ArticleCell reports. Medicine2023
MesoGraph: Automatic profiling of mesothelioma subtypes from histological images.
Article in Cell reports. Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Decoding spatial mTOR signalling reveals distinct roles in the tumour microenvironment.NPJ precision oncology · 2026Article
- MesoRet: a reticulin stain-based deep learning algorithm to assist diffuse mesothelioma subtyping.Pathologica · 2026Article
- A histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images.Nature communications · 2025Article
- DeepTFtyper: an interpretable morphology-aware graph neural network for translating histopathology images into molecular subtypes in small cell lung cancer.Briefings in bioinformatics · 2025Article
- AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology.NPJ digital medicine · 2025Article
- Article
- Interpretable multi-modal artificial intelligence model for predicting gastric cancer response to neoadjuvant chemotherapy.Cell reports. Medicine · 2024Article
Corrections and comments
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Authors and funding
13 authors at 8 institutions in 2 countries.
Funding
Abstract
Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.