Anatomical change during radiotherapy for head and neck cancer, and its effect on delivered dose to the spinal cord

Quantification of deformable image registration uncertainties for dose accumulation on head and neck cancer proton treatments

PURPOSE

Head and neck cancer (HNC) patients in radiotherapy require adaptive treatment plans due to anatomical changes. Deformable image registration (DIR) is used in adaptive radiotherapy, e.g. for deformable dose accumulation (DDA). However, DIR's ill-posedness necessitates addressing uncertainties, often overlooked in clinical implementations. DIR's further clinical implementation is hindered by missing quantitative commissioning and quality assurance tools. This study evaluates one pathway for more quantitative DDA uncertainties.

METHODS

For five HNC patients, each with multiple repeated CTs acquired during treatment, a simultaneous-integrated boost (SIB) plan was optimized. Recalculated doses were warped individually using multiple DIRs from repeated to reference CTs, and voxel-by-voxel dose ranges determined an error-bar for DDA. Followed by evaluating, a previously proposed early-stage DDA uncertainty estimation method tested for lung cancer, which combines geometric DIR uncertainties, dose gradients and their directional dependence, in the context of HNC.

RESULTS

Applying multiple DIRs show dose differences, pronounced in high dose gradient regions. The patient with largest anatomical changes (-13.1 % in ROI body volume), exhibited 33 % maximum uncertainty in contralateral parotid, with 54 % of voxels presenting an uncertainty >5 %. Accumulation over multiple CTs partially mitigated uncertainties. The estimation approach predicted 92.6 % of voxels within ±5 % to the reference dose uncertainty across all patients.

CONCLUSIONS

DIR variations impact accumulated doses, emphasizing DDA uncertainty quantification's importance for HNC patients. Multiple DIR dose warping aids in quantifying DDA uncertainties. An estimation approach previously described for lung cancer was successfully validated for HNC, for SIB plans, presenting different dose gradients, and for accumulated treatments.

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.

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.

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.

Evaluation of a novel CBCT conversion method implemented in a treatment planning system

BACKGROUND

To evaluate a novel CBCT conversion algorithm for dose calculation implemented in a research version of a treatment planning system (TPS).

METHODS

The algorithm was implemented in a research version of RayStation (v. 11B-DTK, RaySearch, Stockholm, Sweden). CBCTs acquired for each ten head and neck (HN), gynecology (GYN) and lung cancer (LNG) patients were collected and converted using the new algorithm (CBCTc). A bulk density overriding technique implemented in the same version of the TPS was used for comparison (CBCTb). A deformed CT (dCT) was created by using deformable image registration of the planning CT (pCT) to the CBCT to reduce anatomical changes. All treatment plans were recalculated on the pCT, dCT, CBCTc and the CBCTb. The resulting dose distributions were analyzed using the MICE toolkit (NONPIMedical AB Sweden, Umeå) with local gamma analysis, with 1% dose difference and 1 mm distance to agreement criteria. A Wilcoxon paired rank sum test was applied to test the differences in gamma pass rates (GPRs). A p value smaller than 0.05 considered statistically significant.

RESULTS

The GPRs for the CBCTb method were systematically lower compared to the CBCTc method. Using the 10% dose threshold and the dCT as reference the median GPRs were for the CBCTc method were 100% and 99.8% for the HN and GYN cases, respectively. Compared to that the GPRs of the CBCTb method were lower with values of 99.8% and 98.0%, for the HN and GYN cases, respectively. The GPRs of the LNG cases were 99.9% and 97.5% for the CBCTc and CBCTb method, respectively. These differences were statistically significant. The main differences between the dose calculated on the CBCTs and the pCTs were found in regions near air/tissue interfaces, which are also subject to anatomical variations.

CONCLUSION

The dose distribution calculated using the new CBCTc method showed excellent agreement with the dose calculated using dCT and pCT and was superior to the CBCTb method. The main reasons for deviations of the calculated dose distribution were caused by anatomical variations between the pCT and the corrected CBCT.

