ArticleBiomedicines2021
Diagnostic Value of Conventional PET Parameters and Radiomic Features Extracted from 18F-FDG-PET/CT for Histologic Subtype Classification and Characterization of Lung Neuroendocrine Neoplasms.
Article in Biomedicines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Role of Chest CT Radiomics in Differentiating Tumorlets and Granulomas: A Preliminary Study.Journal of clinical medicine · 2025Article
- Article
- Application of Radiomics for Differentiating Lung Neuroendocrine Neoplasms.Diagnostics (Basel, Switzerland) · 2025Article
- Applications of Artificial Intelligence and Radiomics in Molecular Hybrid Imaging and Theragnostics for Neuro-Endocrine Neoplasms (NENs).Life (Basel, Switzerland) · 2023Review
- The diagnostic and prognostic role of combined [European radiology · 2023Article
- Somatostatin Receptor-negative and Fluorodeoxyglucose-positron Emission Tomography-positive Lung Neuroendocrine Tumor G1 Exhibiting Cyclic Cushing's Syndrome.Internal medicine (Tokyo, Japan) · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Radiolabeled Somatostatin Analogues for Diagnosis and Treatment of Neuroendocrine Tumors.Cancers · 2022Review
- Clinicopathological, Oncogenic, andContrast media & molecular imaging · 2022Article
- CT Texture Analysis of Pulmonary Neuroendocrine Tumors-Associations with Tumor Grading and Proliferation.Journal of clinical medicine · 2021Article
- Evaluating a Machine Learning Tool for the Classification of Pathological Uptake in Whole-Body PSMA-PET-CT Scans.Tomography (Ann Arbor, Mich.) · 2021Article
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11 authors.
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Abstract
aimTo evaluate if conventional Positron emission tomography (PET) parameters and radiomic features (RFs) extracted by 18F-FDG-PET/CT can differentiate among different histological subtypes of lung neuroendocrine neoplasms (Lu-NENs).
methodsForty-four naïve-treatment patients on whom 18F-FDG-PET/CT was performed for histologically confirmed Lu-NEN (n = 46) were retrospectively included. Manual segmentation was performed by two operators allowing for extraction of four conventional PET parameters (SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG)) and 41 RFs. Lu-NENs were classified into two groups: lung neuroendocrine tumors (Lu-NETs) vs. lung neuroendocrine carcinomas (Lu-NECs). Lu-NETs were classified according to histological subtypes (typical (TC)/atypical carcinoid (AC)), Ki67-level, and TNM staging. The least absolute shrink age and selection operator (LASSO) method was used to select the most predictive RFs for classification and Pearson correlation analysis was performed between conventional PET parameters and selected RFs.
resultsPET parameters, in particular, SUVmax (area under the curve (AUC) = 0.91; cut-off = 5.16) were higher in Lu-NECs vs. Lu-NETs (
conclusionIn our study, conventional PET parameters were able to distinguish Lu-NECs from Lu-NETs, but not TC from AC. RFs did not provide additional information.
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