ArticleTranslational lung cancer research2021
MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer.
Article in Translational lung cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 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
- A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects.Journal of medical radiation sciences · 2024Pooled it
- Radiomics for risk stratification in brain metastases: potential in melanoma brain metastases.Clinical & experimental metastasis · 2026Article
- Predicting the progressive-free survival of ALK-positive lung cancer: machine learning algorithm based on habitat imaging.Journal of thoracic disease · 2025Article
- Pan-immune-inflammation value as an independent prognostic marker in patients with brain metastases.Frontiers in oncology · 2025Article
- Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy.Neuro-oncology · 2024Article
- Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases.Clinical & experimental metastasis · 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
- Enhancing intracranial efficacy prediction of osimertinib in non-small cell lung cancer: a novel approach through brain MRI radiomics.Frontiers in neurology · 2024Article
- CT-based nomogram for early identification of T790M resistance in metastatic non-small cell lung cancer before first-line epidermal growth factor receptor-tyrosine kinase inhibitors therapy.European radiology experimental · 2023Article
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- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Correlation between PD-L1 expression and radiomic features in early-stage lung adenocarcinomas manifesting as ground-glass nodules.Frontiers in oncology · 2022Article
- Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.Frontiers in oncology · 2022Review
- Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation.Korean journal of radiology · 2022Article
- Clinical Biomarkers for Early Identification of Patients with Intracranial Metastatic Disease.Cancers · 2021Review
- Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients.Radiation oncology (London, England) · 2021Article
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Authors and funding
14 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntracranial progression is considered an important cause of treatment failure in anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) patients. Recent advances in targeted therapy and radiomics have generated considerable interest for the exploration of prognostic imaging biomarkers to predict the clinical course. Here, we developed a magnetic resonance imaging (MRI) radiomic signature that can stratify survival and intracranial progression.
methodsWe analyzed 87 brain metastatic lesions in 24 ALK-positive NSCLC patients undergoing ALK-inhibitor ensartinib therapy and divided them into training (n=61) and validation (n=26) sets. Radiomic features were extracted and screened from contrast-enhanced MR images. Combined with these selected features, the Rad-score was calculated with multivariate logistic regression. The predictive model and Rad-score performance were assessed in the training set and validated in the validation set; decision curve analysis was performed with the combined training and validation sets to estimate Rad-score's patient-stratification ability.
resultsThe prediction model constructed with nine selected radiomic features could predict intracranial progression within 51 weeks (AUC =0.84 and 0.85 in the training and validation sets, respectively), while clinical and regular MRI characteristics were independent of progression (P>0.05). The decision-curve analysis showed that the radiomic prediction model was clinically useful. The Kaplan-Meier analysis showed that the progression-free survival (PFS) difference between the high- and low-risk groups distinguished by the Rad-score was significant (P=0.017).
conclusionsRadiomics may provide prognostic information and improve pretreatment risk stratification in ALK-positive NSCLC patients with brain metastases undergoing ensartinib treatment, allowing follow-up and treatment to be tailored to the patient's individual risk profile.
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