1
|
Xiao H, Liu Y, Liang P, Hou P, Zhang Y, Gao J. Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers. BMC Infect Dis 2024; 24:1050. [PMID: 39333962 DOI: 10.1186/s12879-024-09952-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2024] [Accepted: 09/17/2024] [Indexed: 09/30/2024] Open
Abstract
OBJECTIVE To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs). METHODS The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs. RESULTS Of the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed. CONCLUSION Combining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.
Collapse
Affiliation(s)
- Huijuan Xiao
- Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Yihe Liu
- Department of Emergency, the First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zheng zhou, Zhengzhou, 450052, Henan, China
| | - Pan Liang
- Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Ping Hou
- Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Yonggao Zhang
- Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Jianbo Gao
- Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
| |
Collapse
|
2
|
Zheng J, Hao Y, Guo Y, Du M, Wang P, Xin J. An 18F-FDG-PET/CT-based radiomics signature for estimating malignance probability of solitary pulmonary nodule. THE CLINICAL RESPIRATORY JOURNAL 2024; 18:e13751. [PMID: 38725315 PMCID: PMC11082539 DOI: 10.1111/crj.13751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 01/29/2024] [Accepted: 03/28/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND Some solitary pulmonary nodules (SPNs) as early manifestations of lung cancer, it is difficult to determine its nature, which brings great trouble to clinical diagnosis and treatment. Radiomics can deeply explore the essence of images and provide clinical decision support for clinicians. The purpose of our study was to explore the effect of positron emission tomography (PET) with 2-deoxy-2-[fluorine-18] fluoro-d-glucose integrated with computed tomography (CT; 18F-FDG-PET/CT) combined with radiomics for predicting probability of malignancy of SPNs. METHODS We retrospectively enrolled 190 patients with SPNs confirmed by pathology from January 2013 to December 2019 in our hospital. SPNs were benign in 69 patients and malignant in 121 patients. Patients were randomly divided into a training or testing group at a ratio of 7:3. Three-dimensional regions of interest (ROIs) were manually outlined on PET and CT images, and radiomics features were extracted. Synthetic minority oversampling technique (SMOTE) method was used to balance benign and malignant samples to a ratio of 1:1. In the training group, least absolute shrinkage and selection operator (LASSO) regression analyses and Spearman correlation analyses were used to select the strongest radiomics features. Three models including PET model, CT model, and joint model were constructed using multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were plotted to evaluate diagnostic efficiency, calibration degree, and clinical usefulness of all models in training and testing groups. RESULTS The estimative effectiveness of the joint model was superior to the CT or PET model alone in the training and testing groups. For the joint model, CT model, and PET model, area under the ROC curve was 0.929, 0.819, 0.833 in the training group, and 0.844, 0.759, 0.748 in the testing group, respectively. Calibration and decision curves showed good fit and clinical usefulness for the joint model in both training and testing groups. CONCLUSION Radiomics models constructed by combining PET and CT radiomics features are valuable for distinguishing benign and malignant SPNs. The combined effect is superior to qualitative diagnoses with CT or PET radiomics models alone.
Collapse
Affiliation(s)
- Jingchi Zheng
- Radiology DepartmentShengjing Hospital of China Medical UniversityShenyangChina
| | - Yue Hao
- Radiology DepartmentShengjing Hospital of China Medical UniversityShenyangChina
| | | | - Ming Du
- Nuclear Medicine DepartmentShengjing Hospital of China Medical UniversityShenyangChina
| | - Pengyuan Wang
- Nuclear Medicine DepartmentShengjing Hospital of China Medical UniversityShenyangChina
| | - Jun Xin
- Nuclear Medicine DepartmentShengjing Hospital of China Medical UniversityShenyangChina
| |
Collapse
|
3
|
Dondi F, Gatta R, Treglia G, Piccardo A, Albano D, Camoni L, Gatta E, Cavadini M, Cappelli C, Bertagna F. Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review. Rev Endocr Metab Disord 2024; 25:175-186. [PMID: 37434097 PMCID: PMC10808150 DOI: 10.1007/s11154-023-09822-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/30/2023] [Indexed: 07/13/2023]
Abstract
BACKGROUND In the last years growing evidences on the role of radiomics and machine learning (ML) applied to different nuclear medicine imaging modalities for the assessment of thyroid diseases are starting to emerge. The aim of this systematic review was therefore to analyze the diagnostic performances of these technologies in this setting. METHODS A wide literature search of the PubMed/MEDLINE, Scopus and Web of Science databases was made in order to find relevant published articles about the role of radiomics or ML on nuclear medicine imaging for the evaluation of different thyroid diseases. RESULTS Seventeen studies were included in the systematic review. Radiomics and ML were applied for assessment of thyroid incidentalomas at 18 F-FDG PET, evaluation of cytologically indeterminate thyroid nodules, assessment of thyroid cancer and classification of thyroid diseases using nuclear medicine techniques. CONCLUSION Despite some intrinsic limitations of radiomics and ML may have affect the results of this review, these technologies seem to have a promising role in the assessment of thyroid diseases. Validation of preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.
Collapse
Affiliation(s)
- Francesco Dondi
- Nuclear Medicine, ASST Spedali Civili di Brescia, P.le Spedali Civili, 1, Brescia, 25123, Italy
| | - Roberto Gatta
- Dipartimento di Scienze Cliniche e Sperimentali, Università degli Studi di Brescia, Brescia, Italy
| | - Giorgio Treglia
- Clinic of Nuclear Medicine, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano, Switzerland
| | | | - Domenico Albano
- Nuclear Medicine, ASST Spedali Civili di Brescia and Università degli Studi di Brescia, Brescia, Italy
| | - Luca Camoni
- Nuclear Medicine, ASST Spedali Civili di Brescia, P.le Spedali Civili, 1, Brescia, 25123, Italy
| | - Elisa Gatta
- Unit of Endocrinology and Metabolism, ASST Spedali Civili di Brescia and Università degli Studi di Brescia, Brescia, Italy
| | - Maria Cavadini
- Unit of Endocrinology and Metabolism, ASST Spedali Civili di Brescia and Università degli Studi di Brescia, Brescia, Italy
| | - Carlo Cappelli
- Unit of Endocrinology and Metabolism, ASST Spedali Civili di Brescia and Università degli Studi di Brescia, Brescia, Italy
| | - Francesco Bertagna
- Nuclear Medicine, ASST Spedali Civili di Brescia, P.le Spedali Civili, 1, Brescia, 25123, Italy.
