ESTRO 36 Abstract Book

S554 ESTRO 36 _______________________________________________________________________________________________

Conclusion Machine learning methods can be competitive with standard image processing algorithms in the field of organ segmentation. PO-1005 Automatic segmentation of cardiac sub- structures in the treatment of HL C. Fiandra 1 , M. Levis 1 , F. Cadoni 1 , V. De Luca 1 , F. Procacci 1 , A. Cannizzaro 1 , R. Ragona 1 , U. Ricardi 1 1 University of Torino, Oncology, Torino, Italy Purpose or Objective to validate, in the context of treatment of Hodgkin Lymphoma, three commercial software solutions for atlas- based segmentation of cardiac sub-structures Material and Methods 25 patients were selected and then divided into two groups: 15 patients will make up the personalized atlas and 10 patients on which will be applied atlases created in order to assess its quality. For the selection of patients, the following inclusion criteria were selected: patients with HL presentation of a mediastinal mass at the onset of the disease and the availability of CT imaging with contrast. Two expert physicians have delineated on the diagnostic CT with contrast the selected 15 patients cardiac structures: the heart as a whole, the four chambers of the heart, the coronary artery and valvular structures; which will compose the atlas. We use three commercial solutions (Velocity AI, MIM and RayStation) in order to compare their results; the structures delineated by doctors on the 5 control patients will be compared with those automatically drawn by atlases, through the conformality function (Dice Index (DI)). In addition, the atlases underwent a clinical evaluation of the involved physicians: in particular it was asked to a Radiation Oncologist to analyze contours made by the three software on reference patients to evaluate the goodness of the warp made from atlases than those performed by him. Clinical judgments were recorded on a scale of numerical values: 1 = poor; 2 = medium; 3 = good.

Conclusion In general we can say that the contours applied by atlases are valid, even if not yet optimal and they may represent a starting point for the step of contouring, useful to speed up this process; based on the values of Dice Index collected in this study, MIM performs better while RayStation appears the most powerful software from a clinical point of view thus obtaining contours more “similar” to those defined by the Radiation Oncologist. PO-1006 Evaluation of an auto-segmentation software for definition of organs at risk in radiotherapy M.D. Herraiz Lablanca 1 , S. Paul 1 , M. Chiesa 1 , K.H. Grosser 1 , W. Harms 1 1 St. Claraspital, Radioonkologie, Basel, Switzerland Purpose or Objective The aim of this work is to evaluate the capability of a commercial software performing automatic segmentation of relevant structures for radiotherapy planning, as well as the time saving of it use on a daily Basis. Material and Methods The software Smart Segmentation Knowledge Based Contouring (Version 13.6) from Varian Medical System was evaluated according to segmentation quality and time saving. For that purpose, 5 consecutive prostate and breast patients were contoured manually and automatically using the software, recording the time needed in both, manual and automatic contouring with corrections. This task was performed by the RTTs, since they are responsible of the OARs contouring in our department. Segmentation quality was qualitatively scored in four levels: 'excellent”(1), 'good”(2), 'acceptable”(3) and 'not acceptable”(4) and quantitatively evaluated calculating five parameters: Relative difference in volume, DICE similarity coefficient, Sensitivity Index, Inclusiveness Index, Mass Center Location. Results Mean values of the qualitative evaluation and acceptance are summarized in Table 1. The acceptance of the structures automatically contoured is higher for breast cancer than for prostate patients, as well as the mean time saving, that is above four minutes for breast and around 1 minute for prostate.

Results in terms of statistical analysis, the data obtained from the values of Dice Index were compared structure by structure between the three platforms. The Figure 1 shows only structures with a Dice Index more than 0.5 (right atrium, left atrium, the heart, the left side wall, interventricular septum, aortic valve, left ventricle and right ventricle). The differences between the 3 software were calculated and the structures delineated by MIM have more frequently higher values of Dice Index, compared to those of Velocity and RayStation, with respectively 0.03 to 0.01 p-value. Instead the difference between Velocity and RayStation is not statistically significant (p-value = 0.8). Regarding the evaluation of the Radiation Oncologist as compared to DI, values show that RayStation is the software that realizes contours more applicable in clinical practice, with statistically significant differences from Velocity and MIM, with p-value respectively of 0.038 and 0.046. While the difference between Velocity and MIM is not statistically significant (p-value = 0.083).

Good agreement was found between manual and automated segmentation for heart with a mean difference in volume of 7%, DICE of 0.87 and deviation of mass center less than 2mm in all directions and for liver with a mean difference in volume of 11%, DICE of 0.91 and deviation of mass center less than 2mm in all directions. Poor acceptance was found in complex structures as penile bulb, small bowel, sigma and rectum wall anterior and posterior. Conclusion Smart Segmentation software is a useful tool for the delineation of relevant structures for breast although did not generate useful delineation for neither the mammilla nor the esophagus. For relevant structures for prostate as penile bulb, small bowel, sigma and rectum wall anterior and posterior the software was not good enough. Further analysis will be performed including more patients.