ArticleQuantitative imaging in medicine and surgery2024
Fusion of shallow and deep features from
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.European radiology · 2026Pooled it
- Radiomics for prediction of perineural invasion in colorectal cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Radiogenomics: transforming lung cancer care through non-invasive imaging and genomic integration.Medical oncology (Northwood, London, England) · 2025Review
- Aluminum-derived nanotubes for lung cancer detection: a DFT inquisition.Scientific reports · 2025Article
- An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images.Scientific reports · 2025Article
- Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.Scientific reports · 2025Article
- Tumor microenvironment: recent advances in understanding and its role in modulating cancer therapies.Medical oncology (Northwood, London, England) · 2025Review
- Exosome-based miRNA delivery: Transforming cancer treatment with mesenchymal stem cells.Regenerative therapy · 2025Review
- A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [Diagnostics (Basel, Switzerland) · 2025Article
- On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling.Scientific reports · 2025Article
- Article
- Aptamers in Combination Therapies for Enhanced Radiosensitization in Cancer.Iranian journal of biotechnology · 2025Review
- Liquid Biopsy for Medical Imaging Analysis in Cancer Diagnosis.Current pharmaceutical design · 2025Review
- Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks.PeerJ. Computer science · 2025Article
- A comprehensive review on the cellular mechanism of traditional Chinese medicine in the treatment of pediatric lung diseases.BioImpacts : BI · 2025Review
- Article
- Exploring the critical role of SDHA in breast cancer proliferation: implications for novel therapeutic strategies.American journal of translational research · 2025Article
- Explainable 1-mm Peritumoral CT Radiomics forCancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor-sensitizing (EGFR-sensitizing) mutations exhibit a positive response to tyrosine kinase inhibitors (TKIs). Given the limitations of current clinical predictive methods, it is critical to explore radiomics-based approaches. In this study, we leveraged deep-learning technology with multimodal radiomics data to more accurately predict EGFR-sensitizing mutations. Methods: A total of 202 patients who underwent both flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography ( Results: In the classification of EGFR-sensitive mutations, the areas under the curve (AUCs) of ResNet-based deep-shallow features and only shallow features from different multidata were as follows: RES_TRAD, PET/CT Conclusions: Our findings suggest that deep features significantly enhance the detection of EGFR-sensitizing mutations, especially those extracted with ResNet. Moreover, PET/CT images are more effective than CT-only and PET-only images in producing EGFR-sensitizing mutation-related signatures.
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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.