- Nuclear Medicine, ASST Spedali Civili di Brescia and Università degli Studi di Brescia, Brescia, Italy.
| |
Collapse
|
4
|
Alves VM, dos Santos Cardoso J, Gama J. Classification of Pulmonary Nodules in 2-[ 18F]FDG PET/CT Images with a 3D Convolutional Neural Network. Nucl Med Mol Imaging 2024; 58:9-24. [PMID: 38261899 PMCID: PMC10796312 DOI: 10.1007/s13139-023-00821-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/17/2023] [Accepted: 08/08/2023] [Indexed: 01/25/2024] Open
Abstract
Purpose 2-[18F]FDG PET/CT plays an important role in the management of pulmonary nodules. Convolutional neural networks (CNNs) automatically learn features from images and have the potential to improve the discrimination between malignant and benign pulmonary nodules. The purpose of this study was to develop and validate a CNN model for classification of pulmonary nodules from 2-[18F]FDG PET images. Methods One hundred thirteen participants were retrospectively selected. One nodule per participant. The 2-[18F]FDG PET images were preprocessed and annotated with the reference standard. The deep learning experiment entailed random data splitting in five sets. A test set was held out for evaluation of the final model. Four-fold cross-validation was performed from the remaining sets for training and evaluating a set of candidate models and for selecting the final model. Models of three types of 3D CNNs architectures were trained from random weight initialization (Stacked 3D CNN, VGG-like and Inception-v2-like models) both in original and augmented datasets. Transfer learning, from ImageNet with ResNet-50, was also used. Results The final model (Stacked 3D CNN model) obtained an area under the ROC curve of 0.8385 (95% CI: 0.6455-1.0000) in the test set. The model had a sensibility of 80.00%, a specificity of 69.23% and an accuracy of 73.91%, in the test set, for an optimised decision threshold that assigns a higher cost to false negatives. Conclusion A 3D CNN model was effective at distinguishing benign from malignant pulmonary nodules in 2-[18F]FDG PET images. Supplementary Information The online version contains supplementary material available at 10.1007/s13139-023-00821-6.
Collapse
Affiliation(s)
- Victor Manuel Alves
- Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-464 Porto, Portugal
- Department of Nuclear Medicine, University Hospital Center of São João, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal
| | - Jaime dos Santos Cardoso
- Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - João Gama
- Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-464 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| |
Collapse
|
5
|
Matsuoka K, Hirata K, Kokubo N, Maeda T, Tagai K, Endo H, Takahata K, Shinotoh H, Ono M, Seki C, Tatebe H, Kawamura K, Zhang MR, Shimada H, Tokuda T, Higuchi M, Takado Y. Investigating neural dysfunction with abnormal protein deposition in Alzheimer's disease through magnetic resonance spectroscopic imaging, plasma biomarkers, and positron emission tomography. Neuroimage Clin 2023; 41:103560. [PMID: 38147791 PMCID: PMC10944210 DOI: 10.1016/j.nicl.2023.103560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/19/2023] [Accepted: 12/19/2023] [Indexed: 12/28/2023]
Abstract
In Alzheimer's disease (AD), aggregated abnormal proteins induce neuronal dysfunction. Despite the evidence supporting the association between tau proteins and brain atrophy, further studies are needed to explore their link to neuronal dysfunction in the human brain. To clarify the relationship between neuronal dysfunction and abnormal proteins in AD-affected brains, we conducted magnetic resonance spectroscopic imaging (MRSI) and assessed the neurofilament light chain plasma levels (NfL). We evaluated tau and amyloid-β depositions using standardized uptake value ratios (SUVRs) of florzolotau (18F) for tau and 11C-PiB for amyloid-β positron emission tomography in the same patients. Heatmaps were generated to visualize Z scores of glutamate to creatine (Glu/Cr) and N-acetylaspartate to creatine (NAA/Cr) ratios using data from healthy controls. In AD brains, Z score maps revealed reduced Glu/Cr and NAA/Cr ratios in the gray matter, particularly in the right dorsolateral prefrontal cortex (rDLPFC) and posterior cingulate cortex (PCC). Glu/Cr ratios were negatively correlated with florzolotau (18F) SUVRs in the PCC, and plasma NfL levels were elevated and negatively correlated with Glu/Cr (P = 0.040, r = -0.50) and NAA/Cr ratios (P = 0.003, r = -0.68) in the rDLPFC. This suggests that the abnormal tau proteins in AD-affected brains play a role in diminishing glutamate levels. Furthermore, neuronal dysfunction markers including Glu/tCr and NAA/tCr could potentially indicate favorable clinical outcomes. Using MRSI provided spatial information about neural dysfunction in AD, enabling the identification of vulnerabilities in the rDLPFC and PCC within the AD's pathological context.
