ArticleRadiologia brasileira
Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity.
Article in Radiologia brasileira. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage.Scientific reports · 2023Article
- Novel Chest Radiographic Biomarkers for COVID-19 Using Radiomic Features Associated with Diagnostics and Outcomes.Journal of digital imaging · 2021Article
- The role of quantitative imaging in chronic obstructive pulmonary disease.Radiologia brasileiraArticle
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Authors and funding
8 authors.
Funding
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Abstract
objectiveTo determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients. MATERIALS AND
methodsThis was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables.
resultsOf the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (
conclusionA radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.
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