N. Di Muzio

An automatic contour propagation method to follow parotid gland deformation during head-and-neck cancer tomotherapy

We developed an efficient technique to auto-propagate parotid gland contours from planning kVCT to daily MVCT images of head-and-neck cancer patients treated with helical tomotherapy. The method deformed a 3D surface mesh constructed from manual kVCT contours by B-spline free-form deformation to generate optimal and smooth contours. Deformation was calculated by elastic image registration between kVCT and MVCT images. Data from ten head-and-neck cancer patients were considered and manual contours by three observers were included in both kVCT and MVCT images. A preliminary inter-observer variability analysis demonstrated the importance of contour propagation in tomotherapy application: a high variability was reported in MVCT parotid volume estimation (p = 0.0176, ANOVA test) and a larger uncertainty of MVCT contouring compared with kVCT was demonstrated by DICE and volume variability indices (Wilcoxon signed rank test, p < 10(-4) for both indices). The performance analysis of our method showed no significant differences between automatic and manual contours in terms of volumes (p > 0.05, in a multiple comparison Tukey test), center-of-mass distances (p = 0.3043, ANOVA test), DICE values (p = 0.1672, Wilcoxon signed rank test) and average and maximum symmetric distances (p = 0.2043, p = 0.8228 Wilcoxon signed rank tests). Results suggested that our contour propagation method could successfully substitute human contouring on MVCT images.

A two-variable linear model of parotid shrinkage during IMRT for head and neck cancer.

PURPOSE To assess anatomical, clinical and dosimetric pre-treatment parameters, possibly predictors of parotid shrinkage during radiotherapy of head and neck cancer (HNC). MATERIALS Data of 174 parotids from four institutions were analysed; patients were treated with IMRT, with radical and adjuvant intent. Parotid shrinkage was evaluated by the volumetric difference (DeltaV) between parotid volumes at the end and those at the start of the therapy, as assessed by CT images (MVCT for 40 patients, KVCT for 47 patients). Correlation between DeltaVcc/% and a number of dosimetric, clinical and geometrical parameters was assessed. Univariate as well as stepwise logistic multivariate (MVA) analyses were performed by considering as an end-point a DeltaVcc/% larger than the median value. Linear models of DeltaV (continuous variable) based on the most predictive variables found at the MVA were developed. RESULTS Median DeltaVcc/% were 6.95 cc and 26%, respectively. The most predictive independent variables of DeltaVcc at MVA were the initial parotid volume (IPV, OR: 1.100; p=0.0002) and Dmean (OR: 1.059; p=0.038). The main independent predictors of DeltaV% at MVA were age (OR: 0.968; p=0.041) and V40 (OR: 1.0338; p=0.013). DeltaVcc and DeltaV% may be well described by the equations: DeltaVcc=-2.44+0.076 Dmean (Gy)+0.279 IPV (cc) and DeltaV%=34.23+0.192 V40 (%)-0.2203 age (year). The predictive power of the DeltaVcc model is higher than that of the DeltaV% model. CONCLUSIONS IPV/age and Dmean/V40 are the major dosimetric and clinical/anatomic predictors of DeltaVcc and DeltaV%. DeltaVcc and DeltaV% may be well described by bi-linear models including the above-mentioned variables.

The Shape of Parotid DVH Predicts the Entity of Gland Deformation During IMRT for Head and Neck Cancers

The Jacobian of the deformation field of the registration between images taken during Radiotherapy is a measure of compression/expansion of the voxels within an organ. The Jacobian mean value was applied to investigate possible correlations between parotid deformation and anatomical, clinical and dosimetric parameters. Data of 84 patients were analyzed. Parotid deformation was evaluated through Jacobian maps of images taken at the start and at the end of the treatment. Several clinical, geometrical and dosimetric factors were considered. Correlation between Jacobian mean value and these parameters was assessed through Spearman’s test. Univariate and multivariate logistic analyses were performed by considering as the end point the first quartile value of the Jacobian mean value. Parotid dose volume histograms were stratified according to gland deformation, assessing the most predictive dose-volume combination. At multivariate analysis, age (p = 0.02), overlap between tumor volume and parotid gland (p = 0.0006) and the parotid volume receiving more than 10 Gy (p = 0.02) were found as the best independent predictors, by considering Jacobian mean value fist quartile, the parotid volume receiving more than 10 Gy and 40 Gy were found as the most predictive dosimetric parameters. Parotid glands were divided in three different sub-groups (bad-, medium- and good dose volume histogram). The risk to have Jacobian means value lower than first quartile was 39.6% versus 19.6% versus 11.3% in these three groups. By including in the multivariate analysis this “dose volume grouping” parameter, age and bad dose volume histogram were found as the most predictive parameters for large shrinkage. The pattern of parotid deformation may be well predicted by some pre-treatment variables; a bad dose volume histogram seems the most important predictor.

