Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study.

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Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study.

Br J Radiol. 2018 Feb 16;:20170498

Authors: Rth L, M B, A J, Fjp H, Fwr F, Sh H, B C, Jn W, B O, D R, Cr L, Rh B, O R, S TL, M G, K I, P L

Abstract
OBJECTIVES: HPV positive oropharyngeal cancer (OPSCC) is biologically and clinically different from HPV negative OPSCC. Here, we evaluate the use of a radiomic approach to identify the HPV status of OPSCC.
METHODS: Four independent cohorts, totaling 778 OPSCC patients with HPV determined by p16 were collected. We randomly assigned 80% of all data for model training (N=628) and 20% for validation (N=150). On the pre-treatment CT images, 902 radiomic features were calculated from the gross tumor volume (GTV). Multivariable modeling was performed using least absolute shrinkage and selection operator (LASSO). To assess the impact of CT artifacts in predicting HPV (p16), a model was developed on all training data (Mall) and on the artifact-free subset of training data (Mno art). Models were validated on all validation data (Vall), and the subgroups with (Vart) and without (Vno art) artifacts. Kaplan-Meier survival analysis was performed to compare HPV status based on p16 and radiomic model predictions.
RESULTS: The area under the receiver operator curve (AUC) for Mall and Mno art ranged between 0.70-0.80 and was not significantly different for all validation datasets. There was a consistent and significant split between survival curves with HPV status determined by p16 (p=0.007; HR: 0.46), Mall (p=0.036; HR: 0.55) and Mno art (p=0.027; HR: 0.49).
CONCLUSION: This study provides proof of concept that molecular information can be derived from standard medical images and shows potential for radiomics as imaging biomarker of HPV status. Advances in knowledge: Radiomics has the potential to identify clinically relevant molecular phenotypes.

PMID: 29451412 [PubMed - as supplied by publisher]

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