Biology subjects

Vaickus, L. J.

Publications and source records attributed to Vaickus, L. J..

12 recordsLinked to original sources

bioRxiv2026

Interpreting and Validating a Deep Learning Model Predictive of Spatial Morphologic-Molecular Patterns in Lung Adenocarcinoma, Using Ground Truth Immunohistochemistry

Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, exhibits profound histological and molecular heterogeneity. While genomic profiling has identified key oncogenic drivers and immune signatures, its use is limited by cost, technical demands and tissue availability. In addition, spatial transcriptomics provides spatially resolved molecular insights but remains challenging and time-consuming. To address this gap, we developed XpressO-Lung, an explanatory deep learning model that predicts gene expression heterogeneity spatially in tumor and its microenvironment on hematoxylin and eosin based diagnostic (Dx) whole-slide images (WSIs) by learning associations between tissue morphology and the corresponding bulk-transcriptomic data. Utilizing 200 LUAD cases from The Cancer Genome Atlas, XpressO-Lung predicted spatial expression patterns of NAPSA, TP53I3, CD8A, TTF1, KRT7, CDKN2A, FOXO1, KEAP1, RB1 and TP53 on Dx-WSIs with AUCs ranging from 0.64 to 0.92. The predicted spatial gene expression patterns aligned with the known morphologic interactions of the tumor and its microenvironment, capturing biological events directly on Dx-WSIs. These spatio-morpho-molecular associations were further validated using immunohistochemistry on an external set of clinical samples at Dartmouth Health, demonstrating concordance between model-predicted spatial patterns and observed histomorphologic features. By coupling predictive performance with spatial interpretability of gene expression on Dx-WSIs, the XpressO-Lung model bridges histopathology and bulk-transcriptomics, enabling explainable spatio-morpho-genomic analyses to advance biomarker discovery, therapeutic stratification and precision oncology in LUAD.

pathology↗

bioRxiv2026

X-SPATIO: An Explanatory Deep Learning Pipeline for the Prediction and Visualization of Spatially Resolved Biomarker Expression in Triple-Negative Breast Cancer

Histopathologic evaluation remains central to cancer diagnosis and treatment planning, yet the molecular programs underlying distinct tissue morphologies are not routinely accessible in clinical workflows. Spatial transcriptomic/proteomic platforms provide region-specific molecular measurements but are limited by cost, throughput, and scalability. Most computational pathology models rely on either bulk tissue-based gene expression or a focused gene/protein expression-panel prediction, thereby obscuring subregion-specific morpho-molecular relationships and limiting spatial interpretation of a wider gene/protein expression network. This limitation is particularly significant in triple-negative breast cancer (TNBC), which exhibits pronounced spatial heterogeneity across tumor, stroma, and immune compartments. We developed X-SPATIO, a spatially compatible computational pipeline designed to directly link hematoxylin and eosin (H&E) morphology with region-matched mRNA and protein expression. The model was trained on H&E-defined regions of interest paired with spatially-resolved transcriptomic and proteomic data obtained from GeoMx Digital Spatial Profiler. Using a multiple-instance learning approach, X-SPATIO captures morpho-molecular associations, generating spatio-morphologic attention maps that indicate predictive tissue regions. X-SPATIO demonstrated strong performance across biologically relevant spatial biomarkers, achieving area under the curve values ranging [0.79, 0.97]. Attention maps revealed spatial patterns consistent with known biology, indicating alignment between learned features and tissue organization. By integrating spatial molecular ground truth with routine histopathology, X-SPATIO enables cost-effective inference of spatial biomarker expression and establishes a foundation for biologically grounded discovery and precision oncology in TNBC.

pathology↗

bioRxiv2022

Uncovering Additional Predictors of Urothelial Carcinoma from Voided Urothelial Cell Clusters Through a Deep Learning Based Image Preprocessing Technique

