Hypermetabolic pulmonary lesions detection and diagnosis based on PET/CT imaging and deep learning models.

ABSTRACT:

Purpose

This study aims to develop and evaluate deep learning models for the detection and classification of hypermetabolic lung lesions into four categories: benign, lung cancer, pulmonary lymphoma, and metastasis. These categories are defined by their pathological origin, clinical relevance, and therapeutic implications.

Methods

A lesion localisation model was first developed using manually annotated PET/CT images. For classification, a multi-dimensional joint network was employed, incorporating both image patches and two-dimensional projections. Classification performance was quantified by metrics like accuracy, and compared to that of a radiomics model. Additionally, false-positive segmentations were manually reviewed and analysed for clinical evaluation.

Results

SUBMITTER: Hu J 

PROVIDER: S-EPMC12316760 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature