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Highlights • The PIRATES protocol is a novel tumor radiation dose-escalation approach. • PIRATES integrates proton therapy, image guided RT and hybrid hyper-fractionation. • It targets only head and neck cancer patients at high risk of treatment failure. • This Phase I trial aims to test the feasibility and safety of PIRATES., Introduction Radiation dose-escalation for head and neck cancer (HNC) patients aiming to improve cure rates is challenging due to the increased risk of unacceptable treatment-induced toxicities. With “Proton Image-guided Radiation Assignment for Therapeutic Escalation via Selection of locally advanced head and neck cancer patients” (PIRATES), we present a novel treatment approach that is designed to facilitate dose-escalation while minimizing the risk of dose-limiting toxicities for locally advanced HPV-negative HNC patients. The aim of this Phase I trial is to assess the safety & feasibility of PIRATES approach. Methods The PIRATES protocol employs a multi-faceted dose-escalation approach to minimize the risk of dose-limiting toxicities (DLTs): 1) sparing surrounding normal tissue from extraneous dose with intensity-modulated proton therapy, 2) mid-treatment hybrid hyper-fractionation for radiobiologic normal tissue sparing; 3) Magnetic Resonance Imaging (MRI) guided mid-treatment boost volume adaptation, and 4) iso-effective restricted organ-at-risk dosing to mucosa and bone tissues. The time-to-event Bayesian optimal interval (TITE-BOIN) design is employed to address the challenge of the long DLT window of 6 months and find the maximum tolerated dose. The primary endpoint is unacceptable radiation-induced toxicities (Grade 4, mucositis, dermatitis, or Grade 3 myelopathy, osteoradionecrosis) occurring within 6 months following radiotherapy. The second endpoint is any grade 3 toxicity occurring in 3–6 months after radiation. Discussion The PIRATES dose-escalation approach is designed to provide a safe avenue to intensify local treatment for HNC patients for whom therapy with conventional radiation dose levels is likely to fail. PIRATES aims to minimize the radiation damage to the tissue surrounding the tumor volume with the combination of proton therapy and adaptive radiotherapy and within the high dose tumor volume with hybrid hyper-fractionation and not boosting mucosal and bone tissues. Ultimately, if successful, PIRATES has the potential to safety increase local control rates in HNC patients with high loco-regional failure risk. Trial registration: ClinicalTrials.gov ID: NCT04870840; Registration date: May 4, 2021. Netherlands Trial Register ID: NL9603; Registration date: July 15, 2021.

BackgroundPost-treatment symptoms are a focal point of follow-up visits for head and neck cancer patients. While symptoms such as dysphagia and shortness-of-breath early after treatment may motivate additional work up, their precise association with disease control and survival outcomes is not well established.MethodsThis prospective data cohort study of 470 oropharyngeal cancer patients analyzed patient-reported swallowing, choking and shortness-of-breath symptoms at 3-to-6 months following radiotherapy to evaluate their association with overall survival and disease control. Associations between the presence of moderate-to-severe swallowing, choking and mild-to-severe shortness-of-breath and treatment outcomes were analyzed via Cox regression and Kaplan-Meier. The main outcome was overall survival (OS), and the secondary outcomes were local, regional, and distant disease control.ResultsThe majority of patients (91.3%) were HPV-positive. Median follow-up time was 31.7 months (IQR: 21.9-42.1). Univariable analysis showed significant associations between OS and all three symptoms of swallowing, choking, and shortness-of-breath. A composite variable integrating scores of all three symptoms was significantly associated with OS on multivariable Cox regression (p=0.0018). Additionally, this composite symptom score showed the best predictive value for OS (c-index=0.75). Multivariable analysis also revealed that the composite score was significantly associated with local recurrence/progression (p=0.025). Notably, the same significant associations with OS were seen for HPV-positive only subset analysis (pConclusionsQuantitative patient-reported measures of dysphagia and shortness-of-breath 3-to-6 months post-treatment are significant predictors of OS and disease recurrence/progression in OPC patients and in HPV-positive OPC only.

Purpose: In order for radiotherapy to enter the realm of personalized medicine it will be necessary to model and predict individual patient responses to radiotherapy. Methods and Materials: Here we model tumor dynamics as logistic growth and the effect of radiation as a reduction in the tumor carrying capacity, motivated by the impact of radiation on the tumor microenvironment. The model was assessed on weekly tumor volume data collected for two independent cohorts of head and neck cancer patients from XXXX1 and XXXX2 that received 66-70 Gy in standard daily fractions or with accelerated fractionation. To predict response to radiotherapy for individual patients, we developed a new forecasting framework that combined the learned tumor growth rate and carrying capacity reduction fraction (δ) distribution with weekly measurements of tumor volume reduction for a given test patient to estimate δ, which was used to predict patient-specific outcomes. Results: The model fit data from XXXX1 with high accuracy with patient-specific δ and a fixed tumor growth rate across all patients. The model fit data from an independent cohort from XXXX2 with comparable accuracy using the tumor growth rate learned from the XXXX1 cohort, demonstrating transferability of the growth rate. The forecasting framework predicted patient-specific outcomes with 76% sensitivity and 83% specificity for locoregional control and 68% sensitivity and 85% specificity for disease-free survival with the inclusion of four on-treatment tumor volume measurements. Conclusions: These results show that our simple mathematical model can describe a variety of tumor volume dynamics. Further, combining historically observed patient responses with a few patient-specific tumor volume measurements allows for the accurate prediction of patient outcomes, which may inform treatment adaptation and personalization.

