Radiotherapy for Head and Neck Cancer: Evaluation of Triggered Adaptive Replanning in Routine Practice

Does adaptive radiotherapy for head and neck cancer favorably impact dosimetric, clinical, and toxicity outcomes?: A review

PURPOSE

The current review aims to summarize the international experience of the impact of adaptive radiotherapy on dosimetry and clinical and toxicity outcomes. Additionally, it might trigger Radiation Oncologists to use ART and evaluate whether ART improves target volume coverage and/or normal tissue sparing and, consequently, therapeutic results.

MATERIALS AND METHODS

We conducted an electronic literature search of PubMed/MEDLINE and ScienceDirect from January 2007 to January 2023. The search adhered to the PRISMA guidelines and employed keywords such as ART, HNC, parotid gland, and target volume. Furthermore, we examined the reference lists for studies pertinent to the present review. This study included both retrospective and prospective studies that were considered for inclusion.

CONCLUSION

ART replanning appears to be a sustainable strategy to minimize toxicity by improving normal tissue sparing. Furthermore, it can enhance target volume coverage by correctly determining the specific dose to be delivered to the tumor. In conclusion, this review confirmed that ART benefits dosimetric, clinical/therapeutic, and toxicity outcomes.

Radiotherapy Plan Quality Assurance in NRG Oncology Trials for Brain and Head/Neck Cancers: An AI-Enhanced Knowledge-Based Approach

The quality of radiation therapy (RT) treatment plans directly affects the outcomes of clinical trials. KBP solutions have been utilized in RT plan quality assurance (QA). In this study, we evaluated the quality of RT plans for brain and head/neck cancers enrolled in multi-institutional clinical trials utilizing a KBP approach. The evaluation was conducted on 203 glioblastoma (GBM) patients enrolled in NRG-BN001 and 70 nasopharyngeal carcinoma (NPC) patients enrolled in NRG-HN001. For each trial, fifty high-quality photon plans were utilized to build a KBP photon model. A KBP proton model was generated using intensity-modulated proton therapy (IMPT) plans generated on 50 patients originally treated with photon RT. These models were then applied to generate KBP plans for the remaining patients, which were compared against the submitted plans for quality evaluation, including in terms of protocol compliance, target coverage, and organ-at-risk (OAR) doses. RT plans generated by the KBP models were demonstrated to have superior quality compared to the submitted plans. KBP IMPT plans can decrease the variation of proton plan quality and could possibly be used as a tool for developing improved plans in the future. Additionally, the KBP tool proved to be an effective instrument for RT plan QA in multi-center clinical trials.

TransAnaNet: Transformer-based Anatomy Change Prediction Network for Head and Neck Cancer Patient Radiotherapy

Background

Adaptive radiotherapy (ART) can compensate for the dosimetric impact of anatomic change during radiotherapy of head neck cancer (HNC) patients. However, implementing ART universally poses challenges in clinical workflow and resource allocation, given the variability in patient response and the constraints of available resources. Therefore, early identification of head and neck cancer (HNC) patients who would experience significant anatomical change during radiotherapy (RT) is of importance to optimize patient clinical benefit and treatment resources.

Purpose

The purpose of this study is to assess the feasibility of using a vision-transformer (ViT) based neural network to predict radiotherapy induced anatomic change of HNC patients.

Methods

We retrospectively included 121 HNC patients treated with definitive RT/CRT. We collected the planning CT (pCT), planned dose, CBCTs acquired at the initial treatment (CBCT01) and fraction 21 (CBCT21), and primary tumor volume (GTVp) and involved nodal volume (GTVn) delineated on both pCT and CBCTs for model construction and evaluation. A UNet-style ViT network was designed to learn the spatial correspondence and contextual information from embedded image patches of CT, dose, CBCT01, GTVp, and GTVn. The deformation vector field between CBCT01 and CBCT21 was estimated by the model as the prediction of anatomic change, and deformed CBCT01 was used as the prediction of CBCT21. We also generated binary masks of GTVp, GTVn and patient body for volumetric change evaluation. We used data from 100 patients for training and validation, and the remaining 21 patients for testing. Image and volumetric similarity metrics including mean square error (MSE), structural similarity index (SSIM), dice coefficient, and average surface distance were used to measure the similarity between the target image and predicted CBCT.

Results

The predicted image from the proposed method yielded the best similarity to the real image (CBCT21) over pCT, CBCT01, and predicted CBCTs from other comparison models. The average MSE and SSIM between the normalized predicted CBCT to CBCT21 are 0.009 and 0.933, while the average dice coefficient between body mask, GTVp mask, and GTVn mask are 0.972, 0.792, and 0.821 respectively.

