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Background: 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., Purpose: 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., Methods: 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., Results: 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 ± 2.1 mm, and HD95 of 13.9 ± 7.6 mm. When the PET images were included, the performance of our model was improved to DSC of 0.62 ± 0.14, MSD of 2.8 ± 1.7 mm, and HD95 of 10.5 ± 6.5 mm. 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., Conclusions: 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., (© 2024 American Association of Physicists in Medicine.)

Objective . Previous methods for robustness evaluation rely on dose calculation for a number of uncertainty scenarios, which either fails to provide statistical meaning when the number is too small (e.g., ∼8) or becomes unfeasible in daily clinical practice when the number is sufficiently large (e.g., >100). Our proposed deep learning (DL)-based method addressed this issue by avoiding the intermediate dose calculation step and instead directly predicting the percentile dose distribution from the nominal dose distribution using a DL model. In this study, we sought to validate this DL-based statistical robustness evaluation method for efficient and accurate robustness quantification in head and neck (H&N) intensity-modulated proton therapy with diverse beam configurations and multifield optimization. Approach . A dense, dilated 3D U-net was trained to predict the 5th and 95th percentile dose distributions of uncertainty scenarios using the nominal dose and planning CT images. The data set comprised proton therapy plans for 582 H&N cancer patients. Ground truth percentile values were estimated for each patient through 600 dose recalculations, representing randomly sampled uncertainty scenarios. The comprehensive comparisons of different models were conducted for H&N cancer patients, considering those with and without a beam mask and diverse beam configurations, including varying beam angles, couch angles, and beam numbers. The performance of our model trained based on a mixture of patients with H&N and prostate cancer was also assessed in contrast with models trained based on data specific for patients with cancer at either site. Results . The DL-based model's predictions of percentile dose distributions exhibited excellent agreement with the ground truth dose distributions. The average gamma index with 2 mm / 2%, consistently exceeded 97% for both 5th and 95th percentile dose volumes. Mean dose-volume histogram error analysis revealed that predictions from the combined training set yielded mean errors and standard deviations that were generally similar to those in the specific patient training data sets. Significance . Our proposed DL-based method for evaluation of the robustness of proton therapy plans provides precise, rapid predictions of percentile dose for a given confidence level regardless of the beam arrangement and cancer site. This versatility positions our model as a valuable tool for evaluating the robustness of proton therapy across various cancer sites., (Creative Commons Attribution license.)

Purpose: Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool., Methods: For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan models developed in-house. The autoplans were, then, applied to the original, physician-drawn contours, which were used as a ground truth (GT) to compare with the autocontours (RPA). Using a "two one-sided tests" (TOST) procedure, we evaluated whether the autocontour normal tissue dose was equivalent to that of the ground truth by a margin, δ, that we determined based on clinical judgement. We also calculated the number of plans that met established clinically accepted dosimetric criteria., Results: For HN plans, 91.8% and 91.7% of structures met dosimetric criteria for automatic and manual contours, respectively; for cervical plans, 95.6% and 95.7% of structures met dosimetric criteria for automatic and manual contours, respectively. Autocontours were equivalent to the ground truth for 71% and 75% of common DVH metrics for the HN and cervix, respectively., Conclusions: This study shows that dosimetrically equivalent normal tissue contours can be created for HN and cervical cancers using deep learning techniques. In general, differences between the contours did not affect the passing or failing of clinical dose tolerances., (© 2024 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Planning for palliative radiotherapy is performed without the advantage of MR or PET imaging in many clinics. Here, we investigated CT-only GTV delineation for palliative treatment of head and neck cancer. Two multi-institutional datasets of palliative-intent treatment plans were retrospectively acquired: a set of 102 non-contrast-enhanced CTs and a set of 96 contrast-enhanced CTs. The nnU-Net auto-segmentation network was chosen for its strength in medical image segmentation, and five approaches separately trained: (1) heuristic-cropped, non-contrast images with a single GTV channel, (2) cropping around a manually-placed point in the tumor center for non-contrast images with a single GTV channel, (3) contrast-enhanced images with a single GTV channel, (4) contrast-enhanced images with separate primary and nodal GTV channels, and (5) contrast-enhanced images along with synthetic MR images with separate primary and nodal GTV channels. Median Dice similarity coefficient ranged from 0.6 to 0.7, surface Dice from 0.30 to 0.56, and 95th Hausdorff distance from 14.7 to 19.7 mm across the five approaches. Only surface Dice exhibited statistically-significant difference across these five approaches using a two-tailed Wilcoxon Rank-Sum test (p ≤ 0.05). Our CT-only results met or exceeded published values for head and neck GTV autocontouring using multi-modality images. However, significant edits would be necessary before clinical use in palliative radiotherapy., (© 2023. The Author(s).)

