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Background: First aim was to compare ddPCR assays of ctHPVDNA with p16 IHC and qualitative HPV PCR. Second aim was to carry out longitudinal blood sampling to test for association of ctHPVDNA with histological confirmed recurrence. Third aim was to perform a multidimensional assessment which included: (1) clinical features; (2) ctHPVDNA; (3) MRI-based tumor size measurements of primary tumor (PT) and cervical lymph node metastases (CLNM)., Methods: Plasma samples were collected before treatment and during follow-up, and ddPCR assay comprising E6 of HPV16 and HPV 33 and HPV 35 was used., Results: Present study was conducted at diagnosis in 117 patients and revealed a ctHPVDNA sensitivity of 100% (95% CI 95.5-100) and a specificity of 94.4 (95% CI 81.3-99.3), positive predictive value (PPV) of 94.4 (95% CI 81.3-99.3), and negative predictive value (NPP) of 100% (95% CI 89.7-100). During follow-up ctHPVDNA had a sensitivity of 100% (95% CI 72.1-100)% and specificity of 98.4% (95% CI 91.7-100)%, PPV% of 90.9% (95% CI 62.3-98.4) and NPV% of 100% (95% CI 94.3-100) for ability to detect recurrence. Correlation between both the CLNM volume and the sum of PT and CLNM volume was observed., Conclusions: ctHPVDNA was superior to p16 in identification of HPV-OPSCC at diagnosis. Introduction of ctHPVDNA, beyond diagnostic setting, represents a great opportunity to improve follow-up protocol of OPSCC patients., (© 2024 The Author(s). Head & Neck published by Wiley Periodicals LLC.)

The role of molecular markers is increasingly being recognized for head and neck tumors, ranging from benign lesions like paragangliomas to malignancies like squamous cell carcinomas. Multiple studies have recently validated blood tests for circulating tumor tissue-modified viral human papillomavirus DNA (HPV ct-DNA) for posttreatment surveillance of HPV-driven oropharyngeal squamous cell carcinomas. This technology quantifies fragments of circulating DNA that are shed into the blood stream with very high (>95%) positive and negative predictive values and are also highly sensitive in distinguishing tumor HPV-DNA from a noncancerous source. This study has a cohort of 34 patients with HPV-driven oropharyngeal squamous cell carcinomas, having at least 3 sequential imaging studies and ct-DNA values. The study showed a strong positive correlation between the imaging findings and the ct-DNA level in recurrent HPV-positive oropharyngeal squamous cell carcinomas. Findings also include the 100% negative predictive value of HPV ct-DNA tests to rule out tumor recurrence. At our institution, we are now routinely performing the ct-DNA assay for surveillance of treated HPV oropharyngeal squamous cell carcinomas. Correlation among clinical, radiologic, and biomarker findings are now part of routine discussions during the multidisciplinary tumor boards., (© 2024 by American Journal of Neuroradiology.)

Human papillomavirus (HPV) is the most common sexually transmitted infection that proliferates in the squamous epithelium and is the most common source of viral-related neoplasms. Low-risk subtypes (HPV-6 and -11) cause respiratory papillomas (laryngeal, tracheal, and bronchial) and condyloma acuminata of the penis, anus, and perineal region (anogenital warts). High-risk subtypes (HPV-16, -18, -31, and -33) are responsible for oropharyngeal squamous cell carcinoma (SCC) that involves the tongue base, tonsils, posterior pharyngeal wall, and larynx and malignancies of the anogenital region (cancers of the cervix, vagina, vulva, penis, and anal canal). Recent studies have increasingly shown a favorable treatment response and substantial differences in the overall prognosis associated with HPV-associated oropharyngeal cancers. Given this fact, oropharyngeal, cervical, and penile SCCs are classified as HPV-associated and HPV-independent cancers in the current World Health Organization classification. Imaging is essential in the early detection, diagnosis, and staging of HPV-associated cancers. Imaging also helps assess treatment response and postoperative complications and is used for long-term surveillance. HPV-associated oropharyngeal SCCs have well-defined borders and solid and cystic nodal metastases at imaging. Updated screening and vaccination guidelines are currently available that have great potential to decrease the overall disease burden and help control this worldwide public health concern. Novel therapeutic strategies, such as immunotherapies, are being explored, and imaging biomarkers that can predict treatment response and prognosis are being investigated; radiologists play a pivotal role in these efforts. © RSNA, 2024 Supplemental material is available for this article.

Purpose: To evaluate deep learning-based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net., Methods: This single-center retrospective study included 91 patients with OPSCC. The patients were grouped into the development (n = 56), test 1 (n = 13), and test 2 (n = 22) cohorts. In the development cohort, OPSCC was manually segmented on CT, MR, and co-registered CT-MR images, which served as the ground truth. The multimodal and multichannel input images were then trained using a self-configuring nnU-Net. For evaluation metrics, dice similarity coefficient (DSC) and mean Hausdorff distance (HD) were calculated for test cohorts. Pearson's correlation and Bland-Altman analyses were performed between ground truth and prediction volumes. Intraclass correlation coefficients (ICCs) of radiomic features were calculated for reproducibility assessment., Results: All models achieved robust segmentation performances with DSC of 0.64 ± 0.33 (CT), 0.67 ± 0.27 (MR), and 0.65 ± 0.29 (CT-MR) in test cohort 1 and 0.57 ± 0.31 (CT), 0.77 ± 0.08 (MR), and 0.73 ± 0.18 (CT-MR) in test cohort 2. No significant differences were found in DSC among the models. HD of CT-MR (1.57 ± 1.06 mm) and MR models (1.36 ± 0.61 mm) were significantly lower than that of the CT model (3.48 ± 5.0 mm) (p = 0.037 and p = 0.014, respectively). The correlation coefficients between the ground truth and prediction volumes for CT, MR, and CT-MR models were 0.88, 0.93, and 0.9, respectively. MR models demonstrated excellent mean ICCs of radiomic features (0.91-0.93)., Conclusion: The self-configuring nnU-Net demonstrated reliable and accurate segmentation of OPSCC on CT and MRI. The multimodal CT-MR model showed promising results for the simultaneous segmentation on CT and MRI., Clinical Relevance Statement: Deep learning-based automatic detection and segmentation of oropharyngeal squamous cell carcinoma on pre-treatment CT and MRI would facilitate radiologic response assessment and radiotherapy planning., Key Points: • The nnU-Net framework produced a reliable and accurate segmentation of OPSCC on CT and MRI. • MR and CT-MR models showed higher DSC and lower Hausdorff distance than the CT model. • Correlation coefficients between the ground truth and predicted segmentation volumes were high in all the three models., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Background and Purpose: To optimize our previously proposed TransRP, a model integrating CNN (convolutional neural network) and ViT (Vision Transformer) designed for recurrence-free survival prediction in oropharyngeal cancer and to extend its application to the prediction of multiple clinical outcomes, including locoregional control (LRC), Distant metastasis-free survival (DMFS) and overall survival (OS)., Materials and Methods: Data was collected from 400 patients (300 for training and 100 for testing) diagnosed with oropharyngeal squamous cell carcinoma (OPSCC) who underwent (chemo)radiotherapy at University Medical Center Groningen. Each patient's data comprised pre-treatment PET/CT scans, clinical parameters, and clinical outcome endpoints, namely LRC, DMFS and OS. The prediction performance of TransRP was compared with CNNs when inputting image data only. Additionally, three distinct methods (m1-3) of incorporating clinical predictors into TransRP training and one method (m4) that uses TransRP prediction as one parameter in a clinical Cox model were compared., Results: TransRP achieved higher test C-index values of 0.61, 0.84 and 0.70 than CNNs for LRC, DMFS and OS, respectively. Furthermore, when incorporating TransRP's prediction into a clinical Cox model (m4), a higher C-index of 0.77 for OS was obtained. Compared with a clinical routine risk stratification model of OS, our model, using clinical variables, radiomics and TransRP prediction as predictors, achieved larger separations of survival curves between low, intermediate and high risk groups., Conclusion: TransRP outperformed CNN models for all endpoints. Combining clinical data and TransRP prediction in a Cox model achieved better OS prediction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Introduction: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions., Aim: To construct and evaluate a preoperative diagnostic method to predict OCLNM in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features., Methods: A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA), and survival analysis., Results: Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927), and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1, and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC., Conclusion: The proposed MRI-based Resnet50 DL model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: The prognostic role of imaging with [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) in oropharynx cancer (OPC) has been demonstrated in the past. The aim of this study was to assess the prognostic impact of both baseline and post-treatment PET/CT in patients with OPC and treated with chemo- and/or radiotherapy., Methods: The PET/CT parameters of scans performed before and after therapy were collected and analyzed to find significant prognosticators for progression-free survival (PFS) and overall survival (OS). Human papillomavirus (HPV) infection's influence on the prognosis was also taken into account., Results: A total of 66 patients were included in the study. The staging volumetric parameters of PET/CT were significant prognosticators for OS, while the same parameters were affordable predictors for PFS at the restaging evaluation. No significant correlations between HPV infection and PET/CT parameters were reported., Conclusion: The prognostic role of volumetric [18F]FDG PET/CT parameters in patients with OPC was reported.