Background and Purpose: Conventional magnetic resonance imaging (MRI) poses challenges in quantitative analysis because voxel intensity values lack physical meaning. While intensity standardization methods exist, their effects on head and neck MRI have not been investigated. We developed a workflow based on healthy tissue region of interest (ROI) analysis to determine intensity consistency within a patient cohort. Through this workflow, we systematically evaluated intensity standardization methods for MRI of head and neck cancer (HNC) patients. Materials and Methods: Two HNC cohorts (30 patients total) were retrospectively analyzed. One cohort was imaged with heterogenous acquisition parameters (HET cohort), whereas the other was imaged with homogenous acquisition parameters (HOM cohort). The standard deviation of cohort-level normalized mean intensity (SD NMIc), a metric of intensity consistency, was calculated across ROIs to determine the effect of five intensity standardization methods on T2-weighted images. For each cohort, a Friedman test followed by a post-hoc Bonferroni-corrected Wilcoxon signed-rank test was conducted to compare SD NMIc among methods. Results: Consistency (SD NMIc across ROIs) between unstandardized images was substantially more impaired in the HET cohort (0.29 ± 0.08) than in the HOM cohort (0.15 ± 0.03). Consequently, corrected p-values for intensity standardization methods with lower SD NMIc compared to unstandardized images were significant in the HET cohort (p 0.05). In both cohorts, differences between methods were often minimal and nonsignificant. Conclusions: Our findings stress the importance of intensity standardization, either through the utilization of uniform acquisition parameters or specific intensity standardization methods, and the need for testing intensity consistency before performing quantitative analysis of HNC MRI.

Purpose: Osteoradionecrosis (ORN) of the mandible represents a severe, debilitating complication of radiation therapy (RT) for head and neck cancer (HNC). At present, no normal tissue complication probability (NTCP) models for risk of ORN exist. The aim of this study was to develop a multivariable clinical/dose-based NTCP model for the prediction of ORN any grade (ORNI-IV) and grade IV (ORNIV) after RT (+/- chemotherapy) in patients with HNC.Methods and Materials: Included patients with HNC were treated with (chemo-)RT between 2005 and 2015. Mandible bone radiation dose-volume parameters and clinical variables (ie, age, sex, tumor site, pre-RT dental extractions, chemotherapy history, postoperative RT, and smoking status) were considered as potential predictors. The patient cohort was randomly divided into a training (70%) and independent test (30%) cohort. Bootstrapped forward variable selection was performed in the training cohort to select the predictors for the NTCP models. Final NTCP model(s) were validated on the holdback test subset.Results: Of 1259 included patients with HNC, 13.7% (n = 173 patients) developed any grade ORN (ORNI-IV primary endpoint) and 5% (n = 65) ORNIV (secondary endpoint). All dose and volume parameters of the mandible bone were significantly associated with the development of ORN in univariable models. Multivariable analyses identified D30% and pre-RT dental extraction as independent predictors for both ORNI-IV and ORNIV best-performing NTCP models with an area under the curve (AUC) of 0.78 (AUCvalidation = 0.75 [0.69-0.82]) and 0.81 (AUCvalidation = 0.82 [0.74-0.89]), respectively.Conclusions: This study presented NTCP models based on mandible bone D30% and pre-RT dental extraction that predict ORNI-IV and ORNIV (ie, needing invasive surgical intervention) after HNC RT. Our results suggest that less than 30% of the mandible should receive a dose of 35 Gy or more for an ORNI-IV risk lower than 5%. These NTCP models can improve ORN prevention and management by identifying patients at risk of ORN. (C) 2021 The Author(s). Published by Elsevier Inc.

While there has been an overall decline of tobacco and alcohol-related head and neck cancer in recent decades, there has been an increased incidence of HPV-associated oropharyngeal cancer (OPC). Recent research studies and clinical trials have revealed that the cancer biology and clinical progression of HPV-positive OPC is unique relative to its HPV-negative counterparts. HPV-positive OPC is associated with higher rates of disease control following definitive treatment when compared to HPV-negative OPC. Thus, these conditions should be considered unique diseases with regards to treatment strategies and survival. In order to sufficiently characterize HPV-positive OPC and guide treatment strategies, there has been a considerable effort to diagnose, prognose, and track the treatment response of HPV-associated OPC through advanced imaging research. Furthermore, HPV-positive OPC patients are prime candidates for radiation de-escalation protocols, which will ideally reduce toxicities associated with radiation therapy and has prompted additional imaging research to detect radiation-induced changes in organs at risk. This manuscript reviews the various imaging modalities and current strategies for tackling these challenges as well as provides commentary on the potential successes and suggested improvements for the optimal treatment of these tumors.

