Adaptive radiation therapy in head and neck cancer for clinical practice: state of the art and practical challenges

Treatment planning comparison for head and neck cancer between photon, proton, and combined proton-photon therapy - From a fixed beam line to an arc

BACKGROUND AND PURPOSE

This study investigates whether combined proton-photon therapy (CPPT) improves treatment plan quality compared to single-modality intensity-modulated radiation therapy (IMRT) or intensity-modulated proton therapy (IMPT) for head and neck cancer (HNC) patients. Different proton beam arrangements for CPPT and IMPT are compared, which could be of specific interest concerning potential future upright-positioned treatments. Furthermore, it is evaluated if CPPT benefits remain under inter-fractional anatomical changes for HNC treatments.

MATERIAL AND METHODS

Five HNC patients with a planning CT and multiple (4-7) repeated CTs were studied. CPPT with simultaneously optimized photon and proton fluence, single-modality IMPT, and IMRT treatment plans were optimized on the planning CT and then recalculated and reoptimized on each repeated CT. For CPPT and IMPT, plans with different degrees of freedom for the proton beams were optimized. Fixed horizontal proton beam line (FHB), gantry-like, and arc-like plans were compared.

RESULTS

The target coverage for CPPT without adaptation is insufficient (average V95%=88.4 %), while adapted plans can recover the initial treatment plan quality for target (average V95%=95.5 %) and organs-at-risk. CPPT with increased proton beam flexibility increases plan quality and reduces normal tissue complication probability of Xerostomia and Dysphagia. On average, Xerostomia NTCP reductions compared to IMRT are -2.7 %/-3.4 %/-5.0 % for CPPT FHB/CPPT Gantry/CPPT Arc. The differences for IMPT FHB/IMPT Gantry/IMPT Arc are + 0.8 %/-0.9 %/-4.3 %.

CONCLUSION

CPPT for HNC needs adaptive treatments. Increasing proton beam flexibility in CPPT, either by using a gantry or an upright-positioned patient, improves treatment plan quality. However, the photon component is substantially reduced, therefore, the balance between improved plan quality and costs must be further determined.

Intentional deep overfit learning for patient-specific dose predictions in adaptive radiotherapy

BACKGROUND

The framework of adaptive radiation therapy (ART) was crafted to address the underlying sources of intra-patient variation that were observed throughout numerous patients' radiation sessions. ART seeks to minimize the consequential dosimetric uncertainty resulting from this daily variation, commonly through treatment planning re-optimization. Re-optimization typically consists of manual evaluation and modification of previously utilized optimization criteria. Ideally, frequent treatment plan adaptation through re-optimization on each day's computed tomography (CT) scan may improve dosimetric accuracy and minimize dose delivered to organs at risk (OARs) as the planning target volume (PTV) changes throughout the course of treatment.

PURPOSE

Re-optimization in its current form is time-consuming and inefficient. In response to this ART bottleneck, we propose a deep learning based adaptive dose prediction model that utilizes a head and neck (H&N) patient's initial planning data to fine-tune a previously trained population model towards a patient-specific model. Our fine-tuned, patient-specific (FT-PS) model, which is trained using the intentional deep overfit learning (IDOL) method, may enable clinicians and treatment planners to rapidly evaluate relevant dosimetric changes daily and re-optimize accordingly.

METHODS

An adaptive population (AP) model was trained using adaptive data from 33 patients. Separately, 10 patients were selected for training FT-PS models. The previously trained AP model was utilized as the base model weights prior to re-initializing model training for each FT-PS model. Ten FT-PS models were separately trained by fine-tuning the previous model weights based on each respective patient's initial treatment plan. From these 10 patients, 26 ART treatment plans were withheld from training as the test dataset for retrospective evaluation of dose prediction performance between the AP and FT-PS models. Each AP and FT-PS dose prediction was compared against the ground truth dose distribution as originally generated during the patient's course of treatment. Mean absolute percent error (MAPE) evaluated the dose differences between a model's prediction and the ground truth.

