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The presence and extent of cribriform patterned Gleason 4 (G4) glands are associated with poor prognosis following radical prostatectomy. This study used whole-mount prostate histology and multiparametric magnetic resonance imaging (MP-MRI) to evaluate diffusion differences in G4 gland morphology. Fourty-eight patients underwent MP-MRI prior to prostatectomy, of whom 22 patients had regions of both G4 cribriform glands and G4 fused glands (G4CG and G4FG, respectively). After surgery, the prostate was sliced using custom, patient-specific 3D-printed slicing jigs modeled according to the T2-weighted MR image, processed, and embedded in paraffin. Whole-mount hematoxylin and eosin-stained slides were annotated by our urologic pathologist and digitally contoured to differentiate the lumen, epithelium, and stroma. Digitized slides were co-registered to the T2-weighted MRI scan. Linear mixed models were fitted to the MP-MRI data to consider the different hierarchical structures at the patient and slide level. We found that Gleason 4 cribriform glands were more diffusion-restricted than fused glands.

Background: Corpora amylacea (CAM), in benign prostatic acini, contain acute-phase proteins. Do CAM coincide with carcinoma?, Methods: Within 270 biopsies, 83 prostatectomies, and 33 transurethral resections (TURs), CAM absence was designated CAM 0; corpora in less than 5% of benign acini: CAM 1; in 5% to 25%: CAM 2; in more than 25%: CAM 3. CAM were compared against carcinoma presence, clinicopathologic findings, and grade groups (GG) 1 to 2 vs 3 to 5. The frequency of CAM according to anatomic zone was counted. A pilot study was conducted using paired initial benign and repeat biopsies (33 benign, 24 carcinoma)., Results: A total of 68.9% of biopsies, 96.4% of prostatectomies, and 66.7% of TURs disclosed CAM. CAM ≥1 was common at an older age (P = .019). In biopsies, 204 cases (75%) had carcinoma; and CAM of 2 to 3 (compared to 0-1) were recorded in 25.0% of carcinomas but only 7.4% of benign biopsies (P = .005; odds ratio [OR] = 5.1). CAM correlated with high percent Gleason pattern 3, low GG (P = .035), and chronic inflammation (CI). CI correlated inversely with carcinoma (P = .003). CAM disclosed no association with race, body mass index, serum prostate specific antigen (PSA), acute inflammation (in biopsies), atrophy, or carcinoma volume. With CAM 1, the odds of GG 3 to 5 carcinoma, by comparison to CAM 0, decreased more than 2× (OR = 0.48; P = .032), with CAM 2, more than 3× (OR = 0.33; P = .005), and with CAM 3, almost 3× (OR = 0.39, P = .086). For men aged less than 65, carcinoma predictive model was: Score = (2 × age) + (5 × PSA) - (20 × degree of CAM); using our data, area under the ROC curve was 78.17%. When the transition zone was involved by cancer, it showed more CAM than in cases where it was uninvolved (P = .012); otherwise zonal distributions were similar. In the pilot study, CAM ≥1 predicted carcinoma on repeat biopsy (P < .05; OR = 8), as did CAM 2 to 3 (P < .0001; OR = 30). CI was not significant, and CAM retained significance after adjusting for CI., Conclusion: CAM correlate with carcinoma. Whether abundant CAM in benign biopsies adds value amidst high clinical suspicion, warrants further study., (© 2020 Wiley Periodicals, Inc.)

Prostate cancer is the most commonly diagnosed cancer in men, accounting for 27% of the new male cancer diagnoses in 2022. If organ-confined, removal of the prostate through radical prostatectomy is considered curative; however, distant metastases may occur, resulting in a poor patient prognosis. This study sought to determine whether quantitative pathomic features of prostate cancer differ in patients who biochemically experience biological recurrence after surgery. Whole-mount prostate histology from 78 patients was analyzed for this study. In total, 614 slides were hematoxylin and eosin stained and digitized to produce whole slide images (WSI). Regions of differing Gleason patterns were digitally annotated by a genitourinary fellowship-trained pathologist, and high-resolution tiles were extracted from each annotated region of interest for further analysis. Individual glands within the prostate were identified using automated image processing algorithms, and histomorphometric features were calculated on a per-tile basis and across WSI and averaged by patients. Tiles were organized into cancer and benign tissues. Logistic regression models were fit to assess the predictive value of the calculated pathomic features across tile groups and WSI; additionally, models using clinical information were used for comparisons. Logistic regression classified each pathomic feature model at accuracies >80% with areas under the curve of 0.82, 0.76, 0.75, and 0.72 for all tiles, cancer only, noncancer only, and across WSI. This was comparable with standard clinical information, Gleason Grade Groups, and CAPRA score, which achieved similar accuracies but areas under the curve of 0.80, 0.77, and 0.70, respectively. This study demonstrates that the use of quantitative pathomic features calculated from digital histology of prostate cancer may provide clinicians with additional information beyond the traditional qualitative pathologist assessment. Further research is warranted to determine possible inclusion in treatment guidance.