Collapse
Affiliation(s)
- Kiwamu Matsuoka
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan; Department of Psychiatry, Nara Medical University, Nara, Japan.
| | - Kosei Hirata
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Naomi Kokubo
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Takamasa Maeda
- QST Hospital, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Kenji Tagai
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Hironobu Endo
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Keisuke Takahata
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Hitoshi Shinotoh
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan; Neurology Clinic, Chiba, Chiba, Japan
| | - Maiko Ono
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan; Institute for Quantum Life Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Chie Seki
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Harutsugu Tatebe
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Kazunori Kawamura
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Ming-Rong Zhang
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Hitoshi Shimada
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan; Center for Integrated Human Brain Science, Brain Research Institute, Niigata University, Niigata, Japan
| | - Takahiko Tokuda
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Makoto Higuchi
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Yuhei Takado
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan; Institute for Quantum Life Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan.
| |
Collapse
|
6
|
Bomhals B, Cossement L, Maes A, Sathekge M, Mokoala KMG, Sathekge C, Ghysen K, Van de Wiele C. Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules. J Clin Med 2023; 12:7731. [PMID: 38137800 PMCID: PMC10743692 DOI: 10.3390/jcm12247731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax alone. Texture features were derived using the LIFEx software. The eight best-performing first-, second-, and higher-order features for separating benign from malignant nodules, in addition to SUVmax (MaximumGreyLevelSUVbwIBSI184IY), were included for PCA. Two principal components (PCs) were retained, of which the contributions to the total variance were, respectively, 87.6% and 10.8%. When included in a logistic binomial regression analysis, including age and gender as covariates, both PCs proved to be significant predictors for the underlying benign or malignant character of the lesions under study (p = 0.009 for the first PC and 0.020 for the second PC). As opposed to SUVmax alone, which allowed for the accurate classification of 69% of the lesions, the regression model including both PCs allowed for the accurate classification of 77% of the lesions. PCs derived from PCA applied on selected texture features may allow for more accurate characterization of SPN when compared to SUVmax alone.
Collapse
Affiliation(s)
- Birte Bomhals
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
| | - Lara Cossement
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
| | - Alex Maes
- Department of Morphology and Functional Imaging, University Hospital Leuven, 3000 Leuven, Belgium;
- Department of Nuclear Medicine, Katholieke University Leuven, AZ Groeninge, President Kennedylaan 4, 8500 Kortrijk, Belgium
| | - Mike Sathekge
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Kgomotso M. G. Mokoala
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Chabi Sathekge
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Katrien Ghysen
- Department of Pneumology, AZ Groeninge, 8500 Kortrijk, Belgium
| | - Christophe Van de Wiele
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
- Department of Nuclear Medicine, Katholieke University Leuven, AZ Groeninge, President Kennedylaan 4, 8500 Kortrijk, Belgium
| |
Collapse
|
7
|
Albano D, Dondi F, Bauckneht M, Albertelli M, Durmo R, Filice A, Versari A, Morbelli S, Berruti A, Bertagna F. The diagnostic and prognostic role of combined [ 18F]FDG and [ 68Ga]-DOTA-peptides PET/CT in primary pulmonary carcinoids: a multicentric experience. Eur Radiol 2023; 33:4167-4177. [PMID: 36482218 DOI: 10.1007/s00330-022-09326-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 10/26/2022] [Accepted: 11/09/2022] [Indexed: 12/13/2022]
Abstract
OBJECTIVES In the present retrospective multicentric study, we combined [68Ga]-DOTA-peptides and [18F]FDG-PET/CT findings aiming to investigate their capability to differentiate typical (TC) and atypical pulmonary carcinoids (AC) and their prognostic role. METHODS From three centers, 61 patients were retrospectively included. Based on a dual tracer combination we classified PET scans as score 1, [18F]FDG- and [68Ga]-DOTA-peptides negative; score 2, [68Ga]-DOTA-peptides positive and [18F]FDG-negative; score 3, [68Ga]-DOTA-peptides negative and [18F]FDG-positive; score 4, both tracers positive. Moreover, for each patient, the ratios of SUVmax on [68Ga]-DOTA-PET to that on [18F]FDG-PET were calculated (SUVr). RESULTS Thirty-five patients had a final diagnosis of TC. Twenty-two TC (57%) had positive [68Ga]-DOTA-peptides PET; instead, 21/26 (81%) AC had positive [18F]FDG-PET/CT. On dual-tracer analysis, scores 1, 2, 3 and 4 were 13%, 20%, 43% and 24% for all populations; 17%, 26%, 20% and 37% for TC; 8%, 11%, 73% and 8% for AC. Median SUVr was significantly higher in TC than AC (6.4 vs. 0.4, p = 0.011). The best value of SUVr to predict the final diagnosis was 1.05 (AUC 0.889). Relapse or progression of disease happened in 17 patients (11 affected by AC) and death in 10 cases (7 AC). AC diagnosis, positive [18F]FDG-PET, negative DOTA-PET and dual tracer score were significantly correlated with PFS (p = 0.013, p = 0.033, p = 0.029 and p = 0.019), while only AC diagnosis with OS (p = 0.022). CONCLUSION PET/CT findings had also a prognostic role in predicting PFS. Dual-tracer PET behavior may be used to predict the nature of pulmonary carcinoids and select the most appropriate management. KEY POINTS • Combination of [18F]FDG and [68Ga]-DOTA-peptides PET/CT results may help to differentiate between atypical and typical lung carcinoids. • The SUVmax ratio between [18F]FDG and [68Ga]-DOTA-peptides PET may help to differentiate between atypical and typical lung carcinoids. • Histotype and PET/CT features have a prognostic impact on PFS.
Collapse
Affiliation(s)
- Domenico Albano
- Nuclear Medicine, ASST Spedali Civili di Brescia, P.le Spedali Civili 1, 25123, Brescia, Italy.
