Aart A. van 't Veld

3D Variation in delineation of head and neck organs at risk

BackgroundConsistent delineation of patient anatomy becomes increasingly important with the growing use of highly conformal and adaptive radiotherapy techniques. This study investigates the magnitude and 3D localization of interobserver variability of organs at risk (OARs) in the head and neck area with application of delineation guidelines, to establish measures to reduce current redundant variability in delineation practice.MethodsInterobserver variability among five experienced radiation oncologists was studied in a set of 12 head and neck patient CT scans for the spinal cord, parotid and submandibular glands, thyroid cartilage, and glottic larynx. For all OARs, three endpoints were calculated: the Intraclass Correlation Coefficient (ICC), the Concordance Index (CI) and a 3D measure of variation (3D SD).ResultsAll endpoints showed largest interobserver variability for the glottic larynx (ICC = 0.27, mean CI = 0.37 and 3D SD = 3.9 mm). Better agreement in delineations was observed for the other OARs (range, ICC = 0.32-0.83, mean CI = 0.64-0.71 and 3D SD = 0.9-2.6 mm). Cranial, caudal, and medial regions of the OARs showed largest variations. All endpoints provided support for improvement of delineation practice.ConclusionsVariation in delineation is traced to several regional causes. Measures to reduce this variation can be: (1) guideline development, (2) joint delineation review sessions and (3) application of multimodality imaging. Improvement of delineation practice is needed to standardize patient treatments.

The potential of intensity-modulated proton radiotherapy to reduce swallowing dysfunction in the treatment of head and neck cancer: A planning comparative study.

Abstract Background. Predictive models for swallowing dysfunction were developed previously and showed the potential of improved intensity-modulated radiotherapy to reduce the risk of swallowing dysfunction. Still the risk is high. The aim of this study was to determine the potential of swallowing-sparing (SW) intensity-modulated proton therapy (IMPT) in head and neck cancer (HNC) for reducing the risk of swallowing dysfunction relative to currently used photon therapy. Material and methods. Twenty-five patients with oropharyngeal (n = 21) and hypopharyngeal (n = 4) cancer received primary radiotherapy, including bilateral neck irradiation, using standard (ST) intensity-modulated photon therapy (IMRT). Prophylactic (54 Gy) and therapeutic (70 Gy) target volumes were defined. The dose to the parotid and submandibular glands was reduced as much as possible. Four additional radiotherapy plans were created for each patient: SW-IMRT, ST-IMPT, 3-beam SW-IMPT (3B-SW-IMPT) and 7-beam SW-IMPT (7B-SW-IMPT). All plans were optimized similarly, with additional attempts to spare the swallowing organs at risk (SWOARs) in the SW plans. Probabilities of swallowing dysfunction were calculated with recently developed predictive models. Results. All plans complied with standard HNC radiotherapy objectives. The mean parotid gland doses were similar for the ST and SW photon plans, but clearly lower in all IMPT plans (ipsilateral parotid gland ST-IMRT: 46 Gy, 7B-SW-IMPT: 29 Gy). The mean dose in the SWOARs was lowest with SW-IMPT, in particular with 7B-SW-IMPT (supraglottic larynx ST-IMRT: 60 Gy, 7B-SW-IMPT: 40 Gy). The observed dose reductions to the SWOARs translated into substantial overall reductions in normal tissue complication risks for different swallowing dysfunction endpoints. Compared with ST-IMRT, the risk of physician-rated grade 2–4 swallowing dysfunction was reduced on average by 8.8% (95% CI 6.5–11.1%) with SW-IMRT, and by 17.2% (95% CI: 12.7–21.7%) with 7B-SW-IMPT. Conclusion. SWOAR-sparing with proton therapy has the potential to substantially reduce the risk of swallowing dysfunction compared to similar treatment with photons.

Reconstruction of high-resolution 3D dose from matrix measurements: error detection capability of the COMPASS correction kernel method