Highlights • Head and neck cancers radiotherapy (RT) leads to inevitable injury to parotid glands. • We sought to quantify delta-changes of 18FDG-PET metrics in these parotid glands. • Parotid PET delta-changes and clinical variables could predict xerostomia severity., Purpose Head and neck cancers radiotherapy (RT) is associated with inevitable injury to parotid glands and subsequent xerostomia. We investigated the utility of SUV derived from 18FDG-PET to develop metabolic imaging biomarkers (MIBs) of RT-related parotid injury. Methods Data for oropharyngeal cancer (OPC) patients treated with RT at our institution between 2005 and 2015 with available planning computed tomography (CT), dose grid, pre- & first post-RT 18FDG-PET-CT scans, and physician-reported xerostomia assessment at 3–6 months post-RT (Xero 3–6 ms) per CTCAE, was retrieved, following an IRB approval. A CT-CT deformable image co-registration followed by voxel-by-voxel resampling of pre & post-RT 18FDG activity and dose grid were performed. Ipsilateral (Ipsi) and contralateral (contra) parotid glands were sub-segmented based on the received dose in 5 Gy increments, i.e. 0–5 Gy, 5–10 Gy sub-volumes, etc. Median and dose-weighted SUV were extracted from whole parotid volumes and sub-volumes on pre- & post-RT PET scans, using in-house code that runs on MATLAB. Wilcoxon signed-rank and Kruskal-Wallis tests were used to test differences pre- and post-RT. Results 432 parotid glands, belonging to 108 OPC patients treated with RT, were sub-segmented & analyzed. Xero 3–6 ms was reported as: non-severe (78.7%) and severe (21.3%). SUV- median values were significantly reduced post-RT, irrespective of laterality (p = 0.02). A similar pattern was observed in parotid sub-volumes, especially ipsi parotid gland sub-volumes receiving doses 10–50 Gy (p

BackgroundDespite the use of aggressive multimodality treatment, most anaplastic thyroid carcinoma (ATC) patients die within a year of diagnosis. Although the combination of BRAF and MEK inhibitors has recently been approved for use in BRAF-mutated ATC, they remain effective in a minority of patients who are likely to develop drug resistance. There remains a critical clinical need for effective systemic agents for ATC with a reasonable toxicity profile to allow for rapid translational development.Material and MethodsTwelve human thyroid cancer cell lines with comprehensive genomic characterization were used in a high-throughput screening (HTS) of 257 compounds to select agents with maximal growth inhibition. Cell proliferation, colony formation, orthotopic thyroid models, and patient-derived xenograft (PDX) models were used to validate the selected agents.ResultsSeventeen compounds were effective, and docetaxel, LBH-589, and pralatrexate were selected for additional in vitro and in vivo analysis as they have been previously approved by the US Food and Drug Administration for other cancers. Significant tumor growth inhibition (TGI) was detected in all tested models treated with LBH-589; pralatrexate demonstrated significant TGI in the orthotopic papillary thyroid carcinoma model and 2 PDX models; and docetaxel demonstrated significant TGI only in the context of mutant TP53.ConclusionsHTS identified classes of systemic agents that demonstrate preferential effectiveness against aggressive thyroid cancers, particularly those with mutant TP53. Preclinical validation in both orthotopic and PDX models, which are accurate in vivo models mimicking tumor microenvironment, may support initiation of early-phase clinical trials in non-BRAF mutated or refractory to BRAF/MEK inhibition ATC.

BACKGROUND AND PURPOSE: Sarcopenia is associated with decreased survival in head and neck cancer patients treated with radiotherapy. This study sought to determine whether in-clinic multifrequency bioelectrical impedance analysis (BIA) can identify survival-associated sarcopenia in patients with head and neck cancer. MATERIALS AND METHODS: This prospective observational study enrolled 50 patients with head and neck cancer undergoing radiation therapy. Baseline BIA measures of skeletal muscle (SM) mass, fat-free mass (FFM), and fat mass (FM) were compared to CT-based estimates using linear regression. Sex-specific BIA-derived thresholds for sarcopenia were defined by the maximum Youden Index on receiver operator characteristic (ROC) curves. Patients were stratified by sarcopenia status and OS was compared using the Kaplan-Meier method and log-rank test. RESULTS: Among 48 evaluable patients, BIA measures of body composition were strongly correlated with CT measures: SM mass (r = 0.97; R(2) = 0.94; p < 0.0001), FFM (r = 0.97; R(2) = 0.94; p < 0.0001) and FM (r = 0.95; R(2) = 0.90; p < 0.0001). SM mass index < 9.19 kg/m(2) identified sarcopenia men with high sensitivity (91.7%) and specificity (92.9%), whereas in women SM mass index < 6.53 kg/m(2) was sensitive for sarcopenia (100%), but not specific. Patients with sarcopenia, defined by either CT or BIA, exhibited decreased OS (HR=not estimable; CT p = 0.009; BIA p = 0.03). CONCLUSION: BIA provides accurate estimates of body composition in head and neck cancer patients. Implementation of BIA in clinical practice may identify patients with sarcopenia at risk for poor survival.