Could early tumour volume changes assessed on morphological MRI predict the response to chemoradiation therapy in locally-advanced rectal cancer?

AIM To investigate the potential role of an additional magnetic resonance imaging (MRI) examination performed during neoadjuvant chemoradiation therapy (CRT) in the prediction of pathological response in locally advanced rectal cancer (LARC). MATERIAL AND METHODS Forty-eight consecutive patients with LARC underwent neoadjuvant CRT. MRI studies at 1.5 T, including high-resolution T2-weighted sequences that were acquired parallel and perpendicular to the main axis of the tumour were performed before (preMRI), during (midMRI), and 6-8 weeks after the end of CRT (postMRI). Cancer volumes (Vpre, Vmid, Vpost) were drawn manually and the reduction rate calculated (ΔVmid, ΔVpost). According to Rödels pathological tumour regression grade (TRG), patients were considered non-responders (NR; TRG0-2), partial responders (PR; TRG3), and complete responders (CR; TRG4). Multivariate regression analysis was performed to identify the best MRI predictors of NR, PR, and CR. RESULTS Twenty-five patients were considered PR (52%), 13 CR (27%), and 10 NR (22%). Tumour shrinkage mainly occurred shortly after CRT (ΔVmid: CR: 80±10% versus PR: 56±19% versus NR: 28±22%, p=2.2×10-16). Vmid, Vpost, ΔVmid, and ΔVpost correlated with TRG (p<0.001). At multivariate analysis, the combined assessment of Vmid and ΔVmid was selected as the best predictor of response to CRT, in that it distinguishes CR, PR, and NR early and accurately (81.5%). CONCLUSION MidMRI allows final response assessment to neoadjuvant CRT earlier and better than the MRI performed after the end of CRT. MRI findings at midMRI may be useful to tailor patient treatment.

Validation of a method for “dose of the day” calculation in head-neck tomotherapy by using planning ct-to-MVCT deformable image registration

PURPOSE The aim of this study was to test the feasibility and dosimetric accuracy of a method that employs planning CT-to-MVCT deformable image registration (DIR) for calculation of the daily dose for head and neck (HN) patients treated with Helical Tomotherapy (HT). METHODS For each patient, the planning kVCT (CTplan) was deformably registered to the MVCT acquired at the 15th therapy session (MV15) with a B-Spline Free Form algorithm using Mattes mutual information (open-source software 3D Slicer), resulting in a deformed CT (CTdef). On the same day as MVCT15, a kVCT was acquired with the patient in the same treatment position (CT15). The original HT plans were recalculated both on CTdef and CT15, and the corresponding dose distributions were compared; local dose differences <2% of the prescribed dose (DD2%) and 2D/3D gamma-index values (2%-2mm) were assessed respectively with Mapcheck SNC Patient software (Sun Nuclear) and with 3D-Slicer. RESULTS On average, 87.9%±1.2% of voxels were found for DD2% (on average 27 slices available for each patient) and 94.6%±0.8% of points passed the 2D gamma analysis test while the 3D gamma test was satisfied in 94.8%±0.8% of bodys voxels. CONCLUSIONS This study represents the first demonstration of the dosimetric accuracy of kVCT-to-MVCT DIR for dose of the day computations. The suggested method is sufficiently fast and reliable to be used for daily delivered dose evaluations in clinical strategies for adaptive Tomotherapy of HN cancer.

EP-1300: Preoperative, Adaptive Radiotherapy with Tomotherapy concomitant with chemotherapy in rectal cancer

ESTRO 35 2016 _____________________________________________________________________________________________________ Morphologic and/or metabolic imaging re-assessment was performed between 12 and 26 weeks after treatment. Disease-free survival (DFS) was calculated from the end of treatment to the date of first event of disease recurrence. Overall survival (OS) was calculated from the end of treatment to the date of death from any cause or of last follow-up.

