Explainable prediction model for the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma using CNN on CT images.

Several studies have emphasised how positive and negative human papillomavirus (HPV+ and HPV-, respectively) oropharyngeal squamous cell carcinoma (OPSCC) has distinct molecular profiles, tumor characteristics, and disease outcomes. Different radiomics-based prediction models have been proposed, by also using innovative techniques such as Convolutional Neural Networks (CNNs). Although some of these models reached encouraging predictive performances, there evidence explaining the role of radiomic features in achieving a specific outcome is scarce. In this paper, we propose some preliminary results related to an explainable CNN-based model to predict HPV status in OPSCC patients. We extracted the Gross Tumor Volume (GTV) of pre-treatment CT images related to 499 patients (356 HPV+ and 143 HPV-) included into the OPC-Radiomics public dataset to train an end-to-end Inception-V3 CNN architecture. We also collected a multicentric dataset consisting of 92 patients (43 HPV+ , 49 HPV-), which was employed as an independent test set. Finally, we applied Gradient-weighted Class Activation Mapping (Grad-CAM) technique to highlight the most informative areas with respect to the predicted outcome. The proposed model reached an AUC value of 73.50% on the independent test. As a result of the Grad-CAM algorithm, the most informative areas related to the correctly classified HPV+ patients were located into the intratumoral area. Conversely, the most important areas referred to the tumor edges. Finally, since the proposed model provided additional information with respect to the accuracy of the classification given by the visualization of the areas of greatest interest for predictive purposes for each case examined, it could contribute to increase confidence in using computer-based predictive models in the actual clinical practice.

Fully automated 3D machine learning model for HPV status characterization in oropharyngeal squamous cell carcinomas based on CT images.

BACKGROUND: Human papillomavirus (HPV) status plays a major role in predicting oropharyngeal squamous cell carcinoma (OPSCC) survival. This study assesses the accuracy of a fully automated 3D convolutional neural network (CNN) in predicting HPV status using CT images. METHODS: Pretreatment CT images from OPSCC patients were used to train a 3D DenseNet-121 model to predict HPV-p16 status. Performance was evaluated by the ROC Curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. RESULTS: The network achieved a mean AUC of 0.80 ± 0.06. The best-preforming fold had a sensitivity of 0.86 and specificity of 0.92 at the Youden's index. The PPV, NPV, and F1 scores are 0.97, 0.71, and 0.82, respectively. CONCLUSIONS: A fully automated CNN can characterize the HPV status of OPSCC patients with high sensitivity and specificity. Further refinement of this algorithm has the potential to provide a non-invasive tool to guide clinical management.

18F-FDG PET/CT Parameters Enhance MRI Radiomics for Predicting Human Papilloma Virus Status in Oropharyngeal Squamous Cell Carcinoma.

PURPOSE: Predicting human papillomavirus (HPV) status is critical in oropharyngeal squamous cell carcinoma (OPSCC) radiomics. In this study, we developed a model for HPV status prediction using magnetic resonance imaging (MRI) radiomics and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients with OPSCC. MATERIALS AND METHODS: Patients with OPSCC who underwent 18F-FDG PET/CT and contrast-enhanced MRI before treatment between January 2012 and February 2020 were enrolled. Training and test sets (3:2) were randomly selected. 18F-FDG PET/CT parameters and MRI radiomics feature were extracted. We developed three light-gradient boosting machine prediction models using the training set: Model 1, MRI radiomics features; Model 2, 18F-FDG PET/CT parameters; and Model 3, combination of MRI radiomics features and 18F-FDG PET/CT parameters. Area under the receiver operating characteristic curve (AUROC) values were used to analyze the performance of the models in predicting HPV status in the test set. RESULTS: A total of 126 patients (118 male and 8 female; mean age: 60 years) were included. Of these, 103 patients (81.7%) were HPV-positive, and 23 patients (18.3%) were HPV-negative. AUROC values in the test set were 0.762 [95% confidence interval (CI), 0.564-0.959], 0.638 (95% CI, 0.404-0.871), and 0.823 (95% CI, 0.668-0.978) for Models 1, 2, and 3, respectively. The net reclassification improvement of Model 3, compared with that of Model 1, in the test set was 0.119. CONCLUSION: When combined with an MRI radiomics model, 18F-FDG PET/CT exhibits incremental value in predicting HPV status in patients with OPSCC.

