Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation–assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning–based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. Significance: An end-to-end pipeline for deep learning–assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

Tissue section thickness (TST) is an understudied variable in digital pathology that significantly impacts both visual assessments and computational analyses. This study systematically examines the effects of TST on whole slide images (WSIs) and nuclear-level features using thyroid tissue samples ( n = 144) prepared at thicknesses ranging from 0.5 to 10 µm. By minimizing preanalytical variables and batch effects, we aimed to isolate TST as the primary factor in our experiment. Visual assessments indicated that thinner Sects. (0.5–3 µm) were more transparent with distinct cellular features, while thicker Sects. (5–10 µm) appeared darker with increased staining intensity and artifacts. Quantitative analyses were performed using open-source tools such as HistoQC for WSI quality control, HoverFast for nuclear segmentation, and feature extraction with Scikit-learn and Mahotas. Both WSI and nuclear-level metrics were significantly influenced by TST. The Haralick texture feature of difference entropy, which measures texture complexity, showed a 13.7% decrease in nuclei as TST increased, indicating fewer complex textures in thicker sections. Additionally, intensity decreased substantially with thicker tissue, dropping by 26.1% at the WSI level and 30.4% at the nuclear level. WSI contrast exhibited an increase of 92.6% when transitioning from 0.5 to 10 µm. These findings demonstrate that variations in TST can obscure or alter the appearance of biological signals, complicating both visual diagnostics and computationally extracted features. The study highlights the need for standardized tissue section thickness protocols, alongside consistent reporting of these standards, to ensure accuracy and reliability in both visual evaluations and computational analyses within digital pathology workflows.

Integration of digital pathology (DP) into clinical diagnostic workflows is increasingly receiving attention as new hardware and software become available. To facilitate the adoption of DP, the Swiss Digital Pathology Consortium (SDiPath) organized a Delphi process to produce a series of recommendations for DP integration within Swiss clinical environments. This process saw the creation of 4 working groups, focusing on the various components of a DP system (1) scanners, quality assurance and validation of scans, (2) integration of Whole Slide Image (WSI)-scanners and DP systems into the Pathology Laboratory Information System, (3) digital workflow—compliance with general quality guidelines, and (4) image analysis (IA)/artificial intelligence (AI), with topic experts for each recruited for discussion and statement generation. The work product of the Delphi process is 83 consensus statements presented here, forming the basis for “SDiPath Recommendations for Digital Pathology”. They represent an up-to-date resource for national and international hospitals, researchers, device manufacturers, algorithm developers, and all supporting fields, with the intent of providing expectations and best practices to help ensure safe and efficient DP usage.

The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.

Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. Here, we investigated if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis. Deep-learning and image-analysis approaches were employed on 254/266 Periodic acid Schiff-stained whole slide image (WSI) kidney biopsies from participants in the NEPTUNE/CureGN prospective observational cohort studies (135/153 with focal segmental glomerulosclerosis (FSGS) and 119/113 with minimal change disease (MCD)) to segment cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM). One hundred four pathomic features were extracted from these segmented tubular substructures and aggregated at the patient level using summary statistics. In the NEPTUNE dataset, tubular features were quantified at the WSI level and in manually segmented regions of mature interstitial fibrosis and tubular atrophy (IFTA), pre-IFTA, and non-IFTA. Minimum Redundancy Maximum Relevance was then used to select features most associated with disease progression and proteinuria remission. Ridge-penalized Cox models evaluated their predictive discrimination compared to clinical/demographic data and visual-assessment. Models were evaluated in the CureGN dataset. Nine features were predictive of disease progression and/or proteinuria remission. Models with tubular features had high prognostic accuracy in both NEPTUNE and CureGN, and higher prognostic accuracy for both outcomes compared to conventional parameters alone in NEPTUNE. TBM thickness/area and TE flattening and/or reduced cell size progressively increased from non- to pre- and mature IFTA. Previously underrecognized computationally derived and quantifiable tubular characteristics may contribute to improving prognostic accuracy and risk stratification in patients with FSGS/MCD. Future studies are needed to test their generalizability across different diseases and populations before they can be deployed in clinical practice.

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder (http://cohortfinder.com), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream digital pathology and medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com.