ArticleNAR genomics and bioinformatics2021
Two-stage Cox-nnet: biologically interpretable neural-network model for prognosis prediction and its application in liver cancer survival using histopathology and transcriptomic data.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research.Journal of the Egyptian National Cancer Institute · 2026Review
- Artificial intelligence and multi-omics integration in liquid biopsy for genitourinary cancers: a systematic scoping review.International urology and nephrology · 2026Article
- Multimodal data-driven prediction of postoperative recurrence and survival in hepatocellular carcinoma: a narrative review.Journal of gastrointestinal oncology · 2026Review
- AI in Cancer Prognosis: A Systematic Review of Multimodal Models Combining Pathology Images and High-Throughput Omics.Cancer informatics · 2026Article
- DeepCMS: A Feature Selection-Driven Model for Cancer Molecular Subtyping with a Case Study on Testicular Germ Cell Tumors.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting interval from diagnosis to delivery in preeclampsia using electronic health records.Nature communications · 2025Article
- Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis.Briefings in bioinformatics · 2025Article
- Characterization of Non-Monotonic Relationships between Tumor Mutational Burden and Clinical Outcomes.Cancer research communications · 2024Article
- Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview.Journal of translational medicine · 2024Review
- Development of a deep learning model for predicting recurrence of hepatocellular carcinoma after liver transplantation.Frontiers in medicine · 2024Article
- Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data.Communications medicine · 2023Article
- Multimodal data fusion for cancer biomarker discovery with deep learning.Nature machine intelligence · 2023Article
- Multimodal deep learning to predict prognosis in adult and pediatric brain tumors.Communications medicine · 2023Article
- Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images.Frontiers in oncology · 2023Article
- CAMR: cross-aligned multimodal representation learning for cancer survival prediction.Bioinformatics (Oxford, England) · 2023Article
- Cox-nnet v2.0: improved neural-network-based survival prediction extended to large-scale EMR data.Bioinformatics (Oxford, England) · 2021Article
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7 authors.
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Abstract
Pathological images are easily accessible data with the potential of prognostic biomarkers. Moreover, integration of heterogeneous data types from multi-modality, such as pathological image and gene expression data, is invaluable to help predicting cancer patient survival. However, the analytical challenges are significant. Here, we take the hepatocellular carcinoma (HCC) pathological image features extracted by CellProfiler, and apply them as the input for Cox-nnet, a neural network-based prognosis prediction model. We compare this model with the conventional Cox proportional hazards (Cox-PH) model, CoxBoost, Random Survival Forests and DeepSurv, using
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