Development of a prediction model for head and neck volume reduction by clinical factors, dose-volume histogram parameters and radiomics in head and neck cancer†

In external radiotherapy of head and neck (HN) cancers, the reduction of irradiation accuracy due to HN volume reduction often causes a problem. Adaptive radiotherapy (ART) can effectively solve this problem; however, its application to all cases is impractical because of cost and time. Therefore, finding priority cases is essential. This study aimed to predict patients with HN cancers are more likely to need ART based on a quantitative measure of large HN volume reduction and evaluate model accuracy. The study included 172 cases of patients with HN cancer who received external irradiation. The HN volume was calculated using cone-beam computed tomography (CT) for irradiation-guided radiotherapy for all treatment fractions and classified into two groups: cases with a large reduction in the HN volume and cases without a large reduction. Radiomic features were extracted from the primary gross tumor volume (GTV) and nodal GTV of the planning CT. To develop the prediction model, four feature selection methods and two machine-learning algorithms were tested. Predictive performance was evaluated by the area under the curve (AUC), accuracy, sensitivity and specificity. Predictive performance was the highest for the random forest, with an AUC of 0.662. Furthermore, its accuracy, sensitivity and specificity were 0.692, 0.700 and 0.813, respectively. Selected features included radiomic features of the primary GTV, human papillomavirus in oropharyngeal cancer and the implementation of chemotherapy; thus, these features might be related to HN volume change. Our model suggested the potential to predict ART requirements based on HN volume reduction .

Assessment of dosimetric impact of interfractional 6D setup error in tongue cancer treated with IMRT and VMAT using daily kV-CBCT

Background

This study aimed to evaluate the dosimetric influence of 6-dimensional (6D) interfractional setup error in tongue cancer treated with intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) using daily kilovoltage cone-beam computed tomography (kV-CBCT).

Materials and methods

This retrospective study included 20 tongue cancer patients treated with IMRT (10), VMAT (10), and daily kV-CBCT image guidance. Interfraction 6D setup errors along the lateral, longitudinal, vertical, pitch, roll, and yaw axes were evaluated for 600 CBCTs. Structures in the planning CT were deformed to the CBCT using deformable registration. For each fraction, a reference CBCT structure set with no rotation error was created. The treatment plan was recalculated on the CBCTs with the rotation error (R_Error), translation error (T_Error), and translation plus rotation error (T+R_Error). For targets and organs at risk (OARs), the dosimetric impacts of R_Error, T_Error, and T+R_Error were evaluated without and with moderate correction of setup errors.

Results

The maximum dose variation ΔD (%) for D_98% in clinical target volumes (CTV): CTV-60, CTV-54, planning target volumes (PTV): PTV-60, and PTV-54 was -1.2%, -1.9%, -12.0%, and -12.3%, respectively, in the T+R_Error without setup error correction. The maximum ΔD (%) for D_98% in CTV-60, CTV-54, PTV-60, and PTV-54 was -1.0%, -1.7%, -9.2%, and -9.5%, respectively, in the T+R_Error with moderate setup error correction. The dosimetric impact of interfractional 6D setup errors was statistically significant (p < 0.05) for D_98% in CTV-60, CTV-54, PTV-60, and PTV-54.

Conclusions

The uncorrected interfractional 6D setup errors could significantly impact the delivered dose to targets and OARs in tongue cancer. That emphasized the importance of daily 6D setup error correction in IMRT and VMAT.

Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view

Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment. These variations may result in underdosage to the target volume or overdosage to the organ at risk. Replanning during the treatment course can be triggered to overcome this issue. Due to technological, methodological and clinical evolutions, tools for adaptive RT (ART) are becoming increasingly sophisticated. The aim of this paper is to give an overview of the key steps of an H&N ART workflow and tools from the point of view of a group of French-speaking medical physicists and physicians (from GORTEC). Focuses are made on image registration, segmentation, estimation of the delivered dose of the day, workflow and quality assurance for an implementation of H&N offline and online ART. Practical recommendations are given to assist physicians and medical physicists in a clinical workflow.

