ArticleLa Radiologia medica2021
CT texture analysis for prediction of EGFR mutational status and ALK rearrangement in patients with non-small cell lung cancer.
Article in La Radiologia medica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
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Who cites it
50 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
- Systematic Review, Meta-Analysis and Radiomics Quality Score Assessment of CT Radiomics-Based Models Predicting Tumor EGFR Mutation Status in Patients with Non-Small-Cell Lung Cancer.International journal of molecular sciences · 2023Pooled it
- A narrative review of imaging intratumor heterogeneity in non-small cell lung cancer: current advances.Translational lung cancer research · 2025Review
- Radiomic Analysis and Liquid Biopsy in Preoperative CT of NSCLC: An Explorative Experience.Thoracic cancer · 2025Article
- An Enhanced CT-based Radiomics Model for Predicting the Anaplastic Lymphoma Kinase Mutation Status in Lung Adenocarcinoma.Current medical imaging · 2025Article
- Radiomic features and tumor immune microenvironment associated with anaplastic lymphoma kinase-rearranged lung adenocarcinoma and their prognostic value.Frontiers in genetics · 2025Article
- Multiomics-Based Deep Learning Prediction of Prognosis and Therapeutic Response in Patients With Extensive-Stage Small Cell Lung Cancer Receiving Chemoimmunotherapy: A Retrospective Cohort Study.International journal of general medicine · 2025Article
- Diagnostic precision in thyroid-associated ophthalmopathy using multi-center radiomics withScientific reports · 2024Article
- Three-dimensional iodine mapping quantified by dual-energy CT for predicting programmed death-ligand 1 expression in invasive pulmonary adenocarcinoma.Scientific reports · 2024Article
- PredictingTranslational lung cancer research · 2024Article
- An overview on multimodal imaging for the diagnostic workup of pleural mesothelioma.Japanese journal of radiology · 2024Review
- Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- Radiomics in gastrointestinal stromal tumours: an up-to-date review.Japanese journal of radiology · 2023Review
- Radiogenomics: a key component of precision cancer medicine.British journal of cancer · 2023Review
- Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool.La Radiologia medica · 2023Article
- Peritoneal Carcinosis: What the Radiologist Needs to Know.Diagnostics (Basel, Switzerland) · 2023Review
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- A Deep Learning Radiomics Nomogram to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Cervical Cancer: A Two-Center Study.Diagnostics (Basel, Switzerland) · 2023Article
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Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop a CT texture-based model able to predict epidermal growth factor receptor (EGFR)-mutated, anaplastic lymphoma kinase (ALK)-rearranged lung adenocarcinomas and distinguish them from wild-type tumors on pre-treatment CT scans. MATERIALS AND
methodsTexture analysis was performed using proprietary software TexRAD (TexRAD Ltd, Cambridge, UK) on pre-treatment contrast-enhanced CT scans of 84 patients with metastatic primary lung adenocarcinoma. Textural features were quantified using the filtration-histogram approach with different spatial scale filters on a single 5-mm-thick central slice considered representative of the whole tumor. In order to deal with class imbalance regarding mutational status percentages in our population, the dataset was optimized using the synthetic minority over-sampling technique (SMOTE) and correlations with textural features were investigated using a generalized boosted regression model (GBM) with a nested cross-validation approach (performance averaged over 1000 resampling episodes).
resultsALK rearrangements, EGFR mutations and wild-type tumors were observed in 19, 28 and 37 patients, respectively, in the original dataset. The balanced dataset was composed of 171 observations. Among the 29 original texture variables, 17 were employed for model building. Skewness on unfiltered images and on fine texture was the most important features. EGFR-mutated tumors showed the highest skewness while ALK-rearranged tumors had the lowest values with wild-type tumors showing intermediate values. The average accuracy of the model calculated on the independent nested validation set was 81.76% (95% CI 81.45-82.06).
conclusionTexture analysis, in particular skewness values, could be promising for noninvasive characterization of lung adenocarcinoma with respect to EGFR and ALK mutations.
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