Comparison of AI-Based Auto-Segmentation Quality with Different Daily IGRT Imaging Modalities for Adaptive Radiotherapy Treatment Planning

Abstract

Modern AI-based auto-segmentation tools may be applied on daily computed tomography (CT) images in an image-guided radiotherapy course to facilitate evaluation of anatomical variations and adaptive treatment planning. However, most AI auto-segmentation models are trained with high-quality diagnostic or planning CT image data. This study aims to evaluate and compare auto-segmentation quality by an AI-based auto-segmentation system with different daily CT imaging modalities. We retrospectively retrieved daily IGRT images for sixty patients. Among them, twenty patients were treated on helical tomotherapy with daily megavoltage CT (MVCT) scans, twenty patients were treated on conventional Linacs with daily cone-beam CT (CBCT) scans, and twenty patients were treated on a biology-guided radiotherapy (BgRT)-capable machine with daily fan-beam kilovoltage CT (kVCT) scans. With each treatment modality, ten patients received daily CT scans in the pelvic region while the other ten received daily CT scans in the thoracic region. An auto-segmentation system using a convolutional neural network algorithm was trained in-house with historical treatment planning CT and contouring data to generate auto-segmentation models for the pelvic and thoracic regions, respectively. Normal organs were first delineated the auto-segmentation system on the daily CT images and then drawn by an experienced planner. A set of metrics including the dice similarity coefficient (DSC), Jaccard similarity index, and Hausdorff distance were used to evaluate the quality of the auto segmentation results compared with manual contours. The auto-segmentation contours on the kVCT images showed higher average DSC compared to those on the MVCT and CBCT images for all the major organs in both the pelvic and thoracic regions including the bladder, rectum, bowel, left and right femurs, esophagus, heart, left and right lung, and spinal cord. With the kVCT images, the average DSC ranged from 0.52±0.22 to 0.996±0.005. In the pelvic region, the largest absolute difference in DSC was observed for the bowel volume with an average DSC of 0.69±0.16, 0.49±0.27, and 0.32±0.25 for the kVCT, MVCT, and CBCT images, respectively (p-value < 0.05 with unpaired t-tests between kVCT and the other two imaging modalities); in the thoracic region, the largest absolute difference in DSC was observed for the esophagus with an average DSC of 0.63±0.16, 0.22±0.08, and 0.15±0.18 for the kVCT, MVCT, and CBCT images, respectively (p-value < 0.05 with unpaired t-tests between kVCT and the other two imaging modalities). Similar results were observed with other metrics. The AI-based auto segmentation system showed improved agreement with manual contouring when using kVCT images from the BgRT capable machine compared to MVCT or CBCT images. However, manual correction is necessary on auto-segmentation results from all imaging modalities especially for organs with limited contrast from surrounding tissues.