(Tuesday, 7/16/2019) 4:30 PM - 6:00 PM
Room: 304ABC
Purpose: To perform deep and unsupervised characteristic analysis of radiomics features extracted from primary tumors of pre-treatment contrast-enhanced computed-tomography (CE-CT) images of patients with oropharyngeal cancers to construct an unsupervised classifier to stratify patients on the basis of Human-Papilloma-Virus (HPV) status.
Methods: One-hundred-eighty-seven patients with oropharyngeal cancers with known HPV-status (confirmed by immunohistochemistry-P16-protein testing) were retrospectively studied. Radiomics features (n=172) were extracted from CE-CT images of gross-tumor-volume. Levene and Kolmogorov-Smirnov’s tests with absolute-biserial-correlation were used to identify discriminant features between HPV+ and HPV- groups. Given the discriminant features, an optimal Kohonen-Self-Organizing-Map (K-SOM; optimal-topology: [9×9]-neurons) was constructed using an unsupervised-competitive-learning algorithm. The dataset was split (100-iterations) into training and test (67% versus 33% respectively) cohorts using a bootstrapping-permutation-sampling technique. In each iteration, the K-SOM was trained and evaluated by the training and test cohorts respectively. K-SOM neighbor-weight-distance and their corresponding hit/(winner-frequency) maps were computed for 100-iterations. Regions pertaining to the dominant characteristics of the discriminant features were identified in the K-SOM space and used to calculate stratification power of the classifier. A correlation matrix was aclculated for the dominant regions of the K-SOM weight maps (12 maps) and used for further feature characteristic interpretation.
Results: Three feature categories were found to be discriminant between the two groups: Discrete-Orthonormal-Stockwell-Transform (DOST), Gray-Level-Co-Occurrence-Matrix (GLCM), and Morphology. The K-SOM revealed four dominant regions associated with the HPV statuses: high-certainty-HPV-, low-certainty-HPV-, low-certainty-HPV+, and high-certainty-HPV+. Average Area-Under-Receiver-Operating-Characteristic of the optimal K-SOM was 0.88±0.06.
Conclusion: This study presents an unsupervised-intuitive deep radiomics feature analysis that provides a data-driven feature characterization. It not only offers high performance HPV-based patient stratification, but also reveals a full-spectrum of feature gains, variations, similarities, and dissimilarities which are key parameters in building different classifiers for HPV-based stratification of patients with oropharyngeal cancer.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by a grant from Varian Medical Systems, Palo Alto, CA.
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