Conclusions

The proposed method showed promising performance for predicting radiotherapy induced anatomic change, which has the potential to assist in the decision making of HNC Adaptive RT.

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

BACKGROUND

Patients with oropharyngeal cancer (OPC) treated with chemoradiation can experience weight loss and tumor shrinkage, altering the prescribed treatment. Treatment replanning ensures patients do not receive excessive doses to normal tissue. However, it is a time- and resource-intensive process, as it takes 1 to 2 weeks to acquire a new treatment plan, and during this time, overtreatment of normal tissues could lead to increased toxicities. Currently, there are limited prognostic factors to determine which patients will require a replan. There remains an unmet need for predictive models to assist in identifying patients who could benefit from the knowledge of a replan prior to treatment.

PURPOSE

We aimed to develop and evaluate a CT-based radiomic model, integrating clinical and dosimetric information, to predict the need for a replan prior to treatment.

METHODS

A dataset of patients (n = 315) with OPC treated with chemoradiation was used for this study. The dataset was split into independent training (n = 220) and testing (n = 95) datasets. Tumor volumes and organs at risk (OARs) were contoured on planning CT images. PyRadiomics was used to compute radiomic image features (n = 1218) on the original and filtered images from each of the primary tumor, nodal volumes, and ipsilateral and contralateral parotid glands. Nine clinical features and nine dose features extracted from the OARs were collected and those significantly (p < 0.05) associated with the need for a replan in the training dataset were used in a baseline model. Random forest feature selection was applied to select the optimal radiomic features to predict replanning. Logistic regression, Naïve Bayes, support vector machine, and random forest classifiers were built using the non-correlated selected radiomic, clinical, and dose features on the training dataset and performance was assessed in the testing dataset. The area under the curve (AUC) was used to assess the prognostic value.

RESULTS

A total of 78 patients (25%) required a replan. Smoking status, nodal stage, base of tongue subsite, and larynx mean dose were found to be significantly associated with the need for a replan in the training dataset and incorporated into the baseline model, as well as into the combined models. Five predictive radiomic features were selected (one nodal volume, one primary tumor, two ipsilateral and one contralateral parotid gland). The baseline model comprised of clinical and dose features alone achieved an AUC of 0.66 [95% CI: 0.51-0.79] in the testing dataset. The random forest classifier was the top-performing radiomics model and achieved an AUC of 0.82 [0.75-0.89] in the training dataset and an AUC of 0.78 [0.68-0.87] in the testing dataset, which significantly outperformed the baseline model (p = 0.023, testing dataset).

CONCLUSIONS

This is the first study to use radiomics from the primary tumor, nodal volumes, and parotid glands for the prediction of replanning for patients with OPC. Radiomic features augmented clinical and dose features for predicting the need for a replan in our testing dataset. Once validated, this model has the potential to assist physicians in identifying patients that may benefit from a replan, allowing for better resource allocation and reduced toxicities.

Optimal timing of re-planning for head and neck adaptive radiotherapy

BACKGROUND AND PURPOSE

Adaptive radiotherapy (ART) relies on re-planning to correct treatment variations, but the optimal timing of re-planning to account for dose changes in head and neck organs at risk (OARs) is still under investigation. We aimed to find out the optimal timing of re-planning in head and neck ART.

MATERIALS AND METHODS

A total of 110 head and neck cancer patients were retrospectively enrolled. A semi auto-segmentation method was applied to obtain the weekly mean dose (Dmean) to OARs. The K-nearest-neighbour method was used for missing data imputation of weekly Dmean. A dose deviation map was built using the planning Dmean and weekly Dmean values and then used to simulate different ART scenarios consisting of 1 to 6 re-plannings. The difference between accumulated Dmean and planning Dmean before re-planning (ΔDmean_acc_noART) and after re-planning (ΔDmean_acc_ART) were evaluated and compared.

RESULTS

Among all the OARs, supraglottic showed the largest ΔDmean_acc_noART (1.23 ± 3.13 Gy) and most cases of ΔDmean_acc_noART > 3 Gy (26 patients). The 3rd week is suggested in the optimal timing of re-planning for 10 OARs. For all the organs except arytenoid, 2 re-plannings were able to guarantee the ΔDmean_acc_ART below 3 Gy while the average |ΔDmean_acc_ART| was below 1 Gy. ART scenarios of 2_4, 3_4, 3_5 (week of re-planning separated with "_") were able to guarantee ΔDmean_acc_ART of 99 % of patients below 3 Gy simultaneously for 19 OARs.

