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Article

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

1
Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, 10126 Turin, Italy
2
Department of Endocrinology, University Hospital of Brest, 29200 Brest, France
3
Medical Physics Unit, AOU Città della Salute e della Scienza, 10126 Turin, Italy
4
Biolab, Department of Electronics and Telecomunications, Politecnico di Torino, 10129 Turin, Italy
5
Pathology Unit, Department of Oncology, University of Turin, 10126 Turin, Italy
6
Department of Oncology, San Luigi Hospital, University of Turin, 10043 Orbassano, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Alberto Ricci
Biomedicines 2021, 9(3), 281; https://doi.org/10.3390/biomedicines9030281
Received: 5 February 2021 / Revised: 1 March 2021 / Accepted: 4 March 2021 / Published: 10 March 2021
(This article belongs to the Special Issue New Generation Imaging in Oncology)
Aim: To 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). Methods: Forty-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. Results: PET parameters, in particular, SUVmax (area under the curve (AUC) = 0.91; cut-off = 5.16) were higher in Lu-NECs vs. Lu-NETs (p < 0.001). Among RFs, HISTO_Entropy_log10 was the most predictive (AUC = 0.90), but correlated with SUVmax/SUVmean (r = 0.95/r = 0.94, respectively). No statistical differences were found between conventional PET parameters and RFs (p > 0.05) and TC vs. AC classification. Conventional PET parameters were correlated with N+ status in Lu-NETs. Conclusion: In 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. View Full-Text
Keywords: texture analysis; Radiomics; lung neuroendocrine neoplasm; carcinoid tumor; 18FDG-PET/CT texture analysis; Radiomics; lung neuroendocrine neoplasm; carcinoid tumor; 18FDG-PET/CT
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MDPI and ACS Style

Thuillier, P.; Liberini, V.; Rampado, O.; Gallio, E.; De Santi, B.; Ceci, F.; Metovic, J.; Papotti, M.; Volante, M.; Molinari, F.; Deandreis, D. 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. Biomedicines 2021, 9, 281. https://doi.org/10.3390/biomedicines9030281

AMA Style

Thuillier P, Liberini V, Rampado O, Gallio E, De Santi B, Ceci F, Metovic J, Papotti M, Volante M, Molinari F, Deandreis D. 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. Biomedicines. 2021; 9(3):281. https://doi.org/10.3390/biomedicines9030281

Chicago/Turabian Style

Thuillier, Philippe, Virginia Liberini, Osvaldo Rampado, Elena Gallio, Bruno De Santi, Francesco Ceci, Jasna Metovic, Mauro Papotti, Marco Volante, Filippo Molinari, and Désirée Deandreis. 2021. "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" Biomedicines 9, no. 3: 281. https://doi.org/10.3390/biomedicines9030281

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