Head And Neck Patients Research Articles

The impact of oncology nutrition services on weight change among patients with head and neck or pancreatic cancers.

86 Background: Weight loss is prevalent among cancer patients and correlates with lower health-related quality of life and overall survival rates. Individuals diagnosed with head and neck (HN) and pancreatic cancers face a heightened risk of weight loss due to tumor location and treatment-related side effects. Although guidelines advocate early interventions such as referral to a dietitian for tailored nutritional support, the precise impact of oncology nutrition services (ONS) on weight loss remains unclear. In this study, we investigated the longitudinal association between weight change and ONS among patients diagnosed with HN or pancreatic cancers. Methods: A retrospective chart review was conducted for patients diagnosed with HN or pancreatic cancers visited between January 2019 and December 2023. Inclusion criteria encompassed patients who had weight measurements taken within 30 days of their cancer diagnosis. Patient demographics, clinical characteristics, all recorded body weights, and receipt of ONS were extracted from electronic health records. Weight change (%) from the baseline was calculated at each measurement by subtracting the initial weight value. A mixed-effects model was employed to evaluate the impact of ONS on weight change, with time to weight measurement and ONS serving as interaction terms, after adjusting for demographics and clinical characteristics. Results: A cohort of 1305 patients was identified, predominantly comprising males (n=1014, 63%) and individuals of White race (n=1358, 85%), with a diverse ethnic distribution (Hispanic: n=620, 39%). Of these patients, 947 (73%) received ONS at any point following cancer diagnosis. Significant weight loss over time was noted among all patients (-0.53% per 6 months, p<0.001). Baseline weight, sex, ethnicity, receipt of percutaneous endoscopic gastrostomy, total parenteral nutrition, radiation, chemotherapy, and ONS were significantly associated with weight loss. Following adjustment for the covariates, patients receiving ONS showed significantly less weight loss compared to those without ONS (-0.06% [95% confidence interval (CI): -0.10, -0.03] vs. -0.04% [95% CI: -0.38, -0.23] per 6 months, p<0.001). Conclusions: The findings indicate that ONS plays a significant role in attenuating weight loss among patients diagnosed with HN or pancreatic cancer, irrespective of baseline demographics or clinical status. While the magnitude of weight change may appear modest, its implications are substantial long term. Early referrals to ONS should be recommended for HN or pancreatic cancer patients to mitigate weight loss.

Improved quality of life in head and neck cancer patients treated with modern arc radiotherapy techniques - A prospective longitudinal analysis.

The present longitudinal study aimed to evaluate the potential impact of modern radiotherapy (RT) techniques on quality of life (QOL) in patients with head and neck (HNC) cancer. In this single-center prospective study, participants were asked to complete QOL questionnaires that included the EORTC QLQ-C30, QLQ-H&N 35 and utility score by time trade-off (TTO) at three time points (2 weeks, 3 months and 6 months) after completion of RT. All patients were treated by modern RT techniques [volumetric modulated arc therapy (VMAT) or helical tomotherapy (HT)]. Patients who developed recurrence or died before the 6-month follow-up were excluded. Linear mixed models with random intercepts for participants and restricted maximum likelihood estimates were used to assess the effect of our study variables (age, sex, primary site, cancer stage, treatment, radiation dose and radiation method). Overall changes in QOL, utility scores and symptom burdens at different time points were tested using paired t tests. A total of 45 patients were recruited from 2022 to 2023. Those who completed the surveys at 2 weeks with at least 1 follow-up (30 patients, 67%) were enrolled in the final analysis. The majority of these 30 patients were men (76.7%), had oral cancer (40%), had stage III or IV disease (60%), received surgical intervention (63%) and were treated with chemoradiation (80%). A curative total dose of 66 to 70 Gy was delivered to 23 (76.7%) patients, half of whom received HT. Patients who received chemotherapy had significantly lower global QoL scales (mean difference, 27.94; 95% CI, 9.33-46.55; p=0.005). Global QOL, physical function, symptoms of sticky saliva, cough, feelings of illness and weight loss improved significantly between 2 weeks and 3 months. There was no significant difference between 3 and 6 months. Interestingly, improvements in social function, social contact, pain and nutrition reached significance at 6 months. Subgroup analysis revealed greater pain relief over time for patients who underwent HT (p=0.030). Moreover, patients who participated in swallowing rehabilitation programs had a greater decrease in nausea and vomiting (p=0.036). HNC patients treated with modern RT techniques experience improved QOL and physical function over time. The most significant improvement occurs between 2 weeks and 3 months, after which the improvement plateaus. However, social function, social contact, pain and nutrition may require longer recovery intervals after treatment. HT with daily image guidance could provide a therapeutic opportunity for improving pain relief in patients with HNC.