RESULTS

MAPE was calculated within the 10% isodose volume region of interest for each of the AP and FT-PS models dose predictions and averaged across all test adaptive sessions, yielding 5.759% and 3.747% respectively. MAPE differences were compared between AP and FT-PS models across each test session in a test of statistical significance. The differences were statistically significant in a paired t-test with two-tailed p-value equal to3.851 × 10 - 9 $3.851 \times {10}^{ - 9}$ and 95% confidence interval (CI) equal to [-2.483, -1.542]. Furthermore, MAPE was calculated using each individually segmented structure as an ROI. Nineteen of 24 structures demonstrated statistically significant differences between the AP and FT-PS models.

CONCLUSION

We utilized the IDOL method to fine-tune a population-based dose prediction model into an adaptive, patient-specific model. The averaged MAPE across the test dataset was 5.759% for the population-based model versus 3.747% for the fine-tuned, patient-specific model, and the difference in MAPE between models was found to be statistically significant. Our work demonstrates the feasibility of patient-specific models in adaptive radiotherapy, and offers unique clinical benefit by utilizing initial planning data that contains the physician's treatment intent.

Segmentation by test-time optimization for CBCT-based adaptive radiation therapy

PURPOSE

Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) images, which often have severe artifacts and lack soft-tissue contrast, making direct segmentation very challenging. Propagating expert-drawn contours from the pretreatment planning CT through traditional or deep learning (DL)-based deformable image registration (DIR) can achieve improved results in many situations. Typical DL-based DIR models are population based, that is, trained with a dataset for a population of patients, and so they may be affected by the generalizability problem.

METHODS

In this paper, we propose a method called test-time optimization (TTO) to refine a pretrained DL-based DIR population model, first for each individual test patient, and then progressively for each fraction of online ART treatment. Our proposed method is less susceptible to the generalizability problem and thus can improve overall performance of different DL-based DIR models by improving model accuracy, especially for outliers. Our experiments used data from 239 patients with head-and-neck squamous cell carcinoma to test the proposed method. First, we trained a population model with 200 patients and then applied TTO to the remaining 39 test patients by refining the trained population model to obtain 39 individualized models. We compared each of the individualized models with the population model in terms of segmentation accuracy.

RESULTS

The average improvement of the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) of segmentation can be up to 0.04 (5%) and 0.98 mm (25%), respectively, with the individualized models compared to the population model over 17 selected OARs and a target of 39 patients. Although the average improvement may seem mild, we found that the improvement for outlier patients with structures of large anatomical changes is significant. The number of patients with at least 0.05 DSC improvement or 2 mm HD95 improvement by TTO averaged over the 17 selected structures for the state-of-the-art architecture VoxelMorph is 10 out of 39 test patients. By deriving the individualized model using TTO from the pretrained population model, TTO models can be ready in about 1 min. We also generated the adapted fractional models for each of the 39 test patients by progressively refining the individualized models using TTO to CBCT images acquired at later fractions of online ART treatment. When adapting the individualized model to a later fraction of the same patient, the model can be ready in less than a minute with slightly improved accuracy.

CONCLUSIONS

The proposed TTO method is well suited for online ART and can boost segmentation accuracy for DL-based DIR models, especially for outlier patients where the pretrained models fail.

Adaptive Volumetric-Modulated Arc Radiation Therapy for Head and Neck Cancer: Evaluation of Benefit on Target Coverage and Sparing of Organs at Risk

