Abstract:Accurate recognition of Gleason pattern 5 (GP5) prostate adenocarcinoma on needle biopsy is critical as it is associated with disease progression and adverse clinical outcome. Despite important implications of this diagnosis, interobserver variation in the diagnosis of GP5 has not been adequately studied. Digital images of 66 prostate adenocarcinoma cases that potentially contained a GP5 component were distributed to 16 urologic pathologists who were asked to classify whether GP5 was present. Each image was in… Show more
“…Substantial additional variability arises from applying discrete categorizations to glandular differentiation that lies on a continuous spectrum, such as the Gleason pattern 3/4 transition between small glands and poorly defined acinar structures or the Gleason pattern 4/5 transition between fused glands and nests or cords. 12,44,45 Our data show that for regions where pathologists are discordant in Gleason pattern categorization, where the underlying histology is likely closer to the cusp between patterns, the DLS reflects this ambiguity in its prediction scores (Fig. 4b) and demonstrates the potential to assign finer-grained Gleason patterns (Fig.…”
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides. Compared to a reference standard provided by genitourinary pathology experts, the mean accuracy among 29 general pathologists was 0.61 on the validation set. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (
p
= 0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.
“…Substantial additional variability arises from applying discrete categorizations to glandular differentiation that lies on a continuous spectrum, such as the Gleason pattern 3/4 transition between small glands and poorly defined acinar structures or the Gleason pattern 4/5 transition between fused glands and nests or cords. 12,44,45 Our data show that for regions where pathologists are discordant in Gleason pattern categorization, where the underlying histology is likely closer to the cusp between patterns, the DLS reflects this ambiguity in its prediction scores (Fig. 4b) and demonstrates the potential to assign finer-grained Gleason patterns (Fig.…”
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides. Compared to a reference standard provided by genitourinary pathology experts, the mean accuracy among 29 general pathologists was 0.61 on the validation set. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (
p
= 0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.
“…Based on the above-mentioned mutual relation of growth patterns, we hypothesize that small solid nests might be precursor lesions of fused glands prior to lumen-formation. Interestingly, Shah et al found overall consensus among 16 urologic pathologists for calling large (>20 cells) solid fields Gleason pattern 5, but not for medium (10-20 cells) or small (<10 cells) solid nests, further questioning the precise clinicopathologic relevance of smaller sized solid fields [46].…”
Individual growth patterns and cribriform architecture are increasingly considered in risk stratification and clinical decision-making in men with prostate cancer. Our objective was to establish the prognostic value of individual Gleason 5 patterns in a radical prostatectomy (RP) cohort. We reviewed 1064 RPs and recorded Grade Group (GG), pT-stage, surgical margin status, Gleason 4 and 5 growth patterns as well as intraductal carcinoma. The clinical endpoints were biochemical recurrence and post-operative distant metastasis. Gleason pattern 5 was present in 339 (31.9%) RPs, of which 47 (4.4%) presented as primary, 166 (15.6%) as secondary, and 126 (11.8%) as tertiary pattern. Single cells/cords were present in 321 (94.7%) tumors with Gleason pattern 5, solid fields in 90 (26.5%), and comedonecrosis in invasive carcinoma in 32 (9.4%) tumors. Solid fields demonstrated either a small nested morphology (n = 50, 14.7%) or medium to large solid fields (n = 61, 18.0%). Cribriform architecture was present in 568 (53.4%) RPs. Medium to large solid fields and comedonecrosis coincided with cribriform architecture in all specimens, and were not observed in cribriform-negative cases. In multivariable analysis adjusted for Prostate-Specific Antigen, pT-stage, GG, surgical margin status and lymph node metastases, cribriform architecture (Hazard Ratio (HR) 9.9; 95% Confidence Interval (CI) 3.9–25.5, P < 0.001) and comedonecrosis (HR 2.1, 95% CI 1.2–3.7, P = 0.01) were independent predictors for metastasis-free survival, while single cells/cords (HR 1.2; 95% CI 0.7–1.8, P = 0.55) and medium to large solid fields (HR 1.6, 95% CI 0.9–2.7, P = 0.09) were not. In conclusion, comedonecrosis in invasive carcinoma is an independent prognostic Gleason 5 pattern for metastasis-free survival after RP. These data support the current recommendations to routinely include cribriform pattern in pathology reports and indicate that comedonecrosis should also be commented on.
“…The three-dimensional continuity of these patterns is reflected by the substantial inter-observer variability in daily pathology practice. Distinguishing, on one hand, tangentially sectioned Gleason pattern 3 glands from poorly formed and fused Gleason pattern 4 glands, and, on the other hand, poorly formed Gleason pattern 4 glands from Gleason pattern 5 cords on hematoxylin and eosin slides is the principal area of difficulty [12, 14, 27, 28]. Secondly, there are serpentine compact irregular epithelial proliferations, consisting of cribriform Gleason pattern 4 and solid Gleason pattern 5, with decreasing inter-epithelial lumen sizes and frequencies.…”
The Gleason score is one of the most important parameters for therapeutic decision-making in prostate cancer patients. Gleason growth patterns are defined by their histological features on 4- to 5-µm cross sections, and little is known about their three-dimensional architecture. Our objective was to characterize the three-dimensional architecture of prostate cancer growth patterns. Intact tissue punches (n = 46) of representative Gleason growth patterns from radical prostatectomy specimens were fluorescently stained with antibodies targeting Keratin 8/18 and Keratin 5 for the detection of luminal and basal epithelial cells, respectively. Punches were optically cleared in benzyl alcohol–benzyl benzoate and imaged using a confocal laser scanning microscope up to a depth of 500 µm. Gleason pattern 3, poorly formed pattern 4, and cords pattern 5 all formed a continuum of interconnecting tubules in which the diameter of the structures and the lumen size decreased with higher grades. In fused pattern 4, the interconnections between the tubules were markedly closer together. In these patterns, all tumor cells were in direct contact with the surrounding stroma. In contrast, cribriform Gleason pattern 4 and solid pattern 5 demonstrated a three-dimensional continuum of contiguous tumor cells, in which the vast majority of cells had no contact with the surrounding stroma. Transitions between cribriform pattern 4 and solid pattern 5 were seen. There was a decrease in the number and size of intercellular lumens from cribriform to solid growth pattern. Glomeruloid pattern 4 formed an intermediate structure consisting of a tubular network with intraluminal epithelial protrusions close to the tubule splitting points. In conclusion, three-dimensional microscopy revealed two major architectural subgroups of prostate cancer growth patterns: (1) a tubular interconnecting network including Gleason pattern 3, poorly formed and fused Gleason pattern 4, and cords Gleason pattern 5, and (2) serpentine contiguous epithelial proliferations including cribriform Gleason pattern 4 and solid Gleason pattern 5.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.