An operator-independent quality assurance system for automatically generated structure sets

Objective. This study describes geometry-based and intensity-based tools for quality assurance (QA) of automatically generated structures for online adaptive radiotherapy, and designs an operator-independent traffic light system that identifies erroneous structure sets. Approach. A cohort of eight head and neck (HN) patients with daily CBCTs was selected for test development. Radiotherapy contours were propagated from planning computed tomography (CT) to daily cone beam CT (CBCT) using deformable image registration. These propagated structures were visually verified for acceptability. For each CBCT, several error scenarios were used to generate what were judged unacceptable structures. Ten additional HN patients with daily CBCTs and different error scenarios were selected for validation. A suite of tests based on image intensity, intensity gradient, and structure geometry was developed using acceptable and unacceptable HN planning structures. Combinations of one test applied to one structure, referred to as structure-test combinations, were selected for inclusion in the QA system based on their discriminatory power. A traffic light system was used to aggregate the structure-test combinations, and the system was evaluated on all fractions of the ten validation HN patients. Results. The QA system distinguished between acceptable and unacceptable fractions with high accuracy, labeling 294/324 acceptable fractions as green or yellow and 19/20 unacceptable fractions as yellow or red. Significance. This study demonstrates a system to supplement manual review of radiotherapy planning structures. Automated QA is performed by aggregating results from multiple intensity- and geometry-based tests.

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.

Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography (PET), are used in the routine clinic to assist radiation oncologists for accurate GTV delineation. However, the availability of PET imaging may not always be guaranteed. To develop a deep learning segmentation framework for automated GTV delineation of HN cancers using a combination of PET/CT images, while addressing the challenge of missing PET data. Two datasets were included for this study: Dataset I: 524 (training) and 359 (testing) oropharyngeal cancer patients from different institutions with their PET/CT pairs provided by the HECKTOR Challenge; Dataset II: 90 HN patients(testing) from a local institution with their planning CT, PET/CT pairs. To handle potentially missing PET images, a model training strategy named the "Blank Channel" method was implemented. To simulate the absence of a PET image, a blank array with the same dimensions as the CT image was generated to meet the dual-channel input requirement of the deep learning model. During the model training process, the model was randomly presented with either a real PET/CT pair or a blank/CT pair. This allowed the model to learn the relationship between the CT image and the corresponding GTV delineation based on available modalities. As a result, our model had the ability to handle flexible inputs during prediction, making it suitable for cases where PET images are missing. To evaluate the performance of our proposed model, we trained it using training patients from Dataset I and tested it with Dataset II. We compared our model (Model 1) with two other models which were trained for specific modality segmentations: Model 2 trained with only CT images, and Model 3 trained with real PET/CT pairs. The performance of the models was evaluated using quantitative metrics, including Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff Distance (HD95). In addition, we evaluated our Model 1 and Model 3 using the 359 test cases in Dataset I. Our proposed model(Model 1) achieved promising results for GTV auto-segmentation using PET/CT images, with the flexibility of missing PET images. Specifically, when assessed with only CT images in Dataset II, Model 1 achieved DSC of 0.56±0.16, MSD of 3.4±2.1mm, and HD95 of 13.9±7.6mm. When the PET images were included, the performance of our model was improved to DSC of 0.62±0.14, MSD of 2.8±1.7mm, and HD95 of 10.5±6.5mm. These results are comparable to those achieved by Model 2 and Model 3, illustrating Model 1's effectiveness in utilizing flexible input modalities. Further analysis using the test dataset from Dataset I showed that Model 1 achieved an average DSC of 0.77, surpassing the overall average DSC of 0.72 among all participants in the HECKTOR Challenge. We successfully refined a multi-modal segmentation tool for accurate GTV delineation for HN cancer. Our method addressed the issue of missing PET images by allowing flexible data input, thereby providing a practical solution for clinical settings where access to PET imaging may be limited.

Defining the role of intensity modulation in electron conformal therapy for the treatment of head and neck cancer

Bolus electron conformal therapy (BECT) in the treatment of cancers of the head and neck is often limited by an inability to reduce dosimetric hot spots resulting from surface irregularity or tissue heterogeneity. We examined the potential benefits of using intensity modulation for electron therapy (IM-BECT) to reduce hotspots in patients undergoing electron beam therapy for superficial cancers of the head and neck (HN). Twenty patients with HN cancer previously treated with BECT were identified. Each case included the treatment targets and a primary organ at risk (OAR) that were defined by the radiation oncologist. A target +2 cm rind structure was created for analysis of the dose deposition in areas surrounding the target volume as a measure of conformality. Each patient plan was transferred into the novel IM-BECT planning software and each case was recomputed as per the original parameters. Next, each case was replanned with the inclusion of intensity modulation, as well as a new custom conformal bolus that was redesigned for optimized range compensation when paired with an intensity modulator. The plans were then normalized to prescription dose and compared for target coverage/dose and OAR dose. For patients who had a hotspot of 125% or greater, the hotspot was on average reduced by 13.1% with IM-BECT. For IM-BECT, the average primary OAR means dose and target+2cm rind mean dose increased slightly by 10.6% and 6.4%, respectively (primary OAR mean [p = 0.0001], and Target+2cm rind mean [p = 0.0001], paired t-test). IM-BECT is an effective method of reducing hotspots in patients with superficial HN cancer. Improvements came at the expense of slight increases in dose to the underlying tissues. This retrospective planning study represents the first example of IM-BECT to actual HN patient cases. Expanding the role of IM-BECT in other disease sites could potentially compared to conventional BECT.