Urine cytology is commonly used as a screening test for high grade urothelial carcinoma for patients with risk factors or hematuria and is an essential step in longitudinal monitoring of patients with a prior bladder cancer history. However, the semi-subjective nature of current reporting systems for urine cytology (e.g., The Paris System) can hamper reproducibility. For instance, the incorporation of urothelial cell clusters into the classification schema is still an item of debate and perplexity amongst expert cytopathologists, as several previous works have disputed their diagnostic relevance. Recently, several machine learning and morphometric algorithms have been proposed to provide quantitative descriptors of urine cytology specimens in an effort to reduce subjectivity and include automated assessments of cell clusters. However, it remains unclear how these computer algorithms interpret/analyze cell clusters. In this work, we have developed an automated preprocessing tool for urothelial cell cluster assessment that divides urothelial cell clusters into meaningful components for downstream assessment (i.e., population-based studies, workflow automation). Results indicate that cell cluster atypia (i.e., defined by whether the cell cluster harbored multiple atypical cells, thresholded by a minimum number of cells), cell border overlap and smoothness, and total number of clusters are important markers of specimen atypia when considering assessment of urothelial cell clusters. Markers established through techniques to separate cell clusters may have wider applicability for the design and implementation of machine learning approaches for urine cytology assessment.

pathology↗

bioRxiv2021

Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry

BACKGROUND AND AIMSEvaluation for dyssynergia is the most common reason that gastroenterologists refer patients for anorectal manometry, because dyssynergia is amenable to biofeedback by physical therapists. High-definition anorectal manometry (3D-HDAM) is a promising technology to evaluate anorectal physiology, but adoption remains limited by its sheer complexity. We developed a 3D-HDAM deep learning algorithm to evaluate for dyssynergia. METHODSSpatial-temporal data were extracted from consecutive 3D-HDAM studies performed between 2018-2020 at a tertiary institution. The technical procedure and gold standard definition of dyssynergia were based on the London consensus, adapted to the needs of 3D-HDAM technology. Three machine learning models were generated: (1) traditional machine learning informed by conventional anorectal function metrics, (2) deep learning, and (3) a hybrid approach. Diagnostic accuracy was evaluated using bootstrap sampling to calculate area-under-the-curve (AUC). To evaluate overfitting, models were validated by adding 502 simulated defecation maneuvers with diagnostic ambiguity. RESULTS302 3D-HDAM studies representing 1,208 simulated defecation maneuvers were included (average age 55.2 years; 80.5% women). The deep learning model had comparable diagnostic accuracy (AUC=0.91 [95% confidence interval 0.89-0.93]) to traditional (AUC=0.93[0.92-0.95]) and hybrid (AUC=0.96[0.94-0.97]) predictive models in training cohorts. However, the deep learning model handled ambiguous tests more cautiously than other models; the deep learning model was more likely to designate an ambiguous test as inconclusive (odds ratio=4.21[2.78-6.38]) versus traditional/hybrid approaches. CONCLUSIONSBy considering complex spatial-temporal information beyond conventional anorectal function metrics, deep learning on 3D-HDAM technology may enable gastroenterologists to reliably identify and manage dyssynergia in broader practice.

bioinformatics↗

bioRxiv2021

Development of Biologically Interpretable Multimodal Deep Learning Model for Cancer Prognosis Prediction

Robust cancer prognostication can enable more effective patient care and management, which may potentially improve health outcomes. Deep learning has proven to be a powerful tool to extract meaningful information from cancer patient data. In recent years it has displayed promise in quantifying prognostication by predicting patient risk. However, most current deep learning-based cancer prognosis prediction methods use only a single data source and miss out on learning from potentially rich relationships across modalities. Existing multimodal approaches are challenging to interpret in a biological or medical context, limiting real-world clinical integration as a trustworthy prognostic decision aid. Here, we developed a multimodal modeling approach that can integrate information from the central modalities of gene expression, DNA methylation, and histopathological imaging with clinical information for cancer prognosis prediction. Our multimodal modeling approach combines pathway and gene-based sparsely coded layers with patch-based graph convolutional networks to facilitate biological interpretation of the model results. We present a preliminary analysis that compares the potential applicability of combining all modalities to uni- or bi-modal approaches. Leveraging data from four cancer subtypes from the Cancer Genome Atlas, results demonstrate the encouraging performance of our multimodal approach (C-index=0.660 without clinical features; C-index=0.665 with clinical features) across four cancer subtypes versus unimodal approaches and existing state-of-the-art approaches. This work brings insight to the development of interpretable multimodal methods of applying AI to biomedical data and can potentially serve as a foundation for clinical implementations of such software. We plan to follow up this preliminary analysis with an in-depth exploration of factors to improve multimodal modeling approaches on an in-house dataset.

