ESTRO 2020 Abstract Book

S182 ESTRO 2020

Proffered Papers: Proffered papers 19: Auto-contouring using deep learning

OC-0343 Artificial Intelligence Based Auto-Contouring of CTV for Cervical Cancer C. Ma 1 , J. Zhou 1 , X. Xu 1 , J. Guo 1 , M. Han 2 , Y. Gao 2 , Z. Wang 3 , J. Zhou 3 1 The First Affiliated Hospital of Soochow University, Dept. of Radiotherapy & Oncology, Suzhou, China ; 2 United Imaging Intelligence, Dept. of UII SH Algo1, Shanghai, China ; 3 Shanghai United Imaging Healthcare, Dept. of RT BU APPL, Shanghai, China Purpose or Objective To validate an artificial intelligence (AI)-based auto- contouring tool of CTVs for cervical cancer, and to evaluate its potential in improving contouring accuracy and efficiency. Material and Methods 533 CT image sets were collected from patients who received external beam radiotherapy for cervical cancer from January 2013 to June 2019. The CTVs were delineated by ROs according to institutional consensus, and set as reference contours after reviewed by experts. For definitive cases, delineate the pelvic lymphatic drainage area as dCTV1, and the paraventricular area as dCTV2; for postoperative cases, delineate the pelvic lymphatic drainage area as pCTV1. An AI-based auto-contouring tool was constructed for dCTV1, dCTV2 and pCTV1 and tested, respectively. The training/validation/testing cohorts contained 157/20/23 cases for dCTV1/dCTV2, and 270/30/33 cases for pCTV1. Dice coefficient (DC) and mean surface distance (MSD) were calculated for AI contours in testing cohorts. In addition, 4 definitive and 6 postoperative cases were randomly selected from the testing cohort and manually contoured by 3 groups of ROs: junior, intermediate, and senior, with 3 ROs per group. DC were calculated for ROs’ manual contours of each group and compared against those of AI. One month later, the junior RO group re-contoured the same 10 cases by modifying AI contours (AI-assisted junior contours) to evaluate whether AI-assistance improves accuracy. Manual contouring time and AI-assisted contouring time (including both algorithm and modification time) were recorded for efficiency comparison. Results For dCTV1, dCTV2 and pCTV1, DC of AI contours in the testing cohort were 0.88±0.03, 0.70±0.09, 0.86±0.03, respectively; and MSD were 1.32±0.48 mm, 2.42±1.62 mm, 1.15±0.38 mm, respectively. Comparison of DC between AI and ROs’ manual contours of each group showed there were no significant difference between AI and all RO groups in dCTV1, and AI outperformed the junior group for dCTV2 (median DC: 0.75 vs 0.56, P <0.01) and all groups for pCTV1(median DC: 0.75 vs 0.83/0.83/0.83, P <0.05 for all). The junior group performed AI-assisted contouring for dCTV2 and pCTV1 to determine whether AI-assistance brings improvement. Median DC was improved from 0.56 to 0.72 (30.0%, P <0.05) for dCTV2 and from 0.83 to 0.86 (3.6%, P <0.05) for pCTV1. Mean contouring time reduced from 19.0 minutes to 10.1 minutes (46.7%) for dCTV2 and from 43.6 minutes to 14.7 minutes (66.2%) for pCTV1. Conclusion Our study validated an AI-based auto-contouring tool of CTVs for cervical cancer. AI-generated contours showed high accuracy for all three CTVs. The similarity between AI and reference contours was comparable to that of senior RO group. AI-assistance improved both accuracy and

Conclusion Online real-time rotation-including motion-including dose reconstruction was performed for the first time. It agreed well with film measurements. It provides a valuable real- time QA tool that allows easy investigation of motion effects and the efficacy of compensation techniques.