Comparisons of successful and failed Phase III trials of drugs and biologicals tested for mitigation of oral mucositis in patients being treated with radiotherapy with or without concomitant chemotherapy for cancers of the head and neck.

Oral mucositis (OM) remains a significant toxicity among patients being treated with radiotherapy (RT) alone or with concomitant chemotherapy (CRT) for cancers of the head and neck (HNC). Given its clinical significance as an unmet need and its potential commercial viability, the pharmaceutical industry has been actively pursuing an effective intervention. Despite this interest and activity, only a few agents have been studied in Phase III trials (n = 6). The objective of this study was to identify common features that differentiate successful and failed Phase III OM trials. We used the United States Patent and Trademark Office Patent Public Search database to search patents with "oral mucositis" in the claims. We then searched ClinicalTrials.gov and PubMed to determine if Phase III or Phase II trial data for identified biologics/drugs had been published. We assessed each Phase III and Phase II trial for characteristics that may be associated with trial success or failure. We considered a study as a "success" if the primary endpoint reached statistical significance, and we considered a study as "failure" if the primary endpoint did not reach statistical significance. Of the three successful Phase III trials, one investigated avasopasem manganese (Galera Therapeutics) and two examined palifermin (Amgen). The three failed trials included those evaluating dusquetide (Soligenix), iseganan hydrochloride (IntraBiotics Pharmaceuticals), and clonidine (Monopar Therapeutics). We found that differences in the level of sponsor funding, patient inclusion criteria including radiation source and concomitant chemotherapy regimen, and concordance of primary efficacy outcomes between Phase II and Phase III trials influenced outcomes. To properly design clinical trials for OM in HNC patients, it is important that researchers and sponsors take note of specific study characteristics associated with success or failure, particularly with Phase III trials where the risks and costs are the highest.

P34: The prognostic value of pre-treatment sarcopenia in overall survival in head and neck cancer patients: A systematic review

Abstract Background This study aims to determine the prognostic value of pre-treatment sarcopenia, defined radiologically at the cervical (C3) or lumbar (L3) region, in head and neck (HNC) adult patient population undergoing treatment with curative intention. Methods A systematic search in the PubMed and Scopus databases was performed before February 2022. The inclusion criteria applied were adult patients with locally advanced HNC, sarcopenia defined radiologically at the C3 and/or L3 level, and patients receiving primary treatment with curative intent. The certainty of study evidence was assessed using the ROBINS-I scale, the Cochrane method for assessing bias in non-randomised trials. Results Twenty-six studies were included in this review, totalling 6395 adult patients with HNC. It was evident that pre-treatment sarcopenia was associated with worse overall survival outcomes in 22 out of 26 (85%) studies across all the treatment modalities with curative intent. In addition, the gender-specific skeletal muscle index (SMI) sarcopenic cut-off value of <52.4 cm2/m2 for men and <38.5 cm2/m2 for women, was significantly associated with worse OS compared to other cut-off values seen in a minority of studies. Conclusions Despite the retrospective design of the studies and inconsistency in SMI cut-off values, these findings suggest that sarcopenia is a strong prognostic factor of overall survival in HNC patients undergoing primary curative treatment. The clinical implications of these findings suggest that sarcopenia evaluation is a good prognostic marker in the HNC population. Future nutritional interventional studies might focus on reversing the muscle loss and improving the overall outcomes in identified sarcopenic individuals.

A retrospective study on the investigation of potential dosimetric benefits of online adaptive proton therapy for head and neck cancer.

Proton therapy is sensitive to anatomical changes, often occurring in head-and-neck (HN) cancer patients. Although multiple studies have proposed online adaptive proton therapy (APT), there is still a concern in the radiotherapy community about the necessity of online APT. We have performed a retrospective study to investigate the potential dosimetric benefits of online APT for HN patients relative to the current offline APT. Our retrospective study has a patient cohort of 10 cases. To mimic online APT, we re-evaluated the dose of the in-use treatment plan on patients' actual treatment anatomy captured by cone-beam CT (CBCT) for each fraction and performed a templated-based automatic replanning if needed, assuming that these were performed online before treatment delivery. Cumulative dose of the simulated online APT course was calculated and compared with that of the actual offline APT course and the designed plan dose of the initial treatment plan (referred to as nominal plan). The ProKnow scoring system was employed and adapted for our study to quantify the actual quality of both courses against our planning goals. The average score of the nominal plans over the 10 cases is 41.0, while those of the actual offline APT course and our simulated online course is 25.8 and 37.5, respectively. Compared to the offline APT course, our online course improved dose quality for all cases, with the score improvement ranging from 0.4 to 26.9 and an average improvement of 11.7. The results of our retrospective study have demonstrated that online APT can better address anatomical changes for HN cancer patients than the current offline replanning practice. The advanced artificial intelligence based automatic replanning technology presents a promising avenue for extending potential benefits of online APT.