Purpose: Evidence of a volume-outcome association in cancer surgery has shaped the centralization of cancer services; however, it is unknown whether a similar association exists for radiation therapy. The objective of this study was to determine the association between radiation therapy treatment volume and patient outcomes., Methods and Materials: This systematic review and meta-analysis included studies that compared outcomes of patients who underwent definitive radiation therapy at high-volume radiation therapy facilities (HVRFs) versus low-volume facilities (LVRFs). The systematic review used Ovid MEDLINE and Embase. For the meta-analysis, a random effects model was used. Absolute effects and hazard ratios (HRs) were used to compare patient outcomes., Results: The search identified 20 studies assessing the association between radiation therapy volume and patient outcomes. Seven of the studies looked at head and neck cancers (HNCs). The remaining studies covered cervical (4), prostate (4), bladder (3), lung (2), anal (2), esophageal (1), brain (2), liver (1), and pancreatic cancer (1). The meta-analysis demonstrated that HVRFs were associated with a lower chance of death compared with LVRFs (pooled HR, 0.90; 95% CI, 0.87- 0.94). HNCs had the strongest evidence of a volume-outcome association for both nasopharyngeal cancer (pooled HR, 0.74; 95% CI, 0.62-0.89) and nonnasopharyngeal HNC subsites (pooled HR, 0.80; 95% CI, 0.75-0.84), followed by prostate cancer (pooled HR, 0.92; 95% CI, 0.86-0.98). The remaining cancer types showed weak evidence of an association. The results also demonstrate that some centers defined as HVRFs are undertaking very few procedures per annum (<5 radiation therapy cases per year)., Conclusions: An association between radiation therapy treatment volume and patient outcomes exists for most cancer types. Centralization of radiation therapy services should be considered for cancer types with the strongest volume-outcome association, but the effect on equitable access to services needs to be explicitly considered., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Our goal is to develop a novel cobalt-compensator-based IMRT device for low- and middle-income countries that is reliable and cost-effective while delivering treatment plans of equal quality to those from linac-MLC devices. The present study examines the quality of treatment plans using this device., Methods: A commercial treatment planning system (TPS; RayStation v.8B) was commissioned for this device using Monte Carlo simulations from the Geant4 toolkit. Patient-specific compensators were created as regions-of-interest. Thirty clinical head & neck cases were planned and compared to clinical plans with a 6MV linac using IMRT. The mock head and neck plan from TG-119 was used for further validation., Results: PTV objectives were achieved in all 30 plans with PTV V95% >95 %. OAR sparing was similar to clinical plans. There were 14 cases where OAR dose limits exceeded the recommended QUANTEC limits in the clinical plan in order to achieve target coverage. OAR sparing was better in the cobalt compensator plan in 8 cases and worse in 3 cases, in the latter cases exceeding the clinical plan doses by an average of 8.22 % (0.0 %-13.5 %). Average field-by-field gamma pass-rate were 93.7 % (2 %/2mm). Estimated treatment times using the Co-60 compensator device were 1 min 27 s vs 1 min 2 s for the clinical system., Conclusion: This system is the first of its kind to allow for IMRT with a Co-60 device. Data here suggests that the delivery meets plan quality criteria while maintaining short treatment times which may offer a sustainable and cost-low option for IMRT on the global scale., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Purpose: Knowledge-based planning (KBP) has been shown to be an effective tool in quality control for intensity-modulated radiation therapy treatment planning and generating high-quality plans. Previous studies have evaluated its ability to create consistent plans across institutions and between planners within the same institution as well as its use as teaching tool for inexperienced planners. This study evaluates whether planning quality is consistent when using a KBP model to plan across different treatment machines., Materials and Methods: This study used a RapidPlan model (Varian Medical Systems) provided by the vendor, to which we added additional planning objectives, maximum dose limits, and planning structures, such that a clinically acceptable plan is achieved in a single optimization. This model was used to generate and optimize volumetric-modulated arc therapy plans for a cohort of 50 patients treated for head-neck cancer. Plans were generated using the following treatment machines: Varian 2100, Elekta Versa HD, and Varian Halcyon. A noninferiority testing methodology was used to evaluate the hypothesis that normal and target metrics in our autoplans were no worse than a set of clinically-acceptable baseline plans by a margin of 1.8 Gy or 3% dose-volume. The quality of these plans were also compared through the use of common clinical dose-volume histogram criteria., Results: The Versa HD met our noninferiority criteria for 23 of 34 normal and target metrics; while the Halcyon and Varian 2100 machines met our criteria for 24 of 34 and 26 of 34 metrics, respectively. The experimental plans tended to have less volume coverage for prescription dose planning target volume and larger hotspot volumes. However, comparable plans were generated across different treatment machines., Conclusions: These results support the use of a head-neck RapidPlan models in centralized planning workflows that support clinics with different linac models/vendors, although some fine-tuning for targets may be necessary., (© 2022 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

This study aimed to investigate the feasibility of using a knowledge-based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. We created two dose-volume histogram (DVH) prediction models using a commercial knowledge-based planning system (RapidPlan, Varian Medical Systems, Palo Alto, CA) from plans generated by manual planning (MP) and automated planning (AP) approaches. DVHs were predicted for evaluation cohort 1 (EC1) of 25 patients and compared with achieved DVHs of MP and AP plans to evaluate prediction accuracy. Additionally, we predicted DVHs for evaluation cohort 2 (EC2) of 25 patients for which we intentionally generated plans with suboptimal normal tissue sparing while satisfying dose-volume limits of standard practice. Three radiation oncologists reviewed these plans without seeing the DVH predictions. We found that predicted DVH ranges (upper-lower predictions) were consistently wider for the MP model than for the AP model for all normal structures. The average ranges of mean dose predictions among all structures was 9.7 Gy (MP model) and 3.4 Gy (AP model) for EC1 patients. RapidPlan models identified 7 MP plans as outliers according to mean dose or D1% for at least one structure, while none of AP plans were flagged. For EC2 patients, 22 suboptimal plans were identified by prediction. While re-generated AP plans validated that these suboptimal plans could be improved, 40 out of 45 structures with predicted poor sparing were also identified by oncologist reviews as requiring additional planning to improve sparing in the clinical setting. Our study shows that knowledge-based DVH prediction models can be sufficiently accurate for plan quality assurance purposes. A prediction model built by a small cohort automatically-generated plans was effective in detecting suboptimal plans. Such tools have potential to assist the plan quality assurance workflow for individual patients in the clinic., (© 2022 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials., (© 2021. The Author(s).)