CONCLUSIONS

The optimal timing of re-planning was suggested for different organs at risk in head and neck adaptive radiotherapy. Generic scenarios of timing and frequency for re-planning can be applied to guarantee the increase of accumulated mean dose within 3 Gy simultaneously for multiple organs.

Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck Cancer

The delineation of parotid glands in head and neck (HN) carcinoma is critical to assess radiotherapy (RT) planning. Segmentation processes ensure precise target position and treatment precision, facilitate monitoring of anatomical changes, enable plan adaptation, and enhance overall patient safety. In this context, artificial intelligence (AI) and deep learning (DL) have proven exceedingly effective in precisely outlining tumor tissues and, by extension, the organs at risk. This paper introduces a DL framework using the AttentionUNet neural network for automatic parotid gland segmentation in HN cancer. Extensive evaluation of the model is performed in two public and one private dataset, while segmentation accuracy is compared with other state-of-the-art DL segmentation schemas. To assess replanning necessity during treatment, an additional registration method is implemented on the segmentation output, aligning images of different modalities (Computed Tomography (CT) and Cone Beam CT (CBCT)). AttentionUNet outperforms similar DL methods (Dice Similarity Coefficient: 82.65% ± 1.03, Hausdorff Distance: 6.24 mm ± 2.47), confirming its effectiveness. Moreover, the subsequent registration procedure displays increased similarity, providing insights into the effects of RT procedures for treatment planning adaptations. The implementation of the proposed methods indicates the effectiveness of DL not only for automatic delineation of the anatomical structures, but also for the provision of information for adaptive RT support.

International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors

BACKGROUND

Sinonasal neoplasms, whether benign and malignant, pose a significant challenge to clinicians and represent a model area for multidisciplinary collaboration in order to optimize patient care. The International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors (ICSNT) aims to summarize the best available evidence and presents 48 thematic and histopathology-based topics spanning the field.

METHODS

In accordance with prior International Consensus Statement on Allergy and Rhinology documents, ICSNT assigned each topic as an Evidence-Based Review with Recommendations, Evidence-Based Review, and Literature Review based on the level of evidence. An international group of multidisciplinary author teams were assembled for the topic reviews using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses format, and completed sections underwent a thorough and iterative consensus-building process. The final document underwent rigorous synthesis and review prior to publication.

RESULTS

The ICSNT document consists of four major sections: general principles, benign neoplasms and lesions, malignant neoplasms, and quality of life and surveillance. It covers 48 conceptual and/or histopathology-based topics relevant to sinonasal neoplasms and masses. Topics with a high level of evidence provided specific recommendations, while other areas summarized the current state of evidence. A final section highlights research opportunities and future directions, contributing to advancing knowledge and community intervention.

CONCLUSION

As an embodiment of the multidisciplinary and collaborative model of care in sinonasal neoplasms and masses, ICSNT was designed as a comprehensive, international, and multidisciplinary collaborative endeavor. Its primary objective is to summarize the existing evidence in the field of sinonasal neoplasms and masses.

Validation of a deep-learning segmentation model for adult and pediatric head and neck radiotherapy in different patient positions

Background and purpose

Autocontouring for radiotherapy has the potential to significantly save time and reduce interobserver variability. We aimed to assess the performance of a commercial autocontouring model for head and neck (H&N) patients in eight orientations relevant to particle therapy with fixed beam lines, focusing on validation and implementation for routine clinical use.

Materials and methods

Autocontouring was performed on sixteen organs at risk (OARs) for 98 adult and pediatric patients with 137 H&N CT scans in eight orientations. A geometric comparison of the autocontours and manual segmentations was performed using the Hausdorff Distance 95th percentile, Dice Similarity Coefficient (DSC) and surface DSC and compared to interobserver variability where available. Additional qualitative scoring and dose-volume-histogram (DVH) parameters analyses were performed for twenty patients in two positions, consisting of scoring on a 0-3 scale based on clinical usability and comparing the mean (Dmean) and near-maximum (D2%) dose, respectively.

Results

For the geometric analysis, the model performance in head-first-supine straight and hyperextended orientations was in the same range as the interobserver variability. HD95, DSC and surface DSC was heterogeneous in other orientations. No significant geometric differences were found between pediatric and adult autocontours. The qualitative scoring yielded a median score of ≥ 2 for 13/16 OARs while 7/32 DVH parameters were significantly different.

