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Purpose: The aim of this study was to investigate the usefulness of the QUANTEC guideline to prevent xerostomia after intensity-modulated radiotherapy (IMRT) for head and neck cancer (HNC) with respect to follow-up time. In addition, we explored alternative guidelines to further reduce xerostomia. Methods: The QUANTEC guideline suggests a mean dose to the contralateral (Dmeancontra) parotid

Purpose: The aim of this study was to investigate if quantitative MRI-derived metrics from four masticatory muscles could explain mouth-opening limitation/trismus following intensity-modulated radiotherapy (IMRT) for head and neck cancer (HNC). Methods: Fifteen intensity-based MRI metrics were derived from the masseter, lateral and medial pterygoid, and temporalis in T1-weighted scans acquired pre- and post gadolinium injection (T1Pre, T1Post) of 16, of in total 20, patients (8 symptomatic; 8 asymptomatic age/sex/tumor location-matched) treated with IMRT to 70 Gy (median) for HNC in 2005–2009. Trismus was defined as “≥decreased range of motion without impaired eating” (CTCAE.v.3: ≥Grade 1). Trismus status was monitored and MRI scans acquired within 1y post-RT. All MRI-derived metrics were assessed as ΔS=S(T1Pre)-S(T1Post)/S(T1Pre), and were normalized to the corresponding metric of a non-irradiated volume defined in each scan. The T1Pre structures were propagated onto the RT dose distribution, and the max and mean dose (Dmax, Dmean) were extracted. The MRI-derived metrics, Dmax, and Dmean were compared between trismus and non-trismus patients. A two-sided Wilcoxon Signed rank test-based p-value≤0.05 denoted significance. Results: For all four muscles the population mean of Dmax and Dmean was higher for patients with trismus compared to patients without trismus (ΔDmax=2.3–4.9 Gy; ΔDmean=and 2.0–3.8 Gy). The standard deviation (SD), the variance, and the minimum value (min) of ΔS were significantly (p=0.04–0.05) different between patients with and without trismus with trismus patients having significantly lower SD (population median: −0.53 vs. −0.31) and variance (−2.09 vs. −0.73) of the masseter, and significantly lower min of the medial pterygoid (−0.36 vs. −0.19). Conclusion: Quantitative MRI-derived metrics of two masticatory muscles were significantly different between patients with and without trismus following RT for HNC. These metrics could serve as image-based biomarkers to better understand the RT-induced etiology behind trismus, but should be further investigated in the complete cohort.

Purpose: Normal tissue complications following radiotherapy (RT) are commonly estimated from single institutions, limiting generalizability of critical dose-volume thresholds. In this study we use data from five cohorts/institutions to explore dose-volume relations for late rectal bleeding (LRB) after RT for prostate cancer. Methods: The investigated cohorts included 1001 patients treated with various RT techniques for prostate cancer (922 3DCRT, 211 image-guided RT, and 79 intensity-modulated RT patients) to 70–86 Gy@2Gy/fraction in 1991–2007. The rectum extended from the recto-sigmoid flexure to the anal canal, and the median (range) follow-up for LRB was 58 (4–259) months. Rectal cross sectional area, length, and volume were compared between LRB and non- LRB patients (Mann-Whitney-test). The ability of dose metrics to predict moderate-to-severe LRB (prevalence: 14%) was investigated on univariate analysis, UVA, (Spearman’s Rho (Rs) and p-values calculated as medians from 10 0000 Bootstrap-resamples). Dose-volume metrics significantly predicting LRB on UVA (p≤0.05) were considered for multivariate logistic regression, MVA, following removal of correlated metrics (Pearson’s correlation, Pr ≥0.85). Results: Patients with LRB had significantly (p1000 patients) suggest that shorter rectums and intermediate to high doses predict LRB. The wide range of prescribed doses and treatment techniques applied support generalizability of our results to other prostate cancer cohorts.