Developing symptom-specific dietary leaflets to address radiotherapy side effects using an eDelphi method

IntroductionPatients suffering from head and neck (HN) cancer undergoing Radiotherapy (RT) suffer from various debilitating side effects that greatly hinder their eating ability. This leads to patients having a poor nutritional status causing weight loss. This study aimed to assess if an e-Delphi method variation allowed efficiently developing dietary advice leaflets addressing these side effects and achieving a consensus among healthcare professionals. MethodsAn e-Delphi style approach was used. Six participants representing the professions working with HN patients were asked to give feedback on four symptom-specific dietary leaflets. These leaflets were designed based on a previous extensive literature review. After each round, the participants’ suggested changes were applied. Before applying major changes to the leaflet, the participants were asked to vote if they agreed with each major change. ResultsOverall consensus was reached after three rounds since all participants voted “highly likely” to use it in their clinical practice. Most suggestions by the participants agreed with the existing literature. The only change (accepted as a result of voting) that contradicted the literature was concerning sugar intake. ConclusionThe participants reached consensus and developed leaflets that were based on literature recommendations for use for patients that, in their opinion, were acceptable for clinical use. This e-Delphi variation proved to be efficient to achieve consensus among healthcare professionals regarding patient information tools. Implications for practiceThis eDelphi method is an efficient and effective way to revise and achieve consensus regarding the development of patient information material.

Is noncoplanar plan more robust to inter-fractional variations than coplanar plan in treating bilateral HN tumors with pencil-beam scanning proton beams?

Noncoplanar plans (NCPs) are commonly used for proton treatment of bilateral head and neck (HN) malignancies. NCP requires additional verification setup imaging between beams to correct residual errors of robotic couch motion, which increases imaging dose and total treatment time. This study compared the quality and robustness of NCPs with those of coplanar plans (CPs). Under an IRB-approved study, CPs were created retrospectively for 10 bilateral HN patients previously treated with NCPs maintaining identical beam geometry of the original plan but excluding couch rotations. Plan robustness to the inter-fractional variation (IV) of both plans was evaluated through the Dose Volume Histograms (DVH) of weekly quality assurance CT (QACT) sets (39 total). In addition, delivery efficiency for both plans was compared using total treatment time (TTT) and beam-on time (BOT). No significant differences in plan quality were observed in terms of clinical target volume (CTV) coverage (D95) or organ-at-risk (OAR) doses (p>0.4 for all CTVs and OARs). No significant advantage of NCPs in the robustness to IV was found over CP, either. Changes in D95 of QA plans showed a linear correlation (slope=1.006, R2 >0.99) between NCP and CP for three CTV data points (CTV1, CTV2, and CTV3) in each QA plan (117 data points for 39 QA plans). NCPs showed significantly higher beam delivery time than CPs for TTT (539±50vs. 897±142s; p<0.001); however, no significant differences were observed for BOT. NCPs are not more robust to IV than CPs when treating bilateral HN tumors with pencil-beam scanning proton beams. CPs showed plan quality and robustness similar to NCPs while reduced treatment time (∼6min). This suggests that CPs may be a more efficient planning technique for bilateral HN cancer proton therapy.

Generating synthetic images from cone beam computed tomography using self-attention residual UNet for head and neck radiotherapy

Background and purposeAccurate CT numbers in Cone Beam CT (CBCT) are crucial for precise dose calculations in adaptive radiotherapy (ART). This study aimed to generate synthetic CT (sCT) from CBCT using deep learning (DL) models in head and neck (HN) radiotherapy. Materials and methodsA novel DL model, the 'self-attention-residual-UNet' (ResUNet), was developed for accurate sCT generation. ResUNet incorporates a self-attention mechanism in its long skip connections to enhance information transfer between the encoder and decoder. Data from 93 HN patients, each with planning CT (pCT) and first-day CBCT images were used. Model performance was evaluated using two DL approaches (non-adversarial and adversarial training) and two model types (2D axial only vs. 2.5D axial, sagittal, and coronal). ResUNet was compared with the traditional UNet through image quality assessment (Mean Absolute Error (MAE), Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM)) and dose calculation accuracy evaluation (DVH deviation and gamma evaluation (1 %/1mm)). ResultsImage similarity evaluation results for the 2.5D-ResUNet and 2.5D-UNet models were: MAE: 46±7 HU vs. 51±9 HU, PSNR: 66.6±2.0 dB vs. 65.8±1.8 dB, and SSIM: 0.81±0.04 vs. 0.79±0.05. There were no significant differences in dose calculation accuracy between DL models. Both models demonstrated DVH deviation below 0.5 % and a gamma-pass-rate (1 %/1mm) exceeding 97 %. ConclusionsResUNet enhanced CT number accuracy and image quality of sCT and outperformed UNet in sCT generation from CBCT. This method holds promise for generating precise sCT for HN ART.