The Effects of Human Papillomavirus Status and Treatment on the Positive Predictive Value of Post-radiotherapy 18F-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography in Advanced Head and Neck Squamous Cell Carcinoma.

AIMS: The high negative predictive value of post-chemoradiation (CRT) positron emission tomography-computed tomography (PET-CT) is well established in head and neck squamous cell cancers (HNSCC). The positive predictive value (PPV) remains under scrutiny, with increasing evidence that it is affected by several factors. The aim of this study was to assess the PPV of post-treatment PET-CT for residual nodal disease when stratified by treatment modality and tumour human papillomavirus (HPV) status. MATERIALS AND METHODS: This was a retrospective cohort study in a tertiary oncology centre carried out between January 2013 and December 2019. Patients were radically treated with radiotherapy only/CRT for node-positive HNSCC. PET-CT nodal responses were categorised as complete, equivocal (EQR) or incomplete (ICR), and outcomes extracted from electronic records. RESULTS: In total, 480 patients were evaluated, all had a minimum potential follow-up of 2 years, with a median of 39.2 months. The PPV of 12-week PET-CT was significantly different between HPV-positive (22.5%) and HPV-unrelated (52.7%) disease, P < 0.001. It was also significantly different between the CRT (24.8%) and radiotherapy-only (51.1%) groups, P = 0.001. The PPV of an EQR was significantly less than an ICR, irrespective of HPV status and primary treatment modality. In HPV-positive disease, the PPV of an EQR was 9.0% for the CRT group compared with 21.4% for radiotherapy only, P = 0.278. The PPV in those who achieved an ICR was 34.2% in the CRT group, significantly lower than 70.0% in the radiotherapy-only group, P = 0.03. CONCLUSION: The PPV of 12-week PET-CT is significantly lower for HPV-positive compared with HPV-unrelated HNSCC. It is poorer in patients with HPV-positive disease treated with CRT compared with radiotherapy alone.

Efficacy and pregnancy outcomes of focused ultrasound for cervical high-grade squamous intraepithelial lesions.

OBJECTIVE: To investigate the efficacy and adverse effects of focused ultrasound (FU) in the treatment of high-grade squamous intraepithelial lesions (HSIL) and follow up on pregnancy outcomes in patients. METHODS: This retrospective study recruited 57 patients aged 20-40 years with cervical HSIL combined with HR-HPV infection who received FU treatment between September 2019 and April 2022. Clinical data of the patients were obtained from hospital records. HSIL cure rate and cumulative HR-HPV clearance rate were assessed after treatment. Patients were followed up on fertility and pregnancy outcomes after treatment by telephone interviews until April 1, 2023. RESULTS: During a 6-month follow-up, the HSIL cure rate was 73.7%, and a statistical difference between CIN2 and CIN3 (75.6% vs. 66.7%, p = 0.713) was not present. HSIL -recurrence was not observed during the follow-up period, and the median follow-up duration was 12 months. The cumulative HR-HPV clearance rates at the 6- and 12-month follow-ups were 56.1% and 75.4%, respectively. The median clearance time of HR-HPV was 6 (95% confidence interval, 5.46-6.54) months. The clearance rate was higher in HPV16/18 than in non-HPV16/18 (86.7% vs. 62.9%, p = 0.038). After treatment, the successful pregnancy rate in patients with fertility intentions and spontaneous abortion rate were 73.9% and 5.9%, respectively. Preterm birth, preterm premature rupture of membranes, or low-birth-weight infants were not observed. CONCLUSION: FU treatment can regress HSIL and accelerate HR-HPV clearance in young women of childbearing age with cervical HSIL associated with HR-HPV infection, and has no significant adverse effects on pregnancy outcomes.

Interpretable radiomics method for predicting human papillomavirus status in oropharyngeal cancer using Bayesian networks.