Oral squamous cell carcinoma (OSCC) with metastasis to the thyroid gland is exceedingly rare, with limited documentation within the literature. Between 1984 and 2023, only 40 cases of head and neck squamous cell carcinoma (SCC) with thyroid gland metastasis were described in published literature. Herein, we present a distinctive case of second primary oropharyngeal SCC with metastasis to the thyroid, detected during surveillance positron emission tomography (PET) scanning subsequent to negative margin resection and radiation therapy for SCC originating from the hard palate. The underlying mechanisms overseeing metastasis remain elusive, with hypotheses ranging from lymphatic drainage routes connecting the thyroid gland and retropharyngeal lymph nodes to hematologic dissemination. The management of metastases to the thyroid gland is multifaceted, encompassing approaches ranging from lobectomy and total thyroidectomy to palliative interventions. We present this atypical case alongside supportive pathological and radiological findings and a comprehensive review of this rare clinical entity to offer insight into its diagnosis and management., Competing Interests: Declaration of competing interest The authors have no conflicts of interest or financial disclosures., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., (© 2024. The Author(s).)

Background: Patients with oropharyngeal cancer (OPC) treated with chemoradiation can experience weight loss and tumor shrinkage, altering the prescribed treatment. Treatment replanning ensures patients do not receive excessive doses to normal tissue. However, it is a time- and resource-intensive process, as it takes 1 to 2 weeks to acquire a new treatment plan, and during this time, overtreatment of normal tissues could lead to increased toxicities. Currently, there are limited prognostic factors to determine which patients will require a replan. There remains an unmet need for predictive models to assist in identifying patients who could benefit from the knowledge of a replan prior to treatment., Purpose: We aimed to develop and evaluate a CT-based radiomic model, integrating clinical and dosimetric information, to predict the need for a replan prior to treatment., Methods: A dataset of patients (n = 315) with OPC treated with chemoradiation was used for this study. The dataset was split into independent training (n = 220) and testing (n = 95) datasets. Tumor volumes and organs at risk (OARs) were contoured on planning CT images. PyRadiomics was used to compute radiomic image features (n = 1218) on the original and filtered images from each of the primary tumor, nodal volumes, and ipsilateral and contralateral parotid glands. Nine clinical features and nine dose features extracted from the OARs were collected and those significantly (p < 0.05) associated with the need for a replan in the training dataset were used in a baseline model. Random forest feature selection was applied to select the optimal radiomic features to predict replanning. Logistic regression, Naïve Bayes, support vector machine, and random forest classifiers were built using the non-correlated selected radiomic, clinical, and dose features on the training dataset and performance was assessed in the testing dataset. The area under the curve (AUC) was used to assess the prognostic value., Results: A total of 78 patients (25%) required a replan. Smoking status, nodal stage, base of tongue subsite, and larynx mean dose were found to be significantly associated with the need for a replan in the training dataset and incorporated into the baseline model, as well as into the combined models. Five predictive radiomic features were selected (one nodal volume, one primary tumor, two ipsilateral and one contralateral parotid gland). The baseline model comprised of clinical and dose features alone achieved an AUC of 0.66 [95% CI: 0.51-0.79] in the testing dataset. The random forest classifier was the top-performing radiomics model and achieved an AUC of 0.82 [0.75-0.89] in the training dataset and an AUC of 0.78 [0.68-0.87] in the testing dataset, which significantly outperformed the baseline model (p = 0.023, testing dataset)., Conclusions: This is the first study to use radiomics from the primary tumor, nodal volumes, and parotid glands for the prediction of replanning for patients with OPC. Radiomic features augmented clinical and dose features for predicting the need for a replan in our testing dataset. Once validated, this model has the potential to assist physicians in identifying patients that may benefit from a replan, allowing for better resource allocation and reduced toxicities., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: Delta radiomics is a high-throughput computational technique used to describe quantitative changes in serial, time-series imaging by considering the relative change in radiomic features of images extracted at two distinct time points. Recent work has demonstrated a lack of prognostic signal of radiomic features extracted using this technique. We hypothesize that this lack of signal is due to the fundamental assumptions made when extracting features via delta radiomics, and that other methods should be investigated., Purpose: The purpose of this work was to show a proof-of-concept of a new radiomics paradigm for sparse, time-series imaging data, where features are extracted from a spatial-temporal manifold modeling the time evolution between images, and to assess the prognostic value on patients with oropharyngeal cancer (OPC)., Methods: To accomplish this, we developed an algorithm to mathematically describe the relationship between two images acquired at time t = 0 $t = 0$ and t > 0 $t &gt; 0$ . These images serve as boundary conditions of a partial differential equation describing the transition from one image to the other. To solve this equation, we propagate the position and momentum of each voxel according to Fokker-Planck dynamics (i.e., a technique common in statistical mechanics). This transformation is driven by an underlying potential force uniquely determined by the equilibrium image. The solution generates a spatial-temporal manifold (3 spatial dimensions + time) from which we define dynamic radiomic features. First, our approach was numerically verified by stochastically sampling dynamic Gaussian processes of monotonically decreasing noise. The transformation from high to low noise was compared between our Fokker-Planck estimation and simulated ground-truth. To demonstrate feasibility and clinical impact, we applied our approach to 18 F-FDG-PET images to estimate early metabolic response of patients (n = 57) undergoing definitive (chemo)radiation for OPC. Images were acquired pre-treatment and 2-weeks intra-treatment (after 20 Gy). Dynamic radiomic features capturing changes in texture and morphology were then extracted. Patients were partitioned into two groups based on similar dynamic radiomic feature expression via k-means clustering and compared by Kaplan-Meier analyses with log-rank tests (p < 0.05). These results were compared to conventional delta radiomics to test the added value of our approach., Results: Numerical results confirmed our technique can recover image noise characteristics given sparse input data as boundary conditions. Our technique was able to model tumor shrinkage and metabolic response. While no delta radiomics features proved prognostic, Kaplan-Meier analyses identified nine significant dynamic radiomic features. The most significant feature was Gray-Level-Size-Zone-Matrix gray-level variance (p = 0.011), which demonstrated prognostic improvement over its corresponding delta radiomic feature (p = 0.722)., Conclusions: We developed, verified, and demonstrated the prognostic value of a novel, physics-based radiomics approach over conventional delta radiomics via data assimilation of quantitative imaging and differential equations., (© 2024 American Association of Physicists in Medicine.)

Purpose: We investigated the potential of baseline 4'-[methyl- 11 C]-thiothymidine ([ 11 C]4DST) PET for predicting loco-regional control of head and neck squamous cell carcinoma (HNSCC)., Methods: A retrospective analysis was performed using volumetric parameters, such as SUVmax, proliferative tumor volume (PTV), and total lesion proliferation (TLP), of pretreatment [ 11 C]4DST PET for 91 patients with HNSCC with primary lesions in the oral cavity, hypopharynx, supraglottis, and oropharynx, which included p16-negative patients. PTV and TLP were calculated for primary lesions and metastatic lymph nodes combined. We examined the association among the parameters and relapse-free survival and whether case selection focused on biological characteristics improved the accuracy of prognosis prediction., Results: The area under the curves (AUCs) using PTV and TLP were high for the oropharyngeal/hypopharyngeal/supraglottis groups (0.91 and 0.87, respectively), whereas that of SUVmax was 0.66 ( P  < 0.01). On the other hand, the oral group had lower AUCs for PTV and TLP (0.72 and 0.77, respectively). When all cases were examined, the AUCs using PTV and TLP were 0.84 and 0.83, respectively., Conclusion: Baseline [ 11 C]4DST PET/CT volume-based parameters can provide important prognostic information with p16-negative oropharyngeal, hypopharyngeal, and supraglottic cancer patients., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: To develop and validate a multiparametric MRI-based radiomics model with optimal oversampling and machine learning techniques for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC)., Methods: This retrospective, multicenter study included consecutive patients with newly diagnosed and pathologically confirmed OPSCC between January 2017 and December 2020 (110 patients in the training set, 44 patients in the external validation set). A total of 293 radiomics features were extracted from three sequences (T2-weighted images [T2WI], contrast-enhanced T1-weighted images [CE-T1WI], and ADC). Combinations of three feature selection, five oversampling, and 12 machine learning techniques were evaluated to optimize its diagnostic performance. The area under the receiver operating characteristic curve (AUC) of the top five models was validated in the external validation set., Results: A total of 154 patients (59.2 ± 9.1 years; 132 men [85.7%]) were included, and oversampling was employed to account for data imbalance between HPV-positive and HPV-negative OPSCC (86.4% [133/154] vs. 13.6% [21/154]). For the ADC radiomics model, the combination of random oversampling and ridge showed the highest diagnostic performance in the external validation set (AUC, 0.791; 95% CI, 0.775-0.808). The ADC radiomics model showed a higher trend in diagnostic performance compared to the radiomics model using CE-T1WI (AUC, 0.604; 95% CI, 0.590-0.618), T2WI (AUC, 0.695; 95% CI, 0.673-0.717), and a combination of both (AUC, 0.642; 95% CI, 0.626-0.657)., Conclusions: The ADC radiomics model using random oversampling and ridge showed the highest diagnostic performance in predicting the HPV status of OPSCC in the external validation set., Clinical Relevance Statement: Among multiple sequences, the ADC radiomics model has a potential for generalizability and applicability in clinical practice. Exploring multiple oversampling and machine learning techniques was a valuable strategy for optimizing radiomics model performance., Key Points: • Previous radiomics studies using multiparametric MRI were conducted at single centers without external validation and had unresolved data imbalances. • Among the ADC, CE-T1WI, and T2WI radiomics models and the ADC histogram models, the ADC radiomics model was the best-performing model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma. • The ADC radiomics model with the combination of random oversampling and ridge showed the highest diagnostic performance., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