Purpose: Radiation treatment planning for head and neck cancer is a complex process with much variability; automated treatment planning is a promising option to improve plan quality and efficiency. This study compared radiation plans generated from a fully automated radiation treatment planning system to plans generated manually that had been clinically approved and delivered., Methods and Materials: The study cohort consisted of 50 patients treated by a specialized head and neck cancer team at a tertiary care center. An automated radiation treatment planning system, the Radiation Planning Assistant, was used to create autoplans for all patients using their original, approved contours. Common dose-volume histogram (DVH) criteria were used to compare the quality of autoplans to the clinical plans. Fourteen radiation oncologists, each from a different institution, then reviewed and compared the autoplans and clinical plans in a blinded fashion., Results: Autoplans and clinical plans were very similar with regard to DVH metrics for coverage and critical structure constraints. Physician reviewers found both the clinical plans and autoplans acceptable for use; overall, 78% of the clinical plans and 88% of the autoplans were found to be usable as is (without any edits). When asked to choose which plan would be preferred for approval, 27% of physician reviewers selected the clinical plan, 47% selected the autoplan, 25% said both were equivalent, and 0% said neither. Hence, overall, 72% of physician reviewers believed the autoplan or either the clinical or autoplan was preferable., Conclusions: Automated radiation treatment planning creates consistent, clinically acceptable treatment plans that meet DVH criteria and are found to be appropriate on physician review., (Copyright © 2021 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

Purpose: To investigate the impact of computed tomography (CT) image acquisition and reconstruction parameters, including slice thickness, pixel size, and dose, on automatic contouring algorithms., Methods: Eleven scans from patients with head-and-neck cancer were reconstructed with varying slice thicknesses and pixel sizes. CT dose was varied by adding noise using low-dose simulation software. The impact of these imaging parameters on two in-house auto-contouring algorithms, one convolutional neural network (CNN)-based and one multiatlas-based system (MACS) was investigated for 183 reconstructed scans. For each algorithm, auto-contours for organs-at-risk were compared with auto-contours from scans with 3 mm slice thickness, 0.977 mm pixel size, and 100% CT dose using Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD)., Results: Increasing the slice thickness from baseline value of 3 mm gave a progressive reduction in DSC and an increase in HD and MSD on average for all structures. Reducing the CT dose only had a relatively minimal effect on DSC and HD. The rate of change with respect to dose for both auto-contouring methods is approximately 0. Changes in pixel size had a small effect on DSC and HD for CNN-based auto-contouring with differences in DSC being within 0.07. Small structures had larger deviations from the baseline values than large structures for DSC. The relative differences in HD and MSD between the large and small structures were small., Conclusions: Auto-contours can deviate substantially with changes in CT acquisition and reconstruction parameters, especially slice thickness and pixel size. The CNN was less sensitive to changes in pixel size, and dose levels than the MACS. The results contraindicated more restrictive values for the parameters should be used than a typical imaging protocol for head-and-neck., (© 2021 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: To enable generation of high-quality deep learning segmentation models from severely limited contoured cases (e.g., ~10 cases)., Methods: Thirty head and neck computed tomography (CT) scans with well-defined contours were deformably registered to 200 CT scans of the same anatomic site without contours. Acquired deformation vector fields were used to train a principal component analysis (PCA) model for each of the 30 contoured CT scans by capturing the mean deformation and most prominent variations. Each PCA model can produce an infinite number of synthetic CT scans and corresponding contours by applying random deformations. We used 300, 600, 1000, and 2000 synthetic CT scans and contours generated from one PCA model to train V-Net, a 3D convolutional neural network architecture, to segment parotid and submandibular glands. We repeated the training using same numbers of training cases generated from 7, 10, 20, and 30 PCA models, with the data distributed evenly between each PCA model. Performance of the segmentation models was evaluated with Dice similarity coefficients between auto-generated contours and physician-drawn contours on 162 test CT scans for parotid glands and another 21 test CT scans for submandibular glands., Results: Dice values varied with the number of synthetic CT scans and the number of PCA models used to train the network. By using 2000 synthetic CT scans generated from 10 PCA models, we achieved Dice values of 82.8% ± 6.8% for right parotid, 82.0% ± 6.9% for left parotid, and 74.2% ± 6.8% for submandibular glands. These results are comparable with those obtained from state-of-the-art auto-contouring approaches, including a deep learning network trained from more than 1000 contoured patients and a multi-atlas algorithm from 12 well-contoured atlases. Improvement was marginal when >10 PCA models or >2000 synthetic CT scans were used., Conclusions: We demonstrated an effective data augmentation approach to train high-quality deep learning segmentation models from a limited number of well-contoured patient cases., (© 2021 American Association of Physicists in Medicine.)

Purpose: To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated radiation treatment planning workflow., Methods and Materials: Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 radiation oncologists as being "clinically acceptable without requiring edits," "requiring minor edits," or "requiring major edits.", Results: When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, <2 minutes)., Conclusions: We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated radiation treatment planning., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models. We retrospectively reviewed 726 CT and 686 PET images from head and neck cancer patients, who were divided into training or independent testing cohorts. For each patient, radiomics features with different preprocessing were calculated and two clinical variables-HPV status and tumor volume-were also included. A Cox proportional hazards model was built on the training data by using bootstrapped Lasso regression to predict overall survival. The effect of controlled imaging protocols on model performance was evaluated by subsetting the original training and independent testing cohorts to include only patients whose images were obtained using the same imaging protocol and vendor. Tumor volume, HPV status, and two radiomics covariates were selected for the CT model, resulting in an AUC of 0.72. However, volume alone produced a higher AUC, whereas adding radiomics features reduced the AUC. HPV status and one radiomics feature were selected as covariates for the PET model, resulting in an AUC of 0.59, but neither covariate was significantly associated with survival. Limiting the training and independent testing to patients with the same imaging protocol reduced the AUC for CT patients to 0.55, and no covariates were selected for PET patients. Radiomics features were not consistently associated with survival in CT or PET images of head and neck patients, even within patients with the same imaging protocol., Competing Interests: The authors have declared that no competing interests exist.

