Prognostic Analysis of Histopathological Images Using Pre-Trained Convolutional Neural Networks

Lu, L.; Daigle, B.

doi:10.1101/620773

Home/Archive/bioRxiv

bioRxiv · 10.1101/620773

Prognostic Analysis of Histopathological Images Using Pre-Trained Convolutional Neural Networks

Lu, L.·Daigle, B.

View original source ↗Export metadata ↓

Abstract

BackgroundHistopathological images contain rich phenotypic descriptions of the molecular processes underlying disease progression. Convolutional neural networks (CNNs), a state-of-the-art image analysis technique in computer vision, automatically learns representative features from such images which can be useful for disease diagnosis, prognosis, and subtyping. Despite hepatocellular carcinoma (HCC) being the sixth most common type of primary liver malignancy with a high mortality rate, little previous work has made use of CNN models to delineate the importance of histopathological images in diagnosis and clinical survival of HCC.\n\nResultsWe applied three pre-trained CNN models - VGG 16, Inception V3, and ResNet 50 - to extract features from HCC histopathological images. The visualization and classification showed clear separation between cancer and normal samples using image features. In a univariate Cox regression analysis, 21.4% and 16% of image features on average were significantly associated with overall survival and disease-free survival, respectively. We also observed significant correlations between these features and integrated biological pathways derived from gene expression and copy number variation. Using an elastic net regularized CoxPH model of overall survival, we obtained a concordance index (C-index) of 0.789 and a significant log-rank test (p = 7.6E-18) after applying Inception image features. We also performed unsupervised classification to identify HCC subgroups from image features. The optimal two subgroups discovered using Inception image features were significantly associated with both overall (C-index = 0.628 and p = 7.39E-07) and disease-free survival (C-index =0.558 and p = 0.012). Our results suggest the feasibility of feature extraction using pre-trained models, as well as the utility of the resulting features to build an accurate prognosis model of HCC and highlight significant correlations with clinical survival and biological pathways.\n\nConclusionsThe image features extracted from HCC histopathological images using the pre-trained CNN models VGG 16, Inception V3 and ResNet 50 can accurately distinguish normal and cancer samples. Furthermore, these image features are significantly correlated with relevant biological outcomes.

Source connections

Published article ↗

Explore related subjects

bioinformatics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Lu, L., Daigle, B.. 2019-04-26. Prognostic Analysis of Histopathological Images Using Pre-Trained Convolutional Neural Networks. https://doi.org/10.1101/620773

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗

bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