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Pellegrino S, Fonti R, Vallone C, Morra R, Matano E, De Placido S, Del Vecchio S. Coefficient of Variation in Metastatic Lymph Nodes Determined by 18F-FDG PET/CT in Patients with Advanced NSCLC: Combination with Coefficient of Variation in Primary Tumors. Cancers (Basel) 2024; 16:279. [PMID: 38254770 PMCID: PMC10813913 DOI: 10.3390/cancers16020279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/03/2024] [Accepted: 01/06/2024] [Indexed: 01/24/2024] Open
Abstract
Purpose The aim of the present study was to test whether the coefficient of variation (CoV) of 18F-FDG PET/CT images of metastatic lymph nodes and primary tumors may predict clinical outcome in patients with advanced non-small cell lung cancer (NSCLC). Materials and Methods Fifty-eight NSCLC patients who had undergone 18F-FDG PET/CT at diagnosis were evaluated. SUVmax, SUVmean, CoV, MTV and TLG were determined in targeted lymph nodes and corresponding primary tumors along with Total MTV (MTVTOT) and Whole-Body TLG (TLGWB) of all malignant lesions. Univariate analysis was performed using Cox proportional hazards regression whereas the Kaplan-Meier method and log-rank tests were used for survival analysis. Results Fifty-eight metastatic lymph nodes were analyzed and average values of SUVmax, SUVmean, CoV, MTV and TLG were 11.89 ± 8.54, 4.85 ± 1.90, 0.37 ± 0.16, 46.16 ± 99.59 mL and 256.84 ± 548.27 g, respectively, whereas in primary tumors they were 11.92 ± 6.21, 5.47 ± 2.34, 0.36 ± 0.14, 48.03 ± 64.45 mL and 285.21 ± 397.95 g, respectively. At univariate analysis, overall survival (OS) was predicted by SUVmax (p = 0.0363), SUVmean (p = 0.0200) and CoV (p = 0.0139) of targeted lymph nodes as well as by CoV of primary tumors (p = 0.0173), MTVTOT (p = 0.0007), TLGWB (p = 0.0129) and stage (p = 0.0122). Using Kaplan-Meier analysis, OS was significantly better in patients with CoV of targeted lymph nodes ≤ 0.29 than those with CoV > 0.29 (p = 0.0147), meanwhile patients with CoV of primary tumors > 0.38 had a better prognosis compared to those with CoV ≤ 0.38 (p = 0.0137). Finally, we combined the CoV values of targeted lymph nodes and primary tumors in all possible arrangements and a statistically significant difference was found among the four survival curves (p = 0.0133). In particular, patients with CoV of targeted lymph nodes ≤ 0.29 and CoV of primary tumors > 0.38 had the best prognosis. Conclusions The CoV of targeted lymph nodes combined with the CoV of primary tumors can predict prognosis of NSCLC patients.
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Affiliation(s)
- Sara Pellegrino
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (S.P.); (R.F.); (C.V.)
| | - Rosa Fonti
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (S.P.); (R.F.); (C.V.)
| | - Carlo Vallone
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (S.P.); (R.F.); (C.V.)
| | - Rocco Morra
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy; (R.M.); (E.M.); (S.D.P.)
| | - Elide Matano
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy; (R.M.); (E.M.); (S.D.P.)
| | - Sabino De Placido
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy; (R.M.); (E.M.); (S.D.P.)
| | - Silvana Del Vecchio
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (S.P.); (R.F.); (C.V.)
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Lue KH, Chen YH, Chu SC, Chang BS, Lin CB, Chen YC, Lin HH, Liu SH. A comparison of 18 F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study. Nucl Med Commun 2023; 44:1094-1105. [PMID: 37728592 DOI: 10.1097/mnm.0000000000001776] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
OBJECTIVE The performance of 18 F-FDG PET-based radiomics and deep learning in detecting pathological regional nodal metastasis (pN+) in resectable lung adenocarcinoma varies, and their use across different generations of PET machines has not been thoroughly investigated. We compared handcrafted radiomics and deep learning using different PET scanners to predict pN+ in resectable lung adenocarcinoma. METHODS We retrospectively analyzed pretreatment 18 F-FDG PET from 148 lung adenocarcinoma patients who underwent curative surgery. Patients were separated into analog (n = 131) and digital (n = 17) PET cohorts. Handcrafted radiomics and a ResNet-50 deep-learning model of the primary tumor were used to predict pN+ status. Models were trained in the analog PET cohort, and the digital PET cohort was used for cross-scanner validation. RESULTS In the analog PET cohort, entropy, a handcrafted radiomics, independently predicted pN+. However, the areas under the receiver-operating-characteristic curves (AUCs) and accuracy for entropy were only 0.676 and 62.6%, respectively. The ResNet-50 model demonstrated a better AUC and accuracy of 0.929 and 94.7%, respectively. In the digital PET validation cohort, the ResNet-50 model also demonstrated better AUC (0.871 versus 0.697) and accuracy (88.2% versus 64.7%) than entropy. The ResNet-50 model achieved comparable specificity to visual interpretation but with superior sensitivity (83.3% versus 66.7%) in the digital PET cohort. CONCLUSION Applying deep learning across different generations of PET scanners may be feasible and better predict pN+ than handcrafted radiomics. Deep learning may complement visual interpretation and facilitate tailored therapeutic strategies for resectable lung adenocarcinoma.