The purpose of this study is to investigate the dosimetric impact of multi-leaf collimator (MLC) positioning errors on a Varian Halcyon for both random and systematic errors, and to evaluate the effectiveness of portal dosimetry quality assurance in catching clinically significant changes caused by these errors. Both random and systematic errors were purposely added to 11 physician-approved head and neck volumetric modulated arc therapy (VMAT) treatment plans, yielding a total of 99 unique plans. Plans were then delivered on a preclinical Varian Halcyon linear accelerator and the fluence was captured by an opposed portal dosimeter. When comparing dose-volume histogram (DVH) values of plans with introduced MLC errors to known good plans, clinically significant changes to target structures quickly emerged for plans with systematic errors, while random errors caused less change. For both error types, the magnitude of clinically significant changes increased as error size increased. Portal dosimetry was able to detect all systematic errors, while random errors of ±5 mm or less were unlikely to be detected. Best detection of clinically significant errors, while minimizing false positives, was achieved by following the recommendations of AAPM TG-218. Furthermore, high- to moderate correlation was found between dose DVH metrics for normal tissues surrounding the target and portal dosimetry pass rates. Therefore, it may be concluded that portal dosimetry on the Halcyon is robust enough to detect errors in MLC positioning before they introduce clinically significant changes to VMAT treatment plans., (© 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Radiomics studies have demonstrated the potential use of quantitative image features to improve prognostic stratification of patients with head and neck cancer. Imaging protocol parameters that can affect radiomics feature values have been investigated, but the effects of artifacts caused by intrinsic patient factors have not. Two such artifacts that are common in patients with head and neck cancer are streak artifacts caused by dental fillings and beam-hardening artifacts caused by bone. The purpose of this study was to test the impact of these artifacts and if needed, methods for compensating for these artifacts in head and neck radiomics studies. The robustness of feature values was tested by removing slices of the gross tumor volume (GTV) on computed tomography images from 30 patients with head and neck cancer; these images did not have streak artifacts or had artifacts far from the GTV. The range of each feature value over a percentage of the GTV was compared to the inter-patient variability at full volume. To determine the effects of beam-hardening artifacts, we scanned a phantom with 5 cartridges of different materials encased in polystyrene buildup. A cylindrical hole through the cartridges contained either a rod of polylactic acid to simulate water or a rod of polyvinyl chloride to simulate bone. A region of interest was drawn in each cartridge flush with the rod. Most features were robust with up to 50% of the original GTV removed. Most feature values did not significantly differ when measured with the polylactic acid rod or the polyvinyl chloride rod. Of those that did, the size of the difference did not exceed the inter-patient standard deviation in most cases. We conclude that simply removing slices affected by streak artifacts can enable these scans to be included in radiomics studies and that contours of structures can abut bone without being affected by beam hardening if needed., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Radiomics has shown promise in improving models for predicting patient outcomes. However, to maximize the information gain of the radiomics features, especially in larger patient cohorts, the variability in radiomics features owing to differences between scanners and scanning protocols must be accounted for. To this aim, the imaging variability of radiomics feature values was evaluated on 100 computed tomography scanners at 35 clinics by imaging a radiomics phantom using a controlled protocol and the commonly used chest and head protocols of the local clinic. We used a linear mixed-effects model to determine the degree to which the manufacturer and individual scanners contribute to the overall variability. Using a controlled protocol reduced the overall variability by 57% and 52% compared to the local chest and head protocols respectively. The controlled protocol also reduced the relative contribution of the manufacturer to the total variability. For almost all variabilities (manufacturer, scanner, and residual with different preprocesssing), the controlled protocol scans had a significantly smaller variability than the local protocol scans did. For most radiomics features, the imaging variability was small relative to the inter-patient feature variability in non-small cell lung cancer and head and neck squamous cell carcinoma patient cohorts. From this study, we conclude that using controlled scans can reduce the variability in radiomics features, and our results demonstrate the importance of using controlled protocols in prospective radiomics studies.

Purpose We assessed automated contouring of normal structures for patients with head-and-neck cancer (HNC) using a multiatlas deformable-image-registration algorithm to better provide a fully automated radiation treatment planning solution for low- and middle-income countries, provide quantitative analysis, and determine acceptability worldwide. Methods Autocontours of eight normal structures (brain, brainstem, cochleae, eyes, lungs, mandible, parotid glands, and spinal cord) from 128 patients with HNC were retrospectively scored by a dedicated HNC radiation oncologist. Contours from a 10-patient subset were evaluated by five additional radiation oncologists from international partner institutions, and interphysician variability was assessed. Quantitative agreement of autocontours with independently physician-drawn structures was assessed using the Dice similarity coefficient and mean surface and Hausdorff distances. Automated contouring was then implemented clinically and has been used for 166 patients, and contours were quantitatively compared with the physician-edited autocontours using the same metrics. Results Retrospectively, 87% of normal structure contours were rated as acceptable for use in dose-volume-histogram-based planning without edit. Upon clinical implementation, 50% of contours were not edited for use in treatment planning. The mean (± standard deviation) Dice similarity coefficient of autocontours compared with physician-edited autocontours for parotid glands (0.92 ± 0.10), brainstem (0.95 ± 0.09), and spinal cord (0.92 ± 0.12) indicate that only minor edits were performed. The average mean surface and Hausdorff distances for all structures were less than 0.15 mm and 1.8 mm, respectively. Conclusion Automated contouring of normal structures generates reliable contours that require only minimal editing, as judged by retrospective ratings from multiple international centers and clinical integration. Autocontours are acceptable for treatment planning with no or, at most, minor edits, suggesting that automated contouring is feasible for clinical use and in the ongoing development of automated radiation treatment planning algorithms.