We aimed to investigate the effects of 18 F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. Methods: We extracted 1,037 18 F-FDG PET radiomic features quantifying the shape, intensity, and texture of 430 OPSCC primary tumors. The reproducibility of individual features across 3 intensity-normalized images (body-weight SUV, reference tissue activity ratio to lentiform nucleus of brain and cerebellum) and the raw PET data was assessed using an intraclass correlation coefficient (ICC). We investigated the effects of intensity normalization on the features' utility in predicting the human papillomavirus (HPV) status of OPSCCs in univariate logistic regression, receiver-operating-characteristic analysis, and extreme-gradient-boosting (XGBoost) machine-learning classifiers. Results: Of 1,037 features, a high (ICC ≥ 0.90), medium (0.90 > ICC ≥ 0.75), and low (ICC < 0.75) degree of reproducibility across normalization methods was attained in 356 (34.3%), 608 (58.6%), and 73 (7%) features, respectively. In univariate analysis, features from the PET normalized to the lentiform nucleus had the strongest association with HPV status, with 865 of 1,037 (83.4%) significant features after multiple testing corrections and a median area under the receiver-operating-characteristic curve (AUC) of 0.65 (interquartile range, 0.62-0.68). Similar tendencies were observed in XGBoost models, with the lentiform nucleus-normalized model achieving the numerically highest average AUC of 0.72 (SD, 0.07) in the cross validation within the training cohort. The model generalized well to the validation cohorts, attaining an AUC of 0.73 (95% CI, 0.60-0.85) in independent validation and 0.76 (95% CI, 0.58-0.95) in external validation. The AUCs of the XGBoost models were not significantly different. Conclusion: Only one third of the features demonstrated a high degree of reproducibility across intensity-normalization techniques, making uniform normalization a prerequisite for interindividual comparability of radiomic markers. The choice of normalization technique may affect the radiomic features' predictive value with respect to HPV. Our results show trends that normalization to the lentiform nucleus may improve model performance, although more evidence is needed to draw a firm conclusion., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: The study aimed to describe the prevalence, severity, and trajectory of internal lymphedema, external lymphedema, and fibrosis in patients with oral cavity or oropharyngeal (OCOP) cancer., Methods and Materials: One hundred twenty patients with newly diagnosed OCOP cancer were enrolled in a prospective longitudinal study. Recruitment was conducted at a comprehensive medical center. Participants were assessed pretreatment; at end of treatment; and at 3, 6, 9, and 12 months post-cancer treatment. Validated clinician-reported measures and computed tomography were used to assess the study outcomes., Results: Seventy-six patients who completed the 9- or 12-month assessments were included in this report. Examination of the external lymphedema and fibrosis trajectories revealed that the total severity score peaked between the end of treatment and 3 months posttreatment and then decreased gradually over time but did not return to baseline by 12 months posttreatment (P < .001). The longitudinal patterns of severity scores for patients treated with surgery only or with multimodality therapy were similar. Examination of the internal swelling trajectories revealed that all patients experienced a significant increase in sites with swelling immediately posttreatment. For patients treated with surgery only, swelling was minimal and returned to baseline by 9 to 12 months posttreatment. Patients receiving multimodal treatment experienced a gradual decrease in number of sites with swelling during the 12-month posttreatment period that remained significantly above baseline (P < .05). Computed tomography revealed different patterns of changes in prevertebral soft tissue and epiglottic thickness in the surgery-only and multimodality treatment groups during the 12-month posttreatment period. There were minimal changes in thickness in both regions in the surgery-only group. Patients with multimodal treatment had significant increases in thickness in both regions 3 months posttreatment that remained thicker at 12 months than at baseline (P < .001)., Conclusions: Lymphedema and fibrosis are the common complications of OCOP cancer therapy. Routine assessment, monitoring, and timely treatment of lymphedema and fibrosis are critical., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: A reliable and comprehensive cancer prognosis model for oropharyngeal cancer (OPC) could better assist in personalizing treatment. In this work, we developed a vision transformer-based (ViT-based) multilabel model with multimodal input to learn complementary information from available pretreatment data and predict multiple associated endpoints for radiation therapy for patients with OPC., Methods and Materials: A publicly available data set of 512 patients with OPC was used for both model training and evaluation. Planning computed tomography images, primary gross tumor volume masks, and 16 clinical variables representing patient demographics, diagnosis, and treatment were used as inputs. To extract deep image features with global attention, we used a ViT module. Clinical variables were concatenated with the learned image features and fed into fully connected layers to incorporate cross-modality features. To learn the mapping between the features and correlated survival outcomes, including overall survival, local failure-free survival, regional failure-free survival, and distant failure-free survival, we employed 4 multitask logistic regression layers. The proposed model was optimized by combining the multitask logistic regression negative-log likelihood losses of different prediction targets., Results: We employed the C-index and area under the curve metrics to assess the performance of our model for time-to-event prediction and time-specific binary prediction, respectively. Our proposed model outperformed corresponding single-modality and single-label models on all prediction labels, achieving C-indices of 0.773, 0.765, 0.776, and 0.773 for overall survival, local failure-free survival, regional failure-free survival, and distant failure-free survival, respectively. The area under the curve values ranged between 0.799 and 0.844 for different tasks at different time points. Using the medians of predicted risks as the thresholds to identify high-risk and low-risk patient groups, we performed the log-rank test, the results of which showed significantly larger separations in different event-free survivals., Conclusion: We developed the first model capable of predicting multiple labels for OPC simultaneously. Our model demonstrated better prognostic ability for all the prediction targets compared with corresponding single-modality models and single-label models., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: By radiomic analysis of the postcontrast CT images, this study aimed to predict locoregional recurrence (LR) of locally advanced oropharyngeal cancer (OPC) and hypopharyngeal cancer (HPC)., Methods: A total of 192 patients with stage III-IV OPC or HPC from two independent cohort were randomly split into a training cohort with 153 cases and a testing cohort with 39 cases. Only primary tumor mass was manually segmented. Radiomic features were extracted using PyRadiomics, and then the support vector machine was used to build the radiomic model with fivefold cross-validation process in the training data set. For each case, a radiomics score was generated to indicate the probability of LR., Results: There were 94 patients with LR assigned in the progression group and 98 patients without LR assigned in the stable group. There was no significant difference of TNM staging, treatment strategies and common risk factors between these two groups. For the training data set, the radiomics model to predict LR showed 83.7% accuracy and 0.832 (95% CI 0.72, 0.87) area under the ROC curve (AUC). For the test data set, the accuracy and AUC slightly declined to 79.5% and 0.770 (95% CI 0.64, 0.80), respectively. The sensitivity/specificity of training and test data set for LR prediction were 77.6%/89.6%, and 66.7%/90.5%, respectively., Conclusions: The image-based radiomic approach could provide a reliable LR prediction model in locally advanced OPC and HPC. Early identification of those prone to post-treatment recurrence would be helpful for appropriate adjustments to treatment strategies and post-treatment surveillance., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: The objective was to perform risk stratification of oropharyngeal cancer (OPC) for treatment de-escalation based on the radiomics analysis and human papillomavirus (HPV) status., Methods: A total of 265 patients with OPC who underwent baseline contrast-enhanced computed tomography were analyzed, and the patients were grouped into the training (n = 133) and test (n = 132) cohorts at a ratio of 1:1. Intratumoral and peritumoral radiomics features were extracted, and the radiomics signature (Rscore) was calculated using least absolute shrinkage and selection operator regression (LASSO) and Cox regression analyses., Results: Twelve features were selected to establish the radiomics signature (Rscore) of intratumoral regions +10-mm peritumoral regions, which yielded maximum AUCs of 0.835, 0.798, and 0.784 in the training, test, and validation cohorts, respectively. Patients with OPC were divided into the high-risk group, intermediate-risk group, and low-risk group based on the Rscore and HPV status and had different prognoses. Patients in the low-risk group benefit from radiotherapy alone, and patients in the intermediate-risk group only benefitted from chemoradiotherapy., Conclusion: The radiomics signature appears to improve the predictive performance of clinical characteristics for oropharyngeal cancer. The combined stratification of the radiomics signature and HPV status might be preferred to select patients for de-escalated treatment., (© 2022 Wiley Periodicals LLC.)

