Trial reportPloS one2014
Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic.
Trial report in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis 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
66 citing papers in PubMed, 1 synthesis or guideline pooled it, 118 citations in OpenAlex.
- Clinical applications of textural analysis in non-small cell lung cancer.The British journal of radiology · 2018Pooled it
- Pembrolizumab ± paricalcitol in metastatic pancreatic cancer postmaximal cytoreduction.The oncologist · 2025Trial
- Limits of radiomic-based entropy as a surrogate of tumor heterogeneity: ROI-area, acquisition protocol and tissue site exert substantial influence.Scientific reports · 2017Trial
- Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?Medical physics · 2015Trial
- Comparing subjective and objective sonographic heterogeneity in predicting long-term testicular non-viability in cases of testicular torsion.Singapore medical journal · 2026Article
- Radiomics-based explainable artificial intelligence to predict treatment response following lung stereotactic body radiation therapy.The British journal of radiology · 2025Article
- A narrative review of imaging intratumor heterogeneity in non-small cell lung cancer: current advances.Translational lung cancer research · 2025Review
- Article
- PredictingTranslational lung cancer research · 2024Article
- Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature.Biomedical engineering online · 2023Article
- Article
- Review of the use of radiomics to assess the risk of recurrence in early-stage non-small cell lung cancer.Translational lung cancer research · 2023Review
- CT-based radiomics prediction of complete response after stereotactic body radiation therapy for patients with lung metastases.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2023Article
- Statistical Analysis on Impact of Image Preprocessing of CT Texture Patterns and Its CT Radiomic Feature Stability: A Phantom Study.Asian Pacific journal of cancer prevention : APJCP · 2023Article
- Can Imaging Using Radiomics and Fat Fraction Analysis Detect Early Tissue Changes on Historical CT Scans in the Regions of the Pancreas Gland That Subsequently Develop Adenocarcinoma?Diagnostics (Basel, Switzerland) · 2023Article
- Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry.Respirology (Carlton, Vic.) · 2022Review
- Radiomics Signature to Predict Prognosis in Early-Stage Lung Adenocarcinoma (≤3 cm) Patients with No Lymph Node Metastasis.Diagnostics (Basel, Switzerland) · 2022Article
- Computed Tomography Texture Analysis for Predicting Clinical Outcomes in Patients With Metastatic Renal Cell Carcinoma Treated With Immune Checkpoint Inhibitors.The oncologist · 2022Article
- Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results.Cancers · 2022Review
- MRI-based radiomics model can improve the predictive performance of postlaminar optic nerve invasion in retinoblastoma.The British journal of radiology · 2022Article
6 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
7 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundNon-invasive characterization of a tumor's molecular features could enhance treatment management. Quantitative computed tomography (CT) based texture analysis (QTA) has been used to derive tumor heterogeneity information, and the appearance of the tumors has been shown to relate to patient outcome in non-small cell lung cancer (NSCLC) and other cancers. In this study, we examined the potential of tumoral QTA to differentiate K-ras mutant from pan-wildtype tumors and its prognostic potential using baseline pre-treatment non-contrast CT imaging in NSCLC.
methodsTumor DNA from patients with early-stage NSCLC was analyzed on the LungCarta Panel. Cases with a K-ras mutation or pan-wildtype for 26 oncogenes and tumor suppressor genes were selected for QTA. QTA was applied to regions of interest in the primary tumor. Non-parametric Mann Whitney test assessed the ability of the QTA, clinical and patient characteristics to differentiate between K-ras mutation from pan-wildtype. A recursive decision tree was developed to determine whether the differentiation of K-ras mutant from pan-wildtype tumors could be improved by sequential application of QTA parameters. Kaplan-Meier survival analysis assessed the ability of these markers to predict survival.
resultsQTA was applied to 48 cases identified, 27 had a K-ras mutation and 21 cases were pan-wildtype. Positive skewness and lower kurtosis were significantly associated with the presence of a K-ras mutation. A five node decision tree had sensitivity, specificity, and accuracy values (95% CI) of 96.3% (78.1-100), 81.0% (50.5-97.4), and 89.6% (72.9-97.0); respectively. Kurtosis was a significant predictor of OS and DFS, with a lower kurtosis value linked with poorer survival.
conclusionsLower kurtosis and positive skewness are significantly associated with K-ras mutations. A QTA feature such as kurtosis is prognostic for OS and DFS. Non-invasive QTA can differentiate the presence of K-ras mutation from pan-wildtype NSCLC and is associated with patient survival.
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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.