ArticleFrontiers in cell and developmental biology2021
Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Integrative multi-omics analysis identifies LIPA as a prognostic hub and ferroptosis regulator in lung adenocarcinoma.Molecular and cellular biochemistry · 2026Article
- A Clinically Translatable Multimodal Deep Learning Model for HRD Detection from Histopathology Images.Diagnostics (Basel, Switzerland) · 2026Article
- AI in Cancer Prognosis: A Systematic Review of Multimodal Models Combining Pathology Images and High-Throughput Omics.Cancer informatics · 2026Article
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- A machine learning approach for multimodal data fusion for survival prediction in cancer patients.NPJ precision oncology · 2025Article
- Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data.Bioinformatics (Oxford, England) · 2025Article
- Explainable AI for classifying vertebral fracture histology in digital spine pathology.Frontiers in medicine · 2025Article
- Multimodal learning for scalable representation of high-dimensional medical data.Frontiers in digital health · 2025Article
- Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace.Journal of cancer research and clinical oncology · 2024Article
- Construction of a pathomics model for predicting mRNAsi in lung adenocarcinoma and exploration of biological mechanism.Heliyon · 2024Article
- Pathomic model based on histopathological features and machine learning to predict IDO1 status and its association with breast cancer prognosis.Breast cancer research and treatment · 2024Article
- Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review).Oncology reports · 2024Review
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study.Journal of translational medicine · 2022Article
- Feature screening for survival trait with application to TCGA high-dimensional genomic data.PeerJ · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histopathological images and omics profiles play important roles in prognosis of cancer patients. Here, we extracted quantitative features from histopathological images to predict molecular characteristics and prognosis, and integrated image features with mutations, transcriptomics, and proteomics data for prognosis prediction in lung adenocarcinoma (LUAD). Patients obtained from The Cancer Genome Atlas (TCGA) were divided into training set (
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