pathology↗

bioRxiv2021

Improving the Virtual Trichrome Assessment through Bridge Category Models

Non-alcoholic steatohepatitis (NASH) is a liver disease characterized by excessive lipid accumulation and disease progression is typically assessed through inspection of a Trichrome stain for Fibrosis staging. As the public health burden of NASH worsens due to evolving lifestyle habits, pathology laboratory resources will become increasingly strained due to rising demand for specialized stains. Virtual staining processes, computational methods which can synthesize the application of chemical staining reagents, can potentially provide resource savings by obviating the need to acquire specialized stains. Virtual staining technologies are assessed by comparing virtual and real tissue stains for their realism and ability to stage. However, these assessment methods are rife with statistical mistreatment of observed phenomena that are difficult to account for. Bridge category ratings represent a phenomenon where a pathologist may assign two adjacent stages simultaneously, which may bias and/or reduce the power of research findings. Such stage assignments were frequently reported in a large-scale assessment of Virtual Trichrome technologies yet were unaccounted for since no statistical adjustment procedures existed. In this work, we provide an updated assessment of Virtual Trichrome technologies using Bridge Category Models, which account for these bridge ratings. We report that two of four pathologists tended to assign lower Fibrosis stages to virtually stained tissue while the other two pathologists assigned similar stages. These research findings differ when bridge ratings are not accounted for. While promising, these results indicate further room for algorithmic finetuning of Virtual Trichrome technologies.

pathology↗

bioRxiv2021

Mixed Effects Machine Learning Models for Colon Cancer Metastasis Prediction using Spatially Localized Immuno-Oncology Markers

Spatially resolved characterization of the transcriptome and proteome promises to provide further clarity on cancer pathogenesis and etiology, which may inform future clinical practice through classifier development for clinical outcomes. However, batch effects may potentially obscure the ability of machine learning methods to derive complex associations within spatial omics data. Profiling thirty-five stage three colon cancer patients using the GeoMX Digital Spatial Profiler, we found that mixed-effects machine learning (MEML) methods{dagger} may provide utility for overcoming significant batch effects to communicate key and complex disease associations from spatial information. These results point to further exploration and application of MEML methods within the spatial omics algorithm development life cycle for clinical deployment.

pathology↗

bioRxiv2020

Estimating latent positions of actors using Neural Networks in R with GCN4R

Network analysis methods are useful to better understand and contextualize relationships between entities. While statistical and machine learning prediction models generally assume independence between actors, network-based statistical methods for social network data allow for dyadic dependence between actors. While numerous methods have been developed for the R statistical software to analyze such data, deep learning methods have not been implemented in this language. Here, we introduce GCN4R, an R library for fitting graph neural networks on independent networks to aggregate actor covariate information to yield meaningful embeddings for a variety of network-based tasks (e.g. community detection, peer effects models, social influence). We provide an extensive overview of insights and methods utilized by the deep learning community on learning on social and biological networks, followed by a tutorial that demonstrates some of the capabilities of the GCN4R framework to make these methods more accessible to the R research community.

bioinformatics↗

bioRxiv2020

Investigation of Capsule-Inspired Neural Network Approaches for DNA Methylation

DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules - such as gene promoter context, CpG island relationship, or user-defined groupings - and relate them to diagnostic and prognostic outcomes. We demonstrate these models utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep learning analyses interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules.

bioinformatics↗

bioRxiv2020

A Large-Scale Internal Validation Study of Unsupervised Virtual Trichrome Staining Technologies on Non-alcoholic Steatohepatitis Liver Biopsies

Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tissue sections are stained with hematoxylin and eosin (H&E) to grade NASH activity, and stained with trichrome to stage fibrosis. Methods to computationally transform one stain into another on digital whole slide images (WSI) can lessen the need for additional physical staining besides H&E, reducing personnel, equipment, and time costs. Generative adversarial networks (GAN) have shown promise for virtual staining of tissue. We conducted a large-scale validation study of the viability of GANs for H&E to trichrome conversion on WSI (n=574). Pathologists were largely unable to distinguish real images from virtual/synthetic images given a set of twelve Turing Tests. We report high correlation between staging of real and virtual stains ({rho} = 0.86; 95% CI: 0.84-0.88). Stages assigned to both virtual and real stains correlated similarly with a number of clinical biomarkers and progression to End Stage Liver Disease (Hazard Ratio HR = 2.06, CI 95% 1.36-3.12, P < 0.001 for real stains; HR = 2.02, CI 95% 1.40-2.92, p < 0.001 for virtual stains). Our results demonstrate that virtual trichrome technologies may offer a software solution that can be employed in the clinical setting as a diagnostic decision aid.

pathology↗

bioRxiv2020

PathFlow-MixMatch for Whole Slide Image Registration: An Investigation of a Segment-Based Scalable Image Registration Method

Image registration involves finding the best alignment between different images of the same object. In these tasks, the object in question is viewed differently in each of the images (e.g. different rotation or light conditions, etc.). In digital pathology, image registration aligns correspondent regions of tissue from different stereotactic viewpoints (e.g. subsequent deeper sections of the same tissue). These comparisons are important for histological analysis and can facilitate previously unavailable manipulations, such as 3D tissue reconstruction and cell-level alignment of immunohistochemical (IHC) and special stains. Several benchmarks have been established for evaluating image registration techniques for histological tissue; however, little work has evaluated the impact of scaling registration techniques to Giga-Pixel Whole Slide Images (WSI), which are large enough for significant memory limitations, and contain recurrent patterns and deformations that hinder traditional alignment algorithms. Furthermore, as tissue sections often contain multiple, discrete, smaller tissue fragments, it is unnecessary to align an entire image when the bulk of the image is background whitespace and tissue fragments orientations are often agnostic of each other. We present a methodology for circumventing large-scale image registration issues in histopathology and accompanying software. By removing background pixels, parsing the slide into discrete tissue segments, and matching, orienting and registering smaller segment pairs, we recovered registrations with lower Target Registration Error (TRE) when compared to utilizing the unmanipulated WSI. We tested our technique by having a pathologist annotate landmarks from 13 pairs of differently stained liver biopsy slides, performing WSI and segment-based registration techniques, and comparing overall TRE. Preliminary results demonstrate superior performance of registering segment pairs versus registering WSI (difference of median TRE of 44 pixels, p<0.001). Segment matching within WSI is an effective solution for histology image registration but requires further testing and validation to ensure its viability for stain translation and 3D histology analysis.

pathology↗

bioRxiv2020

Preliminary Evaluation of the Utility of Deep Generative Histopathology Image Translation at a Mid-Sized NCI Cancer Center

Evaluation of a tissue biopsy is often required for the diagnosis and prognostic staging of a disease. Recent efforts have sought to accurately quantitate the distribution of tissue features and morphology in digitized images of histological tissue sections, Whole Slide Images (WSI). Generative modeling techniques present a unique opportunity to produce training data that can both augment these models and translate histologic data across different intra-and-inter-institutional processing procedures, provide cost-effective ways to perform computational chemical stains (synthetic stains) on tissue, and facilitate the creation of diagnostic aid algorithms. A critical evaluation and understanding of these technologies is vital for their incorporation into a clinical workflow. We illustrate several potential use cases of these techniques for the calculation of nuclear to cytoplasm ratio, synthetic SOX10 immunohistochemistry (IHC, sIHC) staining to delineate cell lineage, and the conversion of hematoxylin and eosin (H&E) stain to trichome stain for the staging of liver fibrosis.

pathology↗