Introduction: Circulating tumour human papillomavirus DNA (ctHPVDNA) is an emerging tool to assess post-treatment response in patients with HPV+ oropharyngeal squamous cell carcinoma (OPSCC). Its use is not a standard practice, however, with interval F-18 FDG PET/CT and fiberoptic examination preferred. Post-treatment PET/CT at 3 months has a low positive predictive value (PPV), especially in patients with HPV+ OPSCC treated with (chemo)radiation therapy (CRT). We aimed to compare 3-6 month post-treatment PET/CT and ctHPVDNA test results to determine the most effective option for post-treatment response assessment., Methods: Patients with HPV+ OPSCC that underwent commercially available ctHPVDNA blood testing after curative intent treatment were identified. Demographic, clinical, treatment, surveillance and oncologic outcome information were collected for each patient. Specificity and false positive rate were calculated for post-treatment PET/CT and ctHPVDNA., Results: 80% of patients had Stage I disease. 52% of the population was treated with definitive chemoradiation (43%) or accelerated radiation (9%), with the remaining patients treated with transoral robotic surgery (TORS) +/- risk-adapted adjuvant therapy. In total, 25 patients underwent ctHPVDNA testing and PET/CT at 3-6 months after finishing treatment. At 3-6 months post-treatment, specificity of ctHPVDNA and PET/CT was 96% and 56%, correlating to false positive rates of 4% and 44%, respectively., Conclusions: ctHPVDNA is more reliable than PET/CT following treatment in patients with HPV+ OPSCC, and its incorporation in post-treatment response assessment will decrease the rate of anxiety over persistent disease and lead to a decrease in unnecessary medical procedures, including completion of neck dissection., (© 2023 The Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.)

Background and Objective: Recently, deep learning (DL) algorithms showed to be promising in predicting outcomes such as distant metastasis-free survival (DMFS) and overall survival (OS) using pre-treatment imaging in head and neck cancer. Gross Tumor Volume of the primary tumor (GTVp) segmentation is used as an additional channel in the input to DL algorithms to improve model performance. However, the binary segmentation mask of the GTVp directs the focus of the network to the defined tumor region only and uniformly. DL models trained for tumor segmentation have also been used to generate predicted tumor probability maps (TPM) where each pixel value corresponds to the degree of certainty of that pixel to be classified as tumor. The aim of this study was to explore the effect of using TPM as an extra input channel of CT- and PET-based DL prediction models for oropharyngeal cancer (OPC) patients in terms of local control (LC), regional control (RC), DMFS and OS., Methods: We included 399 OPC patients from our institute that were treated with definitive (chemo)radiation. For each patient, CT and PET scans and GTVp contours, used for radiotherapy treatment planning, were collected. We first trained a previously developed 2.5D DL framework for tumor probability prediction by 5-fold cross validation using 131 patients. Then, a 3D ResNet18 was trained for outcome prediction using the 3D TPM as one of the possible inputs. The endpoints were LC, RC, DMFS, and OS. We performed 3-fold cross validation on 168 patients for each endpoint using different combinations of image modalities as input. The final prediction in the test set (100) was obtained by averaging the predictions of the 3-fold models. The C-index was used to evaluate the discriminative performance of the models., Results: The models trained replacing the GTVp contours with the TPM achieved the highest C-indexes for LC (0.74) and RC (0.60) prediction. For OS, using the TPM or the GTVp as additional image modality resulted in comparable C-indexes (0.72 and 0.74)., Conclusions: Adding predicted TPMs instead of GTVp contours as an additional input channel for DL-based outcome prediction models improved model performance for LC and RC., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2023. Published by Elsevier B.V.)

Objectives: Emerging data supports radical intent therapy for oligometastatic (OM) relapsed human papilloma virus (HPV+) related oropharyngeal cancer (OPC). We assess the association of follow-up imaging frequency amongst HPV + OPC, with temporal and spatial patterns of distant relapse, to inform rationalisation of routine post-treatment imaging., Materials and Methods: A retrospective single centre cohort study was carried out of consecutive HPV + OPC patients treated with radical intent (chemo)radiotherapy ((CT)RT) between 2011 and 2019. OM state was defined as ≤ 5 metastasis, none larger than 3 cm (OM s ) or, if interval from last negative surveillance imaging > 6-months, then ≤ 10 metastasis, none larger than 5 cm, (OM p ). Patients not meeting OM s / OM p criteria were deemed to have incurable diffuse metastatic disease (DM diffuse )., Results: 793 HPV-OPC patients were identified with median follow-up 3.15years (range 0.2-8.9). 52 (6.6 %) patients had radiologically identified DM at first failure and were considered for analysis. The median time to recurrence was 15.1 months (range: 2.6-63 months). 87 % of distant metastasis (DM) occurred in the first two years after treatment. Twenty-seven (52 %) patients had OM (OM s or OM p ) at time of failure, with 31 % having OM s . The median time from completion of treatment to diagnosis of DM diffuse vs OM was 22.2 months (range: 2.6-63.1 months) vs 11.6 months (range: 3.5-32.5 months). The probability of being diagnosed with OM vs DM diffuse increased with reducing interval from last negative surveillance scan to imaging identifying DM (≤6 months 88.9 %, 7-12 months 71.4 %, 13-24 months 35 %, > 24 months 22.2 %)., Conclusion: We demonstrate that a reduced interval between last negative imaging and subsequent radiological diagnosis of DM is associated with increased likelihood of identification of OM disease. Consideration of increased frequency of surveillance imaging during the first two years of follow up is supported, particularly for patients at high risk of distant failure., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: To determine the cost-effectiveness of surveillance imaging with PET/CT scan among patients with human papillomavirus-positive oropharyngeal squamous cell carcinoma., Study Design: Cost-effectiveness analysis., Setting: Oncologic care centers in the United States with head and neck oncologic surgeons and physicians., Methods: We compared the cost-effectiveness of 2 posttreatment surveillance strategies: clinical surveillance with the addition of PET/CT scan versus clinical surveillance alone in human papillomavirus-positive oropharyngeal squamous cell carcinoma patients. We constructed a Markov decision model which was analyzed from a third-party payer's perspective using 1-year Markov cycles and a 30-year time horizon. Values for transition probabilities, costs, health care utilities, and their studied ranges were derived from the literature., Results: The incremental cost-effectiveness ratio for PET/CT with clinical surveillance versus clinical surveillance alone was $89,850 per quality-adjusted life year gained. Flexible fiberoptic scope exams during clinical surveillance would have to be over 51% sensitive or PET/CT scan cost would have to exceed $1678 for clinical surveillance alone to be more cost-effective. The willingness-to-pay threshold at which imaging surveillance was equally cost-effective to clinical surveillance was approximately $80,000/QALY., Conclusion: Despite lower recurrence rates of human papillomavirus-positive oropharyngeal cancer, a single PET/CT scan within 6 months after primary treatment remains a cost-effective tool for routine surveillance when its cost does not exceed $1678. The cost-effectiveness of this strategy is also dependent on the clinical surveillance sensitivity (flexible fiberoptic pharyngoscopy), and willingness-to-pay thresholds which vary by country., (© 2023 The Authors. Otolaryngology-Head and Neck Surgery published by Wiley Periodicals LLC on behalf of American Academy of Otolaryngology-Head and Neck Surgery Foundation.)

Rationale and Objectives: The purpose of this study was to evaluate the diagnostic utility of iterative metal artifact reduction (iMAR) in computed tomography (CT)-imaging of oral and oropharyngeal cancers when obscured by dental hardware artifacts and to determine the most appropriate iMAR settings for this purpose., Materials and Methods: The study retrospectively enrolled 27 patients (8 female, 19 male; mean age 64±12.7years) with histologically confirmed oral or oropharyngeal cancer obscured by dental artifacts in contrast-enhanced CT. Raw CT data were reconstructed with ascending iMAR strengths (levels 1/2/3/4/5) and one reconstruction without iMAR (level 0). For subjective analysis, two blinded radiologists rated tumor visualization and artifact severity on a five-point Likert scale. For objective analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) were determined., Results: iMAR reconstructions improved the subjective image quality of tumor edge and contrast, and the objective parameters of tumor SNR and CNR, reaching their optimum at iMAR levels 4 and 5 (P<.001). AI decreased with iMAR reconstructions reaching its minimum at iMAR level 5 (P<.001). Tumor detection rates increased 2.4-fold with iMAR 5, 2.1-fold with iMAR 4, and 1.9-fold with iMAR 3 compared to reconstructions without iMAR. Disadvantages such as algorithm-induced artifacts increased significantly with higher iMAR strengths (P<.05), reaching a maximum with iMAR 5., Conclusion: iMAR significantly improves CT imaging of oral and oropharyngeal cancers, as confirmed by both subjective and objective measures, with best results at highest iMAR strengths., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Matthias Stefan May reports a relationship with Siemens Healthcare that includes: speaking and lecture fees. Wolfgang Wuest reports a relationship with Siemens Healthcare that includes: speaking and lecture fees. Jan-Peter Roth reports a relationship with Siemens Healthcare that includes: speaking and lecture fees. Michael Uder reports a relationship with Siemens Healthcare that includes: speaking and lecture fees. Christian Hofmann reports a relationship with Siemens Healthcare that includes: employment. Bernhard Schmidt reports a relationship with Siemens Healthcare that includes: employment., (Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: Dysphagia is a common toxicity after head and neck (HN) radiation therapy that negatively affects quality of life. We explored the relationship between radiation therapy dose to normal HN structures and dysphagia 1 year after treatment using image-based datamining (IBDM), a voxel-based analysis technique., Methods and Materials: We used data from 104 patients with oropharyngeal cancer treated with definitive (chemo)radiation therapy. Swallow function was assessed pretreatment and 1 year posttreatment using 3 validated measures: MD Anderson Dysphagia Inventory (MDADI), performance status scale for normalcy of diet (PSS-HN), and water swallowing test (WST). For IBDM, we spatially normalized all patients' planning dose matrices to 3 reference anatomies. Regions where the dose was associated with dysphagia measures at 1 year were found by performing voxel-wise statistics and permutation testing. Clinical factors, treatment variables, and pretreatment measures were used in multivariable analysis to predict each dysphagia measure at 1 year. Clinical baseline models were found using backward stepwise selection. Improvement in model discrimination after adding the mean dose to the identified region was quantified using the Akaike information criterion. We also compared the prediction performance of the identified region with a well-established association: mean doses to the pharyngeal constrictor muscles., Results: IBDM revealed highly significant associations between dose to distinct regions and the 3 outcomes. These regions overlapped around the inferior section of the brain stem. All clinical models were significantly improved by including mean dose to the overlap region (P ≤ .006). Including pharyngeal dosimetry significantly improved WST (P = .04) but not PSS-HN or MDADI (P ≥ .06)., Conclusions: In this hypothesis-generating study, we found that mean dose to the inferior section of the brain stem is strongly associated with dysphagia 1 year posttreatment. The identified region includes the swallowing centers in the medulla oblongata, providing a possible mechanistic explanation. Further work including validation in an independent cohort is required., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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.

Background: Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high-risk patients who may be suitable to receive de-escalation or intensified treatment approaches., Purpose: To develop a deep learning (DL)-based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT)., Methods: Two patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre-treatment CT-scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2-year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), disease-specific survival (DSS), overall survival (OS), and disease-free survival (DFS). We proposed DL outcome prediction models with the multi-label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT-scans., Results: The multi-label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2-year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2-year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low-risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set., Conclusion: MLL models demonstrated better discriminative ability for all 2-year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Oropharyngeal cancer (OPC) comprises a group of various malignant tumours that grow in the throat, larynx, mouth, sinuses, and nose. THE RESEARCH AIMS: to investigate the performance of the OPC VMAT model by comparison to clinical plans in terms of dosimetric parameters and normal tissue complication probabilities., Purpose: Tune the model which at least matches the performance of clinical created photon treatment plans and analyse and find the most appropriate strategic plan scheme for OPC., Methods and Materials: The machine learning (ML) plans are compared to the reference plans (clinical plans) based on dose constraints and target coverage. VMAT oropharynx ML model of Raystation development 11B version (non-clinical) was used. A model was trained by using different modalities. A different strategy of machine learning and clinical plans was performed for five patients. The dose Prescribed for OPC is 70 Gy, 2 Gy per fraction (2Gy/Fx). The PTV was derived for the primary tumour and secondary tumour, PTV+7000 cGy and PTV&#95;5425 cGy volumetric modulated arc therapy (VMAT) were used with beams performing a full 360° rotation around the single isocenter., Results: Organs at risk were observed that the volume of L-Eye in clinical plan (AF) for the case1 treatment planning could be successfully used ensuring efficiency and lower than MLVMAT and MLVMAT-org plans were 372 cGy, 697 cGy and 667 cGy respectively, while showed case2, case3, case4 and case5 are better to protect the critical organs in ML plan compare with a clinical plan. DHI for the PTV-7000 and PTV-5425 is between 1 and 1.34, While DCI for PTV-7000 and PTV-5425 is between 0.98 and 1., Competing Interests: Declaration of competing interest None declared., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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.

Purpose: 18 F-Fluorodeoxyglucose positron emission tomography (FDG-PET) parameters are prognostic of oncologic outcomes in human papillomavirus-associated oropharyngeal squamous cell carcinoma (OPSCC). We used FDG-PET imaging biomarkers to select patients for de-escalated chemoradiotherapy (CRT), hypothesizing that acute toxicity will be improved with de-escalation., Methods and Materials: This is a planned interim initial feasibility and acute toxicity report from a phase 2, prospective, nonrandomized study, which enrolled patients with stage I-II p16+ OPSCC. All patients started definitive CRT to 70 Gy in 35 fractions, and those who met de-escalation criteria on midtreatment FDG-PET at fraction 10 completed treatment at 54 Gy in 27 fractions. We report the acute toxicity and patient-reported outcomes for 59 patients with a minimum follow-up of 3 months., Results: There were no statistically significant differences between baseline patient characteristics in the standard and de-escalated cohorts. There were 28 of 59 (47.5%) patients who met FDG-PET de-escalation criteria and collectively received 20% to 30% less dose to critical organs at risk known to affect toxicity. At 3 months posttreatment, patients who received de-escalated CRT lost significantly less weight (median, 5.8% vs 13.0%; P < .001), had significantly less change from baseline in penetration-aspiration scale score (median, 0 vs 1; P = .018), and had significantly fewer aspiration events on repeat swallow study (8.0% vs 33.3%, P = .037) compared with patients receiving standard CRT., Conclusions: Approximately half of patients with early-stage p16+ OPSCC are selected for de-escalation of definitive CRT using midtreatment FDG-PET biomarkers, which resulted in significantly improved rates of observed acute toxicity. Further follow-up is ongoing and will be required to determine whether this de-escalation approach preserves the favorable oncologic outcomes for patients with p16+ OPSCC before adoption., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: Although HPV-positive and negative oropharyngeal cancers are two distinct diseases, Post-Therapeutic Surveillance (PTS) modalities are similar. Adjusting PTS strategies to HPV status will represent a massive practice change that raises the issue of its acceptability, by both physicians and patients., Methods: Two distinct surveys were designed and submitted, respectively, to HPV-positive patients and physicians (surgeons, radiation and medical oncologists) involved in head and neck cancer treatment., Results: 133 patients and 90 physicians have participated to the study. Most patients were reluctant to embrace new PTS options (remote consultations, nurse consultations and smart phone applications). However, 84% of patients would be favorable to use HPV Circulating DNA (HPV Ct DNA) measurement to guide surveillance modalities. 57% of physicians acknowledged that our current PTS strategy is improvable and most of them would accept the use of new monitoring options from the third year of follow-up. 87% of physicians would be interested to participate to a trial comparing the current PTS strategy to a new approach, where monitoring modalities (number of visits, imaging prescription) would depend on HPV Ct DNA level., Conclusions: Patients and physicians are aware that PTS modalities should depend on HPV status. Their adhesion is a prerequisite to any potential changes. Strategies based on HPV Ct DNA measurement should be assessed within a randomized clinical trial., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., (© 2023 Wiley Periodicals LLC.)