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Affiliation(s)
- Kun-Han Lue
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology,
| | - Yu-Hung Chen
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology,
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation,
- School of Medicine, College of Medicine, Tzu Chi University,
| | - Sung-Chao Chu
- School of Medicine, College of Medicine, Tzu Chi University,
- Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation,
| | - Bee-Song Chang
- Department of Cardiothoracic Surgery, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation,
| | - Chih-Bin Lin
- Department of Internal Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation,
| | - Yen-Chang Chen
- School of Medicine, College of Medicine, Tzu Chi University,
- Department of Anatomical Pathology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien,
| | - Hsin-Hon Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan and
- Department of Nuclear Medicine, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Shu-Hsin Liu
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology,
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation,
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Malet J, Ancel J, Moubtakir A, Papathanassiou D, Deslée G, Dewolf M. Assessment of the Association between Entropy in PET/CT and Response to Anti-PD-1/PD-L1 Monotherapy in Stage III or IV NSCLC. Life (Basel) 2023; 13:life13041051. [PMID: 37109580 PMCID: PMC10142835 DOI: 10.3390/life13041051] [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: 03/09/2023] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 04/29/2023] Open
Abstract
Anti-PD-1/PD-L1 therapy indications are broadened in non-small cell lung cancer (NSCLC) although immune checkpoint inhibitors (ICI) do not provide benefits for the entire population. Texture features based on positron emission tomography/computed tomography (PET/CT), especially entropy (based on a gray-level co-occurrence matrix (GLCM)), could be interesting as predictors in NSCLC. The aim of our retrospective study was to evaluate the association between GLCM-entropy and response to anti-PD-1/PD-L1 monotherapy at the first evaluation in stage III or IV NSCLC, comparing patients with progressive disease (PD) and non-progressive disease (non-PD). In total, 47 patients were included. Response Evaluation Criteria in Solid Tumors (RECIST 1.1) were used to evaluate the response to ICI treatment (nivolumab, pembrolizumab, or atezolizumab). At the first evaluation, 25 patients were PD and 22 were non-PD. GLCM-entropy was not predictive of response at the first evaluation. Furthermore, GLCM-entropy was not associated with progression-free survival (PFS) (p = 0.393) or overall survival (OS) (p = 0.220). Finally, GLCM-entropy measured in PET/CT performed before ICI initiation in stage III or IV NSCLC was not predictive of response at the first evaluation. However, this study demonstrates the feasibility of using texture parameters in routine clinical practice. The interest of measuring PET/CT texture parameters in NSCLC remains to be evaluated in larger prospective studies.
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Affiliation(s)
- Julie Malet
- Department of Respiratory Diseases, Reims University Hospital, 45, Rue Cognacq-Jay, 51100 Reims, France
| | - Julien Ancel
- Department of Respiratory Diseases, Reims University Hospital, 45, Rue Cognacq-Jay, 51100 Reims, France
- INSERM UMRS 1250, University of Reims Champagne-Ardenne, 51100 Reims, France
| | - Abdenasser Moubtakir
- Department of Nuclear Medicine, Institut Godinot, 1, Rue du Général Koenig, 51100 Reims, France
| | - Dimitri Papathanassiou
- Department of Nuclear Medicine, Institut Godinot, 1, Rue du Général Koenig, 51100 Reims, France
- UFR de Médecine, Reims-Champagne Ardenne University, 1, Rue Cognacq-Jay, CEDEX 51095 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l'Information et de la Communication), EA 3804, University of Reims Champagne-Ardenne, Moulin de la Housse, BP 1039, CEDEX 51687 Reims, France
| | - Gaëtan Deslée
- Department of Respiratory Diseases, Reims University Hospital, 45, Rue Cognacq-Jay, 51100 Reims, France
- INSERM UMRS 1250, University of Reims Champagne-Ardenne, 51100 Reims, France
| | - Maxime Dewolf
- Department of Respiratory Diseases, Reims University Hospital, 45, Rue Cognacq-Jay, 51100 Reims, France
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Fukai S, Daisaki H, Ishiyama M, Shimada N, Umeda T, Motegi K, Ito R, Terauchi T. Reproducibility of the principal component analysis (PCA)-based data-driven respiratory gating on texture features in non-small cell lung cancer patients with 18 F-FDG PET/CT. J Appl Clin Med Phys 2023; 24:e13967. [PMID: 36943700 PMCID: PMC10161026 DOI: 10.1002/acm2.13967] [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: 01/29/2023] [Revised: 02/15/2023] [Accepted: 03/01/2023] [Indexed: 03/23/2023] Open