One in eight men will be affected by prostate cancer (PCa) in their lives. While the current clinical standard prognostic marker for PCa is the Gleason score, it is subject to inter-reviewer variability. This study compares two machine learning methods for discriminating between cancerous regions on digitized histology from 47 PCa patients. Whole-slide images were annotated by a GU fellowship-trained pathologist for each Gleason pattern. High-resolution tiles were extracted from annotated and unlabeled tissue. Patients were separated into a training set of 31 patients (Cohort A, n = 9345 tiles) and a testing cohort of 16 patients (Cohort B, n = 4375 tiles). Tiles from Cohort A were used to train a ResNet model, and glands from these tiles were segmented to calculate pathomic features to train a bagged ensemble model to discriminate tumors as (1) cancer and noncancer, (2) high- and low-grade cancer from noncancer, and (3) all Gleason patterns. The outputs of these models were compared to ground-truth pathologist annotations. The ensemble and ResNet models had overall accuracies of 89% and 88%, respectively, at predicting cancer from noncancer. The ResNet model was additionally able to differentiate Gleason patterns on data from Cohort B while the ensemble model was not. Our results suggest that quantitative pathomic features calculated from PCa histology can distinguish regions of cancer; however, texture features captured by deep learning frameworks better differentiate unique Gleason patterns.

BACKGROUND: Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. PURPOSE: To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms. STUDY TYPE: Prospective. POPULATION: Thirty-three patients prospectively imaged prior to prostatectomy. FIELD STRENGTH/SEQUENCE: 3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence. ASSESSMENT: Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC). STATISTICAL TEST: Levenes test, P

PURPOSE: This study aims to combine multiparametric magnetic resonance imaging (MRI) and digitized pathology with machine learning to generate predictive maps of histologic features for prostate cancer localization. METHODS AND MATERIALS: Thirty-nine patients underwent MRI prior to prostatectomy. After surgery, tissue was sliced according to MRI orientation using patient-specific 3-dimensionally printed slicing jigs. Whole-mount sections were annotated by our pathologist and digitally contoured to differentiate the lumen and epithelium. Slides were co-registered to the T2-weighted MRI scan. A learning curve was generated to determine the number of patients required for a stable machine-learning model. Patients were randomly stratified into 2 training sets and 1 test set. Two partial least-squares regression models were trained, each capable of predicting lumen and epithelium density. Predicted density values were calculated for each patient in the test dataset, mapped into the MRI space, and compared between regions confirmed as high-grade prostate cancer. RESULTS: The learning-curve analysis showed that a stable fit was achieved with data from 10 patients. Maps indicated that regions of increased epithelium and decreased lumen density, generated from each independent model, corresponded with pathologist-annotated regions of high-grade cancer. CONCLUSIONS: We present a radio-pathomic approach to mapping prostate cancer. We find that the maps are useful for highlighting high-grade tumors. This technique may be relevant for dose-painting strategies in prostate radiation therapy.

The presence and extent of cribriform patterned Gleason 4 (G4) glands are associated with poor prognosis following radical prostatectomy. This study used whole-mount prostate histology and multiparametric magnetic resonance imaging (MP-MRI) to evaluate diffusion differences in G4 gland morphology. Fourty-eight patients underwent MP-MRI prior to prostatectomy, of whom 22 patients had regions of both G4 cribriform glands and G4 fused glands (G4CG and G4FG, respectively). After surgery, the prostate was sliced using custom, patient-specific 3D-printed slicing jigs modeled according to the T2-weighted MR image, processed, and embedded in paraffin. Whole-mount hematoxylin and eosin-stained slides were annotated by our urologic pathologist and digitally contoured to differentiate the lumen, epithelium, and stroma. Digitized slides were co-registered to the T2-weighted MRI scan. Linear mixed models were fitted to the MP-MRI data to consider the different hierarchical structures at the patient and slide level. We found that Gleason 4 cribriform glands were more diffusion-restricted than fused glands. [ABSTRACT FROM AUTHOR]