Estimating Potential Benefits of Online Adaptive Proton Therapy for Head-and-Neck Cancer: A Retrospective Study

Proton therapy is highly sensitive to anatomical changes and setup variations in head-and-neck (HN) treatments. To address this issue, proton centers often acquire patient CT images weekly to monitor patient anatomical changes during the treatment course and perform offline plan adaptation when needed. However, offline adaptation cannot fully account for daily setup variations or the anatomical changes occurring with high frequency. There are a few groups endeavoring to develop advanced technologies to enable online adaptive proton therapy (APT). However, the necessity of online APT remains controversial, as it is unknown that whether online APT will significantly improve treatment quality and outcomes compared to offline APT. The purpose of this study is to estimate the clinical potential of online APT in the management of HN cancers in relation to the current offline APT. Our retrospective study was conducted with four HN patients (35 fractions per patient), who had been treated with intensity modulated proton therapy and had offline adaptation once or twice during their treatment courses. Synthetic CT (sCT) images were generated from 140 daily CBCT images for us to recalculate the dose of the treatment plan in patient's actual treatment anatomy for each treatment fraction and adapt the plan when warranted. These adaptations were assumed to be performed online before treatment delivery to mimic an online APT course. Accumulative doses were calculated for both courses using the CBCT-based sCT images of every fraction for us to compare the target coverage, organ at risk (OAR) sparing, tumor control probability (TCP) and normal tissue complication probability (NTCP). An in-house script was developed to semi-automate this process in a commercial treatment planning system to facilitate our study. All patients would benefit from online APT to different extents. For the first patient, with OAR doses comparable to the actual offline course, the retrospective online APT course improved dose coverages of the three CTVs from 95.2%, 98.64% and 89.53% to 98.88%, 99.81%, 98.97%, which would lead to a 4.52% improvement in TCP. Similarly, online APT would yield a 2.66% improvement in TCP for the second patient. For the third patient, with comparable CTV dose coverages, the mean doses of right parotid and oral cavity were decreased from 29.52 Gy relative biological effectiveness (RBE) and 41.89 Gy RBE to 22.16 Gy RBE and 34.61 Gy RBE, leading to a reduce of 1.67% and 3.40% in NTCP. The mean dose of right parotid was decreased from 21.71 Gy RBE to 19.37 Gy RBE for the last patient, leading to a reduce of 0.73% in NTCP. Our results showed that online APT could better maintain the treatment plan quality than offline APT for all the four patients, despite their significant anatomical changes. Future investigation will focus on collecting more patient data to obtain statistically significant results and help identify the patients to whom the online APT will be of most benefit.

Dynamic Contrast Enhanced MRI as a Biomarker of Tumor Response and Oncologic Outcomes in Head and Neck Cancer: Results of a Single Institution Prospective Imaging Study

We aim to determine the correlation between vascular parameters of Dynamic contrast enhanced (DCE) MRIs and tumor response and outcomes in head and neck (HNC) patients treated with definitive radiation therapy (RT). Eighty-two HNC patients are included in this prospective study in one institute. All patients had malignant head and neck neoplasm indicative of curative- intent treatment. Patients were imaged using MRIs pre-, mid-, and post-RT completion at 8-12 weeks. T2-weighted sequences were used for tumor contouring then it was co-registered to respective DCE images. The response to treatment was checked at mid-radiotherapy (mid-RT) and at the end of RT. Mid-RT MRI was co-registered to baseline images and the manually segmented baseline primary tumor regions of interest were propagated to mid-RT images. Quantitative maps (Ktrans, Kep, Ve and Vp) were generated with the extended Tofts pharmacokinetic models and were used for analysis. These vascular parameters were presented as a mean value and percentile using histogram analysis and the following parameters were extracted using an in-house programming environment script: mean, 5th, 10th, 20th, 30th, 40th, 50th (i.e., median), 60th, 70th, 80th, 90th, 95th percentile. The non-parametric Wilcoxon signed-rank test was used to assess the changes of mid-RT DCE parameters compared to baseline. Recursive partitioning analysis (RPA) was used to identify the delta DCE threshold associated with relapse. We assessed the identified thresholds' correlation with oncological and survival endpoints using Cox regression with and without standard clinical variables. The median age for patients is 61 years old (33-78 range). Never smokers are 39 (47%), 35 (43%) are former smoker and 8 (10%) are current smoker with a mean value of 14 pack per year and 26 standard deviations. Using AJCC 8th edition, 39 (47%) are stage I and 19 (23%) are stage II and stage III and IV are 15 (18%) and 9 (10%) respectively. HPV positive are 72 (88%). For patients with GTV-P at baseline (n = 60), 11 (18%) had mid-RT CR at the primary site which increased to 50 (83%) post-RT. The LC and RFS for the entire cohort were 91.4%, and 79.2% respectively. In GTV-P, none of the pre-radiotherapy DCE parameters were correlated with LC or RFS. Wilcoxon signed rank test was statistically significant in 80, 90 and 95 percentiles with (p<0.05). RPA analysis identified different thresholds for each DCE parameter, and its inclusions to the multivariate model improved its performance. In GTV-P, RPA analysis identified ΔKtrans 40 percentiles >15.6% at mid-RT as the most significant point. When this value of ΔKtrans added to the multivariate analysis it was associated with a significantly better model performance in RFS (p = 0.00001). DCE parameters are a very promising tool to correlate with response and outcomes in H&N cancer patients. Future work is warranted for external validation of our findings.

