Habitat Evolution Imaging Biomarkers to Assess Early Response and Predict Treatment Outcomes in Oropharyngeal Cancer

Abstract

The incidence of oropharyngeal cancer (OPC) has been rapidly increasing in recent years. There is an unmet need for reliable prognostic biomarkers in OPC. This work aims to develop an imaging signature to assess early response and predict outcomes of OPC based on serial FDG-PET and CT imaging analysis. We propose a novel spatiotemporal habitat evolution-based method to analyze the complex response patterns. First, we used a robust two-stage clustering approach to partition the primary tumor and involved lymph nodes into spatially distinct subregions (i.e., habitats) based on the local image intensity and entropy maps. Second, we proposed 27 quantitative image features to characterize the serial volumetric change of the habitats and peritumor/nodal parenchyma at both regional and single-voxel levels between baseline and mid-treatment imaging. We then evaluated the reproducibility of the proposed features with respect to multiple delineations and image registration and removed redundant features with linear correlation above 0.95. Given the remaining features, we developed an imaging signature to predict progression-free survival (PFS) by fitting an L1-regularized Cox regression model in a training cohort of 81 patients treated with concurrent radiation and chemotherapy. Finally, we tested the imaging signature in an independent validation cohort of 81 patients with similar clinical features and treatment. We identified 3 phenotypically distinct subregions based on PET and CT imaging, which were metabolically active and heterogeneous (habitat 1), enhancing and heterogeneous (habitat 2), metabolically inactive and homogeneous (habitat 3). All 3 habitats showed high concordance across the two cohorts with in-group proportion ranging from 0.97 to 0.99. After removing redundant and less stable features (intra-class coefficient < 0.75), 21 features remained. The final Cox model consisted of 3 habitat evolution-based image features. This imaging signature predicted 2-year PFS with a reasonably good accuracy, AUC: 0.74, 95% CI: 0.69-0.78 (training) and 0.70, 95% CI: 0.65-0.74 (validation), and significantly outperformed traditional imaging response metrics such as tumor volume, SUVmax and MTV (AUC: 0.52-0.58). When tested in the validation cohort, the imaging signature stratified patients into high vs low-risk groups with distinct 2-year PFS (64% vs 90%; P = 0.003). On multivariable analysis, the imaging signature was independently associated with PFS (hazard ratio: 5.2, 95% CI: 1.6-17.5, P = 0.008) adjusting for clinical and pathologic factors including stage, HPV status, and smoking history. The proposed habitat evolution-based imaging signature allows reliable assessment of early response and accurate prediction of outcomes in OPC. If validated, it can help identify patients who may benefit from de-intensification therapy or adjuvant immunotherapy.