Simple Summary: Understanding the complex network of high-level relationships within tumors and between surrounding tissue is challenging and not fully understood. Our findings demonstrate that the tumor connectomics framework (TCF) models different networks within the tumors and surrounding tissue that are detectable on imaging. The TCF extracts a set of graph network features for each lesion and provides insight into the different types of interactions of a cancer under investigation. These TCF networks are visualized with the radiological parameters and overlaid onto the structural images for better understanding of the global and regional connections within the lesion and surrounding tissue. This information could be used for improved cancer therapeutic targeting in neoplasms and response within different organ systems. The high-level relationships that form complex networks within tumors and between surrounding tissue is challenging and not fully understood. To better understand these tumoral networks, we developed a tumor connectomics framework (TCF) based on graph theory with machine learning to model the complex interactions within and around the tumor microenvironment that are detectable on imaging. The TCF characterization model was tested with independent datasets of breast, brain, and prostate lesions with corresponding validation datasets in breast and brain cancer. The TCF network connections were modeled using graph metrics of centrality, average path length (APL), and clustering from multiparametric MRI with IsoSVM. The Matthews Correlation Coefficient (MCC), Area Under the Curve-ROC, and Precision-Recall (AUC-ROC and AUC-PR) were used for statistical analysis. The TCF classified the breast and brain tumor cohorts with an IsoSVM AUC-PR and MCC of 0.86, 0.63 and 0.85, 0.65, respectively. The TCF benign breast lesions had a significantly higher clustering coefficient and degree centrality than malignant TCFs. Grade 2 brain tumors demonstrated higher connectivity compared to Grade 4 tumors with increased degree centrality and clustering coefficients. Gleason 7 prostate lesions had increased betweenness centrality and APL compared to Gleason 6 lesions with AUC-PR and MCC ranging from 0.90 to 0.99 and 0.73 to 0.87, respectively. These TCF findings were similar in the validation breast and brain datasets. In conclusion, we present a new method for tumor characterization and visualization that results in a better understanding of the global and regional connections within the lesion and surrounding tissue. [ABSTRACT FROM AUTHOR]

Vasculature plays a crucial role in the progression of prostate cancer (PC). Changes to the prostatic native vessels have not been studied since 2000 when Garcia et al. demonstrated marked media hypercellularity and increased artery thickness in prostatic native arteries within PC. We aim to further evaluate and characterize prostatic native vessels with a more accurate method with the use of virtual slides and digital analysis. Pathologist-annotated whole-mount digital slides from 96 entirely submitted prostatectomies were annotated for PC (color-coded by Gleason) using Omero platform. A subset of 44 cases met criteria for further analysis of media thickness, cellularity, and wall thickness to lumen ratio. Cases were included based on containing ≥5 native arteries (≥100 µm diameter) encased on at least 3 sides by PC, with vessels (≥100 µm diameter) designated as controls if they were ≥ 1000 µm away from PC. Annotated vessels were segmented and processed using Matlab 2023b. Mean media thickness (corrected for oblique sections), media: lumen ratio (based on numbers of pixels), and media cellularity (nuclei count) were studied by analysis with SPSS by linear mixed model with nested random effects for subject and slide to account for repeated measures. Vessels encased by PC showed greater media thickness (p=0.02), cellularity (p=0.02) and wall thickness/lumen ratio (p= <0.001) compared to vessels away from PC. These values showed an increasing trend according to stage in cellularity (p=0.14), media thickness (p=0.12) and wall thickness/ lumen ratio (p= 0.33) with higher stage (pT3). A Gleason group comparison showed a borderline-significant gradewise trend when analyzing wall thickness/lumen ratio (p=0.06). Grade 5 emerged as significantly different (p=0.02) from grades 3 or 4 non-cribriform. Similar to the 2000 study, increased media thickness and hypercellularity of vessels encased by PC were evident compared to controls. Borderline grade-dependent increased vessel cellularity changes were seen, suggesting a possible role in PC progression; the predictive value of these changes for outcome is uncertain. Whether the etiology of changes reflects locally increased intravascular pressure of vessels within tumor should be investigated. • Quantitative digital morphometric analysis of native prostatic vessels was performed. • Findings show distinct changes in vessels encased by prostate cancer, compared to matched vessels in the uninvolved prostate. • Trends, but not significant differences, in native vessels were detected according to tumor grade or to stage. [ABSTRACT FROM AUTHOR]