Using a reduced spot size for intensity-modulated proton therapy potentially improves salivary gland-sparing in oropharyngeal cancer

Purpose: To investigate whether intensity-modulated proton therapy with a reduced spot size (rsIMPT) could further reduce the parotid and submandibular gland dose compared with previously calculated IMPT plans with a larger spot size. In addition, it was investigated whether the obtained dose reductions would theoretically translate into a reduction of normal tissue complication probabilities (NTCPs). Methods: Ten patients with N0 oropharyngeal cancer were included in a comparative treatment planning study. Both IMPT plans delivered simultaneously 70 Gy to the boost planning target volume (PTV) and 54 Gy to the elective nodal PTV. IMPT and rsIMPT used identical three-field beam arrangements. In the IMPT plans, the parotid and submandibular salivary glands were spared as much as possible. rsIMPT plans used identical dose-volume objectives for the parotid glands as those used by the IMPT plans, whereas the objectives for the submandibular glands were tightened further. NTCPs were calculated for salivary dysfunction and xerostomia. Results: Target coverage was similar for both IMPT techniques, whereas rsIMPT clearly improved target conformity. The mean doses in the parotid glands and submandibular glands were significantly lower for three-field rsIMPT (14.7 Gy and 46.9 Gy, respectively) than for three-field IMPT (16.8 Gy and 54.6 Gy, respectively). Hence, rsIMPT significantly reduced the NTCP of patient-rated xerostomia and parotid and contralateral submandibular salivary flow dysfunction (27%, 17%, and 43% respectively) compared with IMPT (39%, 20%, and 79%, respectively). In addition, mean dose values in the sublingual glands, the soft palate and oral cavity were also decreased. Obtained dose and NTCP reductions varied per patient. Conclusions: rsIMPT improved sparing of the salivary glands and reduced NTCP for xerostomia and parotid and submandibular salivary dysfunction, while maintaining similar target coverage results. It is expected that rsIMPT improves quality of life during and after radiotherapy treatment.</p

Insect flight, eye movements, and vision

m een goed inzich te krijgen in de informatieverwerking die plaats vindt in een visueel systeem is het belangrijk te weten wat de gemiddelde eigenschappen zijn van de visuele input (optische signalen) die en visueel systeem onder normale omstandigheden binnenkomt. De temporele (d.w.z. tijdafhankelijke) kenmerken van het organisme zelf. ... Zie: Samenvatting.

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

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

A COMPARISON OF DOSE-RESPONSE MODELS FOR THE PAROTID GLAND IN A LARGE GROUP OF HEAD-AND-NECK CANCER PATIENTS

Purpose: The dose response relationship of the parotid gland has been described most frequently using the Lyman-Kutcher-Burman model. However, various other normal tissue complication probability (NTCP) models exist. We evaluated in a large group of patients the value of six NTCP models that describe the parotid gland dose response 1 year after radiotherapy. Methods and Materials: A total of 347 patients with head-and-neck tumors were included in this prospective parotid gland dose response study. The patients were treated with either conventional radiotherapy or intensitymodulated radiotherapy. Dose volume histograms for the parotid glands were derived from three-dimensional dose calculations using computed tomography scans. Stimulated salivary flow rates were measured before and 1 year after radiotherapy. A threshold of 25% of the pretreatment flow rate was used to define a complication. The evaluated models included the Lyman-Kutcher-Burman model, the mean dose model, the relative seriality model, the critical volume model, the parallel functional subunit model, and the dose threshold model. The goodness of fit (GOF) was determined by the deviance and a Monte Carlo hypothesis test. Ranking of the models was based on Akaike's information criterion (AIC). Results: None of the models was rejected based on the evaluation of the GOF. The mean dose model was ranked as the best model based on the AIC. The TD50 in these models was approximately 39 Gy. Conclusions: The mean dose model was preferred for describing the dose response relationship of the parotid gland. (C) 2010 Elsevier Inc

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

The Potential Benefit of Radiotherapy with Protons in Head and Neck Cancer with Respect to Normal Tissue Sparing: A Systematic Review of Literature

A literature review of in silico planning comparative studies that include proton-based and photon-based irradiation techniques in the treatment of head and neck cancer is presented

POTENTIAL BENEFITS OF SCANNED INTENSITY-MODULATED PROTON THERAPY VERSUS ADVANCED PHOTON THERAPY WITH REGARD TO SPARING OF THE SALIVARY GLANDS IN OROPHARYNGEAL CANCER

Purpose: To test the hypothesis that scanned intensity-modulated proton therapy (IMPT) results in a significant dose reduction to the parotid and submandibular glands as compared with intensity-modulated radiotherapy with photons (IMRT) and three-dimensional conformal radiotherapy (3D-CRT) for oropharyngeal cancer. In addition, we investigated whether the achieved dose reductions would theoretically translate into a reduction of salivary dysfunction and xerostomia. Methods and Materials: Ten patients with NO oropharyngeal carcinoma were used. The intensity-modulated plans delivered simultaneously 70 Gy to the boost planning target volume (PTV2) and 54 Gy to the elective nodal areas (PTV1). The 3D-CRT technique delivered sequentially 70 Gy and 46 Gy to PTV2 and PTV1, respectively. Normal tissue complication probabilities were calculated for salivary dysfunction and xerostomia. Results: Planning target volume coverage results were similar for IMPT and IMRT. Intensity-modulated proton therapy clearly improved the conformity. The 3D-CRT results were inferior to these results. The mean dose to the parotid glands by 3D-CRT (50.8 Gy), IMRT (25.5 Gy), and IMPT (16.8 Gy) differed significantly. For the submandibular glands no significant differences between BIRT and IMPT were found. The dose reductions obtained with IMPT theoretically translated into a significant reduction in normal tissue complication probability. Conclusion: Compared with IMRT and 3D-CRT, IMPT improved sparing of the organs at risk, while keeping similar target coverage results. The dose reductions obtained with IMPT vs. IMRT and 3D-CRT varied widely per individual patient. Intensity-modulated proton therapy theoretically translated into a clinical benefit for most cases, but this requires clinical validation. (C) 2011 Elsevier Inc