BACKGROUND: Lactate dehydrogenase (LDH) is a critical metabolic enzyme. LDH A (LDHA) overexpression is a hallmark of aggressive malignancies and has been linked to tumour initiation, reprogramming and progression in multiple tumour types. However, successful LDHA inhibition strategies have not materialised in the translational and clinical space. We sought to develop a rational strategy for LDHA suppression in the context of solid tumour treatment. METHODS: We utilised a doxycycline-inducible short hairpin RNA (shRNA) system to generate LDHA suppression. Lactate and LDH activity levels were measured biochemically and kinetically using hyperpolarised (13)C-pyruvate nuclear magnetic resonance spectroscopy. We evaluated effects of LDHA suppression on cellular proliferation and clonogenic survival, as well as on tumour growth, in orthotopic models of anaplastic thyroid carcinoma (ATC) and head and neck squamous cell carcinoma (HNSCC), alone or in combination with radiation. RESULTS: shRNA suppression of LDHA generated a time-dependent decrease in LDH activity with transient shifts in intracellular lactate levels, a decrease in carbon flux from pyruvate into lactate and compensatory shifts in metabolic flux in glycolysis and the Krebs cycle. LDHA suppression decreased cellular proliferation and temporarily stunted tumour growth in ATC and HNSCC xenografts but did not by itself result in tumour cure, owing to the maintenance of residual viable cells. Only when chronic LDHA suppression was combined with radiation was a functional cure achieved. CONCLUSIONS: Successful targeting of LDHA requires exquisite dose and temporal control without significant concomitant off-target toxicity. Combinatorial strategies with conventional radiation are feasible as long as the suppression is targeted, prolonged and non-toxic.

PurposeIn order to improve segmentation accuracy in head and neck cancer (HNC) radiotherapy treatment planning for the 1.5T MR-Linac, 3D fat-suppressed T2-weighted MRI sequences were developed and optimized.MethodsAfter initial testing of fat suppression techniques, SPectral Attenuated Inversion Recovery (SPAIR) was chosen as the fat suppression technique. Five candidate SPAIR sequences and a non-suppressed T2-weighted sequence were acquired on five HNC patients on the Unity MR-Linac. The primary tumor, metastatic lymph nodes, parotid glands, and pterygoid muscles were delineated by five segmentors. A robust image quality analysis platform was developed to objectively score the SPAIR sequences based on a combination of qualitative and quantitative metrics.ResultsSequences were analyzed for signal-to-noise (SNR), contrast-to-noise (CNR) compared to fat and muscle, conspicuity, pairwise distance metrics, segmentor assessment, and MR physicist assessment. From this analysis, the non-suppressed sequence was inferior to each of the SPAIR sequences for the primary tumor, lymph nodes, and parotid glands, but was superior for the pterygoid muscles. Two SPAIR sequences consistently received the highest scores among the analysis categories and are recommended for use to Unity MR-Linac users for HNC radiotherapy treatment planning.ConclusionsTwo deliverables resulted from this study. First, an optimized 3D fat-suppressed T2-weighted sequence was developed that can be disseminated to Unity MR-Linac users. Second, a robust image quality analysis process pathway, used to objectively score the various SPAIR sequences, was developed and can be customized and generalized to any image quality optimization. Improved segmentation accuracy with the proposed SPAIR sequence can potentially lead to improved treatment outcomes and reduced toxicity by maximizing target coverage and minimizing organ-at-risk exposure.

Historically USPIO has been used to help with nodal staging but not in primary tumors. The ability to concentrate USPIO may help to differentiate BMT from other types of parotid tumors.

PURPOSE: We aim to characterize the quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters associated with advanced mandibular osteoradionecrosis (ORN) compared with the contralateral normal mandible. METHODS AND MATERIALS: Patients with a diagnosis of advanced ORN after curative-intent radiation treatment of head and neck cancer were prospectively enrolled after institutional review board approval and study-specific informed consent were obtained. Quantitative maps generated with the Tofts and extended Tofts pharmacokinetic models were used for analysis. Manual segmentation of advanced ORN 3-dimensional volume was done using anatomic sequences to create ORN volumes of interest (VOIs). Subsequently, normal mandibular VOIs were segmented on the contralateral healthy mandible of similar volume and anatomic location to create control VOIs. Finally, anatomic sequences were coregistered to DCE sequences, and contours were propagated to the respective parameter maps. RESULTS: Thirty patients were included. The median time to ORN diagnosis after completion of IMRT was 38 months (range, 6–184 months), whereas median time to ORN progression to advanced grade after initial diagnosis was 5.6 months (range, 0–128 months). There were statistically significant higher K(trans) and V(e) in ORN-VOIs compared with controls (0.23 vs 0.07 min(−)(1), and 0.34 vs 0.15; P

Background We aimed to determine whether hypomagnesemia predicts osteoradionecrosis development in patients with squamous cell carcinoma of the oropharynx and oral cavity who received platinum-based concurrent chemoradiation with or without induction therapy. Methods We reviewed data from patients with head and neck cancers who had undergone chemoradiation with weekly cisplatin/carboplatin between January 1, 2010 and December 31, 2014 at our institution. Pathologic features, laboratory test results, disease stage, and social histories were recorded. The association between hypomagnesemia and osteoradionecrosis was analyzed controlling for known confounding factors. Results Hypomagnesemia during cancer treatment was associated with osteoradionecrosis development (HR = 2.72, P = .037) independent of total radiation dose (HR = 1.07, P = .260) and smoking history (HR = 2.05, P = .056) among the patients who received platinum-based induction chemotherapy followed by concurrent chemoradiation. Conclusions Hypomagnesemia was predictive of the development of osteoradionecrosis in patients with cancers of the oropharynx and oral cavity receiving platinum-based induction followed by concurrent chemoradiation.