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health; Nuclear Medicine, University of Brescia, Brescia, Italy.
| | - Francesco Dondi
- Nuclear Medicine, ASST Spedali Civili di Brescia, P.le Spedali Civili 1, 25123, Brescia, Italy
| | - Matteo Bauckneht
- Nuclear Medicine, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Department of Health Sciences (DISSAL), University of Genova, Genova, Italy
| | - Manuela Albertelli
- Endocrinology Unit, IRCCS Ospedale Policlinico San Martino, University of Genova, Genova, Italy
| | - Rexhep Durmo
- Nuclear Medicine, Azienda USL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
- PhD Program in Clinical and Experimental Medicine (CEM), University of Modena and Reggio Emilia, Modena, Italy
| | - Angelina Filice
- Nuclear Medicine, Azienda USL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | - Annibale Versari
- Nuclear Medicine, Azienda USL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | - Silvia Morbelli
- Nuclear Medicine, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Department of Health Sciences (DISSAL), University of Genova, Genova, Italy
| | - Alfredo Berruti
- Medical Oncology Unit, ASST Spedali Civili di Brescia, Brescia, Italy
| | - Francesco Bertagna
- Nuclear Medicine, ASST Spedali Civili di Brescia, P.le Spedali Civili 1, 25123, Brescia, Italy
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health; Nuclear Medicine, University of Brescia, Brescia, Italy
| |
Collapse
|
8
|
Liu Z, Ran H, Yu X, Wu Q, Zhang C. Immunocyte count combined with CT features for distinguishing pulmonary tuberculoma from malignancy among non-calcified solitary pulmonary solid nodules. J Thorac Dis 2023; 15:386-398. [PMID: 36910060 PMCID: PMC9992615 DOI: 10.21037/jtd-22-1024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 12/02/2022] [Indexed: 02/04/2023]
Abstract
Background Tuberculoma is the most common type of surgically removed benign solid solitary pulmonary nodule (SPN) and can lead to a high risk of misdiagnoses for clinicians. This study aimed to discuss the value of the immunocyte count combined with computed tomography (CT) features in distinguishing pulmonary tuberculoma from malignancy among non-calcified solid SPNs. Methods Forty-eight patients with pulmonary tuberculoma and 52 patients with lung cancer were retrospectively included in our study. Univariate and multivariate analyses were conducted to screen the independent predictors. Receiver operating characteristic (ROC) analysis was performed to investigate the validity of the predictive model. Results The univariate and multivariate analyses revealed that a coarse margin, vacuole, lobulation, pleural indentation, cluster of differentiation (CD)3+ T-lymphocyte count, and CD4+ T-lymphocyte count were independent predictors for distinguishing pulmonary tuberculoma from malignancy. The sensitivity, specificity, accuracy, and the area under the ROC curve of the model comprising the CD3+ T-lymphocyte count were 79.2%, 75%, 74.5%, and 0.845 [95% confidence interval (CI), 0.759-0.910], respectively, and those of the model involving the CD4+ T-lymphocyte count were 77.1%, 78.8%, 77.1%, and 0.857 (95% CI, 0.773-0.919), respectively. Conclusions Immunocyte count combined with CT features is efficient in distinguishing pulmonary tuberculoma from malignancy among non-calcified solid SPNs and has applicable clinical value.
Collapse
Affiliation(s)
- Zihao Liu
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Haoyu Ran
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiran Yu
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qingchen Wu
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Cheng Zhang
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| |
Collapse
|
9
|
Wang B, Liu J, Zhang X, Wang Z, Cao Z, Lu L, Lv W, Wang A, Li S, Wu X, Dong X. Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer. EJNMMI Res 2023; 13:14. [PMID: 36779997 PMCID: PMC9925656 DOI: 10.1186/s13550-023-00959-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 01/26/2023] [Indexed: 02/14/2023] Open
Abstract
OBJECTIVES By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. MATERIALS AND METHODS A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan-Meier curves (log-rank analysis) were used to evaluate and compare these models. RESULTS The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812-0.934), 0.759 (95% CI 0.663-0.855) and 0.835 (95% CI 0.745-0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). CONCLUSION Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC.
Collapse
Affiliation(s)
- Bingzhen Wang
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Jinghua Liu
- Department of Nursing, Chengde Central Hospital, Chengde, Hebei China ,grid.11142.370000 0001 2231 800XDepartment of Nursing, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia
| | - Xiaolei Zhang
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Zhongxiao Wang
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Zhendong Cao
- grid.413851.a0000 0000 8977 8425Department of Radiology, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei China
| | - Lijun Lu
- grid.284723.80000 0000 8877 7471School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong China
| | - Wenbing Lv
- grid.440773.30000 0000 9342 2456Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan China
| | - Aihui Wang
- grid.413851.a0000 0000 8977 8425Department of Nuclear Medicine, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei China
| | - Shuyan Li
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Xiaotian Wu
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Xianling Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China. .,Hebei International Research Center of Medical-Engineering, Chengde Medical University, Chengde, Hebei, China.
| |
Collapse
|
10
|
Dondi F, Albano D, Bellini P, Cerudelli E, Treglia G, Bertagna F. Prognostic role of baseline 18F-FDG pet/CT in stage I and stage ii non-small cell lung cancer. Clin Imaging 2023; 94:71-78. [PMID: 36495848 DOI: 10.1016/j.clinimag.2022.11.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 11/14/2022] [Accepted: 11/17/2022] [Indexed: 12/03/2022]
Abstract
OBJECTIVE investigate the prognostic role of baseline 18F-FDG PET/CT in stage I-II NSCLC. MATERIAL AND METHODS 296 patients were included. Clinicopathological features and PET/CT semiquantitative parameters [standardized uptake value (SUV) body weight max (SUVmax), SUV body weight mean (SUVmean), SUV lean body mass (SUVlbm), SUV body surface area (SUVbsa), metabolic tumor volume (MTV), total lesion glycolysis (TLG), ratio SUVmax/liver (S-L) and ratio SUVmax/blood-pool (S-BP) were extracted]. Anova and Kruskall-Wallis tests were used to assess the relationship between these parameters. Kaplan-Meier, univariate and multivariate analysis were performed to search independent prognostic factors for progression free (PFS), overall survival (OS) and disease specific survival (DSS). RESULTS Correlation between PET/CT semiquantitative parameters and histology, stage, size, grading and presence of nodal metastasis were reported. Mean PFS was 28.1 months, relapse/progression of disease occurred in 85 patients (28.7%). Mean OS was 33.3 months, death occurred in 43 patients (14.5%); specific death by NSCLC occurred in 26 subjects (8.8%). Kaplan-Meier analyses revealed most of semiquantitative parameters as predictive for PFS, OS and DSS. For DSS, this was confirmed when dividing between patients with surgery and surgery with other therapies. SUVmax, SUVmean, SUVlbm, SUVbsa and S-L revealed to be independent prognosticators for OS and DSS. S-BP was an independent prognosticator for DSS. SUVmax, SUVmean, SUVlbm, S-L and S-BP were confirmed as independent prognosticators for DSS in the group of patients treated with surgery and subsequent adjuvant therapy. CONCLUSION Baseline 18F-FDG PET/CT semiquantitative parameters are confirmed as prognostic tools for stage I-II NSCLC, in particular for DSS.