Purpose: Endoscopic examinations are frequently-used procedures for patients with head and neck cancer undergoing radiotherapy, but radiation treatment plans are created on computed tomography (CT) scans. Image registration between endoscopic video and CT could be used to improve treatment planning and analysis of radiation-related normal tissue toxicity. The purpose of this study was to explore the feasibility of endoscopy-CT image registration without prospective physical tracking of the endoscope during the examination., Methods: A novel registration technique called Location Search was developed. This technique uses physical constraints on the endoscope's view direction to search for the virtual endoscope coordinates that maximize the similarity between the endoscopic video frame and the virtual endoscopic image. Its performance was tested on phantom and patient images and compared to an established registration technique, Frame-To-Frame Tracking., Results: In phantoms, Location Search had average registration errors of 0.55 ± 0.60 cm for point measurements and 0.29 ± 0.15 cm for object surface measurements. Frame-To-Frame Tracking achieved similar results on some frames, but it failed on others due to the virtual endoscope becoming lost. This weakness was more pronounced in patients, where Frame-To-Frame tracking could not make it through the nasal cavity. On successful patient video frames, Location Search was able to find endoscope positions with an average distance of 0.98 ± 0.53 cm away from the ground truth positions. However, it failed on many frames due to false similarity matches caused by anatomical structural differences between the endoscopic video and the virtual endoscopic images., Conclusions: Endoscopy-CT image registration without prospective physical tracking of the endoscope is possible, but more development is required to achieve an accuracy suitable for clinical translation.

Cancer is a global health issue that disproportionately kills based on stage of disease, cellular pathology, and genetics, to name a few. Another variable to consider in this ongoing fight is treatment machine complexity that leads to elevated development and purchasing cost, leading to a reduced use. Reducing the complexity (in hopes of lowering costs) would benefit underdeveloped, low- and middle-income countries by introducing newer treatment technology, as their currently accepted standards do not meet standards of more advanced, developed countries. In this study, unilateral head and neck (H&N), and prostate cases using volumetric modulated arc therapy (VMAT) were tested with multiple segment widths of 5, 10, 15, and 20mm to create treatable plans. Pinnacle 9.10v was used for planning purposes. A total of 12 cases were planned with varying multileaf collimator (MLC) widths. Treatment plans were evaluated retrospectively. Results show that altering the MLC widths from 5 through 20mm produces both comparable and treatable plans up to 99% and 98% target coverage for H&N and prostate, respectively, albeit clinically significant hot spots were shown to increase with increasing segment width. Furthermore, the results show that increasing widths can produce comparable treatment plans as measured against our current Food and Drug Administration (FDA)-approved treatment devices-leading to an increase in treatment efficacy in economically underdeveloped countries., (Copyright © 2017 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.)

Radiotherapy in a seated position may be indicated for patients who are unable to lie on the treatment couch for the duration of treatment, in scenarios where a seated treatment position provides superior anatomical positioning and dose distributions, or for a low-cost system designed using a fixed treatment beam and rotating seated patient. In this study, we report a novel treatment chair that was constructed to allow for three-dimensional imaging and treatment delivery while ensuring robust immobilization, providing reproducibility equivalent to that in the traditional supine position. Five patients undergoing radiation treatment for head-and-neck cancers were enrolled and were setup in the chair, with immobilization devices created, and then imaged with orthogonal X-rays in a scenario that mimicked radiation treatments (without treatment delivery). Six subregions of the acquired images were rigidly registered to evaluate intra- and interfraction displacement and chair construction. Displacements under conditions of simulated image guidance were acquired by first registering one subregion; the residual displacement of other subregions was then measured. Additionally, we administered a patient questionnaire to gain patient feedback and assess comparison to the supine position. Average inter- and intrafraction displacements of all subregions in the seated position were less than 2 and 3 mm, respectively. When image guidance was simulated, L-R and A-P interfraction displacements were reduced by an average of 1 mm, providing setup of comparable quality to supine setups. The enrolled patients, who had no indication for a seated treatment position, reported no preference in the seated or the supine position. The novel chair design provides acceptable inter- and intrafraction displacement, with reproducibility equivalent to that reported for patients in the supine position. Patient feedback will be incorporated in the refinement of the chair, facilitating treatment of head-and-neck cancer in patients who are unable to lie for the duration of treatment or for use in an economical fixed-beam setup., (© 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

The purpose of this study was to develop an approach to generate artificial computed tomography (CT) images with known deformation by learning the anatomy changes in a patient population for voxel-level validation of deformable image registration. Using a dataset of CT images representing anatomy changes during the course of radiation therapy, we selected a reference image and registered the remaining images to it, either directly or indirectly, using deformable registration. The resulting deformation vector fields (DVFs) represented the anatomy variations in that patient population. The mean deformation, computed from the DVFs, and the most prominent variations, which were captured using principal component analysis (PCA), composed an active shape model that could generate random known deformations with realistic anatomy changes based on those learned from the patient population. This approach was applied to a set of 12 head and neck patients who received intensity-modulated radiation therapy for validation. Artificial planning CT and daily CT images were generated to simulate a patient with known anatomy changes over the course of treatment and used to validate the deformable image registration between them. These artificial CT images potentially simulated the actual patients' anatomies and also showed realistic anatomy changes between different daily CT images. They were used to successfully validate deformable image registration applied to intrapatient deformation.