OBJECTIVES: To develop a simple interpretable Bayesian Network (BN) to classify HPV status in patients with oropharyngeal cancer. METHODS: Two hundred forty-six patients, 216 of whom were HPV positive, were used in this study. We extracted 851 radiomics markers from patients' contrast-enhanced Computed Tomography (CT) images. Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. The area under the curve (AUC) demonstrated BN model performance in 30% of the data reserved for testing. A Support Vector Machine (SVM) based method was also implemented for comparison purposes. RESULTS: The Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. Areas under the Curves (AUC) were found 0.78 and 0.72 on the training and test data, respectively. When using support vector machine (SVM) and 25 features, the AUC was found 0.83 on the test data. CONCLUSIONS: The straightforward structure and power of interpretability of our BN model will help clinicians make treatment decisions and enable the non-invasive detection of HPV status from contrast-enhanced CT images. Higher accuracy can be obtained using more complex structures at the expense of lower interpretability. ADVANCES IN KNOWLEDGE: Radiomics is being studied lately as a simple imaging data based HPV status detection technique which can be an alternative to laboratory approaches. However, it generally lacks interpretability. This work demonstrated the feasibility of using Bayesian networks based radiomics for predicting HPV positivity in an interpretable way.

A comparison of loco-regional relapse pattern in HPV positive vs negative oropharyngeal cancers following radiotherapy; relation to pretreatment FDG-PET and radiotherapy target volumes.

BACKGROUND: Previous studies have shown that a large proportion of relapses in head-and neck squamous cell carcinoma (HNSCC) following radiotherapy (RT) occur in the pretreatment FDG-PET avid volume (GTV-PET). The aim of the current work was to see if this was valid also in an oropharynx squamous cell carcinoma (OPSCC) only population, and to compare the loco-regional relapse pattern between HPV positive and HPV negative patients. MATERIAL AND METHODS: Among 633 OPSCC patients treated between 2009 and 2017, 46 patients with known HPV (p16) status and isolated loco-regional relapse were included. Oncologists contoured relapse volumes (RV) on relapse scans (PET/CT, CT or MR), which were thereafter deformed to match the anatomy of the planning CTs. The point of origin (center of volume) of the deformed RVs were determined and analyzed in relation to the RT target volumes (GTV-PET, GTV and CTVs). The relapse pattern was compared between HPV positive and HPV negative patients using Fischer's exact test. RESULTS: Sixty RVs were contoured in the 46 patients. 55% (95% CI 44-67%) of relapses originated in GTV-PET, while the other RT volumes harbored 12% (5-20%) (GTV), 18% (9-28%) (high risk CTV) and 5% (0-11%) (low risk CTV) of relapses. Six relapses were found outside the RT target volumes. No significant difference in relapse pattern between HPV positive and HPV negative patients was found (p = .95). CONCLUSION: There were no signs of difference in loco-regional relapse pattern between HPV positive and HPV negative patients. In agreement with previous findings, GTV-PET was the most frequent RT target volume of relapse.

Post-treatment 18F-fludeoxyglocuse-positron emission tomography in human papillomavirus-associated oropharyngeal cancer.

BACKGROUND: Human papillomavirus association has changed the landscape of treatment for oropharyngeal squamous cell carcinoma; it remains to be seen whether current post-treatment surveillance schedules are effective. OBJECTIVE: Evaluate whether post-treatment surveillance of oropharyngeal cancer through FDG-PET imaging is modified by human papillomavirus association. METHODS: A prospective cohort analysis of retrospective data was conducted for patients undergoing treatment of oropharyngeal cancer between 2016 and 2018. This study was conducted at a single large tertiary referral center in Brisbane, Australia. RESULTS: Two-hundred and twenty-four patients were recruited for the purposes of the study, 193 (86%) with HPV-associated disease. In this cohort FDG-PET had a sensitivity of 48.3%, specificity of 72.6%, positive predictive value of 23.7%, and negative predictive value of 88.8% in detecting disease recurrence. CONCLUSIONS: FDG-PET in HPV-associated oropharyngeal cancer has significantly lower positive predictive value when compared to non-HPV-associated oropharyngeal cancer. Caution should be used when interpreting positive post-treatment FDG-PET.

Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial.

