of perioperative tissue assessment. In doing so, we explore two opportunities in deep learning for medical image registration, namely, unsupervised, multi-modal deformable image registration and evaluation of the registration. We test this system on prostate needle biopsy cores that were imaged with desorption electrospray ionization mass spectrometry (DESI) and show that we can successfully register DESI and histology images to achieve accurate alignment and, consequently, labelling for future training. This automation is expected to improve the efficiency and development of a deep learning architecture that will benefit the use of mass spectrometry imaging for cancer diagnosis.

PD56-03 EXTERNAL VALIDATION OF AN ARTIFICIAL INTELLIGENCE ALGORITHM FOR PROSTATE CANCER GLEASON GRADING AND TUMOR QUANTIFICATION

You have accessJournal of UrologyProstate Cancer: Detection & Screening VI (PD56)1 Sep 2021PD56-03 EXTERNAL VALIDATION OF AN ARTIFICIAL INTELLIGENCE ALGORITHM FOR PROSTATE CANCER GLEASON GRADING AND TUMOR QUANTIFICATION Bogdana Schmidt, Hriday P. Bhambhvani, Richard E. Fan, Christian Kunder, Chia Sui Kao, John P. Higgins, Mirabela Rusu, and Geoffrey A. Sonn Bogdana SchmidtBogdana Schmidt More articles by this author , Hriday P. BhambhvaniHriday P. Bhambhvani More articles by this author , Richard E. FanRichard E. Fan More articles by this author , Christian KunderChristian Kunder More articles by this author , Chia Sui KaoChia Sui Kao More articles by this author , John P. HigginsJohn P. Higgins More articles by this author , Mirabela RusuMirabela Rusu More articles by this author , and Geoffrey A. SonnGeoffrey A. Sonn More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002090.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate cancer treatment relies on accurate Gleason grading. Gleason scoring has high inter- and intra-rater variability, even among high-volume uropathologists. Deep learning on digital pathology has the potential to address these problems. We aimed to externally validate a deep learning algorithm’s performance on prostate cancer identification and Gleason grading. METHODS: DeepDx Prostate (DeepBio, Seoul, South Korea) is an automated Gleason scoring system that was trained using 1133 prostate core needle biopsy images and validated on 700. We performed external validation using 150 whole mount prostatectomy specimens from which 500 (1mm2) tiles were created and evaluated by 2 uropathologists and the DeepDx algorithm to establish Gleason grade, amount of cancer, and percentage of Gleason pattern 4 and 5 in the tiles. The reference standard was established by consensus of two experienced uropathologists with a third expert to evaluate discordant cases. We defined the main metric as the agreement with the reference standard, measured using quadratic Cohen’s kappa (κ). RESULTS: The DeepDx algorithm achieved overall high agreement with the reference standard (κ 0.79, 95% CI 0.75 - 0.82). It performed well at clinical decision thresholds benign vs malignant, (κ 0.927), and clinically low risk (benign, GG1, or GG2) versus high risk (GG 3-5) disease (κ 0.858). In evaluating benign and GG1 vs GG 2-5, the algorithm had less agreement (κ 0.771). Of 83 tiles classified as GG1 by uropathologists, the algorithm upgraded 53 (64%) to GG2, but median pattern 4 area was 3.9% (IQR 0.0009, 28.703) in those cases. CONCLUSIONS: In this external validation, we found that the DeepDx algorithm had high agreement with expert uropathologists in cancer identification and grading, despite being trained with a different patient population and using biopsy cores instead of prostatectomy specimens. The speed and accuracy of deep learning-based systems has broad applications from allowing clinicians to better counsel patients to facilitating research requiring detailed annotation of datasets not feasible by human pathologists. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1004-e1004 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Bogdana Schmidt More articles by this author Hriday P. Bhambhvani More articles by this author Richard E. Fan More articles by this author Christian Kunder More articles by this author Chia Sui Kao More articles by this author John P. Higgins More articles by this author Mirabela Rusu More articles by this author Geoffrey A. Sonn More articles by this author Expand All Advertisement Loading ...

10 STARTUPS TO WATCH 2021: Early-stage companies advance precision medicine by applying AI, clinical genomics, and other new technologies

10 STARTUPS TO WATCH 2021: Early-stage companies advance precision medicine by applying AI, clinical genomics, and other new technologies

Independent real-world application of a clinical-grade automated prostate cancer detection system.