Multimodal cancer treatment and its association with nutrition care practices in patients with head and neck and esophageal cancer: an international prospective cohort study

Background: Both cancer and its’ treatment contribute to the development of malnutrition, particularly in cancers that impact nutrition intake such as head and neck (HNC) and esophageal (EC) cancers. This study was undertaken to explore the relationship between cancer treatment and nutrition care in patients with HNC and EC. Methods: Adult patients (≥18 years) with newly diagnosed head and neck (HN) or esophageal (ESO) cancers scheduled to receive cancer treatment were enrolled between 2016 and 2018 in the INFORM study, a longitudinal multi-centre prospective cohort study. Baseline clinical characteristics of patients, cancer characteristics, treatment type (chemotherapy/radiotherap y /surgery) and frequency, nutrition risk (Patient Generated Subjective Global Assessment Short Form (PG-SGA SF) and nutrition care were recorded. Results: 100 HNC and 51 EC patients were included. Data were collected across 4 time periods from baseline to 6 months at 11 sites in Canada, Australia Italy, The Netherlands and the United States. Seventy-nine percent of the patients were male with a mean (SD) age of 63 (10) years. At admission, the mean (SD) BMI was 27 (5) kg/m2 and 30% were current smokers. Baseline PGA-SGA SF was ≥ 4 indicating nutrition risk for 59% of the HNC and 77% of the EC patients. The number of cancer treatments was positively associated with increases in enteral (EN) and parenteral nutrition (PN). In HNC patients receiving a single cancer treatment, 39% required EN and with 3 cancer treatment types, 78% required EN. In EC requiring a single cancer treatment, 50% required EN and in patients with 3 cancer treatments 94% required EN. Conclusion: The number of cancer treatment modalities is associated with the intensity of nutrition therapy required to sustain the patients through their cancer journey.

Development of a CT-Based Auto-Segmentation Model for Prostate-Specific Membrane Antigen (PSMA) Positron Emission Tomography-Delineated Tubarial Glands.

Modern inverse planning radiotherapy techniques allow for organs at risk (OARs) to evade radiation doses that they would have been subjected to with earlier techniques. The extent to which patient outcomes may be improved using these techniques depends on the delineation accuracy of target volumes and OARs on medical imagesas well asclinical dose constraints applied to regions of interest (ROIs). The recent discovery of bilateral "tubarial" salivary glands, which were found in the nasopharynx using prostate-specific membrane antigen (PSMA) positron emission tomography (PET), raises concerns over how dose to this region might affect patient outcomes. The dose response of the major salivary glands is known to be variable, and it is possible that the dose in tubarial glands constitutes a missing variable in the optimization of head and neck (HN) radiotherapy plans. A necessary first step towardconducting clinical studies that include the tubarial glands in plan optimization is to develop methods for delineating these glands without the use of PSMA PET images, as their acquisition is not considered a part of the standard of care for HN patients. In this study, we develop an open-source program, Organogenesis, for the auto-segmentation of tubarial glands using only computed tomography (CT) images. A predictive model is trained using contours derived from PSMA PET images, allowing for accurate delineation of tubarial glands, which cannot be manually contoured using CT only. Organogenesis provides a predictive model for tubarial glands that can be iteratively improved on with additional data, creating a viable pathway to clinical studies that can assess the importance of incorporating tubarial glands into HN radiotherapy plan optimization.

Unplanned Interruptions during Head and Neck Radiotherapy prior to and during the COVID-19 Pandemic

