ArticleTherapeutic advances in medical oncology2022
Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC.
Article in Therapeutic advances in medical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Early predicting brain metastases of EGFR positive lung adenocarcinoma patients by CT radiomics.Physical and engineering sciences in medicine · 2026Article
- Development and validation of a dynamic nomogram for predicting brain metastasis in stage III NSCLC patients undergoing definitive chemoradiotherapy.BMC cancer · 2025Article
- A cuproptosis score model and prognostic score model can evaluate clinical characteristics and immune microenvironment in NSCLC.Cancer cell international · 2024Article
- Risk factors for brain metastases in locally advanced non-small cell lung cancer patients treated with radical radiotherapy.Journal of thoracic disease · 2024Article
- Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.NPJ precision oncology · 2023Review
- Article
- Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology.Biomedicines · 2022Article
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21 authors.
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Abstract
Introduction: Despite radical intent therapy for patients with stage III non-small-cell lung cancer (NSCLC), cumulative incidence of brain metastases (BM) reaches 30%. Current risk stratification methods fail to accurately identify these patients. As radiomics features have been shown to have predictive value, this study aims to develop a model combining clinical risk factors with radiomics features for BM development in patients with radically treated stage III NSCLC. Methods: Retrospective analysis of two prospective multicentre studies. Inclusion criteria: adequately staged [ Results: In total, 219 patients were eligible for analysis. Median FU was 59.4 months for the training cohort and 67.3 months for the validation cohort; 21 (15%) and 17 (22%) patients developed BM in the training and validation cohort, respectively. Two relevant clinical features (age and adenocarcinoma histology) and four relevant radiomics features were identified as predictive. The clinical model yielded the highest AUC value of 0.71 (95% CI: 0.58-0.84), better than radiomics or a combination of clinical parameters and radiomics (both an AUC of 0.62, 95% CIs of 0.47-076 and 0.48-0.76, respectively). Conclusion: CT-based radiomics features of primary NSCLC in the current setup could not improve on a model based on clinical predictors (age and adenocarcinoma histology) of BM development in radically treated stage III NSCLC patients.
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