Artificial intelligence (AI)‐based systems applied to histopathology whole‐slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists. We sought to define the performance of an AI‐based automated prostate cancer detection system, Paige Prostate, when applied to independent real‐world data. The algorithm was employed to classify slides into two categories: benign (no further review needed) or suspicious (additional histologic and/or immunohistochemical analysis required). We assessed the sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) of a local pathologist, two central pathologists, and Paige Prostate in the diagnosis of 600 transrectal ultrasound‐guided prostate needle core biopsy regions (‘part‐specimens’) from 100 consecutive patients, and to ascertain the impact of Paige Prostate on diagnostic accuracy and efficiency. Paige Prostate displayed high sensitivity (0.99; CI 0.96–1.0), NPV (1.0; CI 0.98–1.0), and specificity (0.93; CI 0.90–0.96) at the part‐specimen level. At the patient level, Paige Prostate displayed optimal sensitivity (1.0; CI 0.93–1.0) and NPV (1.0; CI 0.91–1.0) at a specificity of 0.78 (CI 0.64–0.89). The 27 part‐specimens considered by Paige Prostate as suspicious, whose final diagnosis was benign, were found to comprise atrophy (n = 14), atrophy and apical prostate tissue (n = 1), apical/benign prostate tissue (n = 9), adenosis (n = 2), and post‐atrophic hyperplasia (n = 1). Paige Prostate resulted in the identification of four additional patients whose diagnoses were upgraded from benign/suspicious to malignant. Additionally, this AI‐based test provided an estimated 65.5% reduction of the diagnostic time for the material analyzed. Given its optimal sensitivity and NPV, Paige Prostate has the potential to be employed for the automated identification of patients whose histologic slides could forgo full histopathologic review. In addition to providing incremental improvements in diagnostic accuracy and efficiency, this AI‐based system identified patients whose prostate cancers were not initially diagnosed by three experienced histopathologists. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy

Prostate cancer is a leading cause of morbidity and mortality for adult males in the US. The diagnosis of prostate carcinoma is usually made on prostate core needle biopsies obtained through a transrectal approach. These biopsies may account for a significant portion of the pathologists’ workload, yet variability in the experience and expertise, as well as fatigue of the pathologist may adversely affect the reliability of cancer detection. Machine-learning algorithms are increasingly being developed as tools to aid and improve diagnostic accuracy in anatomic pathology. The Paige Prostate AI-based digital diagnostic is one such tool trained on the digital slide archive of New York’s Memorial Sloan Kettering Cancer Center (MSKCC) that categorizes a prostate biopsy whole-slide image as either “Suspicious” or “Not Suspicious” for prostatic adenocarcinoma. To evaluate the performance of this program on prostate biopsies secured, processed, and independently diagnosed at an unrelated institution, we used Paige Prostate to review 1876 prostate core biopsy whole-slide images (WSIs) from our practice at Yale Medicine. Paige Prostate categorizations were compared to the pathology diagnosis originally rendered on the glass slides for each core biopsy. Discrepancies between the rendered diagnosis and categorization by Paige Prostate were each manually reviewed by pathologists with specialized genitourinary pathology expertise. Paige Prostate showed a sensitivity of 97.7% and positive predictive value of 97.9%, and a specificity of 99.3% and negative predictive value of 99.2% in identifying core biopsies with cancer in a data set derived from an independent institution. Areas for improvement were identified in Paige Prostate’s handling of poor quality scans. Overall, these results demonstrate the feasibility of porting a machine-learning algorithm to an institution remote from its training set, and highlight the potential of such algorithms as a powerful workflow tool for the evaluation of prostate core biopsies in surgical pathology practices.

Cell membrane and nuclear expression of programmed death ligand-1 in prostate needle biopsy tissue in prostate cancer patients undergoing primary radiation therapy

BackgroundProgrammed death ligand-1 (PD-L1) expression in cancer is often associated with cancer aggressiveness and responsiveness to treatment with PD-1 pathway inhibitors. We conducted a systematic study on the expression of membranous PD-L1 (mPD-L1) and nuclear PD-1-L1 (nPD-L1) in prostate needle biopsy specimens of prostate cancer patients who underwent primary radiotherapy and analyzed the association between PD-L1 expression and clinicopathological characteristics and prognosis of patients. MethodA total of 971 cancer-containing prostate needle biopsy cores from 172 patients were immunohistochemically stained with anti-PD-L1 antibody. The association of PD-L1 expression with Gleason score and tumor volume percentage was evaluated for each biopsy core. Total of 171 patients were divided according to mPD-L1 or nPD-L1 expression, and clinicopathological characteristics were compared between the positive and negative groups. The prognostic significance of mPD-L1, nPD-L1 and common prognostic factors were analyzed in terms of biochemical recurrence. ResultTotal of 15% and 46% of biopsy cores were stained positive for mPD-L1 and nPD-L1, respectively. There was a positive correlation between Gleason score and mPD-L1 and a negative correlation between Gleason score and nPD-L1. Between mPD-L1 and nPD-L1, there was no significant correlation. There was intraindividual heterogeneity in PD-L1 expression among different Gleason scores. For mPD-L1, only pretreatment PSA was significantly higher in the positive group than in the negative, but not Gleason score and T stage. For nPD-L1, Gleason score and T stage were significantly higher in the positive group than in the negative. Both mPD-L1 and nPD-L1 expression were not predictive of BCR-free survival in univariate and multivariate analyses. ConclusionsOur results suggest that PD-1 pathway inhibitor may be a potential therapeutic option in high risk prostate cancer patients as early as neoadjuvant setting. The novel discovery of PD-L1 expression in the nucleus of PC should be subjected to further research.

