Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer.
Aim: To assess the prognostic value of 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)-based radiomics using a machine learning approach in patients with non-small cell lung cancer (NSCLC).Materials and Methods: Ninety-three patients with stage I-III NSCLC who underwent com...
| Publicado en: | Clinical Radiology Vol. 74; no. 6; pp. 467 - 474 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136152332&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136152332 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00099260 23Q jtl: Clinical Radiology issn: 00099260 maglogo: N pubinfo: dt: Jun2019 vid: 74 iid: 6 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 136152332 136152332 NLM30898382 10.1016/j.crad.2019.02.008 NLM30898382 136152332 ppf: 467 ppct: 7 formats: tig: atl: Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer. aug: au: Ahn, H.K. Lee, H. Kim, S.G. Hyun, S.H. affil: Division of Hematology and Oncology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea sug: subj: Image Interpretation, Computer Assisted Methods Tomography, Emission-Computed Methods Lung Neoplasms Fludeoxyglucose F 18 Carcinoma, Non-Small-Cell Lung Lung Predictive Value of Tests Radiopharmaceuticals Survival Analysis Middle Age Reproducibility of Results Female Lung Neoplasms Mortality Carcinoma, Non-Small-Cell Lung Mortality Male Scales Middle Aged: 45-64 years Female Male ab: Aim: To assess the prognostic value of 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)-based radiomics using a machine learning approach in patients with non-small cell lung cancer (NSCLC).Materials and Methods: Ninety-three patients with stage I-III NSCLC who underwent combined PET/computed tomography (CT) followed by curative resection. A total of 35 unique quantitative radiomic features was extracted from the PET images, which included imaging phenotypes such as pixel intensity, shape, and texture. Radiomic features were ranked based on score according to their correlation with disease recurrence status within a 3-year follow-up. The recurrence risk classification performances of machine learning algorithms (random forest, neural network, naive Bayes, logistic regression, and support vector machine) using the 20 best-ranked features were compared using the areas under the receiver operating characteristic curve (AUC) and validated by the random sampling method.Results: Contrast and busyness texture features from neighbourhood grey-level difference matrix were found to be the two best predictors of disease recurrence. The random forest model obtained the best performance (AUC: 0.956, accuracy: 0.901, F1 score: 0.872, precision: 0.905, recall: 0.842), followed by the neural network model (AUC: 0.871, accuracy: 0.780, F1 score: 0.708, precision: 0.755, recall: 0.666).Conclusion: A PET-based radiomic model was developed and validated for risk classification in NSCLC. The machine learning approach with random forest classifier exhibited good performance in predicting the recurrence risk. Radiomic features may help clinicians to improve the risk stratification for clinical practice. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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