Background and purpose To characterize patterns and outcomes of brain MR image changes after proton therapy (PT) for skull base head and neck cancer (HNC). Material and methods Post-treatment MRIs ≥6 months were reviewed for radiation-associated image changes (RAIC) in 127 patients. All patients had received at least a point dose of 40 Gy(RBE) to the brain. The MRIs were rigidly registered to planning CTs and RAIC lesions were contoured both on T1 weighted (post-contrast) and T2 weighted sequences, and dose–volume parameters extracted. Probability of RAIC was calculated using multistate survival analysis. Univariate/multivariate analyses were performed using Cox Regression. Recursive partitioning analysis was used to investigate dose–volume correlates of RAIC development. Results 17.3% developed RAIC. All RAIC events were asymptomatic and occurred in the temporal lobe (14), frontal lobe (6) and cerebellum (2). The median volume of the contrast enhanced RAIC lesion was 0.5 cc at their maximum size. The RAIC resolved or improved in 45.5% of the patients and were stable or progressed in 36.4%. The 3-year actuarial rate of developing RAIC was 14.3%. RAIC was observed in 63% of patients when V67 Gy(RBE) of the brain ≥0.17 cc. Conclusion Small RAIC lesions after PT occurred in 17.3% of the patients; the majority in nasopharyngeal or sinonasal cancer. The estimated dose–volume correlations confirm the importance of minimizing focal high doses to brain when achievable.

Background The incidence of oropharyngeal squamous cell carcinoma (OPSCC) in the US is rapidly increasing, driven largely by the epidemic of human papillomavirus (HPV)-mediated OPSCC. Although survival for patients with HPV mediated OPSCC (HPV+ OPSCC) is generally better than that of patients with non-virally mediated OPSCC, this effect is not uniform. We hypothesized that tobacco exposure remains a critical modifier of survival for HPV+ OPSCC patients. Methods We conducted a retrospective analysis of 611 OPSCC patients with concordant p16 and HPV testing treated at a single institute (2002–2013). Survival analysis was performed using Kaplan-Meier analysis and Cox regression. Recursive partitioning analysis (RPA) was used to define tobacco exposure associated with survival (p Results Tobacco exposure impacted overall survival (OS) for HPV+ patients on univariate and multivariate analysis (p = 0.002, p = 0.003 respectively). RPA identified 30 pack-years (PY) as a threshold at which survival became significantly worse in HPV+ patients. OS and disease-free survival (DFS) for HPV+ > 30 PY patients didn’t differ significantly from HPV- patients (p = 0.72, p = 0.27, respectively). HPV+ > 30 PY patients had substantially lower 5-year OS when compared to their ≤30 PYs counterparts: 78.4% vs 91.6%; p = 0.03, 76% vs 88.3%; p = 0.07, and 52.3% vs 74%; p = 0.05, for stages I, II, and III (AJCC 8th Edition Manual), respectively. Conclusions Tobacco exposure can eliminate the survival benefit associated with HPV+ status in OPSCC patients. Until this effect can be clearly quantified using prospective datasets, de-escalation of treatment for HPV + OPSCC smokers should be avoided.

This manuscript describes a dataset of thoracic cavity segmentations and discrete pleural effusion segmentations we have annotated on 402 computed tomography (CT) scans acquired from patients with non-small cell lung cancer. The segmentation of these anatomic regions precedes fundamental tasks in image analysis pipelines such as lung structure segmentation, lesion detection, and radiomics feature extraction. Bilateral thoracic cavity volumes and pleural effusion volumes were manually segmented on CT scans acquired from The Cancer Imaging Archive "NSCLC Radiomics" data collection. Four hundred and two thoracic segmentations were first generated automatically by a U-Net based algorithm trained on chest CTs without cancer, manually corrected by a medical student to include the complete thoracic cavity (normal, pathologic, and atelectatic lung parenchyma, lung hilum, pleural effusion, fibrosis, nodules, tumor, and other anatomic anomalies), and revised by a radiation oncologist or a radiologist. Seventy-eight pleural effusions were manually segmented by a medical student and revised by a radiologist or radiation oncologist. Interobserver agreement between the radiation oncologist and radiologist corrections was acceptable. All expert-vetted segmentations are publicly available in NIfTI format through The Cancer Imaging Archive at https://doi.org/10.7937/tcia.2020.6c7y-gq39. Tabular data detailing clinical and technical metadata linked to segmentation cases are also available. Thoracic cavity segmentations will be valuable for developing image analysis pipelines on pathologic lungs - where current automated algorithms struggle most. In conjunction with gross tumor volume segmentations already available from "NSCLC Radiomics," pleural effusion segmentations may be valuable for investigating radiomics profile differences between effusion and primary tumor or training algorithms to discriminate between them.