Conclusions

For head-first-supine straight and hyperextended scans, we found that 13/16 OAR autocontours were suited for use in daily clinical practice and subsequently implemented. Further development is needed for other patient orientations before implementation.

In Silico Analysis of Adjuvant Head and Neck Online Adaptive Radiation Therapy

Purpose

Recently developed online adaptive radiation therapy (OnART) systems enable frequent treatment plan adaptation, but data supporting a dosimetric benefit in postoperative head and neck radiation therapy (RT) are sparse. We performed an in silico dosimetric study to assess the potential benefits of a single versus weekly OnART in the treatment of patients with head and neck squamous cell carcinoma in the adjuvant setting.

Methods and Materials

Twelve patients receiving conventionally fractionated RT over 6 weeks and 12 patients receiving hypofractionated RT over 3 weeks on a clinical trial were analyzed. The OnART emulator was used to virtually adapt either once midtreatment or weekly based on the patient's routinely performed cone beam computed tomography. The planning target volume (PTV) coverage, dose heterogeneity, and cumulative dose to the organs at risk for these 2 adaptive approaches were compared with the nonadapted plan.

Results

In total, 13, 8, and 3 patients had oral cavity, oropharynx, and larynx primaries, respectively. In the conventionally fractionated RT cohort, weekly OnART led to a significant improvement in PTV V100% coverage (6.2%), hot spot (-1.2 Gy), and maximum cord dose (-3.1 Gy), whereas the mean ipsilateral parotid dose increased modestly (1.8 Gy) versus the nonadapted plan. When adapting once midtreatment, PTV coverage improved with a smaller magnitude (0.2%-2.5%), whereas dose increased to the ipsilateral parotid (1.0-1.1 Gy) and mandible (0.2-0.7 Gy). For the hypofractionated RT cohort, similar benefit was observed with weekly OnART, including significant improvement in PTV coverage, hot spot, and maximum cord dose, whereas no consistent dosimetric advantage was seen when adapting once midtreatment.

Conclusions

For head and neck squamous cell carcinoma adjuvant RT, there was a limited benefit of single OnART, but weekly adaptations meaningfully improved the dosimetric criteria, predominantly PTV coverage and dose heterogeneity. A prospective study is ongoing to determine the clinical benefit of OnART in this setting.

Analysis of dose distribution reproducibility based on a fluence map of in vivo transit dose using an electronic portal imaging device

Morphological changes can affect distribution of dose in patients. Determination of the dose distribution changes for each fraction radiotherapy can be done by relativein vivodosimetry (IVD). This study analysed the distribution of doses per fraction based on the fluence map recorded by the electronic portal imaging device (EPID) of the patient's transit dose. This research examined cases involving the cervix, breast, and nasopharynx. Transit dose analysis was performed by calculating the gamma index (GI) with composite and field-by-field methods. The gamma passing rate (GPR) value was assessed for its correlation with the subject's body weight. In the case of the nasopharynx, breast, and cervix, the GPR value decreased as the fraction increased. In the case of the nasopharynx, the correlation between the GPR and fraction radiotherapy showed no difference when using either composite or field-by-field methods. However, in cases involving the cervix and breast, there was a difference in the correlation values between the composite and field-by-field methods, where the subject had a significant correlation (p< 0.05) when it was done using a field-by-field method. In addition, the nasopharynx had the highest number of subjects with significant correlation (p< 0.05) between GPR and body weight, followed by the cervix and breast. In the nasopharynx, breast, and cervix, the reproducibility of the dose distribution decreased. This decreased reproducibility was associated with changes in body weight.

Development of programs to predict the occurrence of mucositis from digital imaging and communications in medicine data by machine learning in head and neck volumetric modulated radiotherapy

