Clinical Feasibility of Deep Learning-Based CT during Treatment CBCT Tumor Registration-Segmentation in Thoracic Radiotherapy (RT).

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Abstract

Accurate tumor segmentation on weekly cone-beam computed tomography (CBCT) images is critical for image-guided and adaptive radiation therapy (ART). In thoracic RT, low image contrast, imaging artifact, and geometry and image modality differences from the planning CT (pCT) typically limits accurate tumor segmentation and registration. Here, we explored the clinical feasibility of using 3D recurrent registration-segmentation deep learning (DL) that combines patient-specific anatomic and shape context from higher contrast pCT and planning contours (PACs) for tumor segmentation on during treatment CBCTs.We included the pCT and CBCTs from six patients with locally advanced non-small cell lung cancer (LA-NSCLC) who had underwent RT. Cases were selected with a primary GTV contoured and labeled separately from the nodal GTV. Using rigidly aligned pCT and CBCT as inputs, DL auto-segmented the GTV on week 1 and 6 CBCTs, and these auto-segmented contours were manually inspected by a radiation oncologist that edited the GTV according to clinical standard quality. The Dice similarity coefficient (DSC), Hausdorff distance (HD95), mean surface distance (MSD), surface DSC (sDSC) and added path length (APL) were used to quantitively compare the DL and the edited GTV.The primary GTV was in the right lung in five cases, and left lung in one case. Manual adjustments were typically made at the interface of GTV and lung parenchyma with partial inclusion of adjacent vessels. Hypodensities within the GTV were sometimes not segmented in all axial slices resulting in discontinuous components. The quantitative comparison between the edited and DL-generated GTV is shown in Table 1. For week 1, the average DSC and HD95 were 0.87 and 6.94 mm, respectively. The performance for week 6 was slightly lower than week 1, with a DSC of 0.85 and HD95 of 7.22 mm.The agreement with the generated DL GTV and the edited GTV was high in week 1 and decreased somewhat later during the treatment course possibly due to a higher impact of geometric changes in tumor and adjacent structures. The proposed DL algorithm showed reasonable performance throughout the treatment, supporting its potential for use into clinical routine for LA-NSCLC.Copyright © 2023. Published by Elsevier Inc.