Ionizing radiation is the primary nonsurgical treatment modality for solid tumors. Its effectiveness is impacted by temporal constraints such as fractionation, hypoxia, and development of radioresistant clones. Biomarkers of acute radiation response are essential to developing more effective clinical algorithms. We hypothesized that acute perturbations in tumor lactate levels act as a surrogate marker of radiation response. In vitro experiments were carried out using validated human-derived cell lines from three histologies: anaplastic thyroid carcinoma (ATC), head and neck squamous cell carcinoma (HNSCC), and papillary thyroid carcinoma (PTC). Cellular metabolic activity was measured using standard biochemical assays. In vivo validation was performed using both an orthotopic and a flank derivative of a previously established ATC xenograft murine model. Irradiation of cells and tumors triggered a rapid, dose-dependent, transient decrease in lactate levels that was reversed by free radical scavengers. Acute lactate perturbations following irradiation could identify hypoxic conditions and correlated with hypoxia-induced radioresistance. Mutant TP53 cells and cells in which p53 activity was abrogated (shRNA) demonstrated a blunted lactate response to irradiation, consistent with a radioresistant phenotype. Lactate measurements therefore rapidly detected both induced (i.e., hypoxia) and intrinsic (i.e., mutTP53-driven) radioresistance. We conclude that lactate is a quantitative biomarker of acute genotoxic stress, with a temporal resolution that can inform clinical decision making. Combined with the spatial resolution of newly developed metabolic imaging platforms, this biomarker could lead to the development of truly individualized treatment strategies., (©2015 American Association for Cancer Research.)

Purpose: To develop a quality assurance (QA) workflow by using a robust, curated, manually segmented anatomic region-of-interest (ROI) library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy-simulation computed tomography (CT) with diagnostic CT coregistration., Materials and Methods: Radiation therapy-simulation CT images and diagnostic CT images in 20 patients with head and neck squamous cell carcinoma treated with curative-intent intensity-modulated radiation therapy between August 2011 and May 2012 were retrospectively retrieved with institutional review board approval. Sixty-eight reference anatomic ROIs with gross tumor and nodal targets were then manually contoured on images from each examination. Diagnostic CT images were registered with simulation CT images rigidly and by using four deformable image registration (DIR) algorithms: atlas based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs by using similarity coefficient metrics (ie, Dice similarity coefficient) and surface distance metrics (ie, 95% maximum Hausdorff distance). The nonparametric Steel test with control was used to compare different DIR algorithms with rigid image registration (RIR) by using the post hoc Wilcoxon signed-rank test for stratified metric comparison., Results: A total of 2720 anatomic and 50 tumor and nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for anatomic and target ROI conformance, as shown for most comparison metrics (Steel test, P < .008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures or category and simulation CT section thickness., Conclusion: Development of a formal ROI-based QA workflow for registration assessment demonstrated improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck diagnostic CT and simulation CT allineation, especially for target delineation.

Larynx may alternatively serve as a target or organs at risk (OAR) in head and neck cancer (HNC) image-guided radiotherapy (IGRT). The objective of this study was to estimate IGRT parameters required for larynx positional error independent of isocentric alignment and suggest population-based compensatory margins. Ten HNC patients receiving radiotherapy (RT) with daily CT on-rails imaging were assessed. Seven landmark points were placed on each daily scan. Taking the most superior-anterior point of the C5 vertebra as a reference isocenter for each scan, residual displacement vectors to the other six points were calculated postisocentric alignment. Subsequently, using the first scan as a reference, the magnitude of vector differences for all six points for all scans over the course of treatment was calculated. Residual systematic and random error and the necessary compensatory CTV-to-PTV and OAR-to-PRV margins were calculated, using both observational cohort data and a bootstrap-resampled population estimator. The grand mean displacements for all anatomical points was 5.07 mm, with mean systematic error of 1.1 mm and mean random setup error of 2.63 mm, while bootstrapped POIs grand mean displacement was 5.09 mm, with mean systematic error of 1.23 mm and mean random setup error of 2.61 mm. Required margin for CTV-PTV expansion was 4.6 mm for all cohort points, while the bootstrap estimator of the equivalent margin was 4.9 mm. The calculated OAR-to-PRV expansion for the observed residual setup error was 2.7 mm and bootstrap estimated expansion of 2.9 mm. We conclude that the interfractional larynx setup error is a significant source of RT setup/delivery error in HNC, both when the larynx is considered as a CTV or OAR. We estimate the need for a uniform expansion of 5 mm to compensate for setup error if the larynx is a target, or 3 mm if the larynx is an OAR, when using a nonlaryngeal bony isocenter.

Purpose: To investigate atlas-based auto-segmentation methods to improve the quality of the delineation of low-risk clinical target volumes (CTVs) of unilateral tonsil cancers., Method and Materials: Sixteen patients received intensity modulated radiation therapy for left tonsil tumors. These patients were treated by a total of 8 oncologists, who delineated all contours manually on the planning CT image. We chose 6 of the patients as atlas cases and used atlas-based auto-segmentation to map each the atlas CTV to the other 10 patients (test patients). For each test patient, the final contour was produced by combining the 6 individual segmentations from the atlases using the simultaneous truth and performance level estimation algorithm. In addition, for each test patient, we identified a single atlas that produced deformed contours best matching the physician's manual contours. The auto-segmented contours were compared with the physician's manual contours using the slice-wise Hausdorff distance (HD), the slice-wise Dice similarity coefficient (DSC), and a total volume overlap index., Results: No single atlas consistently produced good results for all 10 test cases. The multiatlas segmentation achieved a good agreement between auto-segmented contours and manual contours, with a median slice-wise HD of 7.4 ± 1.0 mm, median slice-wise DSC of 80.2% ± 5.9%, and total volume overlap of 77.8% ± 3.3% over the 10 test cases. For radiation oncologists who contoured both the test case and one of the atlas cases, the best atlas for a test case had almost always been contoured by the oncologist who had contoured that test case, indicating that individual physician's practice dominated in target delineation and was an important factor in optimal atlas selection., Conclusions: Multiatlas segmentation may improve the quality of CTV delineation in clinical practice for unilateral tonsil cancers. We also showed that individual physician's practice was an important factor in selecting the optimal atlas for atlas-based auto-segmentation., (Copyright © 2014 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

