The rise in the incidence of human papillomavirus (HPV)-related cancers has caused a significant epidemiological shift (1) in oropharyngeal squamous cell carcinoma (OPSCC). It is estimated that HPV causes more than 70% of OPSCC cases in the United States (2). HPV+ OPSCC differs from its HPV− counterpart in response to treatment and disease aggressiveness (3). In order to account for this, the most recent American Joint Committee on Cancer (AJCC) eighth edition tumor staging system was modified to incorporate HPV status, with different staging systems for HPV (p16) positive and negative tumors (4). HPV+ patients tend to respond better to definitive radiotherapy or combined chemoradiotherapy protocols and are less likely than HPV− patients to develop disease recurrence and metastases. Thus, it has become critical to develop biomarkers within the HPV+ and HPV− populations for risk stratification.

Treatment of OPSCC patients is on the cusp of a paradigm shift; current clinical trials are geared toward reducing treatment toxicity for HPV+ patients without compromising survival outcomes since low-risk patients typically could benefit from a lower dose of radiotherapy or less invasive surgical operations (treatment deintensification) (5). However, it is challenging to tailor the most optimal treatment strategy for each patient. Although high T (T3/T4) and N (N3) stages as well as tobacco use are clinically accepted risk factors for HPV+ OPSCC patients, these categorical predictors neglect the oncogenic differences between individual patients (6). A recent phase II randomized controlled trial NRG HN002 reported that patients who are grouped as candidates for treatment de-escalation using a combination of clinicopathologic factors did not meet the goal of 2-year disease-free survival (DFS) in the radiotherapy only arm (7). This exhibits a clear unmet clinical need for the development of objective biomarkers to identify patients who could truly benefit from treatment de-escalation.

On the other hand, the unmet need for HPV− patients is to precisely identify patients at high risk of developing local or regional failure after treatment, patients who might be candidates for targeted treatment escalation (8). While the eighth edition of the AJCC staging modifications that separate HPV+ and HPV− patients is a major advance (9), it still may not be sufficient for accurate risk stratification as it de-emphasizes the importance of nodal metastasis by diagnosing most of the new p16+ OPSCC patients into stages I and II (10).

Radiomics is the process of computational extraction of large numbers of quantitative imaging features, such as texture features, from routine radiologic scans (e.g., MRI and CT) for characterization of the disease (11). These features are able to detect subtle changes in imaging intensity patterns within a local region which in turn may help better describe the cancer phenotype as well as the tumor microenvironment. While radiomic features in the immediate vicinity outside the tumor have shown significant value in differentiating disease subtypes for lung (12) and breast (13) cancers, we are not aware of any work that has attempted to collectively evaluate the role of textural patterns from both within (intratumoral) and outside the tumor (peritumoral) to predict HPV status or to identify their association with disease-specific survival in OPSCC.

In this study, we sought to explore the prognostic value of both intratumoral and peritumoral HPV status-associated radiomic features on CT scans and compared and combined them with clinical and pathologic factors on over 500 OPSCC patients. The prognostic radiomic biomarker for HPV+ OPSCC was validated both internally (237 patients) and externally (120 patients) on two different cohorts. We also evaluated the utility of the radiomic signature to prognosticate DFS within each individual AJCC eighth edition-defined stage group. Finally, this study also involved creation and validation of a clinicopathologic nomogram for estimating DFS for HPV+ OPSCC patients. Figure 1 shows the overall methodology comprising radiomic feature extraction and selection, prognostic signatures, and radiomic nomogram construction and validation.

HPV+ OPSCC has better clinical prognosis and treatment response than the alcohol- and tobacco-related HPV− OPSCC (23). Because of this, treatment deintensification to reduce therapy-related morbidity in “low-risk” HPV+ OPSCC patients is being considered. However, a recent phase II randomized controlled trial by Yom et al. reported that patients in whom therapy was deintensified based on traditional TNM staging information did not meet the goal of 2-year DFS >85% (7). This was likely due to the lack of biomarkers for identifying patients who would most benefit from therapy deintensification. Although the AJCC eighth edition staging system represents a major improvement over the seventh edition, better and more reliable methods for pretreatment prognostication are needed for therapeutic decision-making. The radiomic biomarkers presented in this study aimed to identify those truly low-risk HPV+ patients within both the whole and AJCC eighth edition stage 1 population groups, for whom treatment deintensification should be considered.