PURPOSE: To ascertain the dose-toxicity relationship for the prevalence of self-reported trismus in long-term survivors after intensity-modulated radiotherapy (IMRT) for oropharyngeal carcinoma (OPC). MATERIALS AND METHODS: Self-reported mouth opening was ascertained prospectively via a cross-sectional survey of OPC survivors using the intraoral finger-test. RT dose-volume histograms (DVHs) were generated for the following masticatory regions of interest: medial pterygoid, lateral pterygoid, and masseter muscles which were designated as ipsilateral or contralateral to the primary tumor. Trismus was defined as self-reported mouth opening of

PURPOSE: Using a 200 Head and Neck cancer (HNC) patient cohort, we employ patient similarity based on tumor location, volume, and proximity to organs at risk to predict radiation-associated dysphagia (RAD) in a new patient receiving intensity modulated radiation therapy (IMRT). MATERIAL AND METHODS: All patients were treated using curative-intent IMRT. Anatomical features were extracted from contrast-enhanced tomography scans acquired pre-treatment. Patient similarity was computed using a topological similarity measure, which allowed for the prediction of normal tissues’ mean doses. We performed feature selection and clustering, and used the resulting groups of patients to forecast RAD. We used Logistic Regression (LG) cross-validation to assess the potential toxicity risk of these groupings. RESULTS: Out of 200 patients, 34 patients were recorded as having RAD. Patient clusters were significantly correlated with RAD (p < .0001). The area under the receiver-operator curve (AUC) using pre-established, baseline features gave a predictive accuracy of 0.79, while the addition of our cluster labels improved accuracy to 0.84. CONCLUSION: Our results show that spatial information available pre-treatment can be used to robustly identify groups of RAD high-risk patients. We identify feature sets that considerably improve toxicity risk prediction beyond what is possible using baseline features. Our results also suggest that similarity-based predicted mean doses to organs can be used as valid predictors of risk to organs.

BACKGROUND We report outcomes for patients with T2N0M0 glottic squamous cell carcinoma (SCC) treated with radiation therapy (RT). METHODS Patients who received definitive RT for T2 glottic SCC from 2000 through 2013 were retrospectively reviewed. RESULTS One hundred and thirteen patients were analyzed (median follow-up time 91 months; 85 patients received three-dimensional conformal radiotherapy [3D-CRT] and 28 received intensity-modulated radiation therapy [IMRT]). Fractionation was conventional (58%) or altered (42%); 20 patients (18%) received concurrent chemotherapy. Five-year local control was 83% for the 3D-CRT vs 81% for the IMRT group (P = .76). The ultimate locoregional control at 5 years was 100% for IMRT vs 91% for 3D-CRT (P = .1). The 5-year overall survival (OS) was 78% for 3D-CRT vs 81% for IMRT (P = .83). On multivariate analysis, younger age was the only independent predictor of improved OS (P = .0002). CONCLUSIONS Oncologic and survival outcomes were excellent for patients with T2N0 glottic cancer. Patients treated with IMRT and 3D-CRT had no statistically significant differences in all investigated endpoints.

Highlights • Stability analysis for various radiomic features based on the CT axial images. • 109 radiomic features were calculated. • ICC and CCC were adopted to assess the representation and predictive agreement. • Segmentation variability affects both radiomic features and predictive accuracy., Introduction Accurate segmentation of tumors and quantification of tumor features are important for cancer detection, diagnosis, monitoring, and planning therapeutic intervention. Due to inherent noise components in multi-parametric imaging and inter-observer and intra-observer variations, it is common that various segmentation methods may produce large segmentation errors in tumor volumes and their associated radiomic features. The purpose of this study is to carry out the stability analysis for radiomic features with respect to segmentation variation in oropharyngeal cancer (OPC). Methods In this study, 436 contrast-enhanced computed tomography (CT) axial images were collected from patients with OPC. In order to derive various segmentations of tumor volumes, two additional segmentations were obtained via resizing the original segmented regions of interest (ROIs) based on their geometric information on the boundary. For three ROI image groups, we calculated 109 radiomic features. Then, a logistic regression model was built to investigate the correlation between the radiomic features extracted from GTVp and the response to chemotherapy and radiation in terms of overall survival (OS). Finally, in order to evaluate the stability of each feature with respect to segmentation results, based on the prediction probabilities, we assessed the inter-rater reliability and reproducibility by calculating the intra-class correlation coefficients (ICC) and concordance correlation coefficients (CCC). Results Most radiomic features in this study varied a lot when the ROIs were not well segmented. For both the representation agreement and predictive agreement, the ICC and CCC were below 0.5 for all the features. We still found some robust features with relatively high ICC and CCC compared to most features. For example, 25percentile (ICC = 0.38, CCC = 0.37 in representation agreement and ICC = CCC = 0.27 in predictive agreement) is a quantile based feature, which is robust to the extremely high or low values; and Hu_1_std (ICC = 0.31, CCC = 0.31 in representation agreement) is a feature calculated based on the first Hu moment, which is invariant to the transformation of ROIs. Conclusion In OPC studies, the tumor segmentation variation affects the radiomic features from CT images in terms of both representation and prediction. Some features that are robust to the extreme values or invariant to the transformation of ROIs may be treated as radiomic markers to assist with OPC treatment monitoring and prognostic prediction.