The use of in-vivo dosimetry to identify head and neck cancer patients needing adaptive radiotherapy

BACKGROUND AND PURPOSE

Head and neck cancer (HNC) patients experiencing anatomical changes during their radiotherapy (RT) course may benefit from adaptive RT (ART). We investigated the sensitivity of an electronic portal imaging device (EPID)-based in-vivo dosimetry (EIVD) systemto detect patients that require ART and identified its limitations.

MATERIALS AND METHODS

A retrospective study was conducted for 182 HNC patients: laryngeal cancer without elective lymph nodes (group A), postoperative RT (group B) and primary RT including elective lymph nodes (group C). The effect of anatomical changes on the dose distribution and volumetric changes was quantified. The receiver operating characteristic curve was used to obtain the optimal cut-off value for the gamma passing rate (%GP) with a dose difference of 3% and a distance to agreement of 3mm.

RESULTS

Fifty HNC patients receiving ART were analyzed: 1 in group A, 10 in group B and 39 in group C. Failed fractions (FFs) occurred in 1/1, 6/10 and 23/39 cases before ART in group A, B and C respectively. In the four cases in group B without FFs, only minor dosimetric changes were observed. One of the cases in group C without FFs had significant dosimetric changes (false negative). Three cases received ART because of clinical reasons that cannot be detected by EIVD. The optimal cut-off value for the %GP was 95%/95.2% for old/new generation machines respectively.

CONCLUSION

EIVD in combined with 3D imaging techniques can be synergistic in the detection of anatomical changes in HNC patients who benefit from ART.

Evaluation of dosimetric and volumetric changes in target volumes and organs at risk during adaptive radiotherapy in head and neck cancer: A prospective study

BACKGROUND

During radiation therapy for head and neck malignancies, most patients experience significant anatomical alterations due to loss of weight, changes in tumor volumes, and immobilization issues. Adaptive radiotherapy adapts to the patient's actual anatomy through repetitive imaging and replanning. In the present study, dosimetric and volumetric changes in target volumes and organs at risk during adaptive radiotherapy in head and neck cancer was evaluated.

MATERIAL AND METHODS

Thirty-four locally advanced Head and neck carcinoma patients with histologically proven Squamous Cell Carcinoma for curative treatment were included. Rescan was done at the end of 20 fractions of treatment. All quantitative data were analyzed with paired t-Test and Wilcoxon Signed Rank (Z) test.

RESULTS

Most patients had oropharyngeal carcinoma (52.9%). There were significant volumetric changes in all the parameters - GTV-primary (10.95, p < 0.001), GTV- nodal (5.81, p = 0.001), PTV High Risk (26.1, p < 0.001), PTV - Intermediate Risk (46.9, p = 0.006), PTV - Low Risk (43.9, p = 0.003), lateral neck diameter (0.9, p < 0.001), right parotid volumes (6.36, p < 0.001) and left parotid volumes (4.93, p < 0.001). Dosimetric changes in the organs at risk were non-significant.

CONCLUSION

Adaptive replanning has been seen to be labour intensive. However, the changes in the volumes of both target and the OARs credit a mid-treatment replanning to be done. Long term follow-up is required to assess locoregional control after adaptive radiotherapy in head and neck cancer.

Assessing the generalisability of radiomics features previously identified as predictive of radiation-induced sticky saliva and xerostomia

Background and purpose

While core to the scientific approach, reproducibility of experimental results is challenging in radiomics studies. A recent publication identified radiomics features that are predictive of late irradiation-induced toxicity in head and neck cancer (HNC) patients. In this study, we assessed the generalisability of these findings.

Materials and Methods

The procedure described in the publication in question was applied to a cohort of 109 HNC patients treated with 50-70 Gy in 20-35 fractions using helical radiotherapy although there were inherent differences between the two patient populations and methodologies. On each slice of the planning CT with delineated parotid and submandibular glands, the imaging features that were previously identified as predictive of moderate-to-severe xerostomia and sticky saliva 12 months post radiotherapy (Xer12m and SS12m) were calculated. Specifically, Short Run Emphasis (SRE) and maximum CT intensity (maxHU) were evaluated for improvement in prediction of Xer12m and SS12m respectively, compared to models solely using baseline toxicity and mean dose to the salivary glands.

