ArticleFrontiers in oncology2021
Machine Learning-Based CT Radiomics Analysis for Prognostic Prediction in Metastatic Non-Small Cell Lung Cancer Patients With
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Machine learning models for predicting response to epidermal growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Pooled it
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.Journal of thoracic disease · 2025Article
- Predicting the prognosis of patients with lung adenocarcinoma treated with third-generation EGFR-TKI alone using nomograms based on CT radiomic and clinicopathological factors.Translational lung cancer research · 2025Article
- Joint model based on intratumoral and peritumoral computed tomography radiomics integrated with clinical features for predicting the spread through air spaces in lung adenocarcinoma: a multicenter study.Quantitative imaging in medicine and surgery · 2025Article
- Progression-Free Survival Prediction Model Based on AI-Enhanced Dynamic Radiomics for Personalized EGFR-TKI Treatment Monitoring Patients With Lung Adenocarcinoma.Thoracic cancer · 2025Article
- Evaluating the combined efficacy of Telisotuzumab Vedotin and artificial intelligence in the treatment of non-squamous non-small cell lung cancer: a narrative review focusing on pharmaceutical and technical insights.Frontiers in oncology · 2025Review
- Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC.Scientific reports · 2024Article
- Radiomics of metastatic brain tumor as a predictive image biomarker of progression-free survival in patients with non-small-cell lung cancer with brain metastasis receiving tyrosine kinase inhibitors.Translational oncology · 2024Article
- Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis.Frontiers in pharmacology · 2024Article
- Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma.La Radiologia medica · 2023Article
- Article
- Application value of CT radiomic nomogram in predicting T790M mutation of lung adenocarcinoma.BMC pulmonary medicine · 2023Article
- Pretherapy investigations using highly robust visualized biomarkers from CT imaging by multiple machine-learning techniques toward its prognosis prediction for ALK-inhibitor therapy in NSCLC: a feasibility study.Journal of cancer research and clinical oncology · 2023Article
- Review
- Development and External Validation of a PET Radiomic Model for Prognostication of Head and Neck Cancer.Cancers · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Prognostic and predictive value of radiomic signature in stage I lung adenocarcinomas following complete lobectomy.Journal of translational medicine · 2022Article
- Can CT Radiomics Detect Acquired T790M Mutation and Predict Prognosis in Advanced Lung Adenocarcinoma With Progression After First- or Second-Generation EGFR TKIs?Frontiers in oncology · 2022Article
Corrections and comments
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7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeAs a third-generation
methodsContrast-enhanced and unenhanced chest CT images before osimertinib treatment were collected from 201 and 273 metastatic NSCLC patients with
resultsDisease progression occurred in 174/273 (63.7%) cases. CT morphological features had no ability in predicting patients' prognosis in osimertinib treatment. Univariate COX regression followed by LASSO regression analyses identified 23 and 6 radiomic features from the contrast-enhanced and unenhanced CT with prognostic value, respectively. The 23 contrast-enhanced radiomic features were further used to construct radiomic models using different machine learning strategies. Radiomic model built by SR exhibited superior predictive accuracy than RF, SVR or LASSO model (mean C-index of the 5-fold CV: 0.660
conclusionsRadiomic features extracted from the contrast-enhanced chest CT could be used to evaluate metastatic NSCLC patients' prognosis in osimertinib treatment. Prognostic models combing both radiomic features and clinical factors had a great performance in predicting patients' outcomes.
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