Background: Radiotherapy is essential in the management of head–neck cancer. During the course of radiotherapy, patients may develop significant anatomical changes. Re-planning with adaptive radiotherapy may ensure adequate dose coverage and sparing of organs at risk. We investigated the consequences of adaptive radiotherapy on head–neck cancer patients treated with volumetric-modulated arc radiation therapy compared to simulated non-adaptive plans: Materials and methods: We included in this retrospective dosimetric analysis 56 patients treated with adaptive radiotherapy. The primary aim of the study was to analyze the dosimetric differences with and without an adaptive approach for targets and organs at risk, particularly the spinal cord, parotid glands, oral cavity and larynx. The original plan (OPLAN) was compared to the adaptive plan (APLAN) and to a simulated non-adaptive dosimetric plan (DPLAN). Results: The non-adaptive DPLAN, when compared to OPLAN, showed an increased dose to all organs at risk. Spinal cord D2 increased from 27.91 (21.06–31.76) Gy to 31.39 (27.66–38.79) Gy (p = 0.00). V15, V30 and V45 of the DPLAN vs. the OPLAN increased by 20.6% (p = 0.00), 14.78% (p = 0.00) and 15.55% (p = 0.00) for right parotid; and 16.25% (p = 0.00), 18.7% (p = 0.00) and 20.19% (p = 0.00) for left parotid. A difference of 36.95% was observed in the oral cavity V40 (p = 0.00). Dose coverage was significantly reduced for both CTV (97.90% vs. 99.96%; p = 0.00) and PTV (94.70% vs. 98.72%; p = 0.00). The APLAN compared to the OPLAN had similar values for all organs at risk. Conclusions: The adaptive strategy with re-planning is able to avoid an increase in dose to organs at risk and better target coverage in head–neck cancer patients, with potential benefits in terms of side effects and disease control.

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.

A reinforcement learning approach for finding optimal policy of adaptive radiation therapy considering uncertain tumor biological response

Recent studies have shown that a tumor's biological response to radiation varies over time and has a dynamic nature. Dynamic biological features of tumor cells underscore the importance of using fractionation and adapting the treatment plan to tumor volume changes in radiation therapy treatment. Adaptive radiation therapy (ART) is an iterative process to adjust the dose of radiation in response to potential changes during the treatment. One of the key challenges in ART is how to determine the optimal timing of adaptations corresponding to tumor response to radiation. This paper aims to develop an automated treatment planning framework incorporating the biological uncertainties to find the optimal adaptation points to achieve a more effective treatment plan. First, a dynamic tumor-response model is proposed to predict weekly tumor volume regression during the period of radiation therapy treatment based on biological factors. Second, a Reinforcement Learning (RL) framework is developed to find the optimal adaptation points for ART considering the uncertainty in biological factors with the goal of achieving maximum final tumor control while minimizing or maintaining the toxicity level of the organs at risk (OARs) per the decision-maker's preference. Third, a beamlet intensity optimization model is solved using the predicted tumor volume at each adaptation point. The performance of the proposed RT treatment planning framework is tested using a clinical non-small cell lung cancer (NSCLC) case. The results are compared with the conventional fractionation schedule (i.e., equal dose fractionation) as a reference plan. The results show that the proposed approach performed well in achieving a robust optimal ART treatment plan under high uncertainty in the biological parameters. The ART plan outperformed the reference plan by increasing the mean biological effective dose (BED) value of the tumor by 2.01%, while maintaining the OAR BED within +0.5% and reducing the variability, in terms of the interquartile range (IQR) of tumor BED, by 25%.

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.

Synonymous point mutation of gtfB gene caused by therapeutic X-rays exposure reduced the biofilm formation and cariogenic abilities of Streptococcus mutans

BACKGROUND

The shift of oral microbiota is a critical factor of radiation caries in head and neck cancer patients after the radiotherapy. However, the direct effects of irradiation on the genome and virulence of cariogenic bacteria are poorly described. Here we investigated the genomic mutations and virulence change of Streptococcus mutans (S. mutans), the major cariogenic bacteria, exposed to the therapeutic doses of X-rays.

RESULTS

X-ray reduced the survival fraction of S. mutans and impacted its biofilm formation. We isolated a biofilm formation-deficient mutant #858 whose genome only possessed three synonymous mutations (c.2043 T > C, c.2100C > T, c.2109A > G) in gtfB gene. The "silent mutation" of c.2043 T > C in gtfB gene can cause the down-regulation of all of the gtfs genes' expression and decrease the GtfB enzyme secretion without the effect on the growth due to the codon bias. #858 and synonymous point mutation strain gtfB ^{2043 T>C}, similar to the gtfB gene null mutant Δ gtfB, can significantly decrease the extracellular polysaccharide production, biofilm formation and cariogenic capabilities both in vitro and in vivo compared with wild type.