Volumetric modulated arc therapy (VMAT) with cisplatin for head and neck cancer is often accompanied by symptoms of pharyngeal and oral mucositis. However, no standard medical program exists for the prevention and treatment of mucositis, and the mechanisms of mucositis have not yet been fully proven. Therefore, adaptive radiotherapy (ART), which is a re-planning process, is administered when severe mucositis develops during the treatment period. We extracted the treatment plans of patients who developed severe mucositis from DICOM data and used machine learning to determine its quantitative features. This study aimed to develop a machine learning program that can predict the development of mucositis requiring ART. This study included 61 patients who received concurrent chemotherapy and radiotherapy (RT). For each patient, the equivalent square field size of each segmental irradiation field used for VMAT, dose per segment (Gy), clinical target volume high, and mean dose of the oral cavity (Gy) were calculated. Furthermore, 671 five-dimensional lists were generated from the acquired data. Support vector machine (SVM) and K-nearest neighbor (KNN) were used for machine learning. For the accuracy score, the test size was varied from 10% to 90%, and the random number of data extracted in each test size was further varied from 1 to 100 to calculate a mean accuracy score. The mean accuracy scores of SVM and KNN were 0.981 ± 0.020 and 0.972 ± 0.033, respectively. The presence or absence of ART for mucositis was classified with high accuracy. The classification of the five-dimensional list was implemented with high accuracy, and a program was constructed to predict the onset of mucositis requiring ART before treatment began. This study suggests that it may support preventive measures against mucositis and the completion of RT without having to re-plan.

A multi-center survey on adaptive radiation therapy for head and neck cancer in Malaysia

The survey is to assess the current state of adaptive radiation therapy (ART) for head and neck (H&N) cases among radiotherapy centers in Malaysia and to identify any implementation limitations. An online questionnaire was sent to all radiotherapy centers in Malaysia. The 24-question questionnaire consists of general information about the center, ART practices, and limitations faced in implementing ART. 28 out of 36 radiotherapy centers responded, resulting in an overall response rate of 78%. About 52% of the responding centers rescanned and replanned less than 5% of their H&N patients. The majority (88.9%) of the respondents reported the use Cone Beam Computed Tomography alone or in combination with other modalities to trigger the ART process. The main reasons cited for adopting ART were weight loss, changes in the immobilization fitting, and anatomical variation. The adaptation process typically occurred during week 3 or week 4 of treatment. More than half of the respondents require three days or more from re-simulation to starting a new treatment plan. Both target and organ at risk delineation on new planning CT relied heavily on manual delineation by physicians and physicists, respectively. All centers perform patient-specific quality assurance for their new adaptive plans. Two main limitations in implementing ART are "limited financial resources or equipment" and "limitation on technical knowledge". There is a need for a common consensus to standardize the practice of ART and address these limitations to improve the implementation of ART in Malaysia.

Adaptive Proton Therapy for Pediatric Parameningeal Rhabdomyosarcoma: On-Treatment Anatomic Changes and Timing to Replanning

PURPOSE

To characterize on-treatment changes in GTV morphology in children with parameningeal rhabdomyosarcoma receiving upfront proton therapy with concurrent chemotherapy and thereby provide guidance on the timing of on-treatment imaging and adaptive replanning.

METHODS AND MATERIALS

GTV was delineated on 86 simulation and weekly MR images of 15 prospectively enrolled patients (aged 1-21 years). Temporal changes from baseline in volume and surface (95% Hausdorff distance) were analyzed in relation to the need for plan verification and the resultant doses with hypothetical no treatment adaptation.

RESULTS

The median time was 6 days from the initiation of chemotherapy to CT+MR simulation and 15 days from the simulation to the start of radiotherapy. All but 1 patient showed a continuous decrease in GTV (0.16-1.52%/day) after simulation. At 3 weeks from simulation, 10 of 15 patients exhibited a significant reduction in volume (median, 20%; range, 6-29%). Without replanning, these changes could lead to a reduction in CTV V95 by 7-14% (n = 2) and/or an increase in D0.01 cc/Dmean of adjacent organs at risk by 6-21% of the prescribed target dose (n = 7). Significant dosimetric consequences occurred in cases with (1) a considerable weight gain, (2) shrinkage of the skin surface, or (3) tumor regression in the oral or nasal cavity and sinus that altered air-tissue components in the beam path. The subsequent GTV and dosimetry after 3 weeks from simulation (4 weeks from chemotherapy initiation) demonstrated a relatively stable trend.

CONCLUSIONS

On-treatment imaging at 3 weeks after simulation is recommended, if the simulation is performed at 1 week after the initiation of chemotherapy, to detect significant anatomic changes that could result in >5% deviation from planned target coverage and/or organ doses in pediatric patients with parameningeal rhabdomyosarcoma receiving early proton therapy.

Image-guidance triggered adaptive radiation therapy in head and neck squamous cell carcinoma: single-institution experience and implications for clinical practice

PURPOSE

To report frequency and timing of adaptive radiotherapy (ART) and assess patient, disease, and treatment-related characteristics potentially triggering the need for such adaptive replanning in head and neck squamous cell carcinoma (HNSCC).

