Predictive potential of dynamic contrast-enhanced MRI and plasma-derived angiogenic factors for response to concurrent chemoradiotherapy in human papillomavirus-negative oropharyngeal cancer.

BACKGROUND: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess tumour vascularity, which depends on the process of angiogenesis and affects tumour response to treatment. Our study explored the associations between DCE-MRI parameters and the expression of plasma angiogenic factors in human papilloma virus (HPV)-negative oropharyngeal cancer, as well as their predictive value for response to concurrent chemoradiotherapy (cCRT). PATIENTS AND METHODS: Twenty-five patients with locally advanced HPV-negative oropharyngeal carcinoma were prospectively enrolled in the study. DCE-MRI and blood plasma sampling were conducted before cCRT, after receiving a radiation dose of 20 Gy, and after the completion of cCRT. Perfusion parameters ktrans, kep, Ve, initial area under the curve (iAUC) and plasma expression levels of angiogenic factors (vascular endothelial growth factor [VEGF], connective tissue growth factor [CTGF], platelet-derived growth factor [PDGF]-AB, angiogenin [ANG], endostatin [END] and thrombospondin-1 [THBS1]) were measured at each time-point. Patients were stratified into responders and non-responders based on clinical evaluation. Differences and correlations between measures were used to generate prognostic models for response prediction. RESULTS: Higher perfusion parameter ktrans and higher plasma VEGF levels successfully discriminated responders from non-responders across all measured time-points, whereas higher iAUC and higher plasma PDGF-AB levels were also discriminative at selected time points. Using early intra-treatment measurements of ktrans and VEGF, a predictive model was created with cut-off values of 0.259 min-1 for ktrans and 62.5 pg/mL for plasma VEGF. CONCLUSIONS: Early intra-treatment DCE-MRI parameter ktrans and plasma VEGF levels may be valuable early predictors of response to cCRT in HPV-negative oropharyngeal cancer.

Development of 18F-Fluoromisonidazole Hypoxia PET/CT Diagnostic Interpretation Criteria and Validation of Interreader Reliability, Reproducibility, and Performance.

Tumor hypoxia, an integral biomarker to guide radiotherapy, can be imaged with 18F-fluoromisonidazole (18F-FMISO) hypoxia PET. One major obstacle to its broader application is the lack of standardized interpretation criteria. We sought to develop and validate practical interpretation criteria and a dedicated training protocol for nuclear medicine physicians to interpret 18F-FMISO hypoxia PET. Methods: We randomly selected 123 patients with human papillomavirus-positive oropharyngeal cancer enrolled in a phase II trial who underwent 123 18F-FDG PET/CT and 134 18F-FMISO PET/CT scans. Four independent nuclear medicine physicians with no 18F-FMISO experience read the scans. Interpretation by a fifth nuclear medicine physician with over 2 decades of 18F-FMISO experience was the reference standard. Performance was evaluated after initial instruction and subsequent dedicated training. Scans were considered positive for hypoxia by visual assessment if 18F-FMISO uptake was greater than floor-of-mouth uptake. Additionally, SUVmax was determined to evaluate whether quantitative assessment using tumor-to-background ratios could be helpful to define hypoxia positivity. Results: Visual assessment produced a mean sensitivity and specificity of 77.3% and 80.9%, with fair interreader agreement (κ = 0.34), after initial instruction. After dedicated training, mean sensitivity and specificity improved to 97.6% and 86.9%, with almost perfect agreement (κ = 0.86). Quantitative assessment with an estimated best SUVmax ratio threshold of more than 1.2 to define hypoxia positivity produced a mean sensitivity and specificity of 56.8% and 95.9%, respectively, with substantial interreader agreement (κ = 0.66), after initial instruction. After dedicated training, mean sensitivity improved to 89.6% whereas mean specificity remained high at 95.3%, with near-perfect interreader agreement (κ = 0.86). Conclusion: Nuclear medicine physicians without 18F-FMISO hypoxia PET reading experience demonstrate much improved interreader agreement with dedicated training using specific interpretation criteria.