Purpose: Automated treatment planning strategies are being widely implemented in clinical routines to reduce inter-planner variability, speed up the optimization process, and improve plan quality. This study aims to evaluate the feasibility and quality of intensity-modulated proton therapy (IMPT) plans generated with four different knowledge-based planning (KBP) pipelines fully integrated into a commercial treatment planning system (TPS)., Materials/methods: A data set containing 60 oropharyngeal cancer patients was split into 11 folds, each containing 47 patients for training, five patients for validation, and five patients for testing. A dose prediction model was trained on each of the folds, resulting in a total of 11 models. Three patients were left out in order to assess if the differences introduced between models were significant. From voxel-based dose predictions, we analyze the two steps that follow the dose prediction: post-processing of the predicted dose and dose mimicking (DM). We focused on the effect of post-processing (PP) or no post-processing (NPP) combined with two different DM algorithms for optimization: the one available in the commercial TPS RayStation (RSM) and a simpler isodose-based mimicking (IBM). Using 55 test patients (five test patients for each model), we evaluated the quality and robustness of the plans generated by the four proposed KBP pipelines (PP-RSM, PP-IBM, NPP-RSM, NPP-IBM). After robust evaluation, dose-volume histogram (DVH) metrics in nominal and worst-case scenarios were compared to those of the manually generated plans., Results: Nominal doses from the four KBP pipelines showed promising results achieving comparable target coverage and improved dose to organs at risk (OARs) compared to the manual plans. However, too optimistic post-processing applied to the dose prediction (i.e. important decrease of the dose to the organs) compromised the robustness of the plans. Even though RSM seemed to partially compensate for the lack of robustness in the PP plans, still 65% of the patients did not achieve the expected robustness levels. NPP-RSM plans seemed to achieve the best trade-off between robustness and OAR sparing., Discussion/conclusions: PP and DM strategies are crucial steps to generate acceptable robust and deliverable IMPT plans from ML-predicted doses. Before the clinical implementation of any KBP pipeline, the PP and DM parameters predefined by the commercial TPS need to be modified accordingly with a comprehensive feedback loop in which the robustness of the final dose calculations is evaluated. With the right choice of PP and DM parameters, KBP strategies have the potential to generate IMPT plans within clinically acceptable levels comparable to plans manually generated by dosimetrists., (© 2023 American Association of Physicists in Medicine.)

Background and Purpose: This study provides a review of the literature assessing whether semiquantitative PET parameters acquired at baseline and/or during definitive (chemo)radiotherapy ("prePET" and "iPET") can predict survival outcomes in patients with oropharyngeal squamous cell carcinoma (OPC), and the impact of human papilloma virus (HPV) status., Material and Methods: A literature search was carried out using PubMed and Embase between 2001 to 2021 in accordance with PRISMA., Results: The analysis included 22 FDG-PET/CT studies [1-22], 19 pre-PET and 3 both pre-PET and iPET, The analysis involved 2646 patients, of which 1483 are HPV-positive (17 studies: 10 mixed and 7 HPV-positive only), 589 are HPV-negative, and 574 have unknown HPV status. Eighteen studies found significant correlations of survival outcomes with pre-PET parameters, most commonly primary or "Total" (combined primary and nodal) metabolic tumour volume and/or total lesional glycolysis. Two studies could not establish significant correlations and both employed SUVmax only. Two studies also could not establish significant correlations when taking into account of the HPV-positive population only. Because of the heterogeneity and lack of standardized methodology, no conclusions on optimal cut-off values can be drawn. Ten studies specifically evaluated HPV-positive patients: five showed positive correlation of pre-PET parameters and survival outcomes, but four of these studies did not include advanced T or N staging in multivariate analysis, and two studies only showed positive correlations after excluding high risk patients with smoking history or adverse CT features. Two studies found that prePET parameters predicted treatment outcomes only in HPV-negative but not HPV-positive patients. Two studies found that iPET parameters could predict outcomes in HPV-positive patients but not prePET parameters., Conclusion: The current literature supports high pre-treatment metabolic burden prior to definitive (chemo)radiotherapy can predict poor treatment outcomes for HPV-negative OPC patients. Evidence is conflicting and currently does not support correlation in HPV-positive patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of interests BHK reports research support from the US National Institutes of Health (NIH; grant number K08DE030216-01) and the Radiological Society of North America, honoraria from Dedham Group, and stock options in Monogram Orthopedics. JDS reports research support paid to their institution from Merck, Bristol Myers Squibb (BMS), Regeneron, Debiopharm, and Merck KGA; consulting, scientific advisory board, and travel fees from Castle Biosciences, Genentech, Immunitas, Debiopharm, BMS, Nanobiotix, Tilos, AstraZeneca, LEK Consulting, Catenion, ACI Clinical, Astellas, Stimit, and Merck KGA; expert witness fees; stock options in Immunitas; and equity in Doximity. RH reports payment for consulting from Celgene, Nanobiotix, ISA, Merck, Eisai, BMS, AstraZeneca, Pfizer, Loxo, Genentech, Immunomic Therapeutics, GlaxoSmithKline, Gilead Sciences, Vaccinex, Emmanuel Merck Darmstadt (EMD), Serono, BioNTech, Achilles Therapeutics, Bayer, Coherus Biosciences, Boehringer Ingelheim, and Mirati Therapeutics; and research funding from Boehringer Ingelheim, Merck, BMS, Celgene, AstraZeneca, Genentech, Pfizer, Kura Oncology, EMD, and Serono. SA reports research support from the Agency for Healthcare Research and Quality, National Cancer Institute, National Science Foundation, American Cancer Society, American Society of Clinical Oncology, Amazon, and Patterson Trust; fees from American Society of Clinical Oncology, HighCape Capital, Emmerson Collective; and stock options in Prophet Brand Strategy. HJWLA reports research support from NIH (grant numbers U24CA194354, U01CA190234, U01CA209414, and R35CA22052), and the European Union—European research Council (grant number 866504) and stock and other ownership interests in Onc.Ai, Love Health, Health-AI, and Bayerische Motoren Werke AG. BAB reports a consulting or advisory role with Merck, Debiopharm Group, CUE Biopharma, Maverick Therapeutics, Rakuten Medical, Nanobiotix, Macrogenics, ALX Oncology, IO Biotech, Ipsen, Genentech/Roche, Kura Oncology, Merck KGA, Pharmaceutical Product Development Global, and Exelixis; research funding to their institution from Merck, Aduro Biotech, Formation Biologics, BMS, and CUE Biopharma; and travel, accommodations, or expenses from Merck and Debiopharm Group. RLF reports stock and other ownership interests in Novasenta; a consulting or advisory role with Merck, Pfizer, EMD Serono, Numab, Macrogenics, Aduro Biotech, Novasenta, Sanofi, Zymeworks, and BMS; and research funding from Bristol Myers Squibb, AstraZeneca/MedImmune, Merck, Tesaro, and Novasenta. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objective: Routine haematoxylin and eosin (H&E) photomicrographs from human papillomavirus-associated oropharyngeal squamous cell carcinomas (HPV + OpSCC) contain a wealth of prognostic information. In this study, we developed a high content image analysis (HCIA) workflow to quantify features of H&E images from HPV + OpSCC patients to identify prognostic features and predict patient outcomes., Methods: First, we have developed an open-source HCIA tool for single-cell segmentation and classification of H&E images. Subsequently, we have used our HCIA tool to analyse a set of 889 images from diagnostic H&E slides in a retrospective cohort of HPV + OpSCC patients with favourable (FO, n = 60) or unfavourable (UO, n = 30) outcomes. We have identified and measured 31 prognostic features which were quantified in each sample and used to train a neural network (NN) model to predict patient outcomes., Results: Univariate and multivariate statistical analyses revealed significant differences between FO and UO patients in 31 and 17 variables, respectively (P < 0.05). At the single-image level, the NN model had an overall accuracy of 72.5% and 71.2% in recognising FO and UO patients when applied to test or validation sets, respectively. When considering 10 images per patient, the accuracy of the NN model increased to 86.7% in the test set., Conclusion: Our open-source H&E analysis workflow and predictive models confirm previously reported prognostic features and identifies novel factors which predict HPV + OpSCC outcomes with promising accuracy. Our work supports the use of machine learning in digital pathology to exploit clinically relevant features in routine diagnostic pathology without additional biomarkers., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: We investigated the incidence and predictive factors of retropharyngeal lymph node (RPLN) metastases in patients with oropharyngeal cancer (OPC) undergoing multimodality treatment planning imaging before radiotherapy., Methods: Consecutive patients with OPC treated with curative-intent radiotherapy from 2017 to 2019 were retrospectively analyzed. Treatment planning comprised contrast-enhanced computed tomography (CT), magnetic resonance imaging (MRI), and fluorodeoxyglucose-positron emission tomography (FDG-PET) unless contraindicated., Results: Of 300 patients, 66 (22%) had radiological evidence of RPLN involvement on planning images, compared to 17 (6%) on diagnostic CT alone. On multivariate analysis, RPLN involvement was statistically (p < 0.05) associated with tonsil, soft palate, and posterior pharyngeal wall primaries, and with disease extension to the soft palate or vallecula., Conclusions: Multimodality treatment planning imaging reveals a high rate of RPLN metastases from OPC compared to diagnostic CT alone. Patients with tonsil, soft palate, or posterior pharyngeal wall primaries or disease extending to the soft palate or vallecula appear at higher risk., (© 2023 The Authors. Head & Neck published by Wiley Periodicals LLC.)