Background: For radiotherapy of the head and neck, 5-point mask immobilization is used to stabilize the shoulders. Still, the daily position of the shoulders during treatment may be different from the position in the treatment plan despite correct isocenter setup. The purpose of this study was to determine the interfractional displacement of the shoulders relative to isocenter over the course of treatment and the associated dosimetric effect of this displacement., Methods: The extent of shoulder displacements relative to isocenter was assessed for 10 patients in 5-point thermoplastic masks using image registration and daily CT-on-rails scans. Dosimetric effects on IMRT and VMAT plans were evaluated in Pinnacle based on simulation CTs modified to represent shoulder shifts between 3 and 15 mm in the superior-inferior, anterior-posterior, and right-left directions. The impact of clinically observed shoulder shifts on the low-neck dose distributions was examined., Results: Shoulder motion was 2-5 mm in each direction on average but reached 20 mm. Superior shifts resulted in coverage loss, whereas inferior shifts increased the dose to the brachial plexus. These findings were generally consistent for both IMRT and VMAT plans. Over a course of observed shifts, the dose to 99% of the CTV decreased by up to 101 cGy, and the brachial plexus dose increased by up to 72 cGy., Conclusions: he position of the shoulder affects target coverage and critical structure dose, and may therefore be a concern during the setup of head and neck patients, particularly those with low neck primary disease., (© 2012 Neubauer et al; licensee BioMed Central Ltd.)

Dental restorations, fixed prosthodontics, and implants affect dose distribution in head and neck radiation therapy due to the high atomic number of the materials utilized. The backscatter of electrons from metallic materials due to the impinging treatment x-ray results in localized dose enhancements. These dose enhancements cause localized mucositis in patients who have dental work, a significant clinical complication. We investigated the backscatter effect of 23 configurations of dental work using the EGS4nrc Monte Carlo (MC) simulation system. We found that all-metal fixed partial dentures caused the highest amount of dose enhancement--up to 33%--while amalgam restorations did not cause a significant amount. Restorations with a ceramic veneer caused up to 8% enhancement. Between 3 mm and 5 mm of water-equivalent material almost completely absorbed the backscatter. MC simulations provide an accurate estimate of backscatter dose, and may provide patient-specific estimates in future.

Purpose: To evaluate early swallowing after intensity-modulated radiotherapy for head and neck squamous cell carcinoma and determine factors correlating with aspiration and/or stricture., Methods and Materials: Consecutive patients treated with intensity-modulated radiotherapy with or without chemotherapy between September 2004 and August 2006 at the Dana Farber Cancer Institute/Brigham and Women's Hospital were evaluated with institutional review board approval. Patients underwent swallowing evaluation after completion of therapy; including video swallow studies. The clinical- and treatment-related variables were examined for correlation with aspiration or strictures, as well as doses to the larynx, pharyngeal constrictor muscles, and cervical esophagus. The correlation was assessed with logistic regression analysis., Results: A total of 96 patients were evaluated. Their median age was 55 years, and 79 (82%) were men. The primary site of cancer was the oropharynx in 43, hypopharynx/larynx in 17, oral cavity in 13, nasopharynx in 11, maxillary sinus in 2, and unknown primary in 10. Of the 96 patients, 85% underwent definitive RT and 15% postoperative RT. Also, 28 patients underwent induction chemotherapy followed by concurrent chemotherapy, 59 received concurrent chemotherapy, and 9 patients underwent RT alone. The median follow-up was 10 months. Of the 96 patients, 31 (32%) had clinically significant aspiration and 36 (37%) developed a stricture. The radiation dose-volume metrics, including the volume of the larynx receiving >or=50 Gy (p = 0.04 and p = 0.03, respectively) and volume of the inferior constrictor receiving >or=50 Gy (p = 0.05 and p = 0.02, respectively) were significantly associated with both aspiration and stricture. The mean larynx dose correlated with aspiration (p = 0.003). Smoking history was the only clinical factor to correlate with stricture (p = 0.05) but not aspiration., Conclusion: Aspiration and stricture are common side effects after intensity-modulated radiotherapy for head-and-neck squamous cell carcinoma. The dose given to the larynx and inferior constrictors correlated with these side effects.

The purpose of this study is two-fold: first is to investigate the process of IMRT QA using control charts and second is to compare control chart limits to limits calculated using the standard deviation (sigma). Head and neck and prostate IMRT QA cases from seven institutions in both academic and community settings are considered. The percent difference between the point dose measurement in phantom and the corresponding result from the treatment planning system (TPS) is used for analysis. The average of the percent difference calculations defines the accuracy of the process and is called the process target. This represents the degree to which the process meets the clinical goal of 0% difference between the measurements and TPS. IMRT QA process ability defines the ability of the process to meet clinical specifications (e.g. 5% difference between the measurement and TPS). The process ability is defined in two ways: (1) the half-width of the control chart limits, and (2) the half-width of +/-3sigma limits. Process performance is characterized as being in one of four possible states that describes the stability of the process and its ability to meet clinical specifications. For the head and neck cases, the average process target across institutions was 0.3% (range: -1.5% to 2.9%). The average process ability using control chart limits was 7.2% (range: 5.3% to 9.8%) compared to 6.7% (range: 5.3% to 8.2%) using standard deviation limits. For the prostate cases, the average process target across the institutions was 0.2% (range: -1.8% to 1.4%). The average process ability using control chart limits was 4.4% (range: 1.3% to 9.4%) compared to 5.3% (range: 2.3% to 9.8%) using standard deviation limits. Using the standard deviation to characterize IMRT QA process performance resulted in processes being preferentially placed in one of the four states. This is in contrast to using control charts for process characterization where the IMRT QA processes were spread over three of the four states with none of the processes in the ideal state. Control charts may be used for IMRT QA in clinical trials to categorize process performance, minimize protocol variation and guide process improvements. For the duration of an institution's participation in a protocol, updated control charts can be periodically sent to the protocol QA center to document continued process performance to protocol specifications.