Results

None of the associations previously identified as statistically significant and involving radiomics features in univariate or multivariate models could be reproduced on our cohort.

Conclusion

The discrepancies observed between the results of the two studies delineate limits to the generalisability of the previously reported findings. This may be explained by the differences in the approaches, in particular the imaging characteristics and subsequent methodological implementation. This highlights the importance of external validation, high quality reporting guidelines and standardisation protocols to ensure generalisability, replication and ultimately clinical implementation.

Predicting radiotherapy-induced xerostomia in head and neck cancer patients using day-to-day kinetics of radiomics features

Background and purpose

The images acquired during radiotherapy for image-guidance purposes could be used to monitor patient-specific response to irradiation and improve treatment personalisation. We investigated whether the kinetics of radiomics features from daily mega-voltage CT image-guidance scans (MVCT) improve prediction of moderate-to-severe xerostomia compared to dose/volume parameters in radiotherapy of head-and-neck cancer (HNC).

Materials and Methods

All included HNC patients (N = 117) received 30 or more fractions of radiotherapy with daily MVCTs. Radiomics features were calculated on the contra-lateral parotid glands of daily MVCTs. Their variations over time after each complete week of treatment were used to predict moderate-to-severe xerostomia (CTCAEv4.03 grade ≥ 2) at 6, 12 and 24 months post-radiotherapy. After dimensionality reduction, backward/forward selection was used to generate combinations of predictors.Three types of logistic regression model were generated for each follow-up time: 1) a pre-treatment reference model using dose/volume parameters, 2) a combination of dose/volume and radiomics-based predictors, and 3) radiomics-based predictors. The models were internally validated by cross-validation and bootstrapping and their performance evaluated using Area Under the Curve (AUC) on separate training and testing sets.

Results

Moderate-to-severe xerostomia was reported by 46 %, 33 % and 26 % of the patients at 6, 12 and 24 months respectively. The selected models using radiomics-based features extracted at or before mid-treatment outperformed the dose-based models with an AUCtrain/AUCtest of 0.70/0.69, 0.76/0.74, 0.86/0.86 at 6, 12 and 24 months, respectively.

Conclusion

Our results suggest that radiomics features calculated on MVCTs from the first half of the radiotherapy course improve prediction of moderate-to-severe xerostomia in HNC patients compared to a dose-based pre-treatment model.

Deep learning for dose assessment in radiotherapy by the super-localization of vaporized nanodroplets in high frame rate ultrasound imaging

Objective. External beam radiotherapy is aimed to precisely deliver a high radiation dose to malignancies, while optimally sparing surrounding healthy tissues. With the advent of increasingly complex treatment plans, the delivery should preferably be verified by quality assurance methods. Recently, online ultrasound imaging of vaporized radiosensitive nanodroplets was proposed as a promising tool for in vivo dosimetry in radiotherapy. Previously, the detection of sparse vaporization events was achieved by applying differential ultrasound (US) imaging followed by intensity thresholding using subjective parameter tuning, which is sensitive to image artifacts. Approach. A generalized deep learning solution (i.e. BubbleNet) is proposed to localize vaporized nanodroplets on differential US frames, while overcoming the aforementioned limitation. A 5-fold cross-validation was performed on a diversely composed 5747-frame training/validation dataset by manual segmentation. BubbleNet was then applied on a test dataset of 1536 differential US frames to evaluate dosimetric features. The intra-observer variability was determined by scoring the Dice similarity coefficient (DSC) on 150 frames segmented twice. Additionally, the BubbleNet generalization capability was tested on an external test dataset of 432 frames acquired by a phased array transducer at a much lower ultrasound frequency and reconstructed with unconventional pixel dimensions with respect to the training dataset. Main results. The median DSC in the 5-fold cross validation was equal to ∼0.88, which was in line with the intra-observer variability (=0.86). Next, BubbleNet was employed to detect vaporizations in differential US frames obtained during the irradiation of phantoms with a 154 MeV proton beam or a 6 MV photon beam. BubbleNet improved the bubble-count statistics by ∼30% compared to the earlier established intensity-weighted thresholding. The proton range was verified with a −0.8 mm accuracy. Significance. BubbleNet is a flexible tool to localize individual vaporized nanodroplets on experimentally acquired US images, which improves the sensitivity compared to former thresholding-weighted methods.

