Development of a Vietnamese PET/CT Dataset for Machine Learning-Based Analysis of Non-Small Cell Lung Cancer Images

H. Q. Tuan, T. T. Duong, B. N. Ha, N. H. Quyet, L. V. Tinh, C. V. Tuynh, V. K. Nam, L. T. Q. Dao, C. V. Luong, D. T. M. Linh, D. T. Nhung, N. D. Nguyen, V. Q. Trang

Abstract


Positron Emission Tomography and Computed Tomography (PET/CT), a key imaging modality in nuclear medicine, Combines Anatomical (CT) and functional (PET) data for cancer diagnosis. Despite advancements in machine learning for automated medical image analysis, publicly available PET/CT datasets remain scarce, limiting Artificial Intelligence (AI) research compared to CT and MRI. This study built a publicly accessible PET/CT Vietnamese dataset for Non-Small Cell Lung Cancer (NSCLC). A total of 416 PET/CT scans were collected from three Vietnamese hospitals, including 300 NSCLC cases. Malignant FDG-sensitive lesions, identified via clinical PET/CT reports, were manually segmented in 3D (slice-by-slice) on PET images and validated by three experienced radiologists. The dataset includes both original and annotated DICOM files, along with clinical patient data. It achieved a dice similarity coefficient of 80.3 % and volume similarity of 81.9 %, demonstrating high segmentation accuracy comparable to other studies. This dataset supports AI-driven NSCLC research and contributes to global efforts in automated PET/CT analysis for nuclear medicine applications.

Keywords


Radiology, Nuclear Medince, PET-CT, Lung cancer, Machine learning.

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DOI: https://doi.org/10.55981/aij.2025.1645

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