Biology subjects

Madabhushi, A.

Publications and source records attributed to Madabhushi, A..

4 recordsLinked to original sources

bioRxiv2026

Prototype-based AI triage for 3D pathology

Non-destructive 3D pathology enables high-resolution slide-free imaging of intact clinical specimens, providing comprehensive visualization of tissue structures beyond what conventional slide-based 2D histopathology can provide. However, the scale and complexity of volumetric datasets make exhaustive manual review impractical, motivating AI-assisted triage methods to select a small number of high-risk 2D slices for pathologist review. While prior triage models have shown promise, interpretability is poor and performance can be suboptimal, especially in the nascent field of 3D pathology in which labeled data is limited. We present SCOPE, a Segmentation-guided CrOss-slice PrototypE learning framework for comprehensive risk assessment of 2D levels within 3D pathology datasets. SCOPE combines (i) clustering-based pretraining on large-scale unlabeled volumetric data to initialize morphology-aware prototypes, (ii) segmentation-derived structural priors from publicly available models to guide proto-type learning, and (iii) cross-slice (2.5D) prototype aggregation across neighboring slices to generate slice-level risk predictions. In prostate and esophageal data cohorts, SCOPE consistently outperforms attention-based and prototype-based multiple instance learning baselines for both binary and multiclass prediction tasks, enabling depth-resolved risk profiling for 3D triage based on morphological prototypes that are interpretable to pathologists.

pathology↗

bioRxiv2025

Detection of prostate cancer in 3D pathology datasets via generative immunolabeling

Recent advancements in nondestructive 3D pathology offer a complement to standard histology by enabling comprehensive volumetric analyses of intact clinical specimens (e.g. biopsies). Prior studies have demonstrated the added prognostic value of 3D pathology for prostate cancer risk stratification by correlating 3D microarchitectural features with long-term patient outcomes. However, these analyses relied on coarse manual annotations of cancer-enriched regions for downstream analysis without fine-grained delineation between often-intermixed cancerous and benign glands. To address these limitations, we have developed a 3D computational pipeline: Synthetic Immunolabeling for Generative Heatmaps of Tumor (SIGHT). SIGHT relies on deep learning-based 3D image translation models, trained in a fully supervised fashion, to convert H&E-analog 3D pathology datasets into multiplexed 3D immunofluorescence datasets that facilitate tumor detection. Our implementation of SIGHT synthetically labels two cytokeratin markers that are differentially expressed in cancerous and benign prostate glands, which are used to generate explainable 3D heatmaps of cancer-enriched regions in prostate tissues. Validation of SIGHT against ground-truth annotations from a panel of genitourinary pathologists yields an average F1 score of 0.88 which is comparable to the average inter-pathologist agreement F1 score of 0.90. To demonstrate the value of SIGHT, we developed machine classifiers of recurrence risk based on 3D glandular histomorphometric features from 75 patients. Volumetric glandular analysis in SIGHT-identified cancer-enriched regions vs. all tissue regions yields an average Kaplan-Meier hazard ratio of 3.57 (1.6 - 7.9 CI) vs. 0.92 (0.45 - 1.89 CI).

pathology↗

bioRxiv2025

Deep-learning triage of 3D pathology datasets for comprehensive and efficient pathologist assessments

Standard-of-care slide-based 2D histopathology severely undersamples spatially heterogeneous tissue specimens, with each thin 2D section representing <1% of the entire tissue volume (in the case of a biopsy). Recent advances in non-destructive 3D pathology, such as open-top light-sheet microscopy (OTLS), enable comprehensive high-resolution imaging of large clinical specimens. While fully automated computational analyses of such 3D pathology datasets are being explored, a potential low-risk route for accelerated clinical adoption would be to continue to rely upon pathologists to provide final diagnoses. Since manual review of these massive and complex 3D datasets is infeasible for routine clinical practice, we present CARP3D, a deep learning triage framework that identifies high-risk 2D cross sections within large 3D pathology datasets to enable time-efficient pathologist evaluation. CARP3D assigns risk scores to all 2D levels within a tissue volume by leveraging context from a subset of neighboring depth levels, outperforming models in which predictions are based on isolated 2D levels. In two use cases - risk stratification based on prostate cancer biopsies and screening for dysplasia/cancer in endoscopic biopsies of Barretts esophagus - AI-triaged 3D pathology, enabled by CARP3D, demonstrates the potential to improve the detection of high-risk diseases in comparison to slide-based 2D histopathology while optimizing pathologist workloads.

pathology↗

bioRxiv2025

Quantitative 3D imaging of mouse and human intrahepatic bile ducts in homeostasis and liver injury

Intrahepatic bile ducts (IHBDs) form a complex hierarchical network essential for liver function. Remodeling and expansion of this network during ductular reaction (DR) is a hallmark of liver disease that can be a key indicator of disease severity. Conventional histology fails to capture the full extent of IHBD structural changes following injury due to the complex 3D organization of the IHBD network which limits understanding of DR, especially in human tissue. A major barrier to leveraging 3D imaging as a diagnostic tool is the absence of standardized pipelines for IHBD imaging and analysis. Here, we establish a robust 3D IHBD imaging and analysis workflow and apply it to both mouse and human liver tissues. This pipeline enables quantification of tissue and individual duct ("segment") level features and identifies features of invasive and noninvasive DR. In mouse models, we uncover regional phenotypes, including IHBD diverticula following duct blockage and the formation of anastomosed clusters after hepatocellular injury. Finally, we apply our 3D imaging and analysis workflow to quantify IHBD networks in human liver tissue. This work deepens our understanding of IHBD architecture in homeostasis and injury and lays the groundwork for advanced phenotyping of IHBD morphologies in mice and humans with relevance to next-generation experimental and diagnostic approaches to liver disease.

pathology↗