Standardization of reporting discontinuous tumor involvement in prostatic needle biopsy: a systematic review.

Discontinuous tumor involvement (DTI) is a not uncommon finding in the tumor in prostate needle core biopsies undertaken for diagnosis of prostate cancer (PCa). The objective of this review is to establish a clear definition of DTI in order to provide a standardized method of measurement which reliably reflects pathologic features and disease progression following radical prostatectomy (RP). A systematic literature search was performed using PubMed up to March 2020 to identify studies of PCa patients which included needle biopsies containing DTI and matched subsequent RP treatment with or without follow-up information. The methodology and quality of reporting of DTI are reviewed, compared, and summarized. DTI is a frequent finding in diagnostic biopsy for PCa (up to 30%). Six studies were compared by methods of measurement used for predicting pathologic features and outcomes which are observed in subsequent RP. In most cases with DTI (> 90%), intervening benign tissue in the tumor core was less than 5 mm. DTI found in the biopsy was likely to be associated with a single, irregular tumor nodule going in and out of the plane of the section, but DTI was not associated with multiple small foci of the tumor. Immunohistochemistry (IHC) also demonstrated that about 75% of cases of DTI shared an IHC profile which supports the concept that DTI most likely comes from a homogeneous tumor nodule. Furthermore, DTI was associated with positive surgical margin (PSM) and bilateral tumor in RP specimens. Compared to additive measurement (with the subtraction of intervening benign tissue), linear measurement (including intervening benign tissue) of DTI was more accurately predictive of aggressive disease in the RP including higher pT stage, PSM, and greater actual extent of the tumor. However, the advantage of linear measurement was lost in cases where there was an upgrade from the biopsy to the RP which may result from undersampling. For cases with either very small tumor foci or very extensive cancer volume, no difference was observed in these two methods of measurement. DTI in core biopsies may represent undersampling of a larger irregular nodule but likely does not result from multifocality and is similarly unlikely to represent multiclonality. Linear measurement of DTI was more accurately predictive of post-RP pathologic findings and oncologic prognosis. This method should be applied for patient selection for AS.

Diagnosing 12 prostate needle cores within an hour of biopsy via open-top light-sheet microscopy.

.Significance: Processing and diagnosing a set of 12 prostate biopsies using conventional histology methods typically take at least one day. A rapid and accurate process performed while the patient is still on-site could significantly improve the patient’s quality of life.Aim: We develop and assess the feasibility of a one-hour-to-diagnosis (1Hr2Dx) method for processing and providing a preliminary diagnosis of a set of 12 prostate biopsies.Approach: We developed a fluorescence staining, optical clearing, and 3D open-top light-sheet microscopy workflow to enable 12 prostate needle core biopsies to be processed and diagnosed within an hour of receipt. We analyzed 44 biopsies by the 1Hr2Dx method, which does not consume tissue. The biopsies were then processed for routine, slide-based 2D histology. Three pathologists independently evaluated the 3D 1Hr2Dx and 2D slide-based datasets in a blinded, randomized fashion. Turnaround times were recorded, and the accuracy of our method was compared with gold-standard slide-based histology.Results: The average turnaround time for tissue processing, imaging, and diagnosis was 44.5 min. The sensitivity and specificity of 1Hr2Dx in diagnosing cancer were both .Conclusions: The 1Hr2Dx method has the potential to improve patient care by providing an accurate preliminary diagnosis within an hour of biopsy.

Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies

Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored. To evaluate an expert-level AI-based assistive tool when used by pathologists for the grading of prostate biopsies. This diagnostic study used a fully crossed multiple-reader, multiple-case design to evaluate an AI-based assistive tool for prostate biopsy grading. Retrospective grading of prostate core needle biopsies from 2 independent medical laboratories in the US was performed between October 2019 and January 2020. A total of 20 general pathologists reviewed 240 prostate core needle biopsies from 240 patients. Each pathologist was randomized to 1 of 2 study cohorts. The 2 cohorts reviewed every case in the opposite modality (with AI assistance vs without AI assistance) to each other, with the modality switching after every 10 cases. After a minimum 4-week washout period for each batch, the pathologists reviewed the cases for a second time using the opposite modality. The pathologist-provided grade group for each biopsy was compared with the majority opinion of urologic pathology subspecialists. An AI-based assistive tool for Gleason grading of prostate biopsies. Agreement between pathologists and subspecialists with and without the use of an AI-based assistive tool for the grading of all prostate biopsies and Gleason grade group 1 biopsies. Biopsies from 240 patients (median age, 67 years; range, 39-91 years) with a median prostate-specific antigen level of 6.5 ng/mL (range, 0.6-97.0 ng/mL) were included in the analyses. Artificial intelligence-assisted review by pathologists was associated with a 5.6% increase (95% CI, 3.2%-7.9%; P < .001) in agreement with subspecialists (from 69.7% for unassisted reviews to 75.3% for assisted reviews) across all biopsies and a 6.2% increase (95% CI, 2.7%-9.8%; P = .001) in agreement with subspecialists (from 72.3% for unassisted reviews to 78.5% for assisted reviews) for grade group 1 biopsies. A secondary analysis indicated that AI assistance was also associated with improvements in tumor detection, mean review time, mean self-reported confidence, and interpathologist agreement. In this study, the use of an AI-based assistive tool for the review of prostate biopsies was associated with improvements in the quality, efficiency, and consistency of cancer detection and grading.

Serum total prostate-specific antigen (tPSA): correlation with diagnosis and grading of prostate cancer in core needle biopsy.

Aim To investigate the impact of pre-treatment serum total prostate-specific antigen (PSA) level on prevalence of prostate carcinoma detection in prostate core needle biopsy, and its correlation with established prognostic factors. Methods Prostate needle biopsy samples of 115 patients with available pre-treatment serum total PSA (tPSA) level were analysed. For all cases where morphology alone was insufficient, immunohistochemistry was performed using p63, CKHMW and AMACR antibody panel in order to confirm or exclude the existence of prostate carcinoma. Results Statistically significant positive correlation between serum total PSA values and prevalence of finding prostate carcinoma in needle biopsy specimens was found (p=0.011), as well as in the case when the patients were classified into groups according to tPSA levels (p=0.028). Serum total PSA values and levels (level groups) showed significant positive correlation with Gleason score (p=0.029 and p=0.036, respectively) and Grade Group of prostate carcinomas (p=0.044 and p=0.046, respectively). Sensitivity of the screening test by using 4 ng/mL as cut off value for tPSA was 94.12% (CI: 80.32-99.28%), specificity 8.64% (CI: 3.55-17.00%), positive predictive value 30.19% (CI: 21.65-39.87%) and negative predictive value 77.78% (CI: 39.99-97.19%). Conclusion The increase of serum tPSA value increases the likelihood of finding prostate cancer on needle biopsy specimens. Due to such findings and its positive correlation with a grade of prostate cancer, our study indicates that tPSA can still be considered as a useful tool both in detecting and predicting aggressiveness of prostate cancer.

Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies

Prostate cancer (PrCa) is the second most common cancer among men in the United States. The gold standard for detecting PrCa is the examination of prostate needle core biopsies. Diagnosis can be challenging, especially for small, well-differentiated cancers. Recently, machine learning algorithms have been developed for detecting PrCa in whole slide images (WSIs) with high test accuracy. However, the impact of these artificial intelligence systems on pathologic diagnosis is not known. To address this, we investigated how pathologists interact with Paige Prostate Alpha, a state-of-the-art PrCa detection system, in WSIs of prostate needle core biopsies stained with hematoxylin and eosin. Three AP-board certified pathologists assessed 304 anonymized prostate needle core biopsy WSIs in 8 hours. The pathologists classified each WSI as benign or cancerous. After ~4 weeks, pathologists were tasked with re-reviewing each WSI with the aid of Paige Prostate Alpha. For each WSI, Paige Prostate Alpha was used to perform cancer detection and, for WSIs where cancer was detected, the system marked the area where cancer was detected with the highest probability. The original diagnosis for each slide was rendered by genitourinary pathologists and incorporated any ancillary studies requested during the original diagnostic assessment. Against this ground truth, the pathologists and Paige Prostate Alpha were measured. Without Paige Prostate Alpha, pathologists had an average sensitivity of 74% and an average specificity of 97%. With Paige Prostate Alpha, the average sensitivity for pathologists significantly increased to 90% with no statistically significant change in specificity. With Paige Prostate Alpha, pathologists more often correctly classified smaller, lower grade tumors, and spent less time analyzing each WSI. Future studies will investigate if similar benefit is yielded when such a system is used to detect other forms of cancer in a setting that more closely emulates real practice.