CONCLUSION

The direct exposure of X-ray radiation can affect the genome and virulence of oral bacteria even at therapeutic doses. The synonymous mutations of genome are negligent factors for gene expression and related protein translation due to the codon usage frequency.

Comparison of CBCT conversion methods for dose calculation in the head and neck region

The purpose of this study was to compare different methods of CBCT conversion respect to dose calculation accuracy. Twelve head and neck cancer patients treated with VMAT using simultaneous integrated boost technique were selected for the study. For each patient a planning CT (pCT), a control. CT acquired in the fourth week of treatment and a CBCT scan acquired on the closest day with the control CT were used. In order to re-calculate dose directly on CBCT image sets, a population based approach (CBCTPop) and a Histogram Matching (HM) approach based on rigid (CBCTHM-R) and deformable registration (CBCTHM-D) were used. Additionally, virtual CTs (vCTs) were generated using two deformable image registration algorithms (CTELX and CTANC) of the planning CT to the CBCT by using two different deformable image registration (DIR) algorithms. The corresponding control CTs were selected as ground truth and dose distributions on CBCT were analyzed using 3D global gamma index analysis applying a threshold of 10% with respect to the prescribed dose. Using the 2%/2mm gamma criterion, the results were 89.9%(±8.3%), 94.1%(±5.0%), 94.3%(±5.7%), 96.1%(±3.9%), 93.4%(±6.3%) for the CBCTPop, CBCTHM-R, CBCTHM-D, CTELX and CTANC, respectively. On average, the HM and DIR techniques showed a higher accuracy compared to the population based approach, but Kruskal-Wallis test did not show significant difference among the investigated dose calculation techniques assuming p<0.05. More sophisticated CBCT dose calculation methods seem to improve the dose calculation accuracy, but statistical significance remains to be demonstrated.

Evaluation of Target Volume Location and Its Impact on Delivered Dose Using Cone-Beam Computed Tomography Scans for Patients with Head and Neck Cancer

INTRODUCTION

Within radiation oncology, treatment of head and neck cancer is known for its unique challenges with patient weight loss and body contour changes. This study sought to quantify these changes through measuring the volume and position of specific target structures over the course of radiation treatment and determining if changes in these factors affected what dose was ultimately delivered.

METHODS

This study utilized weekly cone-beam computed tomography (CBCT) images taken immediately before radiation treatments to measure the difference between the expected location and the actual location of clinical target volumes. Minimum and mean doses to planned target volumes (PTVs) were then calculated on the CBCT scans and compared with the expected planned dose.

RESULTS

In the twenty patients included in this single-institutional study, a significant average difference of 2.47% (P < .0001) and 5.06% (P < .0001) was found in the locations of the high-risk and low-risk clinical target volumes, respectively. Software limitations reduced the sample size that could be used to compare delivered and planned dose to nine patients, but of that number, a significant decrease of 10% was found in the minimum dose delivered to both the high-risk (P = .0401) and low-risk (P = .0123) PTVs. Mean doses to the PTVs did not differ significantly and no correlation was found between any volumetric and dosimetric deviations.

CONCLUSION

The results of this study support the presence of volume matching inaccuracies for patients with head and neck cancer with simultaneous altered minimum doses to PTVs. Based on these findings, it is suggested that institutions may benefit from a standardized treatment imaging protocol that would include a minimum of weekly full-trajectory CBCT scans to assess target volume location, particularly those of the inferior nodal volumes.

AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases

Contouring (segmentation) of Organs at Risk (OARs) in medical images is required for accurate radiation therapy (RT) planning. In current clinical practice, OAR contouring is performed with low levels of automation. Although several approaches have been proposed in the literature for improving automation, it is difficult to gain an understanding of how well these methods would perform in a realistic clinical setting. This is chiefly due to three key factors - small number of patient studies used for evaluation, lack of performance evaluation as a function of input image quality, and lack of precise anatomic definitions of OARs. In this paper, extending our previous body-wide Automatic Anatomy Recognition (AAR) framework to RT planning of OARs in the head and neck (H&N) and thoracic body regions, we present a methodology called AAR-RT to overcome some of these hurdles. AAR-RT follows AAR's 3-stage paradigm of model-building, object-recognition, and object-delineation. Model-building: Three key advances were made over AAR. (i) AAR-RT (like AAR) starts off with a computationally precise definition of the two body regions and all of their OARs. Ground truth delineations of OARs are then generated following these definitions strictly. We retrospectively gathered patient data sets and the associated contour data sets that have been created previously in routine clinical RT planning from our Radiation Oncology department and mended the contours to conform to these definitions. We then derived an Object Quality Score (OQS) for each OAR sample and an Image Quality Score (IQS) for each study, both on a 1-to-10 scale, based on quality grades assigned to each OAR sample following 9 key quality criteria. Only studies with high IQS and high OQS for all of their OARs were selected for model building. IQS and OQS were employed for evaluating AAR-RT's performance as a function of image/object quality. (ii) In place of the previous hand-crafted hierarchy for organizing OARs in AAR, we devised a method to find an optimal hierarchy for each body region. Optimality was based on minimizing object recognition error. (iii) In addition to the parent-to-child relationship encoded in the hierarchy in previous AAR, we developed a directed probability graph technique to further improve recognition accuracy by learning and encoding in the model "steady" relationships that may exist among OAR boundaries in the three orthogonal planes. Object-recognition: The two key improvements over the previous approach are (i) use of the optimal hierarchy for actual recognition of OARs in a given image, and (ii) refined recognition by making use of the trained probability graph. Object-delineation: We use a kNN classifier confined to the fuzzy object mask localized by the recognition step and then fit optimally the fuzzy mask to the kNN-derived voxel cluster to bring back shape constraint on the object. We evaluated AAR-RT on 205 thoracic and 298 H&N (total 503) studies, involving both planning and re-planning scans and a total of 21 organs (9 - thorax, 12 - H&N). The studies were gathered from two patient age groups for each gender - 40-59 years and 60-79 years. The number of 3D OAR samples analyzed from the two body regions was 4301. IQS and OQS tended to cluster at the two ends of the score scale. Accordingly, we considered two quality groups for each gender - good and poor. Good quality data sets typically had OQS ≥ 6 and had distortions, artifacts, pathology etc. in not more than 3 slices through the object. The number of model-worthy data sets used for training were 38 for thorax and 36 for H&N, and the remaining 479 studies were used for testing AAR-RT. Accordingly, we created 4 anatomy models, one each for: Thorax male (20 model-worthy data sets), Thorax female (18 model-worthy data sets), H&N male (20 model-worthy data sets), and H&N female (16 model-worthy data sets). On "good" cases, AAR-RT's recognition accuracy was within 2 voxels and delineation boundary distance was within ∼1 voxel. This was similar to the variability observed between two dosimetrists in manually contouring 5-6 OARs in each of 169 studies. On "poor" cases, AAR-RT's errors hovered around 5 voxels for recognition and 2 voxels for boundary distance. The performance was similar on planning and replanning cases, and there was no gender difference in performance. AAR-RT's recognition operation is much more robust than delineation. Understanding object and image quality and how they influence performance is crucial for devising effective object recognition and delineation algorithms. OQS seems to be more important than IQS in determining accuracy. Streak artifacts arising from dental implants and fillings and beam hardening from bone pose the greatest challenge to auto-contouring methods.

Routine Adaptive Replanning of p16-Positive Stage N2b Oropharyngeal Cancer: Quality Improvement or Waste of Time?

PURPOSE/OBJECTIVE(S)

To determine if routinely replanning patients treated for oropharyngeal cancer that is p16-positive and clinical neck stage N2b (AJCC 7th edition) is likely to result in dose changes that will improve patient outcomes to a meaningful degree.