METHODS

Medical records of HNSCC patients treated with definitive intensity modulated radiation therapy (IMRT) with or without concurrent systemic chemotherapy were reviewed retrospectively to identify patients undergoing image-guidance triggered adaptive replanning. Clinico-demographic characteristics of patients undergoing ART were compared with patients treated without adaptation using the chi-square test.

RESULTS

Two hundred patients with squamous cell cancers of the oropharynx, larynx, or hypopharynx treated with definitive IMRT between 2014 to 2019 comprised the study cohort. Twenty-seven (13.5%) patients underwent adaptive replanning during treatment at a median of 17 fractions (inter-quartile range 14-24 fractions). There were no significant differences in the baseline patient (age, gender), disease (site of primary, staging/grouping), and treatment-related characteristics (dose-fractionation, chemotherapy usage) in patients undergoing ART compared to those treated without adaptation. Weight loss during IMRT emerged as a significant factor predicting the need for ART; patients having ≥10% weight loss from baseline were more likely to undergo treatment adaptation compared to patients with <10% weight loss (p = 0.0002). There was variable impact of ART on dose-volume statistics of organs-at-risk such parotid glands and spinal cord.

CONCLUSION

Image-guidance triggered ART for HNSCC is not associated with significant improvement in OAR dosimetry. However, weight loss during definitive IMRT can be a potentially useful trigger for identifying patients who are most likely to benefit from such adaptive replanning.

Development of Machine-Learning Prediction Programs for Delivering Adaptive Radiation Therapy With Tumor Geometry and Body Shape Changes in Head and Neck Volumetric Modulated Arc Therapy

Purpose

During radiation therapy for head and neck cancer using volumetric modulated arc therapy, excessive dosing or underdosing occurs as a result of the decrease in tumor volume and changes in body weight. Adaptive radiation therapy (ART) is performed when significant changes are observed; however, the decision to implement ART depends on the oncologist's subjective judgment. The purpose of this study was to present objective indicators for ART and develop a program to predict the need for ART.

Methods and Materials

The study included 47 patients in the non-ART group and 21 patients in the ART group with shape changes. Patients who received ART could not be covered with the prescribed radiation therapy dose due to shape changes. For each patient, 1112 6-dimensional lists were created, including the number of irradiations, amount of change in the clinical target volume (CTV), rate of change in CTV, mean oral cavity dose, age, and body mass index. Support vector machine and k-nearest neighbor were used for machine learning. The random number of test data to be extracted varied from 1 to 9, and a mean accuracy score was calculated. These programs could predict the need for ART if the accuracy score was high.

Results

The classification accuracy of the list, including the amount of change in the CTV and rate of change in CTV up to 20 fractions, was 0.963 and 0.967 for support vector machine and k-nearest neighbor, respectively.

Conclusions

This program predicted the need for ART with more than 90% accuracy based on shape changes over time in cone beam computed tomography analysis for up to 20 fractions. This may provide significant support for objective decisions to implement ART based on the amount of change over time during treatment.

Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach

Background: During RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far. Methods: CBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations. Results: The proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling. Conclusion: The use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics.

Dose and robustness comparison of nominal, daily and accumulated doses for photon and proton treatment of sinonasal cancer

INTRODUCTION

The aim was to evaluate and compare the dosimetric effect and robustness towards day-to-day anatomical and setup variations in the delivered dose for photon and proton treatments of sinonasal cancer (SNC) patients.

MATERIALS AND METHODS

Photon (VMAT) and proton (IMPT) plans were optimized retrospectively for 24 SNC patients. Synthetic CTs (synCT) were obtained by deforming the planning CT (pCT) to the anatomy of every daily cone-beam CT. Both VMAT and IMPT plans were recalculated on the synCTs. The recalculated daily dose was accumulated over the whole treatment on the pCT. Target coverage and dose to organs and risk (OARs) were evaluated for all patients for the nominal, daily and accumulated dose distribution.

RESULTS

In general, dose to OARs farther away from the target, including brain, chiasm and contralateral optic nerve, was lower for proton plans than photon plans. Whereas, OARs in proximity of the target received a lower dose for photon plans. For proton plans, the target coverage (volume of CTV receiving 95% of prescribed dose), V95%, fell below 99% for 9/24 patients in one or more fractions. For photon plans, 4/24 patients had one or more fractions where V95% fell below 99%. For accumulated doses, V95% was below 99% only in two cases, but above 98% for all patients.