Prognostic Role of Pre- and Post-Treatment [18F]FDG PET/CT in Squamous Cell Carcinoma of the Oropharynx in Patients Treated with Chemotherapy and Radiotherapy.

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.

Circulating tumor HPV DNA in the management of HPV+ oropharyngeal cancer and its correlation with MRI.

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.

Transoral Ultrasound-Guided Core Biopsy for Minimally Invasive Sampling of Hard-to-Access Oropharyngeal Cancers.

Pathological diagnosis plays a pivotal role in risk classification and personalized treatment planning for patients with oropharyngeal cancers. However, challenges arise in cases involving trismus and tumors with submucosal spread, hindering traditional endoscopic biopsies and open incisional biopsies. In this study, we examined the clinical and pathological data of patients with trismus who underwent transoral ultrasound-guided core biopsy (USCB) for their oropharyngeal tumors, comparing this method with existing diagnostic approaches. Seventeen patients presenting with oropharyngeal tumors and trismus underwent transoral USCB for diagnosis. Of these, 14 patients were diagnosed with squamous cell carcinoma, while the remaining 3 were diagnosed with lymphoma. The procedure resulted in minimal wound size and effective bleeding control through compression, without encountering any complications. In conclusion, transoral USCB emerges as a precise diagnostic tool for patients with oropharyngeal tumors and trismus, offering a valuable adjunct to conventional open and endoscopic biopsies.

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.

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.

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

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

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

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

MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.

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.

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.

Preliminary Results from Retrospective Correlation of Circulating Tumor DNA (ct-DNA) with Imaging for HPV-Positive Oropharyngeal Squamous Cell Carcinoma.

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.

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.

Volumetric and geometric changes in the parotid glands and target volume during image-guided radiotherapy for locally advanced oropharyngeal cancers.

PURPOSE: This study aimed to evaluate the volumetric and geometric changes in the parotid glands and target volume during image-guided radiotherapy (IGRT) for locally advanced oropharyngeal cancers. MATERIALS AND METHODS: Twenty patients receiving radiotherapy using IGRT at a dose of 70 Gy/35 fractions/7 weeks for locally advanced oropharyngeal cancers were accrued. Radiotherapy planning computed tomography (CT) scans were performed at pre-radiotherapy (RT), 20, 40, and 60 Gy for each patient. Volume changes in target and parotids along with shifts of parotids were assessed with respect to pre-RT scan after co-registration. In study scans, GTVp and GTVn were recontoured as per particular CT. CTV and PTV were copied from planning CT to study CT. CTV was edited from anatomical barriers, and PTV was edited only from the skin in the study CT. The parotids were recontoured on each study scan. The center of mass (COM) of C2 vertebral body was considered as the reference to evaluate its shifts. RESULTS: There was a statistically significant percentage regression of ipsilateral and contralateral parotid mean volumes at the rate of 0.85%/0.207 cc and 0.98%/0.26 cc per day, respectively. We observed the mean medial shift of center of mass of ipsilateral parotid of 2.23 mm (p = 0.011) and contralateral parotid of 2.67 mm (p = 0.069) at the end of 60 Gy. GTVp (mean) reduced from 41.87 cc at 0 Gy to 31.13 cc (25.65%) at 60 Gy (p = 0.003), while GTVn (mean) reduced from 19.98 cc at 0 Gy to 10.79 cc (45.99%) at 60 Gy (p = 0.003). There was a statistically significant reduction in CTV and PTV volumes at 60 Gy. CONCLUSION: Statistically significant volumetric and geometric changes occurred during intensity-modulated radiation (IMRT), which were most prominent after 40 Gy and were maximum at 60 Gy. There was a medial shift of parotid glands toward the high-dose region. This study can be useful to devise an adaptive radiotherapy strategy.

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.