Purpose/Objective(s)Patients with head and neck (HN) cancers receiving radiotherapy (RT) often require complex coordination between providers and may be at increased risk for treatment interruptions during the ongoing pandemic. We sought to determine if patients with HN cancers receiving RT at our academic center experienced higher rates of RT interruption – defined as any unscheduled miss of fractionated treatment, except for holidays – during the initial 6 months after the March 13th, 2020 pandemic declaration compared to the prior 6-month interval. We also sought to determine predictors associated with interruption risk.Materials/MethodsAll patients receiving RT for HN cancers with first fraction between 9/2019 and 9/2020 were identified from institutional electronic medical records. Candidate predictive factors including demographics and RT details (dose and fractionation) were abstracted. Primary residence zip code was used to obtain median household income. Descriptive statistics were used to compare outcomes regarding unplanned RT interruptions pre (9-2019- 3/12/2020) and during COVID (3/13/2020 -9/2020). Multinomial logistic regression was used to identify factors associated with highest quartile of unplanned RT interruptions. Spearman's rho was used to assess associations between factors and number of unplanned interruptions.Results119 patients were included (oropharynx (n=33), oral cavity (n=31), larynx (n=15), and other including salivary gland, nasopharynx, and thyroid (n= 40)). The cohorts pre and during COVID (N= 55 pre-COVID, 64 during COVID) were balanced. 87 patients (73.1%) had at least one unplanned RT interruption (n=38, 69.1% pre-COVID vs n=49, 76.6% post-COVID). There was not a difference in presence of any (yes/no) unplanned RT interruptions (p =.36) or total number of unplanned RT interruptions between pre vs post COVID subgroups (p=.85) by univariable analysis. The lowest quartile of patients had 0 interruptions, the 2nd quartile had 1 interruption, the 3rd quartile had 2-4 interruptions, and the highest quartile had > 4 interruptions. No demographic factors, nor RT dose or fraction number were associated with risk of being in the highest quartile of unplanned interruptions. No significant association between pre vs post COVID-19 timing and number of unplanned interruptions was found by Spearman's rho.ConclusionNo significant differences in presence or number of unplanned RT interruptions were detected at our institution during the pandemic. Although these results suggest some degree of health system-level resiliency during COVID-19, they unfortunately also confirm persistent shortfalls handicapping reliable on-treatment access to RT. Independent corroborative data will be necessary to conclusively answer whether HN patients are more broadly experiencing heightened barriers to RT during the public health emergency.

Optimizing Radiation Treatment for Head and Neck Cancer with Adapt-to-Shape Planning on a 1.5 MR-Linac

<h3>Purpose/Objective(s)</h3> MR Linac (MRL) has the advantage of using high soft tissue contrast imaging to track the changes in target and critical organs on a daily basis during the entire radiation treatment course. The potential benefits include improved local control and decreased risk of toxicities. We present our experience on using the adapt-to-shape (ATS) technique on a 1.5 MR-Linac for optimized adaptive treatment of our head and neck (HN) patients. <h3>Materials/Methods</h3> Twelve HN patients were treated on a 1.5 MR-Linac in 2021 with one or more adapt-to-shape (ATS) treatment plans. All patients also had backup plans on another Linac unit. We compared the treatment planning software reference plans and backup TPS plans. Adapt-to-position (ATP) plan was routinely used for treatments, while ATS plan was generated based on physician's request, on the observation of daily anatomy change, or on the challenges of ATP plans to meet clinical goals. For patients with ATS plans, CTV volumes and dosimetric criteria of the daily treatments were recorded. ATS plans were compared to ATP plans and the original reference plan. <h3>Results</h3> 12 HN patients were treated in the oropharynx area (n=9), larynx area (n=2), and nasopharynx area (n=1). Median prescription was 69.96Gy (range: 63Gy – 69.96Gy) in 33 fractions (range: 28-33 fractions). MRL reference plans achieved all clinical goals but with lower primary CTV coverage, higher heterogeneity, and higher brainstem and spinal cord maximum dose (p<0.05) when compared to backup plans. 95.5% of the total 351 treatments were performed on the MRL, while 4.5% were performed using the backup plan. A total of 32 ATS plans (15 online, 17 offline) were generated for all 12 patients, and the median number of ATS plans per patient was 2 (range: 1 to 5). Comparing to the original reference plans, the volume change of the primary CTV in ATS plans was -10.2% ± 14.7% (range: -34.1% to 9.1%) in the first half of the treatment course, and -28.1% ± 21.7% (range: -53.1% to -1.6%) in the second half of the treatment course. The daily dose to nearby critical organs was observed to reduce up to 31% after CTV volume shrank. For 8 of the 12 patients, ATP plans struggled to meet the clinical goals of one or more critical organs (brainstem, brachial plexus, optic nerve, parotid) on some treatment days, and ATS plans followed thereafter to verify the contours and adjust optimization objectives to improve the plans. The ATS plans were used as new reference plans for later treatments. With the help of ATS planning, the observed dosimetric values of challenging organs were well below tolerance. <h3>Conclusion</h3> Close monitoring of target and critical organs have the potential to improve local control and reduce toxicities. ATS planning on the MR-Linac system played an important role in optimizing radiation treatment for HN cancer.

Prospective Assessment of Diffusion-Weighted-MRI as a Biomarker of Treatment Response and Disease Control in Head and Neck Cancer