BACKGROUND: Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. ECOG-ACRIN Cancer Research Group E3311 was a multicentre trial wherein patients with HPV-associated oropharyngeal carcinoma were treated surgically and assigned to a pathological risk-based adjuvant strategy of observation, radiation, or concurrent chemoradiation. Despite protocol exclusion of patients with overt radiographic ENE, more than 30% had pathological ENE and required postoperative chemoradiation. We aimed to evaluate a CT-based deep learning algorithm for prediction of ENE in E3311, a diagnostically challenging cohort wherein algorithm use would be impactful in guiding decision-making. METHODS: For this retrospective evaluation of deep learning algorithm performance, we obtained pretreatment CTs and corresponding surgical pathology reports from the multicentre, randomised de-escalation trial E3311. All enrolled patients on E3311 required pretreatment and diagnostic head and neck imaging; patients with radiographically overt ENE were excluded per study protocol. The lymph node with largest short-axis diameter and up to two additional nodes were segmented on each scan and annotated for ENE per pathology reports. Deep learning algorithm performance for ENE prediction was compared with four board-certified head and neck radiologists. The primary endpoint was the area under the curve (AUC) of the receiver operating characteristic. FINDINGS: From 178 collected scans, 313 nodes were annotated: 71 (23%) with ENE in general, 39 (13%) with ENE larger than 1 mm ENE. The deep learning algorithm AUC for ENE classification was 0·86 (95% CI 0·82-0·90), outperforming all readers (p<0·0001 for each). Among radiologists, there was high variability in specificity (43-86%) and sensitivity (45-96%) with poor inter-reader agreement (κ 0·32). Matching the algorithm specificity to that of the reader with highest AUC (R2, false positive rate 22%) yielded improved sensitivity to 75% (+ 13%). Setting the algorithm false positive rate to 30% yielded 90% sensitivity. The algorithm showed improved performance compared with radiologists for ENE larger than 1 mm (p<0·0001) and in nodes with short-axis diameter 1 cm or larger. INTERPRETATION: The deep learning algorithm outperformed experts in predicting pathological ENE on a challenging cohort of patients with HPV-associated oropharyngeal carcinoma from a randomised clinical trial. Deep learning algorithms should be evaluated prospectively as a treatment selection tool. FUNDING: ECOG-ACRIN Cancer Research Group and the National Cancer Institute of the US National Institutes of Health.

Risk Stratification for Oropharyngeal Squamous Cell Carcinoma Using Texture Analysis on CT - A Step Beyond HPV Status.

Background and Purpose: Human papillomavirus-associated oropharyngeal squamous cell carcinoma (OPSCC) is increasingly prevalent. Despite the overall more favorable outcome, the observed heterogeneous treatment response within this patient group highlights the need for additional means to prognosticate and guide clinical decision-making. Promising prediction models using radiomics from primary OPSCC have been derived. However, no model/s using metastatic lymphadenopathy exist to allow prognostication in those instances when the primary tumor is not seen. The aim of our study was to evaluate whether radiomics using metastatic lymphadenopathy allows for the development of a useful risk assessment model comparable to the primary tumor and whether additional knowledge of the HPV status further improves its prognostic efficacy. Materials and Methods: 80 consecutive patients diagnosed with stage III-IV OPSCC between February 2009 and October 2015, known human papillomavirus status, and pre-treatment CT images were retrospectively identified. Manual segmentation of primary tumor and metastatic lymphadenopathy was performed and the extracted texture features were used to develop multivariate assessment models to prognosticate treatment response. Results: Texture analysis of either the primary or metastatic lymphadenopathy from pre-treatment enhanced CT images can be used to develop models for the stratification of treatment outcomes in OPSCC patients. AUCs range from .78 to .85 for the various OPSCC groups tested, indicating high predictive capability of the models. Conclusions: This preliminary study can form the basis multi-centre trial that may help optimize treatment and improve quality of life in patients with OPSCC in the era of personalized medicine.

Prediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models.

PURPOSE: This study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI). METHODS: Pre-treatment MRI images of patients with cervical cancer were collected retrospectively. An HPV DNA oncogene analysis was performed based on cervical biopsy specimens. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1) and T2-weighted images (T2WI). A third feature subset was created as a combined group by concatenating the CE-T1 and T2WI subsets. Feature selection was performed using Pearson's correlation coefficient and wrapper- based sequential-feature selection. Two models were built with each feature subset, using support vector machine (SVM) and logistic regression (LR) classifiers. The models were validated using a five-fold cross-validation technique and compared using Wilcoxon's signed rank and Friedman's tests. RESULTS: Forty-one patients were enrolled in the study (26 were positive for carcinogenic HPV oncogenes, and 15 were negative). A total of 851 features were extracted from each imaging sequence. After feature selection, 5, 17, and 20 features remained in the CE-T1, T2WI, and combined groups, respectively. The SVM models showed 83%, 95%, and 95% accuracy scores, and the LR models revealed 83%, 81%, and 92.5% accuracy scores in the CE-T1, T2WI, and combined groups, respectively. The SVM algorithm performed better than the LR algorithm in the T2WI feature subset (P = 0.005), and the feature sets in the T2WI and the combined group performed better than CE-T1 in the SVM model (P = 0.033 and 0.006, respectively). The combined group feature subset performed better than T2WI in the LR model (P = 0.023). CONCLUSION: Machine learning-based radiomics models based on pre-treatment MRI can detect carcinogenic HPV status with discriminative accuracy.