Abstract
OBJECTIVE Texture analysis is one of the lung cancer countermeasures in the field of radiomics. Even though image quality affects texture features, the reproducibility of principal component analysis (PCA)-based data-driven respiratory gating (DDG) on texture features remains poorly understood. Hence, this study aimed to clarify the reproducibility of PCA-based DDG on texture features in non-small cell lung cancer (NSCLC) patients with 18 F-Fluorodeoxyglucose (18 F-FDG) Positron emission tomography/computed tomography (PET/CT). METHODS Twenty patients with NSCLC who underwent 18 F-FDG PET/CT in routine clinical practice were retrospectively analyzed. Each patient's PET data were reconstructed in two PET groups of no gating (NG-PET) and PCA-based DDG gating (DDG-PET). Forty-six image features were analyzed using LIFEx software. Reproducibility was evaluated using Lin's concordance correlation coefficient ( ρ c ${\rho _c}$ ) and percentage difference (%Diff). Non-reproducibility was defined as having unacceptable strength ( ρ c $({\rho _c}$ < 0.8) and a %Diff of >10%. NG-PET and DDG-PET were compared using the Wilcoxon signed-rank test. RESULTS A total of 3/46 (6.5%) image features had unacceptable strength, and 9/46 (19.6%) image features had a %Diff of >10%. Significant differences between the NG-PET and DDG-PET groups were confirmed in only 4/46 (8.7%) of the high %Diff image features. CONCLUSION Although the DDG application affected several texture features, most image features had adequate reproducibility. PCA-based DDG-PET can be routinely used as interchangeable images for texture feature extraction from NSCLC patients.
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Affiliation(s)
- Shohei Fukai
- Department of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
- Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences, Gunma, Japan
| | - Hiromitsu Daisaki
- Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences, Gunma, Japan
- Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Mitsutomi Ishiyama
- Department of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Naoki Shimada
- Department of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Takuro Umeda
- Department of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kazuki Motegi
- Department of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Ryoma Ito
- Department of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Takashi Terauchi
- Department of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
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Genomic and Glycolytic Entropy Are Reliable Radiogenomic Heterogeneity Biomarkers for Non-Small Cell Lung Cancer. Int J Mol Sci 2023; 24:ijms24043988. [PMID: 36835402 PMCID: PMC9959107 DOI: 10.3390/ijms24043988] [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: 12/29/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 02/18/2023] Open
Abstract
Radiogenomic heterogeneity features in 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) have become popular in non-small cell lung cancer (NSCLC) research. However, the reliabilities of genomic heterogeneity features and of PET-based glycolytic features in different image matrix sizes have yet to be thoroughly tested. We conducted a prospective study with 46 NSCLC patients to assess the intra-class correlation coefficient (ICC) of different genomic heterogeneity features. We also tested the ICC of PET-based heterogeneity features from different image matrix sizes. The association of radiogenomic features with clinical data was also examined. The entropy-based genomic heterogeneity feature (ICC = 0.736) is more reliable than the median-based feature (ICC = -0.416). The PET-based glycolytic entropy was insensitive to image matrix size change (ICC = 0.958) and remained reliable in tumors with a metabolic volume of <10 mL (ICC = 0.894). The glycolytic entropy is also significantly associated with advanced cancer stages (p = 0.011). We conclude that the entropy-based radiogenomic features are reliable and may serve as ideal biomarkers for research and further clinical use for NSCLC.
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Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [ 18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022; 52:759-780. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.
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Affiliation(s)
- Reyhaneh Manafi-Farid
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Emran Askari
- Department of Nuclear Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Mahboobeh Asadi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria.