Clustering is the task of identifying groups of similar subjects according to certain criteria. The AJCC staging system can be thought as a clustering mechanism that groups patients based on their disease stage. This grouping drives prognosis and influences treatment. The goal of this work is to evaluate the efficacy of machine learning algorithms to cluster the patients into discriminative groups to improve prognosis for overall survival (OS) and relapse free survival (RFS) outcomes. We apply clustering over a retrospectively collected data from 644 head and neck cancer patients including both clinical and radiomic features. In order to incorporate outcome information into the clustering process and deal with the large proportion of censored samples, the feature space was scaled using the regression coefficients fitted using a proxy dependent variable, martingale residuals, instead of follow-up time. Two clusters were identified and evaluated using cross validation. The Kaplan Meier (KM) curves between the two clusters differ significantly for OS and RFS (p-value

Imaging in radiation oncology is essential for the evaluation of treatment response in tumors and organs at risk. This influences further treatment decisions and could possibly be used to adapt therapy. This review article focuses on the currently used imaging modalities for response assessment in radiation oncology and gives an overview of new and promising techniques within this field.

Background To establish a predictive model for the fibrotic level of neck muscles after radiotherapy by using radiomic features extracted from the magnetic resonance imaging (MRI) before and after radiotherapy and planning computed tomography (CT) in nasopharyngeal carcinoma patients. Methods A total of one hundred and eighty-six patients were finally enrolled in this study. According to the specific standard, all patients were divided into three different fibrosis groups. Regions of interests (ROI), including sternocleidomastoids (SCMs), trapezius (T), levator scapulae (LS), and scalenus muscles (S), were delineated manually and used for features extraction on IBEX. XGBoost, a machine learning algorithm, was used for the establishment of the prediction model. First, the patients were divided into training cohort (80%) and testing cohort (20%) randomly. Then the image features of CT or delta changes calculated from pre- and post-radiotherapy MRI images on each cohort constituted training and testing datasets. Then, based on the training dataset, a well-trained prediction model was produced. We used five-fold cross-validation to validate the predictive models. Afterward, the model performance was assessed on the ‘testing’ set and reported in terms of area under the receiver operating characteristic curve (AUC) under five scenarios: (I) only T1 sequence, (II) only T2 sequence, (III) only T1 post-contrast (T1 + C) sequence, (IV) Combination of all MRI sequences, (V) only CT. Results Most of the patients enrolled are male (73.1%), mean age was 47 years, receiving concurrent chemo-radiotherapy as the primary treatment (90.9%). By the end of the final follow-up, most of the patients were rated as mild fibrosis (60.8%). We found the prediction model based on the CT image features outperform all MRI features with an AUC of 0.69 and accuracy of 0.65. Contrarily, the model based on features from all MRI sequence showed lower AUC less than 0.5 and lower accuracy less than 0.6. Conclusions The prediction model based on CT radiomics features has better performance in the prediction of the grade of post-radiotherapy neck fibrosis. This might help guide radiotherapy treatment planning to achieve a better quality of life.

Purpose: To evaluate 2 published normal tissue complication probability models for radiation-induced hypothyroidism (RHT) on a large cohort of oropharyngeal carcinoma (OPC) patients who were treated with intensity-modulated radiation therapy (IMRT). Methods and Materials: OPC patients treated with retrievable IMRT Digital Imaging and Communications in Medicine (DICOMs) data and available baseline and follow-up thyroid function tests were included. Mean dose (Dmean) to the thyroid gland (TG) and its volume were calculated. The study outcome was clinical HT at least 6 months after radiation therapy, which was defined as grade ≥2 HT per Common Terminology Criteria for Adverse Events grading system (symptomatic hypothyroidism that required thyroid replacement therapy). Regression analyses and Wilcoxon rank-sum test were used. Receiver operating characteristic curves and area under the curve for the fitted model were calculated. Results: In the study, 360 OPC patients were included. The median age was 58 years. Most tumors (51%) originated from the base of tongue. IMRT-split field was used in 95%, and median radiation therapy dose was 69.96 Gy. In the study, 233 patients (65%) developed clinical RHT that required thyroid replacement therapy. On multivariate analysis higher Dmean and smaller TG volume maintained the statistically significant association with the risk of clinical RHT (P

The accurate determination of sarcopenia is critical for disease management in patients with head and neck cancer (HNC). Quantitative determination of sarcopenia is currently dependent on manually-generated segmentations of skeletal muscle derived from computed tomography (CT) cross-sectional imaging. This has prompted the increasing utilization of machine learning models for automated sarcopenia determination. However, extant datasets of head and neck CT imaging currently do not provide the necessary manually-generated skeletal muscle segmentations at the C3 vertebral level needed for building these models. In this data descriptor, we detail the annotation of a large set of head and neck CT images for use in automated sarcopenia-related clinical decision making and body composition analysis. A set of 394 HNC patients were selected from The Cancer Imaging Archive, and their skeletal muscle and adipose tissue was manually segmented at the C3 vertebral level using sliceOmatic in .tag format. Subsequently, using publicly disseminated Python scripts, we generated corresponding segmentations files in Neuroimaging Informatics Technology Initiative format. In addition to segmentation data, additional clinical demographic data germane to body composition analysis have been retrospectively collected for these patients from the University of Texas MD Anderson Cancer Center databases. These data are a valuable resource for studying sarcopenia and body composition analysis in patients with HNC.