Purpose: The aim of this study was to explore the use of Magnetic Resonance Imaging (MRI)-derived features as indicators of Radiotherapy (RT)-induced normal tissue morbidity. We also investigate the relationship between these features and RT dose in four critical structures. Methods: We demonstrate our approach for four patients treated with RT for base of tongue cancer in 2005–2007. For each patient, two MRI scans (T1-weighted pre (T1pre) and post (T1post) gadolinium contrast-enhancement) were acquired within the first six months after RT. The assessed morbidity endpoint observed in 2/4 patients was Grade 2+ CTCAEv.3 trismus. Four ipsilateral masticatory-related structures (masseter, lateral and medial pterygoid, and the temporal muscles) were delineated on both T1pre and T1post and these scans were co-registered to the treatment planning CT using a deformable demons algorithm. For each structure, the maximum and mean RT dose, and six MRI-derived features (the second order texture features entropy and homogeneity, and the first order mean, median, kurtosis, and skewness) were extracted and compared structure-wise between patients with and without trismus. All MRI-derived features were calculated as the difference between T1pre and T1post, ΔS. Results: For 5/6 features and all structures, ΔS diverged between trismus and non-trismus patients particularly for themore » masseter, lateral pterygoid, and temporal muscles using the kurtosis feature (−0.2 vs. 6.4 for lateral pterygoid). Both the maximum and mean RT dose in all four muscles were higher amongst the trismus patients (with the maximum dose being up to 25 Gy higher). Conclusion: Using MRI-derived features to quantify RT-induced normal tissue complications is feasible. We showed that several features are different between patients with and without morbidity and that the RT dose in all investigated structures are higher amongst patients with morbidity. MRI-derived features, therefore, has the potential to improve predictions of normal tissue morbidity.« less

Purpose: The current practice for evaluating deformable image registration (DIR) is commonly subjective as it involves input from an observer due to lack of an absolute ground truth. We have therefore developed an automated objective method to evaluate DIR in order to identify scan outliers for longitudinal datasets. Methods: The imaging dataset consisted of nine repeated CT scans (CT1–9) from four prostate patients. Six “similar” CT scans (CT1–6) from one patient (PT1) were used as ground truth meanwhile three other CT scans (CT7–9) from PT2–4 served as scan outliers. Voxel-by-voxel DIR-related uncertainties (Distance Discordance Metric, DDM) were evaluated on groups of five CT scans for the following ten scenarios: Scenario 1–5: five out of six CT scans (CT1–6) from PT1 using leave-one-out technique were used; Scenario 6–8: single scan from PT1 was replaced by another CT scan from PT2–4; and Scenario 9–10: two to three CT scans from PT1 were replaced by CT scans from PT2–4. For each scenario, the DDM map was superimposed on CT-1 and two-sample t-test was performed to compare the uncertainty distributions for all 10 scenarios. Results: The mean DDM values were 3.8–4.2 mm for the first five scenarios, 5.1–6.7 mm for scenario 6–8, and 8.3 mm and 8.9 mm for scenario 9 and scenario 10, respectively. The two-sample t-test showed that the DDM distributions for scenarios 1–5 have a similar mean while the distributions for scenarios 6–10 have statistically different means compared to scenario 1–5. Conclusion: The DIR uncertainty distributions as estimated from our method on a set of images from the same patient are of similar magnitude. When images for a patient are replaced by images from other patients, the distribution of the uncertainties changes considerably, motivating the use of DDM as a metric to automatically detect scan outliers.

Purpose: Rectum is the main dose‐limitingorgan in radiotherapy (RT) of prostate cancer (PC). This organ displays considerable motion leading to uncertainties in the rectum dose‐volume histogram (DVH), which is likely to also influence the associations between rectal DVHs and toxicity. The aim of this study was to improve the understanding of how rectum motion influences these associations by introducing and applying a rectum motion model to generate 'motion‐inclusive' DVHs. Methods: Varying amounts of random and systematic organ motion (“shifts”) were generated by altering the standard deviations (SDs) of normal distribution from 0.1 cm to 1 cm in steps of 0.1 cm. In order to simulate random shifts, we perturbed each dose fraction with a unique value of shift drawn from a normal distribution whose SD was proportional to the simulated random shift. The systematic shift was simulated similarly but with a normally distributed shift that remained constant over the entire course of trial. The delivered DVHs (dDVHs) were generated as a number of pseudo DVHs. The dDVHs were associated with prospectively registered late rectal larger than Grade 2 RTOG toxicity (dichotomized; minimum 5 year follow‐up) for a cohort of 232 PC patients treated using 35 dose fractions and compared to the planned rectum DVH (pDVH). The Spearman's rank correlation coefficient (Rs) was used as a measure of the associations. Results: Systematic errors reduced the association at rectum volumes receiving doses above 40 Gy (V40) and doses less than the dose max. The Rs (V40) was insensitive to the magnitude of systematic and random errors. Random errors smoothed out the curve describing Rs vs. Vx. Conclusions: A model for simulating rectal motion has been presented and the corresponding DVHs investigated in relation to rectal toxicity. Systematic errors reduced the association above V40 and doses less than the dose max.

