ArticleNPJ digital medicine2025
Deep multimodal state-space fusion of endoscopic-radiomic and clinical data for survival prediction in colorectal cancer.
Article in NPJ digital medicine, 2025. 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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Who cites it
7 citing papers in PubMed.
- Multi-regional radiomics based on planning CT and information complementarity between GTVp and GTVn: an explainable model for predicting 5-year recurrence risk in HPV-positive oropharyngeal cancer.Radiation oncology (London, England) · 2026Article
- A lightweight improved YOLOv11 framework for intracranial hemorrhage detection.Scientific reports · 2026Article
- Reply to: Clarifying multimodal inputs and attribution in colorectal cancer survival prediction.NPJ digital medicine · 2026Article
- Advancing early detection of chromophobe renal cell carcinoma: a Bayesian optimization approach to machine learning models.Scientific reports · 2026Article
- Neoadjuvant PD-1 inhibitor combined with FLOT versus SOX for locally advanced gastric cancer: a retrospective cohort study.Frontiers in immunology · 2026Article
- A multi-dimensional omics framework identifies GPR35 as a driver of M2 macrophage activation and poor prognosis in colorectal cancer.Frontiers in immunology · 2026Article
- The role of R-loop aberrations in lower-grade gliomas: prognostic, immune, and metabolic implications from multi-omics and machine learning analysis.Frontiers in immunology · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
Integrating complementary surface and cross sectional cues is central to preoperative assessment of colorectal cancer, but technically challenging because endoscopic images and pelvic CT encode anatomy at different scales. Here we present HydraMamba, a multimodal selective state space framework that fuses endoscopy and CT for joint lesion segmentation, lesion detection, and survival prediction. The model couples a shared state space backbone with two lightweight modules. Across the endoscopic dataset and the CT dataset, HydraMamba achieved state-of-the-art lesion analysis (endoscopy: Dice 0.856, F1 0.918; CT: Dice 0.812, F1 0.888) and delivered calibrated survival modeling on the CT dataset (Harrell's C index 0.832, Uno's C@1y 0.853, integrated Brier score 0.161, calibration slope ≈1.01). By unifying endoscopic and CT information in a single coherent architecture, HydraMamba provides an accurate and well-calibrated foundation for lesion analysis and prognostication in colorectal cancer.
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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.