Chaddad, Ahmad, Desrosiers, Christian, Toews, Matthew et Abdulkarim, Bassam.
2017.
« Predicting survival time of lung cancer patients using radiomic analysis ».
Oncotarget, vol. 8, nº 61.
pp. 104393-104407.
Compte des citations dans Scopus : 61.
Prévisualisation |
PDF
Toews M 2017 16029 Predicting survival time of lung cancer.pdf - Version publiée Licence d'utilisation : Creative Commons CC BY. Télécharger (6MB) | Prévisualisation |
Résumé
Objectives: This study investigates the prediction of Non-small cell lung cancer (NSCLC) patient survival outcomes based on radiomic texture and shape features automatically extracted from tumor image data. Materials and Methods: Retrospective analysis involves CT scans of 315 NSCLC patients from The Cancer Imaging Archive (TCIA). A total of 24 image features are computed from labeled tumor volumes of patients within groups defined using NSCLC subtype and TNM staging information. Spearman’s rank correlation, Kaplan-Meier estimation and log-rank tests were used to identify features related to long/short NSCLC patient survival groups. Automatic random forest classification was used to predict patient survival group from multivariate feature data. Significance is assessed at P < 0.05 following Holm-Bonferroni correction for multiple comparisons. Results: Significant correlations between radiomic features and survival were observed for four clinical groups: (group, [absolute correlation range]): (large cell carcinoma (LCC) [0.35, 0.43]), (tumor size T2, [0.31, 0.39]), (non lymph node metastasis N0, [0.3, 0.33]), (TNM stage I, [0.39, 0.48]). Significant log-rank relationships between features and survival time were observed for three clinical groups: (group, hazard ratio): (LCC, 3.0), (LCC, 3.9), (T2, 2.5) and (stage I, 2.9). Automatic survival prediction performance (i.e. below/above median) is superior for combined radiomic features with age-TNM in comparison to standard TNM clinical staging information (clinical group, mean area-under-the-ROC-curve (AUC)): (LCC, 75.73%), (N0, 70.33%), (T2, 70.28%) and (TNM-I, 76.17%). Conclusion: Quantitative lung CT imaging features can be used as indicators of survival, in particular for patients with large-cell-carcinoma (LCC), primary-tumor-sizes (T2) and no lymph-node-metastasis (N0).
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Mots-clés libres: | Fonds d'auteur ÉTS, FAETS |
| Professeur: | Professeur Desrosiers, Christian Toews, Matthew |
| Affiliation: | Génie logiciel et des technologies de l'information, Génie de la production automatisée |
| Date de dépôt: | 08 déc. 2017 20:33 |
| Dernière modification: | 28 janv. 2020 16:31 |
| URI: | https://espace2.etsmtl.ca/id/eprint/16029 |
Actions (Authentification requise)
 |
Dernière vérification avant le dépôt |