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Mushari NA, Soultanidis G, Duff L, Trivieri MG, Fayad ZA, Robson P, Tsoumpas C. Exploring the Utility of Radiomic Feature Extraction to Improve the Diagnostic Accuracy of Cardiac Sarcoidosis Using FDG PET. Front Med (Lausanne) 2022; 9:840261. [PMID: 35295595 PMCID: PMC8920041 DOI: 10.3389/fmed.2022.840261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 02/01/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundThis study aimed to explore the radiomic features from PET images to detect active cardiac sarcoidosis (CS).MethodsForty sarcoid patients and twenty-nine controls were scanned using FDG PET-CMR. Five feature classes were compared between the groups. From the PET images alone, two different segmentations were drawn. For segmentation A, a region of interest (ROI) was manually delineated for the patients' myocardium hot regions with standardized uptake value (SUV) higher than 2.5 and the controls' normal myocardium region. A second ROI was drawn in the entire left ventricular myocardium for both study groups, segmentation B. The conventional metrics and radiomic features were then extracted for each ROI. Mann-Whitney U-test and a logistic regression classifier were used to compare the individual features of the study groups.ResultsFor segmentation A, the SUVmin had the highest area under the curve (AUC) and greatest accuracy among the conventional metrics. However, for both segmentations, the AUC and accuracy of the TBRmax were relatively high, >0.85. Twenty-two (from segmentation A) and thirty-five (from segmentation B) of 75 radiomic features fulfilled the criteria: P-value < 0.00061 (after Bonferroni correction), AUC >0.5, and accuracy >0.7. Principal Component Analysis (PCA) was conducted, with five components leading to cumulative variance higher than 90%. Ten machine learning classifiers were then tested and trained. Most of them had AUCs and accuracies ≥0.8. For segmentation A, the AUCs and accuracies of all classifiers are >0.9, but k-neighbors and neural network classifiers were the highest (=1). For segmentation B, there are four classifiers with AUCs and accuracies ≥0.8. However, the gaussian process classifier indicated the highest AUC and accuracy (0.9 and 0.8, respectively).ConclusionsRadiomic analysis of the specific PET data was not proven to be necessary for the detection of CS. However, building an automated procedure will help to accelerate the analysis and potentially lead to more reproducible findings across different scanners and imaging centers and consequently improve standardization procedures that are important for clinical trials and development of more robust diagnostic protocols.
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Affiliation(s)
- Nouf A. Mushari
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- *Correspondence: Nouf A. Mushari
| | - Georgios Soultanidis
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Lisa Duff
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- Institute of Medical and Biological Engineering, University of Leeds, Leeds, United Kingdom
| | - Maria G. Trivieri
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Zahi A. Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Philip Robson
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Charalampos Tsoumpas
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
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Jiménez Londoño GA, García Vicente AM, Bosque JJ, Amo-Salas M, Pérez-Beteta J, Honguero-Martinez AF, Pérez-García VM, Soriano Castrejón ÁM. SUVmax to tumor perimeter distance: a robust radiomics prognostic biomarker in resectable non-small cell lung cancer patients. Eur Radiol 2022; 32:3889-3902. [PMID: 35133484 DOI: 10.1007/s00330-021-08523-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 10/18/2021] [Accepted: 11/30/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE The purpose of this study was to evaluate the prognostic value of novel geometric variables obtained from pre-treatment [18F]FDG PET/CT with respect to classical ones in patients with non-small cell lung cancer (NSCLC). METHODS Retrospective study including stage I-III NSCLC patients with baseline [18F]FDG PET/CT. Clinical, histopathologic, and metabolic parameters were obtained. After tumor segmentation, SUV and volume-based variables, global texture, sphericity, and two novel parameters, normalized SUVpeak to centroid distance (nSCD) and normalized SUVmax to perimeter distance (nSPD), were obtained. Early recurrence (ER) and short-term mortality (STM) were used as end points. Univariate logistic regression and multivariate logistic regression with respect to ER and STM were performed. RESULTS A cohort of 173 patients was selected. ER was detected in 49/104 of patients with recurrent disease. Additionally, 100 patients died and 53 had STM. Age, pathologic lymphovascular invasion, lymph nodal infiltration, TNM stage, nSCD, and nSPD were associated with ER, although only age (aOR = 1.06, p = 0.002), pathologic lymphovascular invasion (aOR = 3.40, p = 0.022), and nSPD (aOR = 0.02, p = 0.018) were significant independent predictors of ER in multivariate analysis. Age, lymph nodal infiltration, TNM stage, nSCD, and nSPD were predictors of STM. Age (aOR = 1.05, p = 0.006), lymph nodal infiltration (aOR = 2.72, p = 0.005), and nSPD (aOR = 0.03, p = 0.022) were significantly associated with STM in multivariate analysis. Coefficient of variation (COV) and SUVmean/SUVmax ratio did not show significant predictive value with respect to ER or STM. CONCLUSION The geometric variables, nSCD and nSPD, are robust biomarkers of the poorest outcome prediction of patients with NSCLC with respect to classical PET variables. KEY POINTS • In NSCLC patients, it is crucial to find prognostic parameters since TNM system alone cannot explain the variation in lung cancer survival. • Age, lymphovascular invasion, lymph nodal infiltration, and metabolic geometrical parameters were useful as prognostic parameters. • The displacement grade of the highest point of metabolic activity towards the periphery assessed by geometric variables obtained from [18F]FDG PET/CT was a robust biomarker of the poorest outcome prediction of patients with NSCLC.
