ArticleCommunications biology2021
Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06029777 (Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients), which is not on this map. Cited by 27 papers.
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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.
Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients
Who cites it
27 citing papers in PubMed.
- Optimizing surgical decision-making for lung adenocarcinoma by preoperatively identifying pathological high-risk factors: retrospective development and prospective validation of a deep learning model.International journal of surgery (London, England) · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- An explainable imaging-clinical biomarker for non-small cell lung cancer prognostication based on normalised hotspot to centroid distance and [European journal of nuclear medicine and molecular imaging · 2026Article
- NHOC/NHOP as novel biomarkers for predicting lymph node metastasis in NSCLC using PET/CT radiomics and machine learning: a two-center retrospective study.European journal of nuclear medicine and molecular imaging · 2026Article
- A Comprehensive Review on the Application of Artificial Intelligence for Predicting Postsurgical Recurrence Risk in Early-Stage Non-Small Cell Lung Cancer Using Computed Tomography, Positron Emission Tomography, and Clinical Data.Journal of medical radiation sciences · 2025Review
- A Radiogenomic Deep Ensemble Learning Model for Identifying Radionecrosis Following Brain Metastases (BM) Stereotactic Radiosurgery in Patients With Non-small Cell Lung Cancer BM.Advances in radiation oncology · 2025Article
- Reproducibility and interpretability in radiomics: a critical assessmentDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- A novel approach to overcome black box of AI for optical diagnosis in colonoscopy.Scientific reports · 2025Article
- Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.Medical physics · 2025Article
- Explainable graph neural network based on metabolic brain imaging for differential diagnosis of parkinsonism.Frontiers in aging neuroscience · 2025Article
- Survival prediction in diffuse large B-cell lymphoma patients: multimodal PET/CT deep features radiomic model utilizing automated machine learning.Journal of cancer research and clinical oncology · 2024Article
- Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.International journal of radiation oncology, biology, physics · 2024Article
- Cardiac ultrasomics for acute myocardial infarction risk stratification and prediction of all-cause mortality: a feasibility study.Echo research and practice · 2024Article
- Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism.Brain sciences · 2024Article
- A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis.Diagnostics (Basel, Switzerland) · 2024Article
- Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging.medRxiv : the preprint server for health sciences · 2023Article
- Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis.Journal of thoracic disease · 2023Article
- An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT.NPJ digital medicine · 2023Article
- ASO Author Reflections: Role of the Computed Tomography Maximum in Pancreatic Neuroendocrine Tumors.Annals of surgical oncology · 2023Article
- Review
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Authors and funding
7 authors.
Funding
Abstract
Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller sample size requirements and enhanced interpretability. Baseline radiomics and DL models for the prognosis of lung adenocarcinomas were developed and tested using local (n = 617) cohort. The DL models were further tested in an external validation (n = 70) cohort. The local cohort was divided into training and test cohorts. A radiomics risk score (RRS) was developed using Cox-LASSO. Three pretrained DL networks derived from natural images were used to extract the DL features. The features were further guided using radiomics by retaining those DL features whose correlations with the radiomics features were high and Bonferroni-corrected p-values were low. The retained DL features were subject to a Cox-LASSO when constructing DL risk scores (DRS). The risk groups stratified by the RRS and DRS showed a significant difference in training, testing, and validation cohorts. The DL features were interpreted using existing radiomics features, and the texture features explained the DL features well.
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Registered trials
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