Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer

Abstract

The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rapidly increasing. Disease stage and smoking history are often used in current clinical trials to select patients for deintensification therapy, but these features lack sufficient accuracy for predicting disease relapse. Our purpose was to develop an imaging signature to assess early response and predict outcomes of OPSCC. Methods: We retrospectively analyzed 162 OPSCC patients treated with concurrent chemoradiotherapy, equally divided into separate training and validation cohorts with similar clinical characteristics. A robust consensus clustering approach was used to spatially partition the primary tumor and involved lymph nodes into subregions (i.e., habitats) based on 18 F-FDG PET and contrast CT imaging. We proposed quantitative image features to characterize the temporal volumetric change of the habitats and peritumoral/nodal tissue between baseline and midtreatment. The reproducibility of these features was evaluated. We developed an imaging signature to predict progression-free survival (PFS) by fitting an L1-regularized Cox regression model. Results: We identified 3 phenotypically distinct intratumoral habitats: metabolically active and heterogeneous, enhancing and heterogeneous, and metabolically inactive and homogeneous. The final Cox model consisted of 4 habitat evolution-based features. In both cohorts, this imaging signature significantly outperformed traditional imaging metrics, including midtreatment metabolic tumor volume for predicting PFS, with a C-index of 0.72 versus 0.67 (training) and 0.66 versus 0.56 (validation). The imaging signature stratified patients into high-risk versus low-risk groups with 2-y PFS rates of 59.1% versus 89.4% (hazard ratio, 4.4; 95% confidence interval, 1.4-13.4 [training]) and 61.4% versus 87.8% (hazard ratio, 4.6; 95% confidence interval, 1.7-12.1 [validation]). The imaging signature remained an independent predictor of PFS in multivariable analysis adjusting for stage, human papillomavirus status, and smoking history. Conclusion: The proposed imaging signature allows more accurate prediction of disease progression and, if prospectively validated, may refine OPSCC patient selection for risk-adaptive therapy.

Post-treatment FDG PET-CT in head and neck carcinoma: comparative analysis of 4 qualitative interpretative criteria in a large patient cohort

“…Emerging studies exploring the utility of radiomic features extracted from head and neck cancers highlight the potential for more accurate prediction of disease progression using novel imaging signatures which could be augmented by artificial intelligence techniques [32][33][34] . Although there is no current clinical implementation of a radiomic-based decision-support system in this clinical scenario, in the future this may emerge and could result in better patient stratification and personalization of treatment 34 .…”

Radiological tumour classification across imaging modality and histology

“…On the other hand, physiological tumor subregions or habitats such as hypoxia and hypermetabolic activity can provide more meaningful biological information, and they do not necessarily follow this simple geometric paradigm 22 , 23 . One caveat is that these physiological tumor subregions are likely cancer type-specific and imaging modality-dependent, and reliable identification of these subregions requires sophisticated algorithms, such as habitat imaging 24 , 25 .…”

Artificial intelligence in tumor subregion analysis based on medical imaging: A review

“…The volume of the most metabolically active and heterogeneous solid components of the tumor predicted OS and OFP better than conventional imaging markers. In a second study, Wu et al 150 developed an imaging biomarker to assess early treatment response and predicted outcomes in oropharyngeal squamous cell carcinoma (OPSCC). Based on ‐FDG PET and contrast CT imaging, the primary tumor and involved lymph nodes were divided into subregions by individual‐ and population‐level clustering.…”