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Affiliation(s)
| | - Ana Maria García Vicente
- Department of Nuclear Medicine, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain
| | - Jesús J Bosque
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Mariano Amo-Salas
- Department of Mathematics, Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Julián Pérez-Beteta
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | | | - Víctor M Pérez-García
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
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Tsuruta C, Hirata K, Kudo K, Masumori N, Hatakenaka M. DWI-related texture analysis for prostate cancer: differences in correlation with histological aggressiveness and data repeatability between peripheral and transition zones. Eur Radiol Exp 2022; 6:1. [PMID: 35018507 PMCID: PMC8752657 DOI: 10.1186/s41747-021-00252-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 10/25/2021] [Indexed: 11/10/2022] Open
Abstract
Background We investigated the correlation between texture features extracted from apparent diffusion coefficient (ADC) maps or diffusion-weighted images (DWIs), and grade group (GG) in the prostate peripheral zone (PZ) and transition zone (TZ), and assessed reliability in repeated examinations. Methods Patients underwent 3-T pelvic magnetic resonance imaging (MRI) before radical prostatectomy with repeated DWI using b-values of 0, 100, 1,000, and 1,500 s/mm2. Region of interest (ROI) for cancer was assigned to the first and second DWI acquisition separately. Texture features of ROIs were extracted from comma-separated values (CSV) data of ADC maps generated from several sets of two b-value combinations and DWIs, and correlation with GG, discrimination ability between GG of 1–2 versus 3–5, and data repeatability were evaluated in PZ and TZ. Results Forty-four patients with 49 prostate cancers met the eligibility criteria. In PZ, ADC 10% and 25% based on ADC map of two b-value combinations of 100 and 1,500 s/mm2 and 10% based on ADC map with b-value of 0 and 1,500 s/mm2 showed significant correlation with GG, acceptable discrimination ability, and good repeatability. In TZ, higher-order texture feature of busyness extracted from ADC map of 100 and 1,500 s/mm2, and high gray-level run emphasis, short-run high gray-level emphasis, and high gray-level zone emphasis from DWI with b-value of 100 s/mm2 demonstrated significant correlation, excellent discrimination ability, but moderate repeatability. Conclusions Some DWI-related features showed significant correlation with GG, acceptable to excellent discrimination ability, and moderate to good data repeatability in prostate cancer, and differed between PZ and TZ. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-021-00252-y.
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Affiliation(s)
- Chie Tsuruta
- Department of Diagnostic Radiology, School of Medicine, Sapporo Medical University, South 1, West 17, Chuo-ku, Sapporo, 060-8556, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Japan.,Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Naoya Masumori
- Department of Urology, School of Medicine, Sapporo Medical University, Sapporo, Japan
| | - Masamitsu Hatakenaka
- Department of Diagnostic Radiology, School of Medicine, Sapporo Medical University, South 1, West 17, Chuo-ku, Sapporo, 060-8556, Japan.
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Lue KH, Chu SC, Wang LY, Chen YC, Li MH, Chang BS, Chan SC, Chen YH, Lin CB, Liu SH. Tumor glycolytic heterogeneity improves detection of regional nodal metastasis in patients with lung adenocarcinoma. Ann Nucl Med 2021; 36:256-266. [PMID: 34817824 DOI: 10.1007/s12149-021-01698-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/16/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVE The diagnostic performance of 18F-FDG PET for detecting regional lymph node metastasis in resectable lung cancer is variable, and its sensitivity for adenocarcinoma is even lower. We aimed to evaluate the value of 18F-FDG PET-derived features in predicting pathological lymph node metastasis in patients with lung adenocarcinoma. METHODS We retrospectively analyzed pretreatment 18F-FDG PET-derived features of 126 lung adenocarcinoma patients who underwent curative surgery. A logistic regression model was used to analyze the association between study variables and pathological regional lymph node status obtained from the curative surgery. Furthermore, Cox regression analysis was used to test the effect of the study variables on survival outcomes, including disease-free survival (DFS) and overall survival (OS). RESULTS The primary tumor entropy (OR = 1.7, p = 0.014) and visual interpretation of regional nodes via 18F-FDG PET (OR = 2.5, p = 0.026) independently predicted pathological regional lymph node metastasis. The areas under the receiver-operating-characteristic curves were 0.631, 0.671, and 0.711 for visual interpretation, primary tumor entropy, and their combination, respectively. Based on visual interpretation, a primary tumor entropy ≥ 3.0 improved the positive predictive value of positive visual interpretation from 51.2% to 63.0%, whereas an entropy < 3.0 improved the negative predictive value of negative visual interpretation from 75.3% to 82.6%. In cases with positive visual interpretation and low entropy, or negative visual interpretation and high entropy, the nodal metastasis rates were approximately 30%. In the survival analyses, the primary tumor entropy was also independently associated with DFS (HR = 2.7, p = 0.001) and OS (HR = 4.8, p = 0.001). CONCLUSIONS Our preliminary results show that the primary tumor entropy may improve 18F-FDG PET visual interpretation in predicting pathological nodal metastasis in lung adenocarcinoma, and may also show a survival prognostic value. This versatile biomarker may facilitate tailored therapeutic strategies for patients with resectable lung adenocarcinoma.