Collapse
Affiliation(s)
- Francesco Dondi
- Nuclear Medicine, ASST Spedali Civili di Brescia, Brescia, Italy.
| | - Domenico Albano
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili di Brescia, Brescia, Italy
| | - Pietro Bellini
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili di Brescia, Brescia, Italy
| | | | - Giorgio Treglia
- Clinic of Nuclear Medicine, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland; Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano, Switzerland
| | - Francesco Bertagna
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili di Brescia, Brescia, Italy
| |
Collapse
|
11
|
Radiomics and Artificial Intelligence Can Predict Malignancy of Solitary Pulmonary Nodules in the Elderly. Diagnostics (Basel) 2023; 13:diagnostics13030384. [PMID: 36766488 PMCID: PMC9914272 DOI: 10.3390/diagnostics13030384] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023] Open
Abstract
Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic surgeons. Although such lesions are usually benign, the risk of malignancy remains significant, particularly in elderly patients, who represent a large segment of the affected population. Surgical treatment in this subset, which usually presents several comorbidities, requires careful evaluation, especially when pre-operative biopsy is not feasible and comorbidities may jeopardize the outcome. Radiomics and artificial intelligence (AI) are progressively being applied in predicting malignancy in suspicious nodules and assisting the decision-making process. In this study, we analyzed features of the radiomic images of 71 patients with SPN aged more than 75 years (median 79, IQR 76-81) who had undergone upfront pulmonary resection based on CT and PET-CT findings. Three different machine learning algorithms were applied-functional tree, Rep Tree and J48. Histology was malignant in 64.8% of nodules and the best predictive value was achieved by the J48 model (AUC 0.9). The use of AI analysis of radiomic features may be applied to the decision-making process in elderly frail patients with suspicious SPNs to minimize the false positive rate and reduce the incidence of unnecessary surgery.
Collapse
|
12
|
Dondi F, Gatta R, Albano D, Bellini P, Camoni L, Treglia G, Bertagna F. Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [ 18F]FDG PET/CT: A Comparison between Two PET/CT Scanners. J Clin Med 2022; 12:255. [PMID: 36615053 PMCID: PMC9820870 DOI: 10.3390/jcm12010255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/07/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022] Open
Abstract
The aim of this study was to compare two different PET/CT tomographs for the evaluation of the role of radiomics features (RaF) and machine learning (ML) in the prediction of the histological classification of stage I and II non-small-cell lung cancer (NSCLC) at baseline [18F]FDG PET/CT. A total of 227 patients were retrospectively included and, after volumetric segmentation, RaF were extracted. All of the features were tested for significant differences between the two scanners and considering both the scanners together, and their performances in predicting the histology of NSCLC were analyzed by testing of different ML approaches: Logistic Regressor (LR), k-Nearest Neighbors (kNN), Decision Tree (DT) and Random Forest (RF). In general, the models with best performances for all the scanners were kNN and LR and moreover the kNN model had better performances compared to the other. The impact of the PET/CT scanner used for the acquisition of the scans on the performances of RaF was evident: mean area under the curve (AUC) values for scanner 2 were lower compared to scanner 1 and both the scanner considered together. In conclusion, our study enabled the selection of some [18F]FDG PET/CT RaF and ML models that are able to predict with good performances the histological subtype of NSCLC. Furthermore, the type of PET/CT scanner may influence these performances.
Collapse
Affiliation(s)
- Francesco Dondi
- Nuclear Medicine, ASST Spedali Civili Brescia, 25123 Brescia, Italy
| | - Roberto Gatta
- Dipartimento di Scienze Cliniche e Sperimentali, Università degli Studi di Brescia, 25123 Brescia, Italy
| | - Domenico Albano
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy
| | - Pietro Bellini
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy
| | - Luca Camoni
- Nuclear Medicine, ASST Spedali Civili Brescia, 25123 Brescia, Italy
| | - Giorgio Treglia
- Nuclear Medicine, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, University of Lausanne, 1011 Lausanne, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
| | - Francesco Bertagna
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy
| |
Collapse
|
13
|
Ren C, Xu M, Zhang J, Zhang F, Song S, Sun Y, Wu K, Cheng J. Classification of solid pulmonary nodules using a machine-learning nomogram based on 18F-FDG PET/CT radiomics integrated clinicobiological features. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1265. [PMID: 36618813 PMCID: PMC9816842 DOI: 10.21037/atm-22-2647] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 10/13/2022] [Indexed: 11/24/2022]
Abstract
Background To develop and validate an 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and clinico-biological features-based nomogram for distinguishing solid benign pulmonary nodules (BPNs) from malignant pulmonary nodules (MPNs). Methods A total of 280 patients with BPN (n=128) or MPN (n=152) were collected retrospectively and randomized into the training set (n=196) and validation set (n=84). Pretherapeutic clinicobiological markers, PET/CT metabolic features and radiomic features were analyzed and selected to develop prediction models by the machine-learning method [Least Absolute Shrinkage and Selection Operator (LASSO) regression]. These prediction models were validated using the area under the curve (AUC) of the receiver-operator characteristic (ROC) analysis and decision curve analysis (DCA). Then, the factors of the model with the optimal predictive efficiency were used to constructed a nomogram to provide a visually quantitative tool for distinguishing BPN from MPN patients. Results We developed 3 independent models (Clinical Model, Radiomics Model and Combined Model) to distinguish patients with BPN from those with MPN in the training set. The Combined Model was validated to hold the optimal efficiency and clinical utility with the lowest false positive rate (FPR) in classifying the solid pulmonary nodules in two sets (AUCs of 0.91 and 0.94, FPRs of 18.68% and 5.41%, respectively; P<0.05). Thus, the quantitative nomogram was developed based on the Combined Model, and a good consistency between the predictions and the actual observations was validated by the calibration curves. Conclusions This study presents a machine-learning nomogram integrated clinico-biologico-radiological features that can improve the efficiency and reduce the FPR in the noninvasive differentiation of BPN from MPN.
