Article

Interpretable Deep Learning Reveals Biologically Relevant Spatial Gene Expression Patterns in Lung Tumors and their Microenvironment

2025-09-20

Abstract excerpt

Lung adenocarcinoma (LUAD), the most common subtype of non–small cell lung cancer (NSCLC) exhibits profound histological and molecular heterogeneity, hindering accurate prognosis and effective treatment. Current approaches to assess this heterogeneity, such as histopathology, molecular profiling, and spatial transcriptomics are constrained by high costs, long turnaround times, and limited tissue availability, maki...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 56b410f4-51b2-59c5-8739-ef32e3661ec5
DOI: 10.1101/2025.09.17.674367

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Interpretable Deep Learning Reveals Biologically Relevant Spatial Gene Expression Patterns in Lung Tumors and their MicroenvironmentDOI 10.1101/2025.09.17.674367Read

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

Towards interpretable molecular and spatial analysis of the tumor microenvironment from digital histopathology images with HistoTME-v2Semantic proximity Read

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancerSemantic proximity Read

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer.Semantic proximity Read

A deep learning-based multiscale integration of spatial omics with tumor morphologySemantic proximity Read

Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learningSemantic proximity Read

A deep learning-based multiscale integration of spatial omics with tumor morphologySemantic proximity Read

Digital profiling of cancer transcriptomes from histology images with grouped vision attentionSemantic proximity Read

Predicting oncogene mutations of lung cancer using deep learning and histopathologic features on whole-slide imagesSemantic proximity Read

Classifying Non-Small Cell Lung Cancer Histopathology Types and Transcriptomic Subtypes using Convolutional Neural NetworksSemantic proximity Read

Predicting Oncogene Mutations of Lung Cancer Using Deep Learning and Histopathologic Features on Whole-Slide ImagesSemantic proximity Read

A Deep Learning Framework Integrating Tumor Microenvironmental Features Accurately Predicts Multiple Driver Gene Mutations in Lung Cancer Pathology Images.Semantic proximity Read