Histogram analysis of synthetic magnetic resonance imaging: Correlations with histopathological factors in head and neck squamous cell carcinoma.

PURPOSE: To analyse the association between histogram parameters derived from synthetic MRI (SyMRI) and different histopathological factors in head and neck squamous cell carcinoma (HNSCC). METHOD: Sixty-one patients with histologically proven primary HNSCC were prospectively enrolled. The correlations between histogram parameters of SyMRI (T1, T2 and proton density (PD) maps) and histopathological factors were analysed using Spearman analysis. The Mann-Whitney U test or Student's t test was utilized to differentiate histological grades and human papillomavirus (HPV) status. The ROC curves and leave-one-out cross-validation (LOOCV) were used to evaluate the differentiation performance. Bootstrapping was applied to avoid overfitting. RESULTS: Several histogram parameters were associated with histological grade: T1 map (r = 0.291) and PD map (r = 0.294 - 0.382/-0.343), and PD_75th Percentile showed the highest differentiation performance (AUC: 0.721 (ROC) and 0.719 (LOOCV)). Moderately negative correlations were found between p16 status and the histogram parameters: T1 map (r = -0.587 - -0.390), T2 map (r = -0.649 - -0.357) and PD map (r = -0.537 - -0.338). In differentiating HPV infection, Entropy was the most discriminative parameter in each map and T2_Entropy showed the highest diagnostic performance (AUC: 0.851 [ROC] and 0.851 [LOOCV]). Additionally, several histogram parameters were correlated with Ki-67 (r = -0.379/-0.397), epidermal growth factor receptor (EGFR) (r = 0.318/0.322) status and p53 (r = 0.452 - 0.665/-0.607) status. CONCLUSIONS: Histogram parameters derived from SyMRI may serve as a potential biomarker for discriminating relevant histopathological features, including histological differentiation grade, HPV infection, Ki-67, EGFR and p53 statuses.

Human papilomaviru-related oropharyngeal squamous cell carcinoma and radiomics: A new era?

BACKGROUND: The increase of the incidence of human papillomavirus dependent oropharyngeal squamous cell carcinoma is alarming, although we have greatly progressed in the classification and staging of this disease. We now know that human papillomavirus related oropharyngeal squamous cell carcinoma is a sub-type of head and neck squamous cell carcinoma with favourable prognosis and good response to therapy that needs a proper system of classification and staging. Thus, in routine practice it is essential to test patients for the presence of human papillomavirus. The most popular technique to assess human papillomavirus status is immunohistochemistry on biopsy samples with p16, which is an excellent surrogate for high-risk human papillomavirus infection. Another highly sensitive and specific tissue-based technique for the detection of human papillomavirus is RNAscope In situ hybridization that has a prohibitive cost, limiting its use in routine practice. Radiomics is an artificial intelligence based non-invasive method of computational analysis of computed tomography, magnetic resonance imaging, positron emission tomography, and ultrasound images. METHODS: In this review, we summarise the last findings of radiomics applied to human papillomavirus associated oropharyngeal squamous cell carcinoma. RESULTS: A growing body of evidence suggest that radiomics is able to characterise and detect early relapse after treatment, and enable development of tailored therapy of human papillomavirus positive oropharyngeal squamous cell carcinoma.

Development and Testing of a Machine Learning Model Using 18F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma.

OBJECTIVE: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18F-fluorodeoxyglucose (18F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. MATERIALS AND METHODS: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent 18F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. RESULTS: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. CONCLUSION: Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from 18F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

Magnetic resonance imaging based radiomics prediction of Human Papillomavirus infection status and overall survival in oropharyngeal squamous cell carcinoma.

