Radiomic Features Associated With HPV Status on Pretreatment Computed Tomography in Oropharyngeal Squamous Cell Carcinoma Inform Clinical Prognosis

Abstract

PurposeThere is a lack of biomarkers for accurately prognosticating outcome in both human papillomavirus-related (HPV+) and tobacco- and alcohol-related (HPV−) oropharyngeal squamous cell carcinoma (OPSCC). The aims of this study were to i) develop and evaluate radiomic features within (intratumoral) and around tumor (peritumoral) on CT scans to predict HPV status; ii) investigate the prognostic value of the radiomic features for both HPV− and HPV+ patients, including within individual AJCC eighth edition-defined stage groups; and iii) develop and evaluate a clinicopathologic imaging nomogram involving radiomic, clinical, and pathologic factors for disease-free survival (DFS) prediction for HPV+ patients.Experimental DesignThis retrospective study included 582 OPSCC patients, of which 462 were obtained from The Cancer Imaging Archive (TCIA) with available tumor segmentation and 120 were from Cleveland Clinic Foundation (CCF, denoted as SCCF) with HPV+ OPSCC. We subdivided the TCIA cohort into training (ST, 180 patients) and validation (SV, 282 patients) based on an approximately 3:5 ratio for HPV status prediction. The top 15 radiomic features that were associated with HPV status were selected by the minimum redundancy–maximum relevance (MRMR) using ST and evaluated on SV. Using 3 of these 15 top HPV status-associated features, we created radiomic risk scores for both HPV+ (RRSHPV+) and HPV− patients (RRSHPV−) through a Cox regression model to predict DFS. RRSHPV+ was further externally validated on SCCF. Nomograms for the HPV+ population (Mp+RRS) were constructed. Both RRSHPV+ and Mp+RRS were used to prognosticate DFS for the AJCC eighth edition-defined stage I, stage II, and stage III patients separately.ResultsRRSHPV+ was prognostic for DFS for i) the whole HPV+ population [hazard ratio (HR) = 1.97, 95% confidence interval (CI): 1.35–2.88, p < 0.001], ii) the AJCC eighth stage I population (HR = 1.99, 95% CI: 1.04–3.83, p = 0.039), and iii) the AJCC eighth stage II population (HR = 3.61, 95% CI: 1.71–7.62, p < 0.001). HPV+ nomogram Mp+RRS (C-index, 0.59; 95% CI: 0.54–0.65) was also prognostic of DFS (HR = 1.86, 95% CI: 1.27–2.71, p = 0.001).ConclusionCT-based radiomic signatures are associated with both HPV status and DFS in OPSCC patients. With additional validation, the radiomic signature and its corresponding nomogram could potentially be used for identifying HPV+ OPSCC patients who might be candidates for therapy deintensification.

Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma

“…The selection of the 50th percentile of DPT in place of the 50th percentile of ADC PT (even though strongly correlated with HPV status as well) seemed to confirm that the perfusion-free diffusion coefficient, D, may better reveal the specific HPV-positive tumor characteristics related to cell density and tissue microstructure, compared to ADC, as also supported by other investigators [36,38]. The increase in inverse difference moment of ADC in HPV-positive patients indicated a higher homogeneity of metastatic lymph nodes, in accordance with a number of previous studies based on both CT and PET imaging, which reported an increase in homogeneity of CT density and in FDG uptakes [13][14][15][16]39]. This can be explained considering the different mechanisms of carcinogenesis between HPV-positive and HPV-negative HNSCCs, the former being more homogeneously triggered by the HPV infection, the latter being the consequence of more intricate genetic alterations attributable to the toxic effect of alcohol drinking and tobacco smoking [12,33].…”

“…Multiple studies have already developed CT-based radiomic signatures for predicting HPV status [13][14][15][16], while only a few studies focused on MRI-based radiomics, taking advantage of the better soft-tissue contrast of morphological sequences than CT, and of functional information provided by diffusion-and perfusion-weighted techniques [17][18][19]. The clinical utility of DWI in discriminating HPV-positive from HPV-negative patients and its superiority with respect to morphological sequences, i.e., T2-weighted and/or contrastenhanced T1-weighted imaging, has been recently suggested by the investigation of Suh et al [19] and supported by previous correlation studies between DWI and histopathology in HNSCC [20,21].…”

Prediction of HPV status in oropharyngeal squamous cell carcinoma based on radiomics and machine learning algorithms: a multi-cohort study

“…Since radiomics is an innovative quantitative imaging analysis technique that utilizes quantitative metrics through high-throughput extraction, numerous researches have investigated its predictive value of HPV status for OPSCC. In a study by Bolin et al, radiomic biomarkers on CT images were applied to predict HPV status of OPSCC patients using a linear discriminant analysis machine-learning classi er, performing well in both training (AUC = 0.84) and validation cohorts (AUC = 0.70) (13). Beomseok et al reported that MRI phenotyping, based on Radiomics, can also discriminate between HPV positive and negative OPSCC, and may serve as a potential imaging biomarker (14).…”

Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review

“…Since imaging is acquired at the macroscopic scale, radiomics allows for interrogating not only the disease regions of interest, but also surrounding structures such as the peri-tumoral region ( 11 ). Radiology images offer the opportunity to be used in conjunction with machine learning to build diagnostic, prognostic and predictive models ( 12 - 14 ).…”