Quantifying the dosimetric effects of neck contour changes and setup errors on the spinal cord in patients with nasopharyngeal carcinoma: establishing a rapid estimation method

The purpose of this study was to quantify the effect of neck contour changes and setup errors on spinal cord (SC) doses during the treatment of nasopharyngeal carcinoma (NPC) and to establish a rapid dose estimation method. The setup errors and contour changes in 60 cone-beam computed tomography (CBCT) images of 10 NPC patients were analysed in different regions of the neck (C1-C3, C4-C5 and C6-C7). The actual delivered dose to the SC was calculated using the CBCT images, and univariate simulations were performed using the planning CT to evaluate the dose effects of each factor, and an index ${\mathrm{Dmax}}_{\mathrm{displaced}}$ was introduced to estimate the SC dose. Compared with the planned dose, the mean (maximum) Dmax increases in the C1-C3, C4-C5 and C6-C7 regions of the SC were 2.1% (12.3%), 1.8% (8.2%) and 2.5% (9.2%), respectively. The simulation results showed that the effects of setup error in the C1-C3, C4-C5 and C6-C7 regions were 1.5% (9.7%), 0.9% (8.2%) and 1.3% (6.3%), respectively, and the effects of contour change were 0.4% (1.7%), 0.7% (2.5%) and 1.5% (4.9%), respectively. The linear regression model can be used to estimate the dose effect of contour changes (R2 > 0.975) and setup errors (R2 = 0.989). Setup errors may lead to a significant increase in the SC dose in some patients. This study established a rapid dose estimation method, which is of great significance for the daily dose evaluation and the adaptive re-planning trigger of the SC.

Multi-Organ Omics-Based Prediction for Adaptive Radiation Therapy Eligibility in Nasopharyngeal Carcinoma Patients Undergoing Concurrent Chemoradiotherapy

Purpose

To investigate the role of different multi-organ omics-based prediction models for pre-treatment prediction of Adaptive Radiotherapy (ART) eligibility in patients with nasopharyngeal carcinoma (NPC).

Methods and Materials

Pre-treatment contrast-enhanced computed tomographic and magnetic resonance images, radiotherapy dose and contour data of 135 NPC patients treated at Hong Kong Queen Elizabeth Hospital were retrospectively analyzed for extraction of multi-omics features, namely Radiomics (R), Morphology (M), Dosiomics (D), and Contouromics (C), from a total of eight organ structures. During model development, patient cohort was divided into a training set and a hold-out test set in a ratio of 7 to 3 via 20 iterations. Four single-omics models (R, M, D, C) and four multi-omics models (RD, RC, RM, RMDC) were developed on the training data using Ridge and Multi-Kernel Learning (MKL) algorithm, respectively, under 10-fold cross validation, and evaluated on hold-out test data using average area under the receiver-operator-characteristics curve (AUC). The best-performing single-omics model was first determined by comparing the AUC distribution across the 20 iterations among the four single-omics models using two-sided student t-test, which was then retrained using MKL algorithm for a fair comparison with the four multi-omics models.

Results

The R model significantly outperformed all other three single-omics models (all p-value<0.0001), achieving an average AUC of 0.942 (95%CI: 0.938-0.946) and 0.918 (95%CI: 0.903-0.933) in training and hold-out test set, respectively. When trained with MKL, the R model (R_MKL) yielded an increased AUC of 0.984 (95%CI: 0.981-0.988) and 0.927 (95%CI: 0.905-0.948) in training and hold-out test set respectively, while demonstrating no significant difference as compared to all studied multi-omics models in the hold-out test sets. Intriguingly, Radiomic features accounted for the majority of the final selected features, ranging from 64% to 94%, in all the studied multi-omics models.

Conclusions

Among all the studied models, the Radiomic model was found to play a dominant role for ART eligibility in NPC patients, and Radiomic features accounted for the largest proportion of features in all the multi-omics models.