OBJECTIVES: Human papillomavirus- (HPV) positive oropharyngeal squamous cell carcinoma (OPSCC) differs biologically and clinically from HPV-negative OPSCC and has a better prognosis. This study aims to analyze the value of magnetic resonance imaging (MRI)-based radiomics in predicting HPV status in OPSCC and aims to develop a prognostic model in OPSCC including HPV status and MRI-based radiomics. MATERIALS AND METHODS: Manual delineation of 249 primary OPSCCs (91 HPV-positive and 159 HPV-negative) on pretreatment native T1-weighted MRIs was performed and used to extract 498 radiomic features per delineation. A logistic regression (LR) and random forest (RF) model were developed using univariate feature selection. Additionally, factor analysis was performed, and the derived factors were combined with clinical data in a predictive model to assess the performance on predicting HPV status. Additionally, factors were combined with clinical parameters in a multivariable survival regression analysis. RESULTS: Both feature-based LR and RF models performed with an AUC of 0.79 in prediction of HPV status. Fourteen of the twenty most significant features were similar in both models, mainly concerning tumor sphericity, intensity variation, compactness, and tumor diameter. The model combining clinical data and radiomic factors (AUC = 0.89) outperformed the radiomics-only model in predicting OPSCC HPV status. Overall survival prediction was most accurate using the combination of clinical parameters and radiomic factors (C-index = 0.72). CONCLUSION: Predictive models based on MR-radiomic features were able to predict HPV status with sufficient performance, supporting the role of MRI-based radiomics as potential imaging biomarker. Survival prediction improved by combining clinical features with MRI-based radiomics.

Utility of PET-CT in Newly Diagnosed HPV-Associated Oropharyngeal Squamous Cell Carcinoma.

OBJECTIVE: To compare the utility of positron emission tomography-computed tomography (PET-CT) versus contrasted CT neck combined with routine chest imaging for disease staging and treatment planning in human papillomavirus (HPV) associated oropharyngeal squamous cell carcinoma (OPSCC) with clinically evident sites of primary disease. METHODS: All adult patients with primary HPV-associated OPSCC at a single quaternary care cancer center from 2018 to 2019 were reviewed, and those with images available for re-review were included. Primary outcomes included concordance in clinical staging between the 2 imaging modalities of interest (PET-CT vs CT), as well as independent agreement of each with pathologic staging. Analysis was performed via ordinal logistic regression. A secondary outcome was treatment selection after diagnostic imaging, analyzed via chi-squared testing. RESULTS: In total, 100 patients were included for evaluation, of which 89% were male, 91% Caucasian, and mean age was 61.2 years (SD 9.6). Clinical disease staging agreed between imaging modalities in 95% of cases (54 of 57 patients). Pathologic staging agreed with clinical staging from CT neck in 93% of cases (25 of 27 patients; P = .004), and with PET-CT in 82% (14 of 17 patients; P =.003). No differences were observed between the 2 imaging modalities for subsequent treatment selection (P = .39). CONCLUSION: In uncomplicated HPV-associated OPSCC, CT offers equivalent diagnostic accuracy to that of combined whole-body PET-CT for clinical staging, and has no appreciable impact on treatment selection. A reduced reliance on routine PET-CT during initial workup of HPV-associated OPSCC may be favorable for otherwise healthy patients with clinically evident sites of primary disease.

HPV impact on oropharyngeal cancer radiological staging: 7th vs 8th edition of AJCC TNM classification.

PURPOSE: To evaluate the agreement between pathological and radiological staging in oropharyngeal cancer by comparing the 7th and the 8th edition of the AJCC TNM system. METHODS: This retrospective cohort study included 57 cases of oropharyngeal cancer with lymph node metastases staged with the 7th and 8th editions of the AJCC TNM system. Comparison between clinical and radiological features and differences in agreement rates were calculated between radiological and pathological staging for the primary tumor (T) and lymph nodes (N) in HPVpos and HPVneg cases. RESULTS: Comparison of HPVpos and HPVneg revealed a significantly different distribution between early and advanced stages in the 8 th edition, with a relevant number of HPVpos patients redefined from advanced stages whit the 7 th ed. to early stages with 8 th ed. (p < 0.01); no significant differences were found when comparing all diagnostic methods for T and N. CONCLUSIONS: The 8th edition of the AJCC TNM seems to lead to better pretreatment staging. For both HPVpos and HPVneg, the agreement between pretreatment radiological and pathological staging.

Focused ultrasound for high-risk human papillomavirus infection-related low-grade cervical lesions: a prospective cohort study.

