ESTRO 2022 - Abstract Book

S646

Abstract book

ESTRO 2022

1 IRCCS San Raffaele Scientific Institute, Medical Physics, milan, Italy; 2 University Hospital of Parma AOUP, Medical Physics, parma, Italy; 3 Azienda USL-IRCCS di Reggio Emilia, Medical Physics Unit, Department of Advanced Technology, reggio emilia, Italy; 4 veneto institute of oncology IOV-IRCCS, Medical Physics, padova, Italy; 5 Amethyst Radioterapia Italia, San Giovanni Calibita Fatebenefratelli Hospital, Medical Physics, rome, Italy; 6 University Hospital of Udine, Medical Physics, udine, Italy; 7 IRCCS Istituto Nazionale dei Tumori Regina Elena, Medical Physics, rome, Italy; 8 University Hospital Federico II, Medical Physics, naples, Italy; 9 Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Medical Physics, rome, Italy; 10 Fondazione IRCCS Istituto Nazionale dei Tumori, Medical Physics, milan, Italy Purpose or Objective Knowledge-based (KB) plan prediction models were used to assess differences of plan performances between institutes in the context of right whole breast (RWB) irradiation using tangential-fields (TF). The aim of the current investigation was to assess major quantitative parameters that influence inter-Institute variability, possibly suggesting robust and efficient metrics to estimate KB-model transferability. Materials and Methods Ten Institutions set RWB-TF KB models by using RapidPlan (Varian Inc., V.16.1 and earlier), following previously shared methods as same, number of patients (> 70), contouring (national guidelines for CTV/PTV/OARs), techniques (wedged or FiF) and outlier elimination criteria. Two patients of each center (in total n=20, external from the training sets) were used to perform cross validation test using a dedicated Eclipse research station (V.16.1): DVH’s prediction bands of OARs (heart, ipsilateral lung, contralateral lung, contralateral breast) predicted from the 10 models (normalized to 40Gy/15 fraction scheme) were exported and inter-institute variability was quantified by looking to SD of several DVH and dose/statistics parameters. Varian Model Analytics, software was used for seven models (only available for earlier V16.1 version) to quantify the distribution of anatomical (PTV/OARs volumes: Vol) and dosimetric (PTV V95%, D99%, Dmax, SD: PTV_dose) parameters referred to the original training data sets. Then, the Principal Component (PC) of GED (Geometry-based Expected Dose) was derived from a “test KB model” based on the 20 patients considered in the cross-validation; in the same patients, the portion of the right lung in field (d C ) along the isocentric axial slice was also quantified. The association between inter-institute variability of the predicted mean ipsilateral lung doses (Dmean) and Vol, PTV_dose and PC, PTV_dose and d C was investigated

Results

Dosimetry/anatomical parameters showed different distributions for the seven models in the original data sets. In particular, one center always showed no overlap between PTV and ipsilateral lung. For the remaining 6 models, there was a clear association between ipsilateral lung average Dmean value and the PTV median value of D99%, as shown in Figure 1 (R 2 =0.78), with average values of Dmean ranging between 4.7 and 6.1 Gy. PC and d C analyses didn’t show correlation between individual geometrical/anatomical features and inter-institute SD of Dmean, as summarized in Figure 2: the mean value of SD was 13.2%.