Purpose: Dose accumulation following deformable image registration (DIR) is challenging. In this study, we used a statistical sampling approach, which takes into account both DIR uncertainties and patient-specific organ motion, to study the distribution of possible true dose distributions. Methods: The study included ten patients (six CT scans/patient) treated with radiotherapy for prostate cancer. For each patient, the planned dose was re-calculated on the repeated geometries, following rigid registration based on fiducial markers. The dose re-calculated on the first CT served as our snapshot dose distribution (D1) and the average of the first five repeat scans as our treatment course reference dose distribution (Dref). Patient-specific motion and DIR-uncertainties, at each voxel in CT1, were assessed using a previously developed DIR performance measure, the distance discordance metric (DDM). To sample the distribution of possible true, predicted dose distributions (Dpred), we resampled D1 by perturbing the location of each voxel with the corresponding DDM. The three dose distribution approaches are compared for the rectum and the bladder. Results: The bladder generalized equivalent uniform dose (gEUD) from the averaged Dpred was closer to the gEUDref than to the gEUD1 (difference: 0.6 vs. 1.0 Gy). For both structures, the gEUDpred was higher than themore » gEUDref, and significantly higher (p≤0.05) for the rectum (average: 50.8 Gy vs. 48.0 Gy). Conclusion: We have shown that the bladder gEUD values resulting from our DIR-uncertainty inclusive dose sampling approach, Dpred, were closer to the gEUD from Dref than the gEUD values from D1. For the rectum, gEUDpred overestimated gEUDref. Theoretically however, gEUDpred values, sampled from DDM uncertainties are more representative of dose uncertainties.« less

Purpose: The quantitative evaluation of deformable image registration (DIR) is currently challenging due to lack of a ground truth. In this study we test a new method proposed for quantifying multiple-image based DIRrelated uncertainties, for DIR of pelvic images. Methods: 19 patients were analyzed, each with 6 CT scans, who previously had radiotherapy for prostate cancer. Manually delineated structures for rectum and bladder, which served as ground truth structures, were delineated on the planning CT and each subsequent scan. For each patient, voxel-by-voxel DIR-related uncertainties were evaluated, following B-spline based DIR, by applying a previously developed metric, the distance discordance metric (DDM; Saleh et al., PMB (2014) 59:733). The DDM map was superimposed on the first acquired CT scan and DDM statistics were assessed, also relative to two metrics estimating the agreement between the propagated and the manually delineated structures. Results: The highest DDM values which correspond to greatest spatial uncertainties were observed near the body surface and in the bowel due to the presence of gas. The mean rectal and bladder DDM values ranged from 1.1–11.1 mm and 1.5–12.7 mm, respectively. There was a strong correlation in the DDMs between the rectum and bladder (Pearson R = 0.68 for the max DDM). For both structures, DDM was correlated with the ratio between the DIR-propagated and manually delineated volumes (R = 0.74 for the max rectal DDM). The maximum rectal DDM was negatively correlated with the Dice Similarity Coefficient between the propagated and the manually delineated volumes (R= −0.52). Conclusion: The multipleimage based DDM map quantified considerable DIR variability across different structures and among patients. Besides using the DDM for quantifying DIR-related uncertainties it could potentially be used to adjust for uncertainties in DIR-based accumulated dose distributions.