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Affiliation(s)
- Kun-Han Lue
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien, Taiwan
| | - Sung-Chao Chu
- School of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan.,Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Ling-Yi Wang
- Epidemiology and Biostatistics Consulting Center, Department of Medical Research, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.,Department of Pharmacy, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Yen-Chang Chen
- School of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan.,Department of Anatomical Pathology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Ming-Hsun Li
- Department of Anatomical Pathology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Bee-Song Chang
- Department of Cardiothoracic Surgery, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Sheng-Chieh Chan
- School of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan.,Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Yu-Hung Chen
- School of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan. .,Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.
| | - Chih-Bin Lin
- School of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan.,Department of Internal Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Shu-Hsin Liu
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien, Taiwan.,Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
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11
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Litvin AA, Burkin DA, Kropinov AA, Paramzin FN. Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovrem Tekhnologii Med 2021; 13:97-104. [PMID: 34513082 PMCID: PMC8353717 DOI: 10.17691/stm2021.13.2.11] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Indexed: 12/12/2022] Open
Abstract
One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of “virtual biopsy” is of particular relevance in oncology. The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered. Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.
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Affiliation(s)
- A A Litvin
- Professor, Department of Surgical Disciplines, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia; Deputy Head Physician for Medical Aspects, Regional Clinical Hospital of the Kaliningrad Region, 74 Klinicheskaya St., Kaliningrad, 236016, Russia
| | - D A Burkin
- PhD Student in Information Science and Computer Engineering, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia
| | - A A Kropinov
- Therapeutist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
| | - F N Paramzin
- Oncologist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
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12
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Differentiation between non-small cell lung cancer and radiation pneumonitis after carbon-ion radiotherapy by 18F-FDG PET/CT texture analysis. Sci Rep 2021; 11:11509. [PMID: 34075072 PMCID: PMC8169739 DOI: 10.1038/s41598-021-90674-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 05/10/2021] [Indexed: 12/27/2022] Open
Abstract
The differentiation of non-small cell lung cancer (NSCLC) and radiation pneumonitis (RP) is critically essential for selecting optimal clinical therapeutic strategies to manage post carbon-ion radiotherapy (CIRT) in patients with NSCLC. The aim of this study was to assess the ability of 18F-FDG PET/CT metabolic parameters and its textural image features to differentiate NSCLC from RP after CIRT to develop a differential diagnosis of malignancy and benign lesion. We retrospectively analyzed 18F-FDG PET/CT image data from 32 patients with histopathologically proven NSCLC who were scheduled to undergo CIRT and 31 patients diagnosed with RP after CIRT. The SUV parameters, metabolic tumor volume (MTV), total lesion glycolysis (TLG) as well as fifty-six texture parameters derived from seven matrices were determined using PETSTAT image-analysis software. Data were statistically compared between NSCLC and RP using Wilcoxon rank-sum tests. Diagnostic accuracy was assessed using receiver operating characteristics (ROC) curves. Several texture parameters significantly differed between NSCLC and RP (p < 0.05). The parameters that were high in areas under the ROC curves (AUC) were as follows: SUVmax, 0.64; GLRLM run percentage, 0.83 and NGTDM coarseness, 0.82. Diagnostic accuracy was improved using GLRLM run percentage or NGTDM coarseness compared with SUVmax (p < 0.01). The texture parameters of 18F-FDG uptake yielded excellent outcomes for differentiating NSCLC from radiation pneumonitis after CIRT, which outperformed SUV-based evaluation. In particular, GLRLM run percentage and NGTDM coarseness of 18F-FDG PET/CT images would be appropriate parameters that can offer high diagnostic accuracy.
