Update on Pathologic Conditions, Imaging Findings, Prevention, and Management of Human Papillomavirus-related Neoplasms.

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.

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

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

PET/CT based transformer model for multi-outcome prediction in oropharyngeal cancer.

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.

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

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

Metastatic oropharyngeal squamous cell carcinoma to the thyroid: A case report and review of literature.

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.

Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.

We aimed to investigate the effects of 18F-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 18F-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.

Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net.

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.

Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics.

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 $t > 0$ . 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 18F-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.

Predictive value of local control by 4'-[methyl-11C]-thiotymidine PET volume parameters in p16-negative oropharyngeal, hypopharyngeal, and supraglottic squamous cell carcinoma.

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.

Cost-Effectiveness Analysis of PET-CT Surveillance After Treatment of Human Papillomavirus-Positive Oropharyngeal Cancer.

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.

From planned neck dissection to PET response to circulating tumour DNA: changes in post-treatment assessment of HPV-driven oropharyngeal cancer†.

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.

Longitudinal Pattern of Lymphedema and Fibrosis in Patients With Oral Cavity or Oropharyngeal Cancer: A Prospective Study.

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.

Vision Transformer-Based Multilabel Survival Prediction for Oropharynx Cancer After Radiation Therapy.

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.

Association of follow-up imaging frequency with temporal incidence and patterns of distant failure following (chemo) radiotherapy for HPV related oropharyngeal cancer.

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 (OMs) or, if interval from last negative surveillance imaging > 6-months, then ≤ 10 metastasis, none larger than 5 cm, (OMp). Patients not meeting OMs / OMp criteria were deemed to have incurable diffuse metastatic disease (DMdiffuse). 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 (OMs or OMp) at time of failure, with 31 % having OMs. The median time from completion of treatment to diagnosis of DMdiffuse 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 DMdiffuse 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.

Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer.

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.

Predicting the need for a replan in oropharyngeal cancer: A radiomic, clinical, and dosimetric model.

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.

Radiomics analysis for the prediction of locoregional recurrence of locally advanced oropharyngeal cancer and hypopharyngeal cancer.

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.

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

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

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

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