PET/CT images provide a rich data source for clinical prediction models in head and neck squamous cell carcinoma (HNSCC). Deep learning models often use images in an end-to-end fashion with clinical data or no additional input for predictions. However, in the context of HNSCC, the tumor region of interest may be an informative prior in the generation of improved prediction performance. In this study, we utilize a deep learning framework based on a DenseNet architecture to combine PET images, CT images, primary tumor segmentation masks, and clinical data as separate channels to predict progression-free survival (PFS) in days for HNSCC patients. Through internal validation (10-fold cross-validation) based on a large set of training data provided by the 2021 HECKTOR Challenge, we achieve a mean C-index of 0.855 ± 0.060 and 0.650 ± 0.074 when observed events are and are not included in the C-index calculation, respectively. Ensemble approaches applied to cross-validation folds yield C-index values up to 0.698 in the independent test set (external validation), leading to a 1st place ranking on the competition leaderboard. Importantly, the value of the added segmentation mask is underscored in both internal and external validation by an improvement of the C-index when compared to models that do not utilize the segmentation mask. These promising results highlight the utility of including segmentation masks as additional input channels in deep learning pipelines for clinical outcome prediction in HNSCC.

Highlights • mpMRI coupled to deep learning can generate reasonable OPC tumor segmentations. • Using multiple input channels may positively impact segmentation performance. • Deep learning segmentations were non-inferior to ground truth as per Turing test., Background/Purpose Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance. Materials/Methods GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2 + T1, T2 + ADC, T2 + Ktrans, T2 + Ve, all five channels [ALL]) primarily using the Dice similarity coefficient (DSC). False-negative DSC (FND), false-positive DSC, sensitivity, positive predictive value, surface DSC, Hausdorff distance (HD), 95% HD, and mean surface distance were also assessed. For the best model, ground truth and DL-generated segmentations were compared through a blinded Turing test using three physician observers. Results Models yielded mean DSCs from 0.71 ± 0.12 (ALL) to 0.73 ± 0.12 (T2 + T1). Compared to the T2 model, performance was significantly improved for FND, sensitivity, surface DSC, HD, and 95% HD for the T2 + T1 model (p 0.05). Most models demonstrated significant correlations between tumor size and HD or Surface DSC (p 0.05). On average, there were no significant differences between ground truth and DL-generated segmentations for all observers (p > 0.05). Conclusion DL using mpMRI provides reasonably accurate segmentations of OPC GTVp that may be comparable to ground truth segmentations generated by clinical experts. Incorporating additional mpMRI channels may increase the performance of FND, sensitivity, surface DSC, HD, and 95% HD, and improve model robustness to tumor size.

Background/PurposeSarcopenia is a prognostic factor in patients with head and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) calculated from cervical neck SM segmentations. However, SM segmentation requires manual input, which is time-consuming and variable. Therefore, we developed a fully-automated approach to segment cervical vertebra SM.Materials/Methods390 HNC patients with corresponding contrast-enhanced computed tomography (CT) scans were utilized (300-training, 90-testing). Ground-truth single-slice SM segmentations at the C3 vertebra were manually generated. A multi-stage deep learning pipeline was developed, where a 3D ResUNet auto-segmented the C3 section (33 mm window), the middle slice of the section was auto-selected, and a 2D ResUNet auto-segmented the auto-selected slice. Both the 3D and 2D approaches trained five sub-models (5-fold cross-validation) and combined sub-model predictions on the test set using majority vote ensembling. Model performance was primarily determined using the Dice similarity coefficient (DSC). Predicted SMI was calculated using the auto-segmentation cross-sectional area. Finally, using established SMI cutoffs, we performed a Kaplan-Meier analysis to determine associations with overall survival.ResultsMean test set DSC of the 3D and 2D models were 0.96 and 0.95, respectively. Predicted SMI had high correlation to the ground-truth SMI in males and females (r>0.96). Predicted SMI stratified patients for overall survival in males (log-rank p = 0.01) but not females (log-rank p = 0.07), consistent with ground-truth SMI.ConclusionWe developed a high-performance, multi-stage, fully-automated approach to segment cervical vertebra SM. Our study is an essential step towards fully-automated sarcopenia-related decision-making.

Auto-segmentation of primary tumors in oropharyngeal cancer using PET/CT images is an unmet need that has the potential to improve radiation oncology workflows. In this study, we develop a series of deep learning models based on a 3D Residual Unet (ResUnet) architecture that can segment oropharyngeal tumors with high performance as demonstrated through internal and external validation of large-scale datasets (training size = 224 patients, testing size = 101 patients) as part of the 2021 HECKTOR Challenge. Specifically, we leverage ResUNet models with either 256 or 512 bottleneck layer channels that are able to demonstrate internal validation (10-fold cross-validation) mean Dice similarity coefficient (DSC) up to 0.771 and median 95% Hausdorff distance (95% HD) as low as 2.919 mm. We employ label fusion ensemble approaches, including Simultaneous Truth and Performance Level Estimation (STAPLE) and a voxel-level threshold approach based on majority voting (AVERAGE), to generate consensus segmentations on the test data by combining the segmentations produced through different trained cross-validation models. We demonstrate that our best performing ensembling approach (256 channels AVERAGE) achieves a mean DSC of 0.770 and median 95% HD of 3.143 mm through independent external validation on the test set. Concordance of internal and external validation results suggests our models are robust and can generalize well to unseen PET/CT data. We advocate that ResUNet models coupled to label fusion ensembling approaches are promising candidates for PET/CT oropharyngeal primary tumors auto-segmentation, with future investigations targeting the ideal combination of channel combinations and label fusion strategies to maximize segmentation performance.