<h3>Purpose/Objective(s)</h3> To determine diffusion-weighted MRI (DWI) parameters associated with tumor response and local control (LC) in head and neck (HNC) patients treated with definitive radiation therapy (RT). <h3>Materials/Methods</h3> Eighty-seven HNC patients enrolled in an active prospective imaging study at MD Anderson Cancer Center were included in the analysis. Patients had MRIs prior to, at mid-RT, and post-treatment completion. Inclusion criteria include adult with histologic evidence of malignant head and neck neoplasm indicated for curative-intent treatment with RT with/without chemotherapy; good performance status (ECOG score 0-2); and with no contraindications to MRI. Patients were scanned using a full-body MRI scanner with two large four-channel flex phased-array coils. We used fat-suppressed T2-weighted turbo spin echo for tumor segmentation which was co-registered to respective DWIs for extraction of apparent diffusion coefficient (ADC) measurements. Sequence parameters were (TR/TE (ms); pixel size/slice thickness (mm); b-values (s/mm2)): T2-weighted (5280/80; 0.5/2; N/A), BLADE (5900/50; 2/4; 0 & 800), RESOLVE (9380/63; 2/4; 0 & 800). Treatment response was assessed at mid-RT and at 8-12 weeks post-RT using RECIST 1.1 criteria and was defined as: complete response (CR) vs non-complete response (non-CR). Pre-RT ADC was correlated with RT response (CR vs non-CR) at mid- and post-RT time points. Mann-Whitney test was used to compare ADC values between the mid-treatment CR group and the non-CR group. Recursive partitioning analysis (RPA) was done to identify ADC threshold associated with relapse. <h3>Results</h3> Fifty-nine patients with grossly visible primary tumor volume prior to RT start were included in this analysis. Patients' disease and treatment criteria are shown in Table 1. Pre-treatment ADC was not correlated with tumor response or oncologic outcomes (P>0.05). For patients with CR at the end of RT, there was a significant increase in mean ADC at mid-RT compared to baseline ((1.8 ± 0.29) × 10^–3 mm²/s <i>vs</i> (1.37 ± 0.22) × 10^–3 mm²/s, <i>p</i> <0.0001). However, patients with no-CR had no statistically significant increase (p>0.05). Median follow-up was 32 months. Patients with ≤106% delta ADC at mid-RT had significantly worse local control at 2-year compared to patients with higher delta ADC (48% vs 96%, <i>p</i> <0.0001). <h3>Conclusion</h3> Delta ADC changes is a strong predictor of local recurrence in head and neck cancer patients. Multi-institutional data is needed for validation of our results.

Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans.

Simple SummaryThe fast and accurate definition of structures is a main limiting factor in online adaptive proton therapy. In this study, different methods to include structure propagation uncertainties in the optimization were compared with adaptation using physician-drawn structures, uncorrected propagated structures, and no adaptation. While adaptation with physician-drawn structures resulted in the best adaptive plan quality and no adaptation in the worst, manual structure correction could be replaced by a fast ‘plausibility check’, and plans could be adapted with correction-free adaptation strategies.Currently, adaptive strategies require time- and resource-intensive manual structure corrections. This study compares different strategies: optimization without manual structure correction, adaptation with physician-drawn structures, and no adaptation. Strategies were compared for 16 patients with pancreas, liver, and head and neck (HN) cancer with 1–5 repeated images during treatment: ‘reference adaptation’, with structures drawn by a physician; ‘single-DIR adaptation’, using a single set of deformably propagated structures; ‘multi-DIR adaptation’, using robust planning with multiple deformed structure sets; ‘conservative adaptation’, using the intersection and union of all deformed structures; ‘probabilistic adaptation’, using the probability of a voxel belonging to the structure in the optimization weight; and ‘no adaptation’. Plans were evaluated using reference structures and compared using a scoring system. The reference adaptation with physician-drawn structures performed best, and no adaptation performed the worst. For pancreas and liver patients, adaptation with a single DIR improved the plan quality over no adaptation. For HN patients, integrating structure uncertainties brought an additional benefit. If resources for manual structure corrections would prevent online adaptation, manual correction could be replaced by a fast ‘plausibility check’, and plans could be adapted with correction-free adaptation strategies. Including structure uncertainties in the optimization has the potential to make online adaptation more automatable.

Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.

PurposeAdaptive radiotherapy requires auto‐segmentation in patients with head and neck (HN) cancer. In the current study, we propose an auto‐segmentation model using a generative adversarial network (GAN) on magnetic resonance (MR) images of HN cancer for MR‐guided radiotherapy (MRgRT).Material and methodsIn the current study, we used a dataset from the American Association of Physicists in Medicine MRI Auto‐Contouring (RT‐MAC) Grand Challenge 2019. Specifically, eight structures in the MR images of HN region, namely submandibular glands, lymph node level II and level III, and parotid glands, were segmented with the deep learning models using a GAN and a fully convolutional network with a U‐net. These images were compared with the clinically used atlas‐based segmentation.ResultsThe mean Dice similarity coefficient (DSC) of the U‐net and GAN models was significantly higher than that of the atlas‐based method for all the structures (p < 0.05). Specifically, the maximum Hausdorff distance (HD) was significantly lower than that in the atlas method (p < 0.05). Comparing the 2.5D and 3D U‐nets, the 3D U‐net was superior in segmenting the organs at risk (OAR) for HN patients. The DSC was highest for 0.75–0.85, and the HD was lowest within 5.4 mm of the 2.5D GAN model in all the OARs.ConclusionsIn the current study, we investigated the auto‐segmentation of the OAR for HN patients using U‐net and GAN models on MR images. Our proposed model is potentially valuable for improving the efficiency of HN RT treatment planning.