Collapse
Affiliation(s)
- Caiyue Ren
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, China;,Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Mingxia Xu
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, China;,Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Jiangang Zhang
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, China;,Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Fuquan Zhang
- College of Physics, Sichuan University, Chengdu, China
| | - Shaoli Song
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China;,Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China;,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China;,Center for Biomedical Imaging, Fudan University, Shanghai, China;,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| | - Yun Sun
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China;,Department of Research and Development, Shanghai Proton and Heavy Ion Center, Shanghai, China
| | - Kailiang Wu
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China;,Department of Radiotherapy, Shanghai Proton and Heavy Ion Center, Shanghai, China
| | - Jingyi Cheng
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China;,Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China;,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China;,Center for Biomedical Imaging, Fudan University, Shanghai, China;,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| |
Collapse
|
14
|
Value of dynamic metabolic curves and artificial neural network prediction models based on 18F-FDG PET/CT multiphase imaging in differentiating nonspecific solitary pulmonary lesions: a pilot study. Nucl Med Commun 2022; 43:1204-1216. [DOI: 10.1097/mnm.0000000000001627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
15
|
Dondi F, Albano D, Bellini P, Camoni L, Treglia G, Bertagna F. Relationship between Baseline [ 18F]FDG PET/CT Semiquantitative Parameters and BRCA Mutational Status and Their Prognostic Role in Patients with Invasive Ductal Breast Carcinoma. Tomography 2022; 8:2662-2675. [PMID: 36412681 PMCID: PMC9680390 DOI: 10.3390/tomography8060222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 11/07/2022] Open
Abstract
AIM To assess the relationship between [18F]FDG PET/CT, breast cancer gene (BRCA) status, and their prognostic role in patients with ductal breast cancer (DBC). METHODS Forty-one women were included. PET/CT semiquantitative parameters such as standardized uptake value (SUV) body weight max (SUVmax), SUV body weight mean (SUVmean), SUV lean body mass (SUVlbm), SUV body surface area (SUVbsa), metabolic tumor volume (MTV), total lesion glycolysis (TLG), ratio SUVmax/blood-pool (S-BP), and ratio SUVmax/liver (S-L) were also extracted. The relationship between these parameters, BRCA, and other clinicopathological features were evaluated. Kaplan-Meier, univariate, and multivariate analyses were performed to find independent prognosticators for progression free (PFS) and overall survival (OS). RESULTS Significant positive correlations between BRCA status and SUVmax (p-value 0.025), SUVlbm (p-value 0.016), and SUVbsa (p-value 0.018) were reported. Mean PFS was 53.90 months with relapse/progression of disease occurring in nine (22.0%) patients; mean OS was 57.48 months with death occurring in two (4.9%) patients. Survival curves revealed TLG, MTV, and BRCA status as prognosticator for PFS; BRCA was also a prognosticator for OS. Univariate and multivariate analyses did not confirm such insights. CONCLUSION We reported a correlation between some PET/CT parameters and BRCA status; some insights on their prognostic role have been underlined.
Collapse
Affiliation(s)
- Francesco Dondi
- Nuclear Medicine, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Domenico Albano
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Pietro Bellini
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Luca Camoni
- Nuclear Medicine, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Giorgio Treglia
- Nuclear Medicine, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera italiana, 6900 Lugano, Switzerland
- Correspondence:
| | - Francesco Bertagna
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| |
Collapse
|
16
|
Le QC, Arimura H, Ninomiya K, Kodama T, Moriyama T. Can Persistent Homology Features Capture More Intrinsic Information about Tumors from 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Images of Head and Neck Cancer Patients? Metabolites 2022; 12:metabo12100972. [PMID: 36295874 PMCID: PMC9610853 DOI: 10.3390/metabo12100972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/09/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
This study hypothesized that persistent homology (PH) features could capture more intrinsic information about the metabolism and morphology of tumors from 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) images of patients with head and neck (HN) cancer than other conventional features. PET/CT images and clinical variables of 207 patients were selected from the publicly available dataset of the Cancer Imaging Archive. PH images were generated from persistent diagrams obtained from PET/CT images. The PH features were derived from the PH PET/CT images. The signatures were constructed in a training cohort from features from CT, PET, PH-CT, and PH-PET images; clinical variables; and the combination of features and clinical variables. Signatures were evaluated using statistically significant differences (p-value, log-rank test) between survival curves for low- and high-risk groups and the C-index. In an independent test cohort, the signature consisting of PH-PET features and clinical variables exhibited the lowest log-rank p-value of 3.30 × 10−5 and C-index of 0.80, compared with log-rank p-values from 3.52 × 10−2 to 1.15 × 10−4 and C-indices from 0.34 to 0.79 for other signatures. This result suggests that PH features can capture the intrinsic information of tumors and predict prognosis in patients with HN cancer.