CONCLUSION

Photon and proton treatment have different strengths regarding OAR sparing. The robustness was high for both treatment modalities. Patient selection for either proton or photon radiation therapy of SNC patients should be based on a case-by-case comparison.

Criteria for Verification and Replanning Based on the Adaptive Radiotherapy Protocol "Best for Adaptive Radiotherapy" in Head and Neck Cancer

No clear criteria have yet been established to guide decision-making for patient selection and the optimal timing of adaptive radiotherapy (ART) based on image-guided radiotherapy (IGRT). We have developed a novel protocol—the Best for Adaptive Radiotherapy (B-ART) protocol—to guide patient selection for ART. The aim of the present study is to describe this protocol, to evaluate its validity in patients with head and neck (HN) cancer, and to identify the anatomical and clinical predictors of the need for replanning. We retrospectively evaluated 82 patients with HN cancer who underwent helical tomotherapy (HT) and subsequently required replanning due to soft tissue changes upon daily MVCT. Under the proposed criteria, patients with anatomical changes >3 mm on three to four consecutive scans are candidates for ART. We compared the volumes on the initial CT scan (iCT) and the replanning CT (rCT) scan for the clinical target volumes (CTV1, referring to primary tumor or tumor bed and CTV2, metastatic lymph nodes) and for the parotid glands (PG) and body contour (B-body). The patients were stratified by primary tumor localization, clinical stage, and treatment scheme. The main reasons for replanning were: (1) a planning target volume (PTV) outside the body contour (n = 70; 85.4%), (2) PG shrinkage (n = 69; 84.1%), (3) B-body deviations (n = 69; 84.1%), and (4) setup deviations (n = 40; 48.8%). The replanning decision was made, on average, during the fourth week of treatment (n = 47; 57.3%). The mean reductions in the size of the right and left PG volumes were 6.31 cc (20.9%) and 5.98 cc (20.5%), respectively (p < 0.001). The reduction in PG volume was ≥30% in 30 patients (36.6%). The volume reduction in all of the anatomical structures was statistically significant. Four variables—advanced stage disease (T3−T4), chemoradiation, increased weight loss, and oropharyngeal localization—were significantly associated with the need for ART. The B-ART protocol provides clear criteria to eliminate random errors, and to allow for an early response to relevant changes in target volumes.

Quality management in radiotherapy treatment delivery

Radiation Oncology continues to rely on accurate delivery of radiation, in particular where patients can benefit from more modulated and hypofractioned treatments that can deliver higher dose to the target while optimising dose to normal structures. These deliveries are more complex, and the treatment units are more computerised, leading to a re-evaluation of quality assurance (QA) to test a larger range of options with more stringent criteria without becoming too time and resource consuming. This review explores how modern approaches of risk management and automation can be used to develop and maintain an effective and efficient QA programme. It considers various tools to control and guide radiation delivery including image guidance and motion management. Links with typical maintenance and repair activities are discussed, as well as patient-specific quality control activities. It is demonstrated that a quality management programme applied to treatment delivery can have an impact on individual patients but also on the quality of treatment techniques and future planning. Developing and customising a QA programme for treatment delivery is an important part of radiotherapy. Using modern multidisciplinary approaches can make this also a useful tool for department management.

Image-Guided Radiation Therapy for Squamous Cell Cancer of the Head and Neck in a Specialized Peruvian Public Hospital

Squamous cell cancer of the head and neck (SCCHN) often requires adjuvant radiotherapy. Radiotherapy for SCCHN is a challenge because the head and neck contain several critical organs that should receive minimal doses of radiation. These organs include the eyes, parotid glands, brainstem, spinal cord, mandible, and thyroid gland. Approaches like image-guided radiotherapy (IGRT) combined with volumetric modulated arc therapy hold the promise to focus radiation to the planning target volume and spare nearby structures while observing potential changes to patient anatomy during treatment to determine whether replanning is required. IGRT, however, requires the frequent imaging of patients to update the treatment plan. In this retrospective study, we present our findings of SCCHN patients treated in a public hospital in Peru. The patients reflected overall demographic trends associated with SCCHN. Each patient was imaged using computed tomography once before radiotherapy and once by cone-beam computed tomography (CBCT) during treatment, for a total of two images. Tumor displacement, planning target volume, gross tumor volume, and neck diameter were compared between the two images. Among the measurements, only a small statistically significant increase in gross tumor volume was observed between the images. However, a minority of patients did experience changes to anatomy, which highlights the need for continued research into criteria to determine which patients are likely to benefit from treatment replanning due to intra-treatment anatomical changes. Alternatively, a lack of frequent CBCT imaging before each session, due to high patient flows and limited staff resources, made it difficult to observe transient changes and trends in each patient. We conclude that the treatment and outcome improvements associated with IGRT are likely associated with frequent imaging during radiotherapy and properly selecting which patients will benefit most from this resource-intensive technique.