Objectives: Accurate pre-treatment imaging determination of extranodal extension (ENE) could facilitate the selection of appropriate initial therapy for HPV-positive oropharyngeal squamous cell carcinoma (HPV + OPSCC). Small studies have associated 7 CT features with ENE with varied results and agreement. This article seeks to determine the replicable diagnostic performance of these CT features for ENE., Methods: Five expert academic head/neck neuroradiologists from 5 institutions evaluate a single academic cancer center cohort of 75 consecutive HPV + OPSCC patients. In a web-based virtual laboratory for imaging research and education, the experts performed training on 7 published CT features associated with ENE and then independently identified the "single most (if any) suspicious" lymph node and presence/absence of each of the features. Inter-rater agreement was assessed using percentage agreement, Gwet's AC1, and Fleiss' kappa. Sensitivity, specificity, and positive and negative predictive values were calculated for each CT feature based on histologic ENE., Results: All 5 raters identified the same node in 52 cases (69%). In 15 cases (20%), at least one rater selected a node and at least one rater did not. In 8 cases (11%), all raters selected a node, but at least one rater selected a different node. Percentage agreement and Gwet's AC1 coefficients were > 0.80 for lesion identification, matted/conglomerated nodes, and central necrosis. Fleiss' kappa was always < 0.6. CT sensitivity for histologically confirmed ENE ranged 0.18-0.94, specificity 0.41-0.88, PPV 0.26-0.36, and NPV 0.78-0.96., Conclusions: Previously described CT features appear to have poor reproducibility among expert head/neck neuroradiologists and poor predictive value for histologic ENE., Key Points: • Previously described CT imaging features appear to have poor reproducibility among expert head and neck subspecialized neuroradiologists as well as poor predictive value for histologic ENE. • Although it may still be appropriate to comment on the presence or absence of these CT features in imaging reports, the evidence indicates that caution is warranted when incorporating these features into clinical decision-making regarding the likelihood of ENE., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Background and Objectives: Anatomical variations in the head, neck and chest are common, and are observed as occasional findings on computed tomography (CT). Although anatomical variations are mostly asymptomatic and do not cause any negative influence on the body function, they may jeopardize diagnosis and may be confused with pathological conditions. The presence of variations may also limit surgical access during tumor removal. The aim of this study was to investigate the prevalence of six anatomical variations-os acromiale, episternal ossicles, cervical rib, Stafne bone cavity, azygos lobe and tracheal bronchus-in an open-access computed tomography dataset obtained from oropharyngeal cancer patients. Materials and Methods: A total of 606 upper-chest and neck computed-tomography scans (79.4% male and 20.6% female) were retrospectively investigated. Sex difference was evaluated using the z-test for two proportions. Results: Os acromiale, episternal ossicles, cervical rib, Stafne bone cavity, azygos lobe, and tracheal bronchus were present in 3.1%, 2.2%, 0.2%, 0%, 0.3% and 0.5%, respectively, of all patients. Os acromiale was identified as meso-acromion in 86.6%, and as pre-acromion in 17.4%, of all acromia. Episternal ossicles were present unilaterally in 58.3%, and bilaterally in 41.7%, of all sterna. Only the cervical rib showed a sex difference in prevalence. Conclusions: awareness of these variations is important for radiologists interpreting head, neck and chest CTs; for example, those of oropharyngeal cancer patients. This study also illustrates the applicability of publicly available datasets in prevalence-based anatomical research. While most of the variations investigated in the present study are well-known, the episternal ossicles are not well explored, and need further investigation.

Purpose: The aim of this study was to evaluate an automated treatment planning method for robustly optimized intensity modulated proton therapy (IMPT) plans for oropharyngeal carcinoma patients and to compare the results with manually optimized robust IMPT plans., Methods and Materials: An atlas regression forest-based machine learning (ML) model for dose prediction was trained on CT scans, contours, and dose distributions of robust IMPT plans of 88 oropharyngeal cancer (OPC) patients. The ML model was combined with a robust voxel and dose volume histogram-based dose mimicking optimization algorithm for 21 perturbed scenarios to generate a machine-deliverable plan from the predicted dose distribution. Machine learning optimization (MLO) configuration was performed using a cross-validation approach with 3 × 8 tuning patients and comprised of adjustments to the mimicking optimization, to generate higher-quality MLO plans. Independent testing of the MLO algorithm was performed with another 25 patients. Plan quality of clinical and MLO plans was evaluated by the clinical target volume (D98% voxel-wise minimum dose >94%), organ at risk (OAR) doses, and the normal tissue complication probability (NTCP) (sum (Σ) of grade-2 and grade-3 dysphagia and xerostomia)., Results: Adequate robust target coverage was achieved in 24/25 clinical plans and in 23/25 MLO plans in the primary clinical target volume (CTV). In the elective CTV, 22/25 clinical plans and 24/25 MLO plans passed the robust target coverage evaluation threshold. The MLO average Σgrade 2 and Σgrade 3 NTCPs were comparable to the clinical plans (Σgrade 2 NTCPs: clinical 47.5% vs MLO 48.4%, Σgrade 3 NTCPs: clinical 11.9% vs MLO 12.3%). Significant increases in OAR average doses in MLO plans were found in the pharynx constrictor muscles (163 cGy, P = .002) and cervical esophagus (265 cGy, P = .002). The MLO plans were created within 45 minutes., Conclusion: This study showed that automated MLO planning can generate robustly optimized MLO plans with quality comparable to clinical plans in OPC patients., (Copyright © 2022. Published by Elsevier Inc.)