A novel semi auto-segmentation method for accurate dose and NTCP evaluation in adaptive head and neck radiotherapy

BACKGROUND AND PURPOSE

Accurate segmentation of organs-at-risk (OARs) is crucial but tedious and time-consuming in adaptive radiotherapy (ART). The purpose of this work was to automate head and neck OAR-segmentation on repeat CT (rCT) by an optimal combination of human and auto-segmentation for accurate prediction of Normal Tissue Complication Probability (NTCP).

MATERIALS AND METHODS

Human segmentation (HS) of 3 observers, deformable image registration (DIR) based contour propagation and deep learning contouring (DLC) were carried out to segment 15 OARs on 15 rCTs. The original treatment plan was re-calculated on rCT to obtain mean dose (D_mean) and consequent NTCP-predictions. The average D_mean and NTCP-predictions of the three observers were referred to as the gold standard to calculate the absolute difference of D_mean and NTCP-predictions (|ΔD_mean| and |ΔNTCP|).

RESULTS

The average |ΔD_mean| of parotid glands in HS was 1.40 Gy, lower than that obtained with DIR and DLC (3.64 Gy, p < 0.001 and 3.72 Gy, p < 0.001, respectively). DLC showed the highest |ΔD_mean| in middle Pharyngeal Constrictor Muscle (PCM) (5.13 Gy, p = 0.01). DIR showed second highest |ΔD_mean| in the cricopharyngeal inlet (2.85 Gy, p = 0.01). The semi auto-segmentation (SAS) adopted HS, DIR and DLC for segmentation of parotid glands, PCM and all other OARs, respectively. The 90th percentile |ΔNTCP|was 2.19%, 2.24%, 1.10% and 1.50% for DIR, DLC, HS and SAS respectively.

CONCLUSIONS

Human segmentation of the parotid glands remains necessary for accurate interpretation of mean dose and NTCP during ART. Proposed semi auto-segmentation allows NTCP-predictions within 1.5% accuracy for 90% of the cases.

Adaptive radiation therapy: When, how and what are the benefits that literature provides?

PURPOSE

To identify from the current literature when is the right time to replan and to assign thresholds for the optimum process of replanning. Nowadays, adaptive radiotherapy (ART) for head and neck cancer plays an exceptional role consisting of an evaluation procedure of the prominent anatomical and dosimetric variations. By performing complex radiotherapy methods, the credibility of the therapeutic result is crucial. Image guided radiotherapy (IGRT) was developed to ensure locoregional control and thus changes that might occur during radiotherapy be dealt with.

MATERIALS AND METHODS

An electronic research of articles published in PubMed/MEDLINE and Science Direct databases from January 2004 to October 2020 was performed. Among a total of 127 studies assessed for eligibility, 85 articles were ultimately retained for the review.

RESULTS

The most noticeable changes have been reported in the middle fraction of the treatment. Therefore, the suggested optimal time to replan is between the third and the fourth week. Anatomical deviations>1cm in the external contour, average weight loss>10%, violation in the dose coverage of the targets>5%, and violation in the dose of the peripherals were some of the thresholds that are currently used, and which lead to replanning.

CONCLUSION

ART may decrease toxicity and improve local-control. Whether it is beneficial or not, depends ultimately on each patient. However, more investigation of the changes should be performed in future prospective studies to obtain more accurate results.

Alert system for monitoring changes in patient anatomy during radiation therapy of head and neck cancer

