Revealing tumor habitats from texture heterogeneity analysis for classification of lung cancer malignancy and aggressiveness

Cherezo, Dmitri (Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida, USA) ; Goldgof, Dmitri (Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida, USA) ; Hall, Lawrence (Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida, USA) ; Gillies, Robert (Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA) ; Schabath, Matthew (Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Depeursinge, Adrien (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis); Biomedical Imaging Group, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland)

We propose an approach for characterizing structural heterogeneity of lung cancer nodules using Computed Tomography Texture Analysis (CTTA). Measures of heterogeneity were used to test the hypothesis that heterogeneity can be used as predictor of nodule malignancy and patient survival. To do this, we use the National Lung Screening Trial (NLST) dataset to determine if heterogeneity can represent differences between nodules in lung cancer and nodules in non-lung cancer patients. 253 participants are in the training set and 207 participants in the test set. To discriminate cancerous from non-cancerous nodules at the time of diagnosis, a combination of heterogeneity and radiomic features were evaluated to produce the best area under receiver operating characteristic curve (AUROC) of 0.85 and accuracy 81.64%. Second, we tested the hypothesis that heterogeneity can predict patient survival. We analyzed 40 patients diagnosed with lung adenocarcinoma (20 short-term and 20 long-term survival patients) using a leave-one-out cross validation approach for performance evaluation. A combination of heterogeneity features and radiomic features produce an AUROC of 0.9 and an accuracy of 85% to discriminate long- and short-term survivors.


Type d'article:
scientifique
Faculté:
Economie et Services
Ecole:
HEG-VS
Institut:
Institut Informatique de gestion
Classification:
Informatique
Date:
2019-03
Pagination:
9 p.
Veröffentlicht in:
Scientific reports
Numérotation (vol. no.):
Mars 2019, vol. 9, article no. 4500
DOI:
ISSN:
2045-2322
Le document apparaît dans:



 Datensatz erzeugt am 2019-04-15, letzte Änderung am 2019-06-13

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