Purpose/objective: The purpose of the study is to externally validate published 18 F-FDG-PET radiomic models for outcome prediction in patients with oropharyngeal cancer treated with chemoradiotherapy., Material/methods: Outcome data and pre-radiotherapy PET images of 100 oropharyngeal cancer patients (stage IV:78) treated with concomitant chemotherapy to 66-69 Gy/30 fr were available. Tumors were segmented using a previously validated semi-automatic method; 450 radiomic features (RF) were extracted according to IBSI (Image Biomarker Standardization Initiative) guidelines. Only one model for cancer-specific survival (CSS) prediction was suitable to be independently tested, according to our criteria. This model, in addition to HPV status, SUVmean and SUVmax, included two independent meta-factors (F i ), resulting from combining selected RF clusters. In a subgroup of 66 patients with complete HPV information, the global risk score R was computed considering the original coefficients and was tested by Cox regression as predictive of CSS. Independently, only the radiomic risk score R F derived from F i was tested on the same subgroup to learn about the radiomics contribution to the model. The metabolic tumor volume (MTV) was also tested as a single predictor and its prediction performances were compared to the global and radiomic models. Finally, the validation of MTV and the radiomic score R F were also tested on the entire dataset., Results: Regarding the analysis of the subgroup with HPV information, with a median follow-up of 41.6 months, seven patients died due to cancer. R was confirmed to be associated to CSS (p value = 0.05) with a C-index equal 0.75 (95% CI=0.62-0.85). The best cut-off value (equal to 0.15) showed high ability in patient stratification (p=0.01, HR=7.4, 95% CI=1.6-11.4). The 5-year CSS for R were 97% (95% CI: 93-100%) vs 74% (56-92%) for low- and high-risk groups, respectively. R F and MTV alone were also significantly associated to CSS for the subgroup with an almost identical C-index. According to best cut-off value (R F >0.12 and MTV>15.5cc), the 5-year CSS were 96% (95% CI: 89-100%) vs 65% (36-94%) and 97% (95% CI: 88-100%) vs 77% (58-93%) for RF and MTV, respectively. Results regarding RF and MTV were confirmed in the overall group., Conclusion: A previously published PET radiomic model for CSS prediction was independently validated. Performances of the model were similar to the ones of using only the MTV, without improvement of prediction accuracy., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: To externally validate a pre-treatment MR-based radiomics model predictive of locoregional control in oropharyngeal squamous cell carcinoma (OPSCC) and to assess the impact of differences between datasets on the predictive performance., Methods: Radiomic features, as defined in our previously published radiomics model, were extracted from the primary tumor volumes of 157 OPSCC patients in a different institute. The developed radiomics model was validated using this cohort. Additionally, parameters influencing performance, such as patient subgroups, MRI acquisition, and post-processing steps on prediction performance will be investigated. For this analysis, matched subgroups (based on human papillomavirus (HPV) status of the tumor, T-stage, and tumor subsite) and a subgroup with only patients with 4-mm slice thickness were studied. Also the influence of harmonization techniques (ComBat harmonization, quantile normalization) and the impact of feature stability across observers and centers were studied. Model performances were assessed by area under the curve (AUC), sensitivity, and specificity., Results: Performance of the published model (AUC/sensitivity/specificity: 0.74/0.75/0.60) drops when applied on the validation cohort (AUC/sensitivity/specificity: 0.64/0.68/0.60). The performance of the full validation cohort improves slightly when the model is validated using a patient group with comparable HPV status of the tumor (AUC/sensitivity/specificity: 0.68/0.74/0.60), using patients acquired with a slice thickness of 4 mm (AUC/sensitivity/specificity: 0.67/0.73/0.57), or when quantile harmonization was performed (AUC/sensitivity/specificity: 0.66/0.69/0.60)., Conclusion: The previously published model shows its generalizability and can be applied on data acquired from different vendors and protocols. Harmonization techniques and subgroup definition influence performance of predictive radiomics models., Key Points: • Radiomics, a noninvasive quantitative image analysis technique, can support the radiologist by enhancing diagnostic accuracy and/or treatment decision-making. • A previously published model shows its generalizability and could be applied on data acquired from different vendors and protocols., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: The aim of this study was to investigate the value of metabolic tumor imaging using hybrid PET for the preoperative detection of extranodal extension (ENE) in lymph node metastases of oropharyngeal squamous cell carcinoma (OPSCC)., Methods: We performed a retrospective analysis of a consecutive cohort of patients with OPSCC treated with primary surgery with or without adjuvant (chemo-) radiotherapy at the Kantonsspital Sankt-Gallen and the University Hospital Zurich, Switzerland, from 2010 until 2019. Hybrid PET was compared to conventional cross-sectional imaging with MRI and CT. Histopathological presence of ENE of neck dissection specimen served as gold standard., Results: A total number of 234 patients were included in the study, 95 (40.6%) of which had pathological ENE (pENE). CT has a good specificity with 93.7%; meanwhile, MRI was the most sensitive diagnostic method (72.0%). The nodal metabolic tumor parameters (SUV max , TLG, MTV) were significantly higher in patients with positive ENE (p < 0.001 for all three parameters) than in patients with negative ENE (p < 0.001, for all three parameters)., Conclusions: CT achieved the best specificity, while MRI had the best sensitivity to detect ENE. Nodal metabolic tumor parameters differed significantly between ENE-positive/negative and p16-positive/negative patients. Hence, quantitative data obtained by metabolic imaging might predict presence of ENE and, therefore, could be helpful in customizing therapy management., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The purpose of this study was to determine the negative predictive value (NPV) of a 12- to 14-wk posttreatment PET/CT for 2-y progression-free survival (PFS) and locoregional control (LRC) in patients with p16-positive locoregionally advanced oropharyngeal cancer (LA-OPC). Study was a secondary endpoint in NRG-HN002, a noncomparative phase II trial in p16-positive LA-OPC, stage T1-T2, N1-N2b or T3, N0-N2b, and ≤10 pack-year smoking. Patients were randomized in a 1:1 ratio to reduced-dose intensity-modulated radiotherapy (IMRT) with or without cisplatin. Methods: PET/CT scans were reviewed centrally. Tumor response evaluations for the primary site, right neck, and left neck were performed using a 5-point ordinal scale (Hopkins criteria). Overall scores were then assigned as negative, positive, or indeterminate. Patients with a negative score for all 3 evaluation sites were given an overall score of negative. The hypotheses were NPV for PFS and LRC at 2-y posttreatment ≤ 90% versus >90% (1-sided P value, 0.10). Results: A total of 316 patients were enrolled, of whom 306 were randomized and eligible. Of these, 131 (42.8%) patients consented to a posttherapy PET/CT, and 117 (89.3%) patients were eligible for PET/CT analysis. The median time from the end of treatment to PET/CT scan was 94 d (range, 52-139 d). Estimated 2-y PFS and LRC rates in the analysis subgroup were 91.3% (95% CI, 84.6, 95.8%) and 93.8% (95% CI, 87.6, 97.5%), respectively. Posttreatment scans were negative for residual tumor for 115 patients (98.3%) and positive for 2 patients (1.7%). NPV for 2-y PFS was 92.0% (90% lower confidence bound [LCB] 87.7%; P = 0.30) and for LRC was 94.5% (90% LCB 90.6%; P = 0.07). Conclusion: In the context of deintensification with reduced-dose radiation, the NPV of a 12- to 14-wk posttherapy PET/CT for 2-y LRC is estimated to be >90%, similar to that reported for patients receiving standard chemoradiation. However, there is insufficient evidence to conclude that the NPV is >90% for PFS., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: No large-scale prospective randomized study with a long-term follow-up period has evaluated the survival outcomes of preconcurrent chemoradiotherapy (CCRT) 18-fluorodeoxyglucose positron emission tomography-computed tomography ( 18 FDG PET-CT) in patients with non-human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC)., Patients and Methods: We included patients with stage I-IVA p16-negative OPSCC receiving definitive CCRT and categorized them into two groups according to pre-CCRT 18 FDG PET-CT and compared their outcomes: the case group consisted of patients who underwent pre-CCRT 18 FDG PET-CT, whereas the comparison group consisted of patients who did not receive pre-CCRT 18 FDG PET-CT., Results: The final cohort consisted of 3942 patients (1663 and 2279 in the case and comparison groups, respectively). According to multivariable Cox regression analysis, pre-CCRT 18 FDG PET-CT was not a significant prognostic factor for overall survival in patients with stages I-II of p16-negative OPSCC receiving standard CCRT. The adjusted hazard ratio (95% confidence interval) of all-cause death for the patients with advanced stages (III-IVA) of p16-negative OPSCC receiving pre-CCRT 18 FDG PET-CT was 0.75 (0.87-0.94, P = 0.0236)., Conclusions: Routine use of pre-CCRT 18 FDG PET-CT is not necessary for each patient with p16-negative OPSCC. Pre-CCRT 18 FDG PET-CT is associated with improved survival in patients with stage III-IVA p16-negative OSCC, but might be not in those with stage I-II p16-negative OPSCC. No large-scale prospective randomized study with a long-term follow-up period has evaluated the survival outcomes of preconcurrent chemoradiotherapy (CCRT) 18-fluorodeoxyglucose positron emission tomography-computed tomography ( 18 FDG PET-CT) in patients with p16-negative oropharyngeal squamous cell carcinoma (OPSCC). Our study is the first, largest, homogenous modality study on PET-CT including a long-term follow-up cohort to examine the survival outcomes of pre-CCRT 18 FDG PET-CT or non-pre-CCRT PET-CT for patients with p16-negative OPSCC receiving standard CCRT stratified by different clinical stages. Routine use of pre-CCRT 18 FDG PET-CT is not necessary for each patient with p16-negative OPSCC. Pre-CCRT 18 FDG PET-CT is associated with improved survival in patients with stage III-IVA p16-negative OPSCC, but might be not in those with stage I-II p16-negative OPSCC., (© 2023. The Author(s).)

Aim: Fluorescence imaging (FI) using indocyanine green (ICG) is a noteworthy alternative technique for sentinel lymph node (SLN) detection without radiation exposure in oral cavity and oropharynx cancer. However, conventional FI is monochrome, so the visibility is limited. This study assessed whether color FI using the HyperEye Medical System (HEMS) is feasible as an alternative for SLN detection., Methods: Patients with previously untreated cT1-2N0 oral or oropharyngeal cancer who were to undergo primary tumor resection and elective neck dissection (END) in our hospital were enrolled from November 2012 to March 2016. The patients underwent SLN detection for biopsy via the HEMS following the injection of ICG solution around the primary lesion before neck dissection. The visibility and the diagnostic accuracy of the imaging were evaluated., Results: SLNs were visualized in all eight cases; however, transcutaneous fluorescence detection was not observed in all cases. Utilizing color mode imaging simplified harvest by clearly discriminating SLNs from surrounding structures, while the monochrome mode proved to be more sensitive for detection. Two cases showed occult metastases on both sentinel and regional nodes. The identification and false negative rates were 100% and 0%, respectively. There were no complications incurred due to this method., Conclusion: Our results suggest that color FI with the HEMS allows for the accurate and safe harvest of SLNs with a preparatory skin incision. Although there is room for improvement of sensitivity, this easy-to-handle procedure might provide the potential to expand the role of the ICG method for SLN detection in head and neck cancer., (© 2022 John Wiley & Sons Australia, Ltd.)