ArticleScientific reports2020
Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed, 57 citations in OpenAlex.
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Polycationic dendrimers synergizes with gefitinib to overcome EGFRDiscover oncology · 2026Article
- Review
- Non-invasive identification of TKI-resistant NSCLC: a multi-model AI approach for predicting EGFR/TP53 co-mutations.BMC pulmonary medicine · 2025Article
- Radiogenomics-based prediction ofZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Machine Learning-Based Radiomics Analysis for Identifying KRAS Mutations in Non-Small-Cell Lung Cancer from CT Images: Challenges, Insights and Implications.Life (Basel, Switzerland) · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- CT-based radiomics and deep learning to predict EGFR mutation status in lung adenocarcinoma.Frontiers in oncology · 2025Article
- Deep learning on brain metastasis for predicting EGFR genotype and EGFR-TKI therapy response in metastatic NSCLC: a multicenter study.Frontiers in bioengineering and biotechnology · 2025Article
- Lung cancer in patients who have never smoked - an emerging disease.Nature reviews. Clinical oncology · 2024Review
- Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC.European radiology · 2023Article
- Lung cancer associated with combustion particles and fine particulate matter (PMBiochemical pharmacology · 2023Review
- Article
- An Explainable Radiogenomic Framework to Predict Mutational Status of KRAS and EGFR in Lung Adenocarcinoma Patients.Bioengineering (Basel, Switzerland) · 2023Article
- Cancer Therapeutic siRNA Delivery and Imaging by Nitrogen- and Neodymium-Doped Graphene Quantum Dots.ACS biomaterials science & engineering · 2023Article
- Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study.Diagnostics (Basel, Switzerland) · 2023Article
- Radiomics for prediction of response to EGFR-TKI based on metastasis/brain parenchyma (M/BP)-interface.La Radiologia medica · 2022Article
- Development and externally validate MRI-based nomogram to assess EGFR and T790M mutations in patients with metastatic lung adenocarcinoma.European radiology · 2022Article
- Nasopharyngeal Carcinoma Progression: Accumulating Genomic Instability and Persistent Epstein-Barr Virus Infection.Current oncology (Toronto, Ont.) · 2022Review
- Multiple instance learning for lung pathophysiological findings detection using CT scans.Medical & biological engineering & computing · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
EGFR and KRAS are the most frequently mutated genes in lung cancer, being active research topics in targeted therapy. The biopsy is the traditional method to genetically characterise a tumour. However, it is a risky procedure, painful for the patient, and, occasionally, the tumour might be inaccessible. This work aims to study and debate the nature of the relationships between imaging phenotypes and lung cancer-related mutation status. Until now, the literature has failed to point to new research directions, mainly consisting of results-oriented works in a field where there is still not enough available data to train clinically viable models. We intend to open a discussion about critical points and to present new possibilities for future radiogenomics studies. We conducted high-dimensional data visualisation and developed classifiers, which allowed us to analyse the results for EGFR and KRAS biological markers according to different combinations of input features. We show that EGFR mutation status might be correlated to CT scans imaging phenotypes; however, the same does not seem to hold for KRAS mutation status. Also, the experiments suggest that the best way to approach this problem is by combining nodule-related features with features from other lung structures.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.