The purpose of this study is to validate a previously developed algorithm for alerting clinicians when to consider re-CT simulation due to changes in the patient's anatomy during radiation therapy of head and neck cancer. Cone beam computed tomography (CBCT) data were collected prospectively for 77 patients. Each CBCT was mathematically compared to a reference CBCT using the gamma index. We defined the match quality parameter (MQP) as an indicator of CBCT image similarity, where a negative MQP value indicates a poorer CBCT match than the match between the first two CBCT acquired during treatment. If three consecutive MQP values were below a chosen threshold, an "alert" is triggered to indicate action required, for example, possible re-CT simulation. The timing of image review requests made by the radiation therapists and any re-CT/re-plan decisions were documented for each patient's treatment course. The MQP for each patient (including any re-plans) was calculated in a manner that was blinded from the clinical process. The MQP as a function of fraction number was compared to actual clinical decisions in the treatment progress to evaluate alert system performance. There was a total of 93 plans (including re-plans) with 34 positives (action required) and 59 negatives (no action required). The sensitivity of the alert system was 0.76 and the false positive rate was 0.37. Only 1 case out of the 34 positive cases would have been missed by both the alert system and our clinical process. Despite the false negatives and false positives, analysis of the timing of alert triggers showed that the alert system could have resulted in seven fewer clinical misses. The alert system has the potential to be a valuable tool to complement human judgment and to provide a quality assurance safeguard to help improve the delivery of radiation treatment of head and neck cancer.

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

Purpose and Objective

A proportion of patients receiving radiotherapy for head and neck squamous cell carcinoma (HNSCC) require ad hoc treatment re-planning. The aim of this retrospective study is to analyze the patients who required ad hoc re-planning and to identify factors, which may predict need for re-planning.

Materials and Methods

A single center evaluation of all patients receiving radical or adjuvant (chemo)radiotherapy (CRT) for HNSCC between January and December 2016 was undertaken. Patients who underwent ad hoc re-planning during the treatment were identified in electronic records. Reasons for re-planning were categorized as: weight loss, tumor shrinkage, changes in patient position and immobilization-related factors. Potential trigger factors for adaptive radiotherapy such as patient characteristics, primary tumor site, stage, concomitant chemotherapy, weight loss ratios, radical/adjuvant treatment, and nutritional interventions were investigated.

Results

31/290 (10.6%) HNSCC patients who underwent radical/adjuvant radiotherapy required re-planning. The adaptive radiotherapy (ART) was performed at a mean fraction of 15. The most common documented reasons for re-planning were tumor shrinkage (35.5%) and weight loss (35.5%). Among the patient/tumor/treatment factors, nasopharyngeal primary site (p = 0.013) and use of concurrent chemotherapy with radiotherapy (p = 0.034) were found to be significantly correlated with the need for re-planning.

Conclusion

Effective on-treatment verification schedules and close follow up of patients especially with NPC primary and/or treated with concurrent chemoradiotherapy are crucial to identify patients requiring ART. We suggest an individualized triggered approach to ART rather than scheduled strategies as it is likely to be more feasible in terms of utilization of workload and resources.

Analysis of dose using CBCT and synthetic CT during head and neck radiotherapy: A single centre feasibility study

OBJECTIVES

The study aimed to assess the suitability of deformable image registration (DIR) software to generate synthetic CT (sCT) scans for dose verification during radiotherapy to the head and neck. Planning and synthetic CT dose volume histograms were compared to evaluate dosimetric changes during the treatment course.

METHODS

Eligible patients had locally advanced (stage III, IVa and IVb) oropharyngeal cancer treated with primary radiotherapy. Weekly CBCT images were acquired post treatment at fractions 1, 6, 11, 16, 21 and 26 over a 30 fraction treatment course. Each CBCT was deformed with the planning CT to generate a sCT which was used to calculate the dose at that point in the treatment. A repeat planning CT2 was acquired at fraction 16 and deformed with the fraction 16 CBCT to compare differences between the calculations mid-treatment.

RESULTS

20 patients were evaluated generating 138 synthetic CT sets. The single fraction mean dose to PTV_HR between the synthetic and planning CT did not vary, although dose to 95% of PTV_HR was smaller at week 6 compared to planning (difference 2.0%, 95% CI (0.8 to 3.1), p = 0.0). There was no statistically significant difference in PRV_brainstem or PRV_spinal cord maximum dose, although greater variation using the sCT calculations was reported. The mean dose to structures based on the fraction 16 sCT and CT2 scans were similar.

CONCLUSIONS

Synthetic CT provides comparable dose calculations to those of a repeat planning CT; however the limitations of DIR must be understood before it is applied within the clinical setting.