METHODS

In 10 consecutive patients treated with primary radiotherapy (RT) and concurrent weekly chemotherapy for p16-positive N2b oropharyngeal carcinoma, we prospectively evaluated dose changes from replanning for the final 4 or 2 weeks of RT of a 7-week RT program.

RESULTS

Replanning for the final 4 or 2 weeks improved planning target volume coverage by an average of 4 and 2 percentage points, respectively. For all normal structures, the dose change was small (<1 Gy) with replanning.

CONCLUSIONS

In patients with p16-positive N2b oropharynx cancer, the value of replanning RT is a small improvement in target coverage with minimal improvement in normal tissue sparing. In response to our study, some of the physicians in our group replan most node-positive oropharyngeal cancer cases while others think routine replanning is not valuable.

Analysis of geometric variation of neck node levels during image-guided radiotherapy for nasopharyngeal carcinoma: recommended planning margins

Background

To quantify the geometrical changes of each neck nodal level (NNL) and estimate the geometric planning target volume (PTV) margin during image-guided radiotherapy (IGRT) for nasopharyngeal cancer (NPC).

Methods

Twenty patients with locally advanced NPC underwent one planning computed tomography (CT_plan) and 6 weekly repeat CT (CT_rep) scans during chemoradiotherapy. Each CT_rep was rigidly registered to the CT_plan. All the NNLs were manually delineated in each transverse CT section. When comparing the NNL in CT_rep with CT_plan, their volumes, displacement of the center of the mass, and the shortest perpendicular distance (SPD) were automatically calculated. This was followed by calculation of the systematic and random errors, overlapping index (OI), and dice similarity coefficient (DSC). With PTVs isotropically expanded from NNL by 1, 2, 3, 4, and 5 mm, they were compared with NNL itself; OI >0.95 was defined as the acceptable geometrical coverage. The Mann-Whitney test was used for statistical analysis.

Results

All volumes, OI, and DSC of the NNLs (not including level IA) showed a linear decrease over time throughout the treatment course. The volume of NNLs decreased by 1-6% in the first week and 10-21% in the sixth week. The mean SPD was 1.3-1.7 and 1.9-3.5 mm in the first and sixth week respectively. The DSCs for nodal level IB, II, III, and IV were >0.7 and that of level V was <0.7 throughout the treatment course. For level IA and VI, DSC was <0.7 after the 2nd week. To maintain the OI >0.95, 2-5 mm was needed to expand the different NNLs.

Conclusions

The geometrical changes of each NNL are substantial and the necessary margin of 2-5 mm depended on individual NNL is needed to maintain geometrical coverage throughout the course of IGRT for NPC.

Head and neck cancer: improving outcomes with a multidisciplinary approach

For early-stage head and neck cancer (HNC), surgery (S) or radiotherapy (RT) is a standard treatment. The multidisciplinary approach, which includes multimodality treatment with S followed by RT, with or without chemotherapy (CT) or concurrent chemoradiotherapy (CRT), is required for locally advanced head and neck cancer (LAHNC). CRT improves prognosis, locoregional control (LRC), and organ function in LAHNC, compared to RT alone. Prognosis in recurrent/metastatic HNC (R/M HNC) is dismal. Platinum-based CT, combined with the anti-Epidermal Growth Factor Receptor (EGFR) antibody (Ab) cetuximab, is used in first-line setting, while no further validated options are available at progression. The complexity of disease is, in part, due to the heterogeneity of organs and functions involved and the need for a multimodality approach. In addition, the patient population (often elderly and/or patients with smoking and alcohol habits) argues for an individually tailored treatment plan. Furthermore, treatment goals - which include cure, organ, and function preservation, quality of life and palliation - must also be considered. Thus, optimal management of patients with HNC should involve a range of healthcare professionals with relevant expertise. The purpose of the present review is to 1) highlight the importance and necessity of the multidisciplinary approach in the treatment of HNC; 2) update the knowledge regarding modern surgical techniques, new medical and RT treatment approaches, and their combination; 3) identify the treatment scenario for LAHNC and R/M HNC; and 4) discuss the current role of immunotherapy in HNC.