ReviewStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2020
Radiomics and deep learning in lung cancer.
Review in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 148 papers, 3 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
148 citing papers in PubMed, 3 syntheses or guidelines pooled it, 264 citations in OpenAlex.
- Current status, hotspots, and trends in cancer prevention, screening, diagnosis, treatment, and rehabilitation: A bibliometric analysis.Oncology research · 2025Pooled it
- Pooled it
- Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.European journal of nuclear medicine and molecular imaging · 2022Pooled it
- Trial
- Immediate post-microwave ablation computed tomography model may better predict short-term response of stage I non-small cell lung cancer.World journal of radiology · 2026Article
- Quantitative CT analysis in lung cancer research: a bibliometric analysis based on Web of Science [2005-2025].Journal of thoracic disease · 2026Article
- Comparison of radiomics handcrafted features and deep features inBMC cancer · 2026Article
- Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- CT-Derived Radiomic Features for the Non-Invasive Differentiation of Mediastinal Lymphadenopathy in Lung Cancer and Sarcoidosis.Biomedicines · 2026Article
- Artificial intelligence in lung cancer: a bibliometric analysis of emerging trends and research hotspots.Annals of medicine and surgery (2012) · 2026Article
- LungDxFormer: a transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification.Scientific reports · 2026Article
- Radiosensitivity Prediction of Tumor Patient Based on Deep Fusion of Pathological Images and Genomics.Bioengineering (Basel, Switzerland) · 2026Article
- Multi-Phasic CECT Peritumoral Radiomics Predict Treatment Response to Bevacizumab-Based Chemotherapy in RAS-Mutated Colorectal Liver Metastases.Bioengineering (Basel, Switzerland) · 2026Article
- Explainable AI for Predicting Mortality Risk in Metastatic Cancer: Retrospective Cohort Study Using the Memorial Sloan Kettering-Metastatic Dataset.JMIR cancer · 2026Article
- LungGANDetectAI: a GAN-augmented and attention-guided deep learning framework for accurate and explainable lung cancer detection.Scientific reports · 2026Article
- Multitask learning for early treatment response and survival prediction in lung cancer radiotherapy using sequential CBCT imaging.Frontiers in oncology · 2026Article
- Clinical applications of deep learning in distinguishing benign from malignant pulmonary nodules in computed tomography scans.Archives of medical science : AMS · 2026Article
- Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Non-invasive Prediction of Lung Cancer Histological Differentiation via Radiomics and Multi-binary Classification ModelsCurrent medical imaging · 2026Article
- Assessing clinician performance using a multi-modality clinical decision-support system for lung cancer prognostication.Scientific reports · 2025Article
88 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.
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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.