In this work, we investigated the ability of both intratumoral and peritumoral radiomic biomarkers on CT scans to predict HPV status for a large cohort of 462 OPSCC patients. Additionally, we addressed the OPSCC prognosis prediction problem independently within the HPV+ and HPV− populations. The radiomic signature which was prognostic for the whole HPV+ population was also prognostic within the AJCC eighth edition stage I and stage II patients. Finally, the radiomic features were combined with pathologic staging factors to form a radiomic nomogram for individualized prognosis estimation for HPV+ OPSCC patients.

Currently, measuring p16 protein expression via immunohistochemistry is the recommended test for determining HPV status. However, distinct populations of patients exist in whom the tumors overexpress p16 but are in fact negative for HPV-DNA or mRNA expression and vice versa (24). Consequently, using p16 testing alone to determine HPV status results in some misclassified patients. Additional biomarkers are needed to complement the p16 testing. In the first experiment, we demonstrated that radiomic features from within the tumor and annular rings of 0–15 mm outside the tumor on CT imaging could reasonably predict HPV status of OPSCC, with an overall accuracy of 76%. A recent study by Leijenaar et al. designed a statistical framework for HPV status prediction, and they found that HPV+ tumors are more homogeneous in CT densities (25). This finding is in alignment with the result of this study. Specifically, HPV+ tumors possess a more homogeneous morphologic appearance in terms of CT texture patterns compared with HPV− tumors, which in turn is characterized by the Haralick correlation of information measured within the tumor ( Figure 2A , second column). A higher value of the Haralick correlation indicates less pixel intensity disorders and decreased morphologic appearance heterogeneity for HPV+ tumors compared with HPV− tumors. Similar to the findings we report, Bagher-Ebadian et al. reported that HPV+ tumors have consistently lower energy components for seven frequency bands quantified by the DOST features (26). Although the discriminating textural features we identified are different from previous studies, the interpretations of the features are similar. However, we also found higher gray-level intensity values of HPV+ tumors compared with HPV− tumors on CT, which appears to be at odds to the findings of the study by Leijenaar et al. (25). In addition, our study represents the first study for integrating CT radiomic both within and around the tumor for OPSCC HPV status prediction. We demonstrate the superior discriminability of peritumoral CT radiomic features, which appears to suggest discriminable differences of the microenvironment in the regions immediately outside the tumor. HPV− patients are best characterized by a combination of local intensity disorder and microscale heterogeneity in gradient orientation, particularly outside the tumor. Specifically, a higher peritumoral variation of the gradient orientation defined by the CoLlAGe sum of variance was observed in HPV− compared with HPV+ tumors. Reduced expression of Haralick information capturing low level of correlation between adjacent pixels was also an important component of the peritumoral radiomic signature for HPV− patients. Furthermore, HPV− peritumoral regions were characterized by abrupt changes in edges, ripples, and intensity smoothness, as detected by the elevated expression of Laws features. These findings are consistent with previous studies, which showed that HPV+ tumors have less stroma overall, have smoother borders to the nests and leading edges, and are more homogeneously cellular usually without keratin production (27–29).