What information can we gain from performing adaptive radiotherapy of head and neck cancer patients from the past 10 years?

The aim of the review was to present the current literature status about replanning regarding anatomical and dosimetric changes in the target and OARs in the head and neck region during radiotherapy, to discuss and to analyze factors influencing the decision for adaptive radiotherapy of head and neck cancer patients. Significant progress has been made in head and neck patients' evaluation and qualification for adapted radiotherapy over the past ten years. Many factors leading to anatomical and dosimetric changes during treatment have been identified. Based on the literature, the most common factors triggering re-plan are weight loss, tumor and nodal changes, and parotid glands shrinkage. The fluctuations in dose distribution in the clinical area are significant predictive factors for patients' quality of life and the possibility of recovery. It has been shown that re-planning influence clinical outcomes: local control, disease free survival and overall survival. Regarding literature studies, it seems that adaptive radiotherapy would be the most beneficial for tumors of immense volume or those in the nearest proximity of the OARs. All researchers agree that the timing of re-planning is a crucial challenge, and there are still no clear consensus guidelines for time or criteria of re-planning. Nowadays, thanks to significant technological progress, the decision is mostly made based on observation and supported with IGRT verification. Although further research is still needed, adaptive strategies are evolving and now became the state of the art of modern radiotherapy.

Patient positioning and immobilization procedures for hybrid MR-Linac systems

Hybrid magnetic resonance (MR)-guided linear accelerators represent a new horizon in the field of radiation oncology. By harnessing the favorable combination of on-board MR-imaging with the possibility to daily recalculate the treatment plan based on real-time anatomy, the accuracy in target and organs-at-risk identification is expected to be improved, with the aim to provide the best tailored treatment. To date, two main MR-linac hybrid machines are available, Elekta Unity and Viewray MRIdian. Of note, compared to conventional linacs, these devices raise practical issues due to the positioning phase for the need to include the coil in the immobilization procedure and in order to perform the best reproducible positioning, also in light of the potentially longer treatment time. Given the relative novelty of this technology, there are few literature data regarding the procedures and the workflows for patient positioning and immobilization for MR-guided daily adaptive radiotherapy. In the present narrative review, we resume the currently available literature and provide an overview of the positioning and setup procedures for all the anatomical districts for hybrid MR-linac systems.

Deformable image registration to assist clinical decision for radiotherapy treatment adaptation for head and neck cancer patients

Head and neck (H&N) cancer patients often present anatomical and geometrical changes in tumors and organs at risk (OARs) during radiotherapy treatment. These changes may result in the need to adapt the existing treatment planning, using an expert's subjective opinion, for offline adaptive radiotherapy and a new treatment planning before each treatment, for online adaptive radiotherapy. In the present study, a fast methodology is proposed to assist in planning adaptation clinical decision using tumor and parotid glands percentage volume changes during treatment. The proposed approach was applied to 40 Η&Ν cases, with one planning Computed Tomography (pCT) image and CBCT scans for 6 weeks of treatment per case. Deformable registration was used for each patient's pCT image alignment to its weekly CBCT. The calculated transformations were used to align each patient's anatomical structures to the weekly anatomy. Clinical target volume (CTV) and parotid gland volume percentage changes were calculated in each case. The accuracy of the achieved image alignment was validated qualitatively and quantitatively. Furthermore, statistical analysis was performed to test if there is a statistically significant correlation between CTV and parotid glands volume percentage changes. Average MDA for CTV and parotid glands between corresponding structures defined by an expert in CBCTs and automatically calculated through registration was 1.4 ± 0.1 mm and 1.5 ± 0.1 mm, respectively. The mean registration time of the first CBCT image registration for 40 cases was lower than 3.4 min. Five patients show more than 20% tumor volume change. Six patients show more than 30% parotid glands volume change. Ten out of 40 patients proposed for planning adaptation. All the statistical tests performed showed no correlation between CTV/parotid glands percentage volume changes. The aim to assist in clinical decision making on a fast and automatic way was achieved using the proposed methodology, thereby reducing workload in clinical practice.