ESTRO 2022 - Abstract Book

S778

Abstract book

ESTRO 2022

MO-0888 Automatic detection and segmentation of GTV for locally advanced cervical cancer in T2W MR images

R. Rouhi 1,2 , S. Niyoteka 1,2 , P. Laurent 1,2 , S. Achkar 1 , A. Carré 1,2 , A. Leroy 2,3 , S. Espenel 1 , C. Chargari 1,2 , E. Deutsch 1,2 , C. Robert 1,2 1 Department of radiation oncology, Gustave Roussy Cancer Campus, Villejuif, France; 2 Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800, Villejuif, France; 3 Therapanacea, Artificial Intelligence, Paris, France Purpose or Objective Locally advanced cervical cancer (LACC) is one of the most frequent malignant tumors among women. LACC is treated partly with ionizing radiation which makes accurate detection and segmentation of cervical tumors a keystone in the treatment of LACC. Manual segmentation of Gross Tumor Volume (GTV) is time-consuming in the radiotherapy (RT) and brachytherapy (BT) workflows and prone to variability and poor reproducibility. Despite its importance, automatic tumor detection and segmentation have rarely been applied to the pelvic region. In this work, we present a fully automatic method to detect and segment the GTVs for LACC in T2-Weighted (T2W) MR images. To the best of our knowledge, it is the first work presenting a comprehensive comparison between different state-of-the-art deep-learning based methods for GTV segmentation in cervical cancer. Materials and Methods For this study, T2W pre-RT images acquired by 18 imaging devices from 82 patients treated for LACC at Gustave Roussy Cancer Campus were gathered. The images were randomly divided into two sub-cohorts of 80% and 20% for respectively model training and model validation. Different recently developed deep neural network models were trained for 2D/3D MRI volume segmentation. Model performance was assessed quantitatively based on Dice Similarity Coefficient (DSC) and 95 th Hausdorff distance (HD95). For tackling the overfitting issue and increasing the generalizability of the segmentation, standard data augmentation techniques like random rotation, flip, zoom, contrast adjustment, Gaussian noise, and smoothing were applied to the MRI volumes used in the training set. Results The results showed the effectiveness of 2D-segmentation by SegResNet compared with the other state-of-the-art models with average values of DSC = 0.775 and HD95 = 13.9 mm for the segmentation of GTV in LACC. The model achieved the best results of DSC = 0.863 and HD95=8.9.