Collapse
Affiliation(s)
- Quoc Cuong Le
- Ho Chi Minh City Oncology Hospital, Ho Chi Minh City 700000, Vietnam
| | - Hidetaka Arimura
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka City 812-8582, Japan
- Correspondence:
| | - Kenta Ninomiya
- Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, San Diego, CA 92037, USA
| | - Takumi Kodama
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka City 812-8582, Japan
| | - Tetsuhiro Moriyama
- Institute of Mathematics for Industry, Kyushu University, Fukuoka City 819-0395, Japan
| |
Collapse
|
17
|
Could [18F]FDG PET/CT or PET/MRI Be Useful in Patients with Skull Base Osteomyelitis? Diagnostics (Basel) 2022; 12:diagnostics12092035. [PMID: 36140437 PMCID: PMC9497608 DOI: 10.3390/diagnostics12092035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 08/19/2022] [Indexed: 11/24/2022] Open
|
18
|
Emerging Role of FAPI PET Imaging for the Assessment of Benign Bone and Joint Diseases. J Clin Med 2022; 11:jcm11154514. [PMID: 35956129 PMCID: PMC9369955 DOI: 10.3390/jcm11154514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 08/02/2022] [Indexed: 12/30/2022] Open
|
19
|
Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:1329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
Collapse
Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| |
Collapse
|
20
|
Wu YJ, Wu FZ, Yang SC, Tang EK, Liang CH. Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education. Diagnostics (Basel) 2022; 12:diagnostics12051064. [PMID: 35626220 PMCID: PMC9139351 DOI: 10.3390/diagnostics12051064] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/14/2022] [Accepted: 04/22/2022] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient–doctor cooperation and shared decision making.
Collapse
Affiliation(s)
- Yun-Ju Wu
- Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung 80201, Taiwan;
| | - Fu-Zong Wu
- Institute of Education, National Sun Yat-Sen University, 70, Lien-Hai Road, Kaohsiung 804241, Taiwan;
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan
- Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Correspondence:
| | - Shu-Ching Yang
- Institute of Education, National Sun Yat-Sen University, 70, Lien-Hai Road, Kaohsiung 804241, Taiwan;
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan;
| |
Collapse
|
21
|
Lai YC, Wu KC, Tseng NC, Chen YJ, Chang CJ, Yen KY, Kao CH. Differentiation Between Malignant and Benign Pulmonary Nodules by Using Automated Three-Dimensional High-Resolution Representation Learning With Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography. Front Med (Lausanne) 2022; 9:773041. [PMID: 35372415 PMCID: PMC8971840 DOI: 10.3389/fmed.2022.773041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 02/14/2022] [Indexed: 11/26/2022] Open
Abstract
Background The investigation of incidental pulmonary nodules has rapidly become one of the main indications for 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET), currently combined with computed tomography (PET-CT). There is also a growing trend to use artificial Intelligence for optimization and interpretation of PET-CT Images. Therefore, we proposed a novel deep learning model that aided in the automatic differentiation between malignant and benign pulmonary nodules on FDG PET-CT. Methods In total, 112 participants with pulmonary nodules who underwent FDG PET-CT before surgery were enrolled retrospectively. We designed a novel deep learning three-dimensional (3D) high-resolution representation learning (HRRL) model for the automated classification of pulmonary nodules based on FDG PET-CT images without manual annotation by experts. For the images to be localized more precisely, we defined the territories of the lungs through a novel artificial intelligence-driven image-processing algorithm, instead of the conventional segmentation method, without the aid of an expert; this algorithm is based on deep HRRL, which is used to perform high-resolution classification. In addition, the 2D model was converted to a 3D model. Results All pulmonary lesions were confirmed through pathological studies (79 malignant and 33 benign). We evaluated its diagnostic performance in the differentiation of malignant and benign nodules. The area under the receiver operating characteristic curve (AUC) of the deep learning model was used to indicate classification performance in an evaluation using fivefold cross-validation. The nodule-based prediction performance of the model had an AUC, sensitivity, specificity, and accuracy of 78.1, 89.9, 54.5, and 79.4%, respectively. Conclusion Our results suggest that a deep learning algorithm using HRRL without manual annotation from experts might aid in the classification of pulmonary nodules discovered through clinical FDG PET-CT images.
Collapse
Affiliation(s)
- Yung-Chi Lai
- Department of Nuclear Medicine, PET Center, China Medical University Hospital, Taichung, Taiwan
| | - Kuo-Chen Wu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung, Taiwan
| | - Neng-Chuan Tseng
- Division of Nuclear Medicine, Tungs’ Taichung MetroHarbor Hospital, Taichung, Taiwan
| | - Yi-Jin Chen
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung, Taiwan
| | - Chao-Jen Chang
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung, Taiwan
| | - Kuo-Yang Yen
- Department of Nuclear Medicine, PET Center, China Medical University Hospital, Taichung, Taiwan
- Department of Biomedical Imaging and Radiological Science, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
| | - Chia-Hung Kao
- Department of Nuclear Medicine, PET Center, China Medical University Hospital, Taichung, Taiwan
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung, Taiwan
- Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
- *Correspondence: Chia-Hung Kao, ,
| |
Collapse
|
22
|
Zarogoulidis P, Kosmidis CS, Hohenforst-Schmidt W, Matthaios D, Sapalidis K, Petridis D, Perdikouri EI, Courcoutsakis N, Hatzibougias D, Arnaoutoglou C, Freitag L, Ioannidis A, Huang H, Tolis C, Bai C, Turner JF. Radial-EBUS: CryoBiopsy Versus Conventional Biopsy: Time-Sample and C-Arm. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063569. [PMID: 35329255 PMCID: PMC8955438 DOI: 10.3390/ijerph19063569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/14/2022] [Accepted: 03/10/2022] [Indexed: 12/21/2022]
Abstract
Introduction: Diagnosis of lung nodules is still under investigation. We use computed tomography scans and positron emission tomography in order to identify their origin. Patients and Methods: In our retrospective study, we included 248 patients with a single lung nodule or multiple lung nodules of size ≥1 cm. We used a radial-endobronchial ultrasound and a C-Arm. We used a 1.1 mm cryoprobe versus a 22G needle vs. forceps/brush. We compared the sample size of each biopsy method with the number of cell-block slices. Results: Central lesions indifferent to the method provided the same mean number of cell-block slices (0.04933–0.02410). Cryobiopsies provide less sample size for peripheral lesions due to the higher incidence of pneumothorax (0.04700–0.02296). Conclusion: The larger the lesion ≥2 cm, and central, more cell-blocks are produced indifferent to the biopsy method (0.13386–0.02939). The time of the procedure was observed to be less when the C-Arm was used as an additional navigation tool (0.14854–0.00089).