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13
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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14
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Prognostic Value of 18F-FDG-PET Parameters in Patients with Small Cell Lung Cancer: A Meta-Analysis and Review of Current Literature. Diagnostics (Basel) 2021; 11:diagnostics11020174. [PMID: 33530446 PMCID: PMC7912276 DOI: 10.3390/diagnostics11020174] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/18/2021] [Accepted: 01/22/2021] [Indexed: 01/29/2023] Open
Abstract
Many studies have suggested a prognostic value of one or several positron emission tomography (PET) parameters in patients with small cell lung cancer (SCLC). However, studies are often small, and there is a considerable interstudy disagreement about which PET parameters have a prognostic value. The objective of this study was to perform a review and meta-analysis to identify the most promising PET parameter for prognostication. PubMed®, Cochrane, and Embase® were searched for papers addressing the prognostic value of any PET parameter at any treatment phase with any endpoint in patients with SCLC. Pooled hazard ratios (HRs) were calculated by a random effects model for the prognostic value of the baseline maximum standardized uptake value (SUVmax) and metabolic tumor volume (MTV). The qualitative analysis included 38 studies, of these, 19 studies were included in the meta-analyses. The pooled results showed that high baseline MTV was prognostic for overall survival (OS) (HR: 2.83 (95% confidence interval [CI]: 2.00–4.01) and progression-free survival (PFS) (HR: 3.11 (95% CI: 1.99–4.90)). The prognostic value of SUVmax was less pronounced (OS: HR: 1.50 (95% CI: 1.17–1.91); PFS: HR: 1.24 (95% CI: 0.94–1.63)). Baseline MTV is a strong prognosticator for OS and PFS in patients with SCLC. MTV has a prognostic value superior to those of other PET parameters, but whether MTV is superior to other prognosticators of tumor burden needs further investigation.
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15
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Chen YH, Wang TF, Chu SC, Lin CB, Wang LY, Lue KH, Liu SH, Chan SC. Incorporating radiomic feature of pretreatment 18F-FDG PET improves survival stratification in patients with EGFR-mutated lung adenocarcinoma. PLoS One 2020; 15:e0244502. [PMID: 33370365 PMCID: PMC7769431 DOI: 10.1371/journal.pone.0244502] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 12/10/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND To investigate the survival prognostic value of the radiomic features of 18F-FDG PET in patients who had EGFR (epidermal growth factor receptor) mutated lung adenocarcinoma and received targeted TKI (tyrosine kinase inhibitor) treatment. METHODS Fifty-one patients with stage III-IV lung adenocarcinoma and actionable EGFR mutation who received first-line TKI were retrospectively analyzed. All patients underwent pretreatment 18F-FDG PET/CT, and we calculated the PET-derived radiomic features. Cox proportional hazard model was used to examine the association between the radiomic features and the survival outcomes, including progression-free survival (PFS) and overall survival (OS). A score model was established according to the independent prognostic predictors and we compared this model to the TNM staging system using Harrell's concordance index (c-index). RESULTS Forty-eight patients (94.1%) experienced disease progression and 41 patients (80.4%) died. Primary tumor SUV entropy > 5.36, and presence of pleural effusion were independently associated with worse OS (both p < 0.001) and PFS (p = 0.001, and 0.003, respectively). We used these two survival predictors to devise a scoring system (score 0-2). Patients with a score of 1 or 2 had a worse survival than those with a score of 0 (HR for OS: 3.6, p = 0.006 for score 1, and HR: 21.8, p < 0.001 for score 2; HR for PFS: 2.2, p = 0.027 for score 1 and HR: 8.8, p < 0.001 for score 2). Our scoring system surpassed the TNM staging system (c-index = 0.691 versus 0.574, p = 0.013 for OS, and c-index = 0.649 versus 0.517, p = 0.004 for PFS). CONCLUSIONS In this preliminary study, combining PET radiomics with clinical risk factors may improve survival stratification in stage III-IV lung adenocarcinoma with actionable EFGR mutation. Our proposed scoring system may assist with optimization of individualized treatment strategies in these patients.