Adaptive radiotherapy for head and neck cancer reduces the requirement for rescans during treatment due to spinal cord dose

BACKGROUND

Patients treated with radiotherapy for head and neck (H&N) cancer often experience anatomical changes. The potential compromises to Planning Target Volume (PTV) coverage or Organ at Risk (OAR) sparing has prompted the use of adaptive radiotherapy (ART) for these patients. However, implementation of ART is time and resource intensive. This study seeks to define a clinical trigger for H&N re-plans based on spinal cord safety using kV Cone-Beam Computed Tomography (CBCT) verification imaging, in order to best balance clinical benefit with additional workload.

METHODS

Thirty-one H&N patients treated with Volumetric Modulated Arc Therapy (VMAT) who had a rescan CT (rCT) during treatment were included in this study. Contour volume changes between the planning CT (pCT) and rCT were determined. The original treatment plan was calculated on the pCT, CBCT prior to the rCT, pCT deformed to the anatomy of the CBCT (dCT), and rCT (considered the gold standard). The dose to 0.1 cc (D0.1cc) spinal cord was evaluated from the Dose Volume Histograms (DVHs).

RESULTS

The median dose increase to D0.1cc between the pCT and rCT was 0.7 Gy (inter-quartile range 0.2-1.9 Gy, p < 0.05). No correlation was found between contour volume changes and the spinal cord dose increase. Three patients exhibited an increase of 7.0-7.2 Gy to D0.1cc, resulting in a re-plan; these patients were correctly identified using calculations on the CBCT/dCT.

CONCLUSIONS

An adaptive re-plan can be triggered using spinal cord doses calculated on the CBCT/dCT. Implementing this trigger can reduce patient appointments and radiation dose by eliminating up to 90% of additional un-necessary CT scans, reducing the workload for radiographers, physicists, dosimetrists, and clinicians.

Pretreatment Prediction of Adaptive Radiation Therapy Eligibility Using MRI-Based Radiomics for Advanced Nasopharyngeal Carcinoma Patients

Background and purpose: Adaptive radiotherapy (ART) can compensate for the dosimetric impacts induced by anatomic and geometric variations in patients with nasopharyngeal carcinoma (NPC); Yet, the need for ART can only be assessed during the radiation treatment and the implementation of ART is resource intensive. Therefore, we aimed to determine tumoral biomarkers using pre-treatment MR images for predicting ART eligibility in NPC patients prior to the start of treatment. Methods: Seventy patients with biopsy-proven NPC (Stage II-IVB) in 2015 were enrolled into this retrospective study. Pre-treatment contrast-enhanced T1-w (CET1-w), T2-w MR images were processed and filtered using Laplacian of Gaussian (LoG) filter before radiomic features extraction. A total of 479 radiomics features, including the first-order (n = 90), shape (n = 14), and texture features (n = 375), were initially extracted from Gross-Tumor-Volume of primary tumor (GTVnp) using CET1-w, T2-w MR images. Patients were randomly divided into a training set (n = 51) and testing set (n = 19). The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for radiomic model construction in training set to select the most predictive features to predict patients who were replanned and assessed in the testing set. A double cross-validation approach of 100 resampled iterations with 3-fold nested cross-validation was employed in LASSO during model construction. The predictive performance of each model was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC). Results: In the present cohort, 13 of 70 patients (18.6%) underwent ART. Average AUCs in training and testing sets were 0.962 (95%CI: 0.961-0.963) and 0.852 (95%CI: 0.847-0.857) with 8 selected features for CET1-w model; 0.895 (95%CI: 0.893-0.896) and 0.750 (95%CI: 0.745-0.755) with 6 selected features for T2-w model; and 0.984 (95%CI: 0.983-0.984) and 0.930 (95%CI: 0.928-0.933) with 6 selected features for joint T1-T2 model, respectively. In general, the joint T1-T2 model outperformed either CET1-w or T2-w model alone. Conclusions: Our study successfully showed promising capability of MRI-based radiomics features for pre-treatment identification of ART eligibility in NPC patients.

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformation-based planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of re-irradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.