ESTRO 2020 Abstract Book

S361 ESTRO 2020

statistically significant regression constant of DeltaD of - 1.63 per cm (HR=0.195) with p-value of 0.017

change (clinical action needed, Cat3). The external validation dataset consisted of 760 TI EPID images from 266 fractions (31 patients) treated at institute B with VMAT or hybrid plans (static beams and VMAT arcs). Features in both datasets were extracted in the same way. For patients in the validation set, a cone beam CT (CBCT) scan was made before each fraction. Contours were propagated from the planning CT to the CBCTs using Mirada (Mirada Medical Ltd., Oxford, UK), and the dose was recalculated. DVH metrics for targets and organs-at-risk (OARs) were extracted for each fraction, and compared to the planned dose. Mann-Whitney U tests were performed to evaluate statistical significance of deviations in DVH metrics between each pair of HMM categories. Results The HMM achieved 78.9% accuracy compared to threshold classification based on the average γ value alone (a surrogate for clinical classification). The confusion matrix (Fig.1) shows that the HMM overestimates the amount of fractions in Cat2 compared to both Cat1 and Cat3. Fig.2 shows that for lungs-GTV, heart and mediastinum, there is a trend towards higher deviations in DVH metrics with classification into higher categories by the HMM.

Conclusion As in the original study this validation study demonstrates significant impact on survival due to estimated setup errors in the direction of the heart, even with daily online IGRT corrections. This indicates that dose to heart impacts survival for these patients. However, in contrast to the original study the effect of DeltaD only starts 16 months after RT. This difference in the two studies might reflect differences in treatment or patient cohorts at the two centers, and calls for additional external validation PH-0651 Validating a hidden Markov model for lung anatomical change classification using EPID dosimetry C. Wolfs 1 , N. Varfalvy 2 , R. Canters 1 , S. Nijsten 1 , L. Archambault 3 , F. Verhaegen 1 1 GROW-School for Oncology and Developmental Biology - Maastricht University Medical Centre, Department of Radiation Oncology - Maastro, Maastricht, The Netherlands ; 2 CHU de Québec, Département de Radio- oncologie, Québec QC, Canada ; 3 CHU de Québec/Université Laval, Département de Radio- oncologie/Physics Department, Québec QC, Canada Purpose or Objective A hidden Markov model (HMM) for classifying gamma (γ) analysis results of in vivo electronic portal imaging device (EPID) measurements into different categories of anatomical change for lung cancer patients was externally validated. The relation between model classification and differences in dose-volume histogram (DVH) metrics was also analyzed. Material and Methods The HMM was developed at institute A, and trained on features extracted from γ analysis maps of 2197 in vivo time-integrated (TI) EPID images from 490 fractions (22 patients, treated with 3D-CRT or IMRT), using (3%,3mm) criteria, 10% low dose threshold, and the EPID image of the first treatment fraction as reference. The model inputs were the average γ value, standard deviation, and average value of the top 1% of γ values, averaged over all beams in a fraction. The HMM classified each fraction into one of three categories: no anatomical change (Cat1), some change (no clinical action needed, Cat2) and severe

Fig.1: Confusion matrix comparing HMM classification to threshold classification based on the average γ value.