ArticleFrontiers in oncology2023
A deep learning approach for automatic tumor delineation in stereotactic radiotherapy for non-small cell lung cancer using diagnostic PET-CT and planning CT.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 11 citations in OpenAlex.
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- Fractal-driven self-supervised learning enhances early-stage lung cancer GTV segmentation: a novel transfer learning framework.Japanese journal of radiology · 2026Article
- A full-scale attention-augmented CNN-transformer model for segmentation of oropharyngeal mucosa organs-at-risk in radiotherapy.Physical and engineering sciences in medicine · 2025Article
- A monocentric retrospective analysis of 10-year overall survival after stereotactic body radiotherapy for medically inoperable non-small cell lung cancer.Oncology letters · 2025Article
- A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology.Bioengineering (Basel, Switzerland) · 2025Article
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- Evaluating deep learning auto-contouring for lung radiation therapy: A review of accuracy, variability, efficiency and dose, in target volumes and organs at risk.Physics and imaging in radiation oncology · 2025Review
- EXACT-Net: Framework for EHR-Guided Lung Tumor Auto-Segmentation for Non-Small Cell Lung Cancer Radiotherapy.Cancers · 2024Article
- Impact of contrast-enhanced CT in the dosimetry of SBRT for liver metastases treated with MR-Linac.Radiation oncology (London, England) · 2024Article
- Accurate and robust auto-segmentation of head and neck organ-at-risks based on a novel CNN fine-tuning workflow.Journal of applied clinical medical physics · 2024Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 ( Results: The average 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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