OBJECTIVES: To assess the efficacy and safety of focused ultrasound (FU) for high-risk human papillomavirus (HR-HPV) infection-related cervical low-grade squamous intraepithelial lesions (LSIL). METHODS: Of 185 patients who met the inclusion criteria for this prospective study from October 2020 to November 2021, 95 received FU and 90 were followed up only. At the six-month follow-up, the HR-HPV clearance and LSIL regression rates of the groups were compared and factors affecting HR-HPV clearance were analyzed. The safety and side effects of FU were evaluated. RESULTS: No significant difference was found in the baseline clinical data between the two groups (p > 0.05). At the six-month follow-up, the HR-HPV clearance rates were 75.6% in the FU group and 25.6% in the observation group (p = 0.000). The LSIL regression rates were 89.5% in the FU group and 56.4% in the observation group (p = 0.000). Multivariate logistic regression analysis showed that the HR-HPV clearance rate in the FU group was 9.03 times higher than that in the observation group (95% confidence interval [CI], 3.75-21.73, p = 0.000), and the clearance rate of single-type HR-HPV infections was 5.28 times higher than that of multi-type infections (95% CI, 1.83-15.23, p = 0.002). The mean intraoperative bleeding was 1.8 ± 0.6 (1-3) mL; the mean intraoperative pain score was 2.6 ± 1.0 (1-6). CONCLUSIONS: For patients with HR-HPV infection-related histological LSIL, FU can eliminate HR-HPV infection and cause lesions to regress in a short time, with few adverse effects and good tolerance.

Correlation between radiologic and pathologic extranodal extension in HPV-associated oropharyngeal cancer: Systematic review.

Pretreatment determination of extranodal extension (ENE) has significant clinical implications in human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC). Unfortunately there is no gold-standard imaging modality for radiological assessment of ENE in HPV+ OPSCC, leading to subjective assessments and complex decision making concerning ENE. A systematic review of diagnostic test accuracy was therefore undertaken, with five databases systemically searched to evaluate the diagnostic performance of an imaging modality for detection of ENE in HPV+ OPSCC. A meta-analysis was conducted on four CT studies using a random-effects model. While a narrative synthesis was provided for the studies using PET/CT and "CT and MRI." Out of 1772 hits, six studies were included in the review. Meta-analysis on four CT studies showed CT had an overall sensitivity of 77% and specificity of 60%. PET/CT had a sensitivity of 37.5% and specificity of 97%. "CT and MRI" had a sensitivity of 62% and specificity of 78%. Further diagnostic studies involving CT, PET/CT and MRI are ultimately required.

Expression patterns of uPAR, TF and EGFR and their potential as targets for molecular imaging in oropharyngeal squamous cell carcinoma.

The clinical introduction of molecular imaging for the management of oropharyngeal squamous cell carcinoma (OPSCC) relies on the identification of relevant cancer­specific biomarkers. The application of three membrane­bound receptors, namely urokinase­type plasminogen activator receptor (uPAR), tissue factor (TF) and EGFR have been previously explored for targeted imaging and therapeutic strategies in a broad range of solid cancers. The present study aimed to investigate the expression patterns of uPAR, EGFR and TF by immunohistochemistry (IHC) to evaluate their potential for targeted imaging and prognostic value in OPSCC. In a retrospective cohort of 93 patients with primary OPSCC, who were balanced into the 45 human papillomavirus (HPV)­positive and 48 HPV­negative groups, the IHC­determined expression profiles of uPAR, TF and EGFR in large biopsy or tumor resection specimens were analyzed. Using the follow­up data, overall survival (OS) and recurrence­free survival were measured. Specifically, associations between survival outcome, biomarker expression and clinicopathological factors were examined using Cox proportional hazards model and log­rank test following Kaplan­Meier statistics. After comparing the expression pattern of biomarkers within the tumor compartment with that in the adjacent normal tissues, uPAR and TF exhibited a highly tumor­specific expression pattern, whereas EGFR showed a homogeneous expression within the tumor compartment as well as a consistent expression in the normal mucosal epithelium and salivary gland tissues. The positive expression rate of uPAR, TF and EGFR in the tumors was 98.9, 76.3 and 98.9%, respectively. No statistically significant association between biomarker expression and survival outcome could be detected. Higher uPAR expression levels had a trend towards reduced OS according to results from univariate analysis (P=0.07; hazard ratio=2.01; 95% CI=0.92­4.37). Taken together, these results suggest that uPAR, TF and EGFR may be suitable targets for molecular imaging and therapy in OPSCC. In particular, uPAR may be an attractive target owing to their high positive expression rates in tumors and a highly tumor­specific expression pattern.