The COMPASS system (IBA Dosimetry) is a quality assurance (QA) tool which reconstructs 3D doses inside a phantom or a patient CT. The dose is predicted according to the RT plan with a correction derived from 2D measurements of a matrix detector. This correction method is necessary since a direct reconstruction of the fluence with a high resolution is not possible because of the limited resolution of the matrix used, but it comes with a blurring of the dose which creates inaccuracies in the dose reconstruction. This paper describes the method and verifies its capability to detect errors in the positioning of a MLC with 10 mm leaf width in a phantom geometry. Dose reconstruction was performed for MLC position errors of various sizes at various locations for both rectangular and intensity-modulated radiotherapy (IMRT) fields and compared to a reference dose. It was found that the accuracy with which an error in MLC position is detected depends on the location of the error relative to the detectors in the matrix. The reconstructed dose in an individual rectangular field for leaf positioning errors up to 5 mm was correct within 5% in 50% of the locations. At the remaining locations, the reconstruction of leaf position errors larger than 3 mm can show inaccuracies, even though these errors were detectable in the dose reconstruction. Errors larger than 9 mm created inaccuracies up to 17% in a small area close to the penumbra. The QA capability of the system was tested through gamma evaluation. Our results indicate that the mean gamma provided by the system is slightly increased and that the number of points above gamma 1 ensures error detection for QA purposes. Overall, the correction kernel method used by the COMPASS system is adequate to perform QA of IMRT treatment plans with a regular MLC, despite local inaccuracies in the dose reconstruction.

Clinical introduction of a linac head-mounted 2D detector array based quality assurance system in head and neck IMRT

BACKGROUND AND PURPOSE IMRT QA is commonly performed in a phantom geometry but the clinical interpretation of the results in a 2D phantom plane is difficult. The main objective of our work is to move from film measurement based QA to 3D dose reconstruction in a patient CT scan. In principle, this could be achieved using a dose reconstruction method from 2D detector array measurements as available in the COMPASS system (IBA Dosimetry). The first step in the clinical introduction of this system instead of the currently used film QA procedures is to test the reliability of the dose reconstruction. In this paper we investigated the validation of the method in a homogeneous phantom with the film QA procedure as a reference. We tested whether COMPASS QA results correctly identified treatment plans that did or did not fulfil QA requirements in head and neck (H&N) IMRT. MATERIALS AND METHODS A total number of 24 treatments were selected from an existing database with more than 100 film based H&N IMRT QA results. The QA results were classified as either good, just acceptable or clinically rejected (mean gamma index <0.4, 0.4-0.5 or >0.5, respectively with 3%/3mm criteria). Film QA was repeated and compared to COMPASS QA with a MatriXX detector measurement performed on the same day. RESULTS Good agreement was found between COMPASS reconstructed dose and film measured dose in a phantom (mean gamma 0.83±0.09, 1SD with 1%/1mm criteria, 0.33±0.04 with 3%/3mm criteria). COMPASS QA results correlated well with film QA, identifying the same patients with less good QA results. Repeated measurements with film and COMPASS showed changes in delivery after a modified MLC calibration, also visible in a standard MLC check in COMPASS. The time required for QA reduced by half by using COMPASS instead of film. CONCLUSIONS Agreement of COMPASS QA results with film based QA supports its clinical introduction for a phantom geometry. A standard MLC calibration check is sensitive to <1mm changes that could be significant in H&N IMRT. These findings offer opportunities to further investigate the method based on a 2D detector array to 3D dose reconstruction in a patient anatomy.

Multivariate modeling of complications with data driven variable selection: Guarding against overfitting and effects of data set size

PURPOSE Multivariate modeling of complications after radiotherapy is frequently used in conjunction with data driven variable selection. This study quantifies the risk of overfitting in a data driven modeling method using bootstrapping for data with typical clinical characteristics, and estimates the minimum amount of data needed to obtain models with relatively high predictive power. MATERIALS AND METHODS To facilitate repeated modeling and cross-validation with independent datasets for the assessment of true predictive power, a method was developed to generate simulated data with statistical properties similar to real clinical data sets. Characteristics of three clinical data sets from radiotherapy treatment of head and neck cancer patients were used to simulate data with set sizes between 50 and 1000 patients. A logistic regression method using bootstrapping and forward variable selection was used for complication modeling, resulting for each simulated data set in a selected number of variables and an estimated predictive power. The true optimal number of variables and true predictive power were calculated using cross-validation with very large independent data sets. RESULTS For all simulated data set sizes the number of variables selected by the bootstrapping method was on average close to the true optimal number of variables, but showed considerable spread. Bootstrapping is more accurate in selecting the optimal number of variables than the AIC and BIC alternatives, but this did not translate into a significant difference of the true predictive power. The true predictive power asymptotically converged toward a maximum predictive power for large data sets, and the estimated predictive power converged toward the true predictive power. More than half of the potential predictive power is gained after approximately 200 samples. Our simulations demonstrated severe overfitting (a predicative power lower than that of predicting 50% probability) in a number of small data sets, in particular in data sets with a low number of events (median: 7, 95th percentile: 32). Recognizing overfitting from an inverted sign of the estimated model coefficients has a limited discriminative value. CONCLUSIONS Despite considerable spread around the optimal number of selected variables, the bootstrapping method is efficient and accurate for sufficiently large data sets, and guards against overfitting for all simulated cases with the exception of some data sets with a particularly low number of events. An appropriate minimum data set size to obtain a model with high predictive power is approximately 200 patients and more than 32 events. With fewer data samples the true predictive power decreases rapidly, and for larger data set sizes the benefit levels off toward an asymptotic maximum predictive power.

Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

PURPOSE To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. RESULTS It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. CONCLUSIONS The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

Differences in delineation guidelines for head and neck cancer result in inconsistent reported dose and corresponding NTCP

PURPOSE To test the hypothesis that delineation of swallowing organs at risk (SWOARs) based on different guidelines results in differences in dose-volume parameters and subsequent normal tissue complication probability (NTCP) values for dysphagia-related endpoints. MATERIALS AND METHODS Nine different SWOARs were delineated according to five different delineation guidelines in 29 patients. Reference delineation was performed according to the guidelines and NTCP-models of Christianen et al. Concordance Index (CI), dosimetric consequences, as well as differences in the subsequent NTCPs were calculated. RESULTS The median CI of the different delineation guidelines with the reference guidelines was 0.54 for the pharyngeal constrictor muscles, 0.56 for the laryngeal structures and 0.07 for the cricopharyngeal muscle and esophageal inlet muscle. The average difference in mean dose to the SWOARs between the guidelines with the largest difference (maxΔD) was 3.5±3.2Gy. A mean ΔNTCP of 2.3±2.7% was found. For two patients, ΔNTCP exceeded 10%. CONCLUSIONS The majority of the patients showed little differences in NTCPs between the different delineation guidelines. However, large NTCP differences >10% were found in 7% of the patients. For correct use of NTCP models in individual patients, uniform delineation guidelines are of great importance.

Statistical Validation of Normal Tissue Complication Probability Models

PURPOSE To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. METHODS AND MATERIALS A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. RESULTS Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. CONCLUSION Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use.

Electronic portal images (EPIs) based position verification for the breast simultaneous integrated boost (SIB) technique

BACKGROUND AND PURPOSE To develop a method based on electronic portal images (EPIs) for the position verification of breast cancer patients that are treated with a simultaneous integrated boost (SIB) technique. METHOD 3D setup errors of the breast outline and the thoracic wall were determined from EPIs of the tangential treatment fields and anterior posterior (AP) verification field. The method was verified with repeated CT scans of 38 patients with an average setup error larger than 5 mm. RESULT The 3D position deviation of the boost volume can best be determined from the position deviation of the breast outline in the ventrodorsal direction and the thoracic wall in the lateral and longitudinal directions from the tangential and AP EPIs. The method gives an average overestimation of the deviation of the boost volume in the ventrodorsal, lateral and longitudinal directions by 28%, 20% and 6%, respectively and an average underestimation of the deviation of the whole breast by 32%, 17% and 39%. CONCLUSIONS The described method is superior to using tangential EPIs only and is recommended for position verification of breast cancer patients that are treated with a SIB technique if no Cone beam CT (CBCT) or fiducial markers can be used.

Evaluation of DVH-based treatment plan verification in addition to gamma passing rates for head and neck IMRT.

BACKGROUND AND PURPOSE Treatment plan verification of intensity modulated radiotherapy (IMRT) is generally performed with the gamma index (GI) evaluation method, which is difficult to extrapolate to clinical implications. Incorporating Dose Volume Histogram (DVH) information can compensate for this. The aim of this study was to evaluate DVH-based treatment plan verification in addition to the GI evaluation method for head and neck IMRT. MATERIALS AND METHODS Dose verifications of 700 subsequent head and neck cancer IMRT treatment plans were categorised according to gamma and DVH-based action levels. Fractionation dependent absolute dose limits were chosen. The results of the gamma- and DVH-based evaluations were compared to the decision of the medical physicist and/or radiation oncologist for plan acceptance. RESULTS Nearly all treatment plans (99.7%) were accepted for treatment according to the GI evaluation combined with DVH-based verification. Two treatment plans were re-planned according to DVH-based verification, which would have been accepted using the evaluation alone. DVH-based verification increased insight into dose delivery to patient specific structures increasing confidence that the treatment plans were clinically acceptable. Moreover, DVH-based action levels clearly distinguished the role of the medical physicist and radiation oncologist within the Quality Assurance (QA) procedure. CONCLUSIONS DVH-based treatment plan verification complements the GI evaluation method improving head and neck IMRT-QA.