Hypermetabolic pulmonary lesions detection and diagnosis based on PET/CT imaging and deep learning models.
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...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 52; no. 10; pp. 3792 - 3807 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187119026&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187119026 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Aug2025 vid: 52 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187119026 184223330 10.1007/s00259-025-07215-0 187119026 ppf: 3792 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Hypermetabolic pulmonary lesions detection and diagnosis based on PET/CT imaging and deep learning models. aug: au: Hu, Jiajia Cheng, Ran Quan, Meilin Peng, Yao Yang, Zi Zhang, Qing Ji, Faquan Chen, Yangchun Li, Biao Wen, Ning affil: https://ror.org/01hv94n30 Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Second Road, Huangpu District, Shanghai, China sug: ab: 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: The study retrospectively included 647 cases (409 males/238 females) over more than 8 years from five centres, divided into an internal dataset (426 cases from Shanghai Ruijin Hospital), an external test set I (151 cases from four other institutions), and an external test set II (70 cases from a new imaging device). The localisation model achieved detection rates of 81.19%, 75.48%, and 77.59% on the internal, external test set I, and external test set II, respectively. The classification model outperformed the radiomics approach, with area-under-curves of 88.4%, 80.7%, and 66.6%, respectively. Most false-positive segmentations were clinically acceptable, corresponding to suspicious lesions in adjacent regions, particularly lymph nodes. Conclusion: Deep learning models based on PET/CT imaging can effectively detect, segment, and classify hypermetabolic lung lesions, and identify suspicious adjacent lesions. These results highlight the potential of artificial intelligence in clinical decision-making and lung disease diagnosis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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