In the second experiment, we evaluated the prognostic ability of the HPV status-associated radiomics features found in experiment 1, to separately stratify HPV+ and HPV− OPSCC patients into high- and low-risk groups based on DFS. We constructed dedicated radiomic risk scores (RRS_HPV+, RRS_HPV−), which yielded significant risk stratification based on DFS in the validation set for both HPV+ and HPV− populations. A previous study by Leijenaar et al. externally validated the prognostic value of intratumoral radiomic signatures in a larger cohort of 542 OPSCC (C-index = 0.628, p < 0.001) but did not consider the variation of the results by HPV status (30). Vallières et al. used radiomic features from pretreatment FDG-PET and CT images of 300 patients from four different cohorts to prognosticate outcomes in head and neck cancer (31). They obtained C-indices of 0.63, 0.88, and 0.60 for local, regional, and distant recurrence-free survival. Aerts et al. trained a prognostic radiomic model on 422 patients with lung cancer and validated on 231 patients with head and neck cancers (32), achieving a concordance index of 0.69 (p < 0.001) on the validation set. However, unlike our study, this study did not consider HPV status as an independent prognostic indicator for outcome prediction. The prognostic ability of the radiomic risk score (RRS_HPV+) was also evaluated within each of the AJCC eighth edition-defined stage groups. Although various treatment deintensification strategies have been proposed by multiple clinical trials based on clinicopathologic factors, there is a lack of reliable biomarkers for risk-stratifying OPSCC patients within individual stage groups. In the HN002 trial, patients meeting criteria of either T1-T2 N1-N2b M0 or T3 N0 N-2b M0 (AJCC seventh ed.) with a ≤10 pack-year smoking history are selected as candidates for therapy deintensification (7). Their results showed that patients randomly assigned to the non-chemotherapy arm did not meet the goal of 2-year DFS >85%. The authors also reported that for these patients using a low-than-standard-dose radiotherapy resulted in a higher rate of locoregional failure. This indicates that risk stratification based on grouping of clinicopathologic factors alone is not robust and there is a clear unmet clinical need for developing more granular and more objective biomarkers to identify patients who could truly benefit from treatment de-escalation. Clinically, stage I and stage II patients are the current target for treatment de-escalation. However, a subset of these patients still had poor survival outcome and would not benefit from treatment de-escalation (33). The CT radiomic risk score developed in this study represents a potential useful tool for guiding treatment intensities within the early-stage HPV+ OPSCC patients. By applying the threshold defined from the training set, the learned radiomic risk scores could further separate the stage I and stage II HPV+ patients into high- vs. low-risk groups based on disease-free survival. As such, the radiomic biomarker presented in this study could potentially help to distinguish patients within the current AJCC eighth edition definition of low risk as to which patients will benefit from treatment deintensification vs. those who will not. The findings of this study are consistent with a recently published study, where a histology-based imaging biomarker (MuNi) was found to be associated with survival for stage I and stage II patients (34). Noticeably, the prognostic radiomic features identified in this study are mainly from peritumoral compartments. One possible explanation for this is that the peritumoral radiomic features are associated with tumor-infiltrating HPV-specific immune responses prior to treatment, which have been more commonly found floating around the tumor without actual penetration or action into the tumor core and are strongly associated with prognosis (35, 36).

In the third experiment, we developed a radiomic nomogram M_p+RRS for HPV+ DFS prediction. Currently, the conventional TNM and AJCC staging systems are routinely used for risk stratification and prognosis estimation. They reflect tumor size (T), lymph node status (N), and cancer metastasis (M). However, these staging factors could not capture the intratumor heterogeneity, which has been shown to be a significant prognostic factor. The radiomic nomogram combined the pathologic staging information with the radiomic features extracted from the entire tumor on CT scans, enabling for robust pretreatment survival estimation. Combining the RRS_HPV+ from experiment 2 with the pathologic T stage resulted in a nomogram that leads to a more individualized prognosis prediction. With refinement and improvement, this type of radiomic approach might guide more tailored treatment for patients with better survival outcome. Compared with risk stratification using only the conventional staging factors, the radiomic nomogram had an improved DFS estimation. A previous study by Fakhry et al. showed that a nomogram integrating clinicopathologic factors (i.e., HPV status, T and N stages) could reliably predict progression-free survival (37), which is in alignment with our results. Based on our results, T3 stage is significantly associated with worse DFS for HPV+ patients, while N1 and N2 stages are significantly associated with worse DFS for HPV− patients in both univariable and multivariable analyses. With regard to the radiomic nomogram for head and neck cancer, Zhang et al. built multiparametric MRI-based radiomic nomograms for predicting nasopharyngeal carcinoma prognosis and obtained C-index of 0.776 for PFS prediction (38). Yuan et al. proved that a nomogram consisting of MRI radiomic signatures and TNM stage could better predict head and neck cancer prognosis with a C-index of 0.72 on the validation set (39). The prognostic performance difference between our new CT-based nomogram and the MRI-based nomogram may be on the higher image resolution offered by multiparametric MRI, although CT tends to be used more routinely compared with MRI for head and neck cancer. We also noted that both patient cohorts from Zhang et al. (38) and Yuan et al. (39) comprised a majority of advanced stage head and neck cancer patients (100% and 70.6%), while our training and validation cohorts consist of only 17% and 26.6% of stage III and no stage IV OPSCC patients. This may also influence the performance of the model since intratumor heterogeneity could be more easily captured within those aggressive tumors.

Our study does have several limitations. First, the prognostic biomarker validation on a single cohort was done in a retrospective manner. Second, we predicted neither benefit of existing treatments for the two populations nor treatment response within the individual AJCC eighth edition-defined stage groups. These aims will be part of our future study involving large multisite and multimodality evaluation of radiomic signatures in predicting treatment response for the two populations. Third, we acknowledge the limitation of our dataset from the TCIA in terms of HPV status based on p16 testing which may not accurately reflect the true transcriptionally active HPV status, at least for a small percentage of patients. Nonetheless, this study demonstrates that CT radiomic features could, in theory, complement the existing p16 testing method in distinguishing HPV status.

Despite the aforementioned limitations, this study is the first to show the role of combined intratumoral and peritumoral radiomic features in predicting HPV status of OPSCC patients. It is also the first study to incorporate both radiomic signatures and corresponding nomograms for prognosis prediction for HPV+ and HPV− patients. If confirmed in prospective clinical trials, this radiomic nomogram pipeline could enrich the existing AJCC eighth staging systems for risk-stratifying OPSCC patients. One can imagine a strategy where numerous sources of data go into predictive models for patient care. Especially attractive here is that all patients received pretreatment cross-sectional CT scans so the data are already garnered in digitized form, easily available for radiomics-based nomograms for prognosis prediction.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by Cleveland Clinic. The patients/participants provided their written informed consent to participate in this study.

BS: conceptualization, data curation, formal analysis, methodology, investigation, writing—original draft, and writing—review and editing. KY: conceptualization, investigation, and writing—review and editing. JG: writing—review and editing. CL: writing—review and editing. LL: formal analysis and investigation. NB: writing—review and editing. CK: writing—review and editing. PT: resources and investigation. PF: conceptualization, investigation, and writing—review and editing. SK: resources, investigation, and writing—review and editing. JSL: resources, investigation, and writing—review and editing. AM: conceptualization, methodology, investigation, supervision, funding acquisition, project administration, and writing—review and editing. All authors contributed to the article and approved the submitted version.

Research reported in this publication was supported by the National Cancer Institute under award numbers 1U24CA199374-01, R01 CA24999201A1, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1, 1U01CA239055-01, 1U01CA248226-01, and 1U54CA254566-01; National Heart, Lung and Blood Institute 1R01HL15127701A1; National Institute for Biomedical Imaging and Bioengineering 1R43EB028736-01; National Center for Research Resources under award number 1 C06 RR12463-01; VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service; the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program (W81XWH-19-1-0668), the Prostate Cancer Research Program (W81XWH-15-1-0558, W81XWH-20-1-0851), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), and the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404); the Kidney Precision Medicine Project (KPMP) Glue Grant; the Ohio Third Frontier Technology Validation Fund; the Clinical and Translational Science Collaborative of Cleveland (UL1TR0002548) from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH roadmap for Medical Research; and the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University. KY was supported by the Computational Genomic Epidemiology of Cancer (CoGEC) Program at Case Comprehensive Cancer Center (T32CA094186).

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the U.S. Department of Veterans Affairs, the Department of Defense, or the United States Government.

AM is an equity holder in Elucid Bioimaging and in Inspirata Inc. In addition, he has served as a scientific advisory board member for Inspirata Inc., AstraZeneca, Bristol Myers Squibb, and Merck. Currently, he serves on the advisory board of Aiforia Inc. He also has sponsored research agreements with Philips, AstraZeneca, Boehringer Ingelheim, and Bristol Myers Squibb. His technology has been licensed to Elucid Bioimaging. He is also involved in a NIH U24 grant with PathCore Inc. and three different R01 grants with Inspirata Inc. SK is a consultant for Merck and Regeneron, receives research support from Merck and Bristol Myers Squibb, and reports honoraria from UpToDate.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.