The purpose of this study was to evaluate patient setup in head and neck IMRT using daily portal imaging. At our institution, orthogonal digital portal images are taken daily to check patient positioning prior to head and neck IMRT treatment. Isocenter misalignments are corrected using a couch shift (3mm action level). Therapists also compare the DRRs and portal images looking at points more distant from the isocenter, particular in the supraclavicular region, and re-position the patient's shoulders in the mask if considered necessary. The daily isocenter shifts (C2 region) and frequency of patient repositioning were investigated by review of record-and-verify records for 15 patients. The magnitude of the shoulder repositioning was evaluated for 10 of these patients by comparing portal images and plan DRRs for a point 8 cm inferior of isocenter (T2-T4). For all patients, pretreatment isocenter discrepancies 3mm or smaller were recorded for a median of 92.5% of fractions (range: 71.4 -100%). Patients were repositioned in the immobilization mask before treatment for a median of 14% of fractions (3-34%). Thirty percent of these were for shoulder shifts of 1cm or larger. Twenty percent of patients needed shoulder shifts of 1cm or more for more than 7/35 fractions, meaning that without setup based on daily imaging, parts of the CTV would have received less than 95% of the prescribed dose. In conclusion, with our current immobilization, isocenter positioning accuracy is excellent, while correct shoulder position is more variable, particularly for a small subset of patients. Frequent imaging of head and neck IMRT patients is essential to accurate delivery of therapy, with shoulder position an important factor.

Purpose: To investigate experimentally the impact of different head-and-neck intensity-modulated radiation therapy (IMRT) planning techniques on doses to the skin and shallow targets., Methods and Materials: A semicylindrical phantom was constructed with micro-MOSFET dosimeters (Thomson-Nielson, Ottawa, Ontario, Canada) at 0-, 3-, 6-, 9-, and 12-mm depths. The planning target volume (PTV) was pulled back 0, 3, or 5 mm from the body contour. The IMRT plans were created to maximize PTV coverage, with one of the following strategies: (a) aim for a maximum 110% hotspot, with 115% allowed; (b) aims for a maximum 105% hotspot; (c) aims for a maximum 105% hotspot and 50% of skin to get a maximum 70% of the prescribed dose; and (d) aim for 99% of the PTV volume to receive 90-93% of prescribed dose, with a maximum 105% hotspot, and with the dose to the skin structure minimized. Doses delivered using a linear accelerator were measured. Setup uncertainty was simulated by intentionally shifting the phantom in a range of +/-8 mm, and calculating the delivered dose for a range of systematic and random uncertainties., Results: From lowest to highest skin dose, the planning strategies were in the order of c, d, b, and a, but c showed a tendency to underdose tissues at depth. Delivered doses varied by 10-20%, depending on planning strategy. For typical setup uncertainties, cumulative dose reduction to a point 6 mm deep was <4%., Conclusions: It is useful to use skin as a sensitive structure, but a minimum dose constraint must be used for the PTV if unwanted reductions in dose to nodes near the body surface are to be avoided. Setup uncertainties are unlikely to give excessive reductions in cumulative dose.

Purpose: Many patients receiving fractionated radiotherapy (RT) for head-and-neck cancer have marked anatomic changes during their course of treatment, including shrinking of the primary tumor or nodal masses, resolving postoperative changes/edema, and changes in overall body habitus/weight loss. We conducted a pilot study to quantify the magnitude of these anatomic changes with systematic CT imaging., Methods and Materials: Fourteen assessable patients were enrolled in this pilot study. Eligible patients had to have a pathologic diagnosis of head-and-neck cancer, be treated with definitive external beam RT, and had have gross primary and/or cervical nodal disease measuring at least 4 cm in maximal diameter. All patients were treated using a new commercial integrated CT-linear accelerator system (EXaCT) that allows CT imaging at the daily RT sessions while the patient remains immobilized in the treatment position. CT scans were acquired three times weekly during the entire course of RT, and both gross tumor volumes (GTVs: primary tumor and involved lymph nodes) and normal tissues (parotid glands, spinal canal, mandible, and external contour) were manually contoured on every axial slice. Volumetric and positional changes relative to a central bony reference (the center of mass of the C2 vertebral body) were determined for each structure., Results: Gross tumor volumes decreased throughout the course of fractionated RT, at a median rate of 0.2 cm(3) per treatment day (range, 0.01-1.95 cm(3)/d). In terms of the percentage of the initial volume, the GTVs decreased at a median rate of 1.8%/treatment day (range, 0.2-3.1%/d). On the last day of treatment, this corresponded to a median total relative loss of 69.5% of the initial GTV (range, 9.9-91.9%). In addition, the center of the mass of shrinking tumors changed position with time, indicating that GTV loss was frequently asymmetric. At treatment completion, the median center of the mass displacement (after corrections for daily setup variation) was 3.3 mm (range, 0-17.3 mm). Parotid glands also decreased in volume (median, 0.19 cm(3)/d range, 0.04-0.84 cm(3)/d), and generally shifted medially (median, 3.1 mm; range, 0-9.9 mm) with time. This medial displacement of the parotid glands correlated highly with the weight loss that occurred during treatment., Conclusion: Measurable anatomic changes occurred throughout fractionated external beam RT for head-and-neck cancers. These changes in the external contour, shape, and location of the target and critical structures appeared to be significant during the second half of treatment (after 3-4 weeks of treatment) and could have potential dosimetric impact when highly conformal treatment techniques are used. These data may, therefore, be useful in the development of an adaptive RT scheme (periodic adjustment of the conformal treatment plan) that takes into account such treatment-related anatomic changes. In theory, such a strategy would maximize the therapeutic ratio of RT.