182 Development and internal validation of a nomogram predicting biochemical recurrence after early salvage radiotherapy in prostate cancer patients treated with radical prostatectomy

INTRODUCTION AND OBJECTIVES: Previous studies have shown that the efficacy of salvage radiotherapy (SRT) in men with biochemical recurrence (BCR) after radical prostatectomy (RP) is strongly related to PSA value at SRT. Contemporary guidelines suggest that SRT,when indicated, should be given at a PSA 0.5 ng/ml (early SRT eSRT). Such approach might be comparable in terms of cancer control to adjuvant RT while significantly reducing the number of patients exposed to RT. However, there is no available model for the prediction of BCR after eSRT. We developed and internally validated a model predicting BCR in patients treated with eSRT after RP for PCa METHODS: The study included 342 patients who received eSRT for BCR after RP at 6 European tertiary care centers between 1993 and 2006. Early SRT was defined as a salvage treatment given at a PSA 0.5 ng/ml. All patients had pT3, pN0, / positive surgical margins (SM). BCR after RP and eSRT was defined as two consecutive PSA values 0.2 ng/mL. No patient received adjuvant hormonal therapy. Radiotherapy consisted of a local radiation delivered to the prostate and seminal vesicle bed alone. Univariable (UVA) and multivariable (MVA) Cox regression models predicting BCR after eSRT were fitted. Predictors consisted of PSA at eSRT, time from RP to BCR, SM status, pT stage, radiotherapy dose and pathological Gleason score (6 vs. 7 vs. 8 or more). Regression-based coefficients were then used to develop a nomogram predicting BCR at 5 years after eSRT. The accuracy of the nomogram was quantified with the Harrel’s concordance index and the calibration plot method. Two hundred bootstrap resamples were used for internal validation. RESULTS: Mean follow-up was 81 months (median 68 months). Mean RT dose was 68 Gy (median 66.6 Gy,range:60-75.6 Gy). BCR-free survival rates at 2, 5 and 8 years after eSRT were 78.4, 61.5 and 56.8%, respectively. At univariable Cox regression analyses, pathological Gleason sum, PSA at eSRT and SM status were indipendent predictors of BCR after eSRT (all p 0.005). The results were confirmed at MVA where all mentioned predictors, including PSA at eSRT, were significantly associated with BCR after eSRT (all p 0.004). The coefficient-based nomogram demonstrated a bootstrapcorrected predictive accuracy of 74.7%. CONCLUSIONS: More than half of patients treated with eSRT for BCR after surgery had undetectable PSA at 5 years. We report the first nomogram predicting BCR in these patients. Our model might be used to assess the impact of eSRT according to each individual characteristics. Source of Funding: None

228 ADJUVANT RADIOTHERAPY LEADS TO SUPERIOR BIO-CHEMICAL RECURRENCE FREE SURVIVAL COMPARED TO EARLY SALVAGE RADIOTHERAPY IN PATIENTS WITH LOCALLY ADVANCED PROSTATE CANCER: RESULTS OF A MATCHED-CONTROLLED MULTI-INSTITUTIONAL ANALYSIS

Efficacy Endpoints for Abiraterone Acetate vs Placebo Endpoint AA (n 797) Placebo (n 398) HR (95% CI) P Value OS, median 14.8 mos 10.9 mos 0.65 (0.54, 0.77) 0.0001 TTPP, median 10.2 mos 6.6 mos 0.58 (0.46, 0.73) 0.0001 rPFS, median 5.6 mos 3.6 mos 0.67 (0.58, 0.78) 0.0001 PSA response 38% 10% – Objective response a 14% (n 55/ 392) 2.8% (n 5/ 181) 5.1 (2.1, 12.5) a RECIST in subjects with measurable disease at baseline; relative risk.

EP-1852: Predictive role of FDG-PET/CT image-derived parameters in locally advanced oropharyngeal cancer

S871 ________________________________________________________________________________ patients with high-risk extremity soft tissue sarcoma. A twotier registration was used to align the tumor VOI within each dynamic frame at TP1 and align the volumes at TP2 to the volumes at TP1. After registration, the voxel-wise transfer constant K within a VOI covering the whole tumor normalized to a reference region of normal tissue area closed to the tumor was calculated. The responder threshold was determined by linear regression via evaluating the 95% confidence interval [-T, T] in the residuals from the reference region. The difference of the voxel-wise ΔK within the tumor between TP1 and TP2 was calculated. Three classes of voxels within the tumor VOI were determined: voxels having ΔK value exceed threshold T were designated in red, below -T were designated in blue, and otherwise designated in green indicating no significant change. The volume fractions with respect to three subvolumes of the tumor VOI were computed as F+ (red voxels), F-(blue voxels) and F0 (green voxels).

EP-1725: Predictors of diarrhea after whole-pelvis post-prostatectomy radiotherapy

S807 ________________________________________________________________________________ was maintained as long as the effect metric used for Cox regression had a linear correlation with the true effect metric of at least 0.50. The conclusions held if the trial cohort consisted of an expected high benefit population (22% reduced sample size), but the effect was even stronger if the cohort was a population with modest expected benefit (31% reduced sample size).