Training and Validation of Deep Learning-Based Auto-Segmentation Models for Lung Stereotactic Ablative Radiotherapy Using Retrospective Radiotherapy Planning Contours.

Jordan Wong¹, Vicky Huang², Joshua A Giambattista^3,4, Tony Teke⁵, Carter Kolbeck⁴, Jonathan Giambattista⁴, Siavash Atrchian⁵

¹Radiation Oncology, British Columbia Cancer - Vancouver, Vancouver, BC, Canada.

²Medical Physics, British Columbia Cancer - Fraser Valley, Surrey, BC, Canada.

³Radiation Oncology, Saskatchewan Cancer Agency, Regina, SK, Canada.

⁴Limbus AI Inc, Regina, SK, Canada.

⁵Medical Physics/Radiation Oncology, British Columbia Cancer - Kelowna, Kelowna, BC, Canada.

PubMed PMC DOI PDF

Abstract

PURPOSE: Deep learning-based auto-segmented contour (DC) models require high quality data for their development, and previous studies have typically used prospectively produced contours, which can be resource intensive and time consuming to obtain. The aim of this study was to investigate the feasibility of using retrospective peer-reviewed radiotherapy planning contours in the training and evalua…

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