Collapse
Affiliation(s)
- Paul Zarogoulidis
- Pulmonary-Oncology Department, General Clinic Euromedica, Private Hospital, 54645 Thessaloniki, Greece
- Correspondence:
| | - Christoforos S. Kosmidis
- Surgical Department, University Hospital of Thessaloniki AHEPA, Aristotle University of Thessaloniki (AUTH), 1st St. Kiriakidi Street, 54621 Thessaloniki, Greece; (C.S.K.); (K.S.)
| | - Wolfgang Hohenforst-Schmidt
- Sana Clinic Group Franken, Department of Cardiology/Pulmonology/Intensive Care/Nephrology, “Hof” Clinics, University of Erlangen, 91052 Hof, Germany;
| | - Dimitrios Matthaios
- Department of Medical Oncology, Rhodes General Hospital, 85133 Rhodes, Greece;
| | - Konstantinos Sapalidis
- Surgical Department, University Hospital of Thessaloniki AHEPA, Aristotle University of Thessaloniki (AUTH), 1st St. Kiriakidi Street, 54621 Thessaloniki, Greece; (C.S.K.); (K.S.)
| | - Dimitrios Petridis
- Department of Food Science and Technology, International Hellenic University, 54621 Thessaloniki, Greece;
| | | | - Nikos Courcoutsakis
- Department of Radiology and Medical Imaging, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | | | - Christos Arnaoutoglou
- Department of Obstetrics & Gynecology, Papageorgiou Hospital, Aristotle University of Thessaloniki, 54621 Thessaloniki, Greece;
| | - Lutz Freitag
- Pulmonary Department, University Hospital of Zurich, 8004 Zurich, Switzerland;
| | - Aristeidis Ioannidis
- Department of Respiratory & Critical Care Medicine, Changhai Hospital, The Second Military Medical University, Shanghai 200001, China; (A.I.); (H.H.); (C.B.)
| | - Haidong Huang
- Department of Respiratory & Critical Care Medicine, Changhai Hospital, The Second Military Medical University, Shanghai 200001, China; (A.I.); (H.H.); (C.B.)
| | - Christos Tolis
- Oncoderm Private Oncology Clinic, 45221 Ioannina, Greece;
| | - Chong Bai
- Department of Respiratory & Critical Care Medicine, Changhai Hospital, The Second Military Medical University, Shanghai 200001, China; (A.I.); (H.H.); (C.B.)
| | - J. Francis Turner
- Department of Medicine, University of Tennessee Graduate School of Medicine, Knoxville, TN 37001, USA;
| |
Collapse
|
23
|
Dondi F, Pasinetti N, Gatta R, Albano D, Giubbini R, Bertagna F. Comparison between Two Different Scanners for the Evaluation of the Role of 18F-FDG PET/CT Semiquantitative Parameters and Radiomics Features in the Prediction of Final Diagnosis of Thyroid Incidentalomas. J Clin Med 2022; 11:jcm11030615. [PMID: 35160067 PMCID: PMC8836668 DOI: 10.3390/jcm11030615] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/19/2022] [Accepted: 01/25/2022] [Indexed: 12/24/2022] Open
Abstract
The aim of this study was to compare two different tomographs for the evaluation of the role of semiquantitative PET/CT parameters and radiomics features (RF) in the prediction of thyroid incidentalomas (TIs) at 18F-FDG imaging. A total of 221 patients with the presence of TIs were retrospectively included. After volumetric segmentation of each TI, semiquantitative parameters and RF were extracted. All of the features were tested for significant differences between the two PET scanners. The performances of all of the features in predicting the nature of TIs were analyzed by testing three classes of final logistic regression predictive models, one for each tomograph and one with both scanners together. Some RF resulted significantly different between the two scanners. PET/CT semiquantitative parameters were not able to predict the final diagnosis of TIs while GLCM-related RF (in particular GLCM entropy_log2 e GLCM entropy_log10) together with some GLRLM-related and GLZLM-related features presented the best predictive performances. In particular, GLCM entropy_log2, GLCM entropy_log10, GLZLM SZHGE, GLRLM HGRE and GLRLM HGZE resulted the RF with best performances. Our study enabled the selection of some RF able to predict the final nature of TIs discovered at 18F-FDG PET/CT imaging. Classic semiquantitative and volumetric PET/CT parameters did not reveal these abilities. Furthermore, a good overlap in the extraction of RF between the two scanners was underlined.
Collapse
Affiliation(s)
- Francesco Dondi
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (F.D.); (R.G.); (F.B.)
| | - Nadia Pasinetti
- Radiation Oncology Department, ASST Valcamonica Esine and Università degli Studi di Brescia, 25040 Brescia, Italy;
| | - Roberto Gatta
- Dipartimento di Scienze Cliniche e Sperimentali dell’Università degli Studi di Brescia, 25123 Brescia, Italy;
| | - Domenico Albano
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (F.D.); (R.G.); (F.B.)
- Correspondence:
| | - Raffaele Giubbini
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (F.D.); (R.G.); (F.B.)
| | - Francesco Bertagna
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (F.D.); (R.G.); (F.B.)
| |
Collapse
|