ESTRO 2023 - Abstract Book

S93

Saturday 13 May

ESTRO 2023

External validation of a fully-automated workflow for adaptive multi-criteria planning for EMBRACE II cervical cancer HDR- BT - without any workflow adjustments or manual tuning of Autoplans - showed overall superior quality of Auto compared to Manual for dosimetry, loading pattern and clinician’s preference.

OC-0131 Deep learning-based segmentation in cervical HDR brachytherapy with two types of applicators R. Ni 1 , K. Han 2,3 , B. Haibe-Kains 1,2 , A. Rink 1,2,3,4 1 University of Toronto, Department of Medical Biophysics, Toronto, Canada; 2 University Health Network, Princess Margaret Cancer Center, Toronto, Canada; 3 University of Toronto, Department of Radiation Oncology, Toronto, Canada; 4 University Health Network, TECHNA Institute, Toronto, Canada Purpose or Objective Manually delineating OARs and targets is a time-consuming process in cervix brachytherapy (BT). Deep learning (DL)-based automatic segmentation approaches have demonstrated promising delineation results with significantly reduced time. Applicator selection is a key step to delivering appropriate treatment that primarily depends on disease extent and anatomy. However, whether the mixing of various applicators will diminish the model performance and how to retain the model’s generalizability among different applicator types remain unclear. In this study, we addressed this research gap by developing DL model with a dataset of both interstitial ring and tandem (R&T) and Syed Neblett template (S-N) and assessed the transfer learning (TL) strategies when using the existing model to auto-delineate patients with another type of applicator. Materials and Methods A dataset of 165 T2-weighted MR images (130 with R&T and 35 with S-N) with clinically used contours was built from 74 cervical cancer patients (39 R&T and 35 S-N). Bladder, rectum, sigmoid, small bowel and HR-CTV were segmented. First, The R&T model was trained with 119 R&T cases using a self-adapting U-Net-based framework (nnU-Net). This baseline network (RTmodel) provided the initial weights for TL on the S-N dataset (25 finetuning cases for TL25 and 10 testing cases). The S-N testing results segmented by the RTmodel and finetuned model were used to assess the TL efficiency. A Mixed model was trained with both R&T training set and S-N finetuning set, and tested on two testing sets separately. Second, we examined TL data requirements by using a different number of fine-tuning cases (n=5-20). Additionally, training- from-scratch models were trained with the same subsets to be compared with the fine-tuned models. Segmentation performance was evaluated by four metrics. Results