Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Spatially aware graph neural networks and cross-level molecular profile prediction in colon cancer histopathology: a retrospective multi-cohort study

25 Oct 2022

Abstract excerpt

BACKGROUND: Digital whole-slide images are a unique way to assess the spatial context of the cancer microenvironment. Exploring these spatial characteristics will enable us to better identify cross-level molecular markers that could deepen our understanding of cancer biology and related patient outcomes. METHODS: We proposed a graph neural network approach that emphasises spatialisation of tumour tiles towards a...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to AI in cancer detection
Share to Radiomics and Machine Learning in Medical Imaging
Share to Single-cell and spatial transcriptomics

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 36307192
DOI: 10.1016/s2589-7500(22)00168-6

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Spatially aware graph neural networks and cross-level molecular profile prediction in colon cancer histopathology: a retrospective multi-cohort studyPMID 36307192Read ↗

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE SlidesSemantic proximity Read ↗

Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility StudySemantic proximity Read ↗

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor HeterogeneitySemantic proximity Read ↗

Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot studySemantic proximity Read ↗

Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortalitySemantic proximity Read ↗

INSIGHT: Spatially resolved survival modelling from routine histology crosslinked with molecular profiling reveals prognostic epithelial-immune axes in stage II/III colorectal cancerSemantic proximity Read ↗

Novel deep learning algorithm predicts the status of molecular pathways and key mutations in colorectal cancer from routine histology imagesSemantic proximity Read ↗

Multicenter self-supervised computational pathology identifies prognostic histomorphological phenotypes in colorectal cancerSemantic proximity Read ↗

Integrating cellular graph embeddings with tumor morphological features to predict in-silico spatial transcriptomics from H&E imagesSemantic proximity Read ↗

Predictive modelling of highly multiplexed tumour tissue images by graph neural networksSemantic proximity Read ↗

Image-Based Consensus Molecular Subtyping in Rectal Cancer Biopsies and Response to Neoadjuvant ChemoradiotherapySemantic proximity Read ↗

SurGen: 1020 H&E-stained whole-slide images with survival and genetic markers.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.