ArticleFrontiers in oncology2021
Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of random forest-based models in prognostication of gastrointestinal tract malignancies: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- The Prognostic and Biological Value of PGF-Based H&E Pathomics in Hepatocellular Carcinoma.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- AI in Cancer Prognosis: A Systematic Review of Multimodal Models Combining Pathology Images and High-Throughput Omics.Cancer informatics · 2026Article
- Integrating pathology genomics and single-cell genomics to identify lactate metabolism-related prognostic features and therapeutic strategies for melanoma.Apoptosis : an international journal on programmed cell death · 2025Article
- Multimodal analysis of whole slide images in colorectal cancer.NPJ digital medicine · 2025Article
- NETosis Genes and Pathomic Signature: A Novel Prognostic Marker for Ovarian Serous Cystadenocarcinoma.Journal of imaging informatics in medicine · 2025Article
- Pathomics-based machine learning model predicts interferon-gamma expression in head and neck squamous cell carcinoma patients.Frontiers in immunology · 2025Article
- Pathomic and bioinformatics analysis of clinical-pathological and genomic factors for pancreatic cancer prognosis.Scientific reports · 2024Article
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- 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
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- Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer.Clinical and experimental otorhinolaryngology · 2024Article
- Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer.Translational oncology · 2024Article
- Establishment of a pathomic-based machine learning model to predict CD276 (B7-H3) expression in colon cancer.Frontiers in oncology · 2023Article
- Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images.Frontiers in oncology · 2023Article
- Classification of Subgroups with Immune Characteristics Based on DNA Methylation in Luminal Breast Cancer.International journal of molecular sciences · 2022Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.
methodsWe downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).
resultsThere were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.
conclusionsThese results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.
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