ReviewBiosensors2025
Deep Learning-Driven Multimodal Integration of miRNA and Radiomic for Lung Cancer Diagnosis.
Review in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Engineered small extracellular vesicles in hematologic malignancies: mechanisms, therapeutic strategies, and translational challenges.Clinical and experimental medicine · 2026Review
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Artificial neural network modeling and optimization of an electrochemical biosensor for plasma miR-155-based breast cancer detection.Scientific reports · 2026Article
- CAR-T therapy in non-small cell lung cancer: Clinical prospects, potential, and strategies for cardiotoxicity management.Translational oncology · 2026Article
- The clinical significance of circulating microRNAs as biomarkers in lung cancer diagnosis and prognosis.Discover oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Lung cancer remains one of the most common and deadly malignancies worldwide. Current diagnosis and staging primarily rely on biopsy techniques, which fail to comprehensively characterize the molecular profiles and tumor microenvironment. Current studies demonstrate the promising performance (AUC = 82%) of miRNA-based predictive models, but exclusive reliance on miRNA signatures is limited by incomplete capture of tumor heterogeneity. Integrating imaging and genomic data can further enhance model accuracy, with functional nanomaterials serving as core advanced biosensing platforms to bridge miRNA sensing and radiomic fusion. Consequently, integrating imaging and genomic data can further enhance model accuracy. Recent research employing DenseNet architecture for the multimodal fusion of miRNA and radiomic features achieved an AUC of 0.98 with 85.7% sensitivity. This review summarizes advances in miRNA biomarkers, deep learning-driven radiogenomics, and critical roles of functional nanomaterials in biosensing-enabled multimodal integration, along with challenges and future directions for clinical translation.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.