ESTRO 2023 - Abstract Book

S1338

Digital Posters

ESTRO 2023

Conclusion MRI deep learning auto segmentation of brain OARs can segment all OARs except the lacrimal glands, which are hard to visualise on MRI. Editing clinical contours on MRI before model training improves performance. Implementing an MRI deep learning auto-segmentation in the RT pathway will improve contour consistency and efficiency but requires careful editing of OARs on MRI.

PO-1641 Clinical evaluation of autosegmentation using AI with manual segmentation of breast tissue

R. Stoica 1 , C. Pop-Casandra 2 , R.G. Curca 3 , A.M. Radu 4 , B. Chivu 4 , S.C. Vlad 3 , B. Anghel 1 , T. Popescu 5 , D. Grama 6 , M. Stanescu 6 , L. Bicsi 6 , D. Du ș e 6 1 Sanador, Radiation Oncology, Bucharest, Romania; 2 “Prof. Dr. Ion Chiricuta” Institute of Oncology, Radiation Oncology, Cluj-Napoca, Romania; 3 Neolife Medical Center, Radiation Oncology, Bucharest, Romania; 4 “Prof. Dr. Alexandru Trestioreanu” Institute of Oncology, Radiation Oncology, Bucharest, Romania; 5 Amethyst Radiotherapy Group, Radiation Oncology, Cluj-Napoca, Romania; 6 Synaptiq Tehnologies, AI Research, Cluj-Napoca, Romania Purpose or Objective In radiotherapy large inter-observer variability has been proven to influence the delineation of target volumes and near organs at risk (OARs) in breast cancer treatment preparation. This study evaluates and compares the quality of the breast contours of a deep learning Artificial Intelligence (AI) network, trained on a curated dataset of breasts volumes in breast cancer patients using “ESTRO consensus guideline on target volume delineation for elective radiation therapy of early-stage breast cancer”. Materials and Methods In this comparison 10 patients DICOM-RT datasets of breast cancer patients are used. The patients were initially treated for early-stage breast cancer in a local chain of radiotherapy clinics. The manual contouring is done by 5 referring Radiation Oncologists respecting ESTRO consensus guidelines and used as a reference, called next the Gold Standard (GS). The contours generated by the AI are then corrected by the same 5 ROs (AI-corrected — AI-c). We perform automatic segmentation using deep learning algorithms trained on a small database (37 left and 41 right breast delineations). We analyze the variability between the Gold Standard and the AI-corrected contours quantitatively, by computing three indexes: Dice Similarity Coefficient (DSC), 95 Hausdorff Distance (95 HD), and Mean Distance to Conformity (MDC). Finally, we conduct an A/B experiment with mixed GS and AI-corrected breast contours, and 3 expert ROs are asked to grade them from 1 to 3 (1 – acceptable, 2 – acceptable after minor corrections, 3 – acceptable after major corrections). The experiment gives us a qualitative perspective of the differences between manual and AI-corrected contouring procedures.

Results