A Deep Learning Approach for Automatic Tumor Delineation in Stereotactic Radiotherapy for Non-small Cell Lung Cancer Using Diagnostic PET-CT and Planning CT

Overview

Journal Front Oncol

Specialty Oncology

Date 2023 Aug 21

PMID 37601687

Authors

Xuyao Yu

Lian He

Yuwen Wang

Yang Dong

Yongchun Song

Zhiyong Yuan

Ziye Yan

Wei Wang

Affiliations

Soon will be listed here.

Abstract

Introduction: Accurate delineation of tumor targets is crucial for stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC). This study aims to develop a deep learning-based segmentation approach to accurately and efficiently delineate NSCLC targets using diagnostic PET-CT and SBRT planning CT (pCT).

Methods: The diagnostic PET was registered to pCT using the transform matrix from registering diagnostic CT to the pCT. We proposed a 3D-UNet-based segmentation method to segment NSCLC tumor targets on dual-modality PET-pCT images. This network contained squeeze-and-excitation and Residual blocks in each convolutional block to perform dynamic channel-wise feature recalibration. Furthermore, up-sampling paths were added to supplement low-resolution features to the model and also to compute the overall loss function. The dice similarity coefficient (), precision, recall, and the average symmetric surface distances were used to assess the performance of the proposed approach on 86 pairs of diagnostic PET and pCT images. The proposed model using dual-modality images was compared with both conventional 3D-UNet architecture and single-modality image input.

Results: The average of the proposed model with both PET and pCT images was 0.844, compared to 0.795 and 0.827, when using 3D-UNet and nnUnet. It also outperformed using either pCT or PET alone with the same network, which had of 0.823 and 0.732, respectively.

Discussion: Therefore, our proposed segmentation approach is able to outperform the current 3D-UNet network with diagnostic PET and pCT images. The integration of two image modalities helps improve segmentation accuracy.

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