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Affiliation(s)
- Yu-Hung Chen
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Tso-Fu Wang
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
- Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Sung-Chao Chu
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
- Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Chih-Bin Lin
- Department of Internal Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Ling-Yi Wang
- Epidemiology and Biostatistics Consulting Center, Department of Medical Research and Department of Pharmacy, Tzu Chi General Hospital, Hualien, Taiwan
| | - Kun-Han Lue
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien, Taiwan
| | - Shu-Hsin Liu
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien, Taiwan
| | - Sheng-Chieh Chan
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
- * E-mail:
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16
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Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer? Eur Radiol 2020; 31:4156-4165. [PMID: 33247345 DOI: 10.1007/s00330-020-07507-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 10/04/2020] [Accepted: 11/11/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVES We aimed at investigating the origin of the correlations between tumor volume and 18F-FDG-PET texture indices in lung cancer. METHODS Eighty-five consecutive patients with newly diagnosed non-small cell lung cancer (NSCLC) underwent a 18F-FDG-PET/CT scan before treatment. Seven phantom spheres uniformly filled with 18F-FDG, and covering a range of activities and volumes similar to that found in lung tumors, were also scanned. Established texture indices were computed for lung tumors and homogeneous spheres. The dependence between textural indices and volume in homogeneous spheres was modeled and then used to predict texture indices in lung tumors. Correlation analyses were carried out between predicted and texture features measured in lung tumors. Cox proportional hazards regression was used to investigate the associations between overall survival and volume-adjusted textural features. RESULTS All textural features showed strong, non-linear correlations with volume, both in tumors and homogeneous spheres. Correlations between predicted versus measured texture features were very high for contrast (r2 = 0.91), dissimilarity (r2 = 0.90), ZP (r2 = 0.90), GLNN (r2 = 0.86), and homogeneity (r2 = 0.82); high for entropy (r2 = 0.50) and HILAE (r2 = 0.53); and low for energy (r2 = 0.30). Cox regressions showed that among volume-adjusted features, only HILAE was associated with overall survival (b = - 0.35, p = 0.008). CONCLUSION We have shown that texture indices previously found to be correlated with a number of clinically relevant outcomes might not provide independent information apart from that driven by their correlation with tumor volume, suggesting that these metrics might not be suitable as intratumor heterogeneity markers. KEY POINTS • Associations between texture FDG-PET indices and overall survival have been widely reported in lung cancer, with tumor volume also being associated with overall survival, and therefore, it is still unclear whether the predictive power of textural indices is simply driven by this correlation. • Our results demonstrated strong non-linear correlations between textural indices and volume, showing an analogous behavior for lung tumors from patients and homogeneous spheres inserted in phantoms. • Our findings showed that texture FDG-PET indices might not provide independent information apart from that driven by their correlation with tumor volume.
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17
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Shao X, Niu R, Shao X, Jiang Z, Wang Y. Value of 18F-FDG PET/CT-based radiomics model to distinguish the growth patterns of early invasive lung adenocarcinoma manifesting as ground-glass opacity nodules. EJNMMI Res 2020; 10:80. [PMID: 32661639 PMCID: PMC7359213 DOI: 10.1186/s13550-020-00668-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/02/2020] [Indexed: 11/12/2022] Open
Abstract
Background To establish and validate 18F-fluorodeoxyglucose (18F-FDG) PET/CT-based radiomics model and use it to predict the intermediate-high risk growth patterns in early invasive adenocarcinoma (IAC). Methods Ninety-three ground-glass nodules (GGNs) from 91 patients with stage I who underwent a preoperative 18F-FDG PET/CT scan and histopathological examination were included in this study. The LIFEx software was used to extract 52 PET and 49 CT radiomic features. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop radiomics signatures. We used the receiver operating characteristics curve (ROC) to compare the predictive performance of conventional CT parameters, radiomics signatures, and the combination of these two. Also, a nomogram based on conventional CT indicators and radiomics signature score (rad-score) was developed. Results GGNs were divided into lepidic group (n = 18) and acinar-papillary group (n = 75). Four radiomic features (2 for PET and 2 for CT) were selected to calculate the rad-score, and the area under the curve (AUC) of rad-score was 0.790, which was not significantly different as the attenuation value of the ground-glass opacity component on CT (CTGGO) (0.675). When rad-score was combined with edge (joint model), the AUC increased to 0.804 (95% CI [0.699–0.895]), but which was not significantly higher than CTGGO (P = 0.109). Furthermore, the decision curve of joint model showed higher clinical value than rad-score and CTGGO, especially under the purpose of screening for intermediate-high risk growth patterns. Conclusion PET/CT-based radiomics model shows good performance in predicting intermediate-high risk growth patterns in early IAC. This model provides a useful method for risk stratification, clinical management, and personalized treatment.
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Affiliation(s)
- Xiaonan Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Rong Niu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Xiaoliang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Zhenxing Jiang
- Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China
| | - Yuetao Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China. .,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China.
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2-[ 18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease. Methods 2020; 188:84-97. [PMID: 32497604 DOI: 10.1016/j.ymeth.2020.05.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/22/2020] [Accepted: 05/27/2020] [Indexed: 12/15/2022] Open
Abstract
Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[18F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[18F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.
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Krarup MMK, Nygård L, Vogelius IR, Andersen FL, Cook G, Goh V, Fischer BM. Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool. Radiother Oncol 2020; 144:72-78. [PMID: 31733491 DOI: 10.1016/j.radonc.2019.10.012] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 10/01/2019] [Accepted: 10/17/2019] [Indexed: 02/06/2023]
Abstract
AIM The aim was to validate promising radiomic features (RFs)1 on 18F-flourodeoxyglucose positron emission tomography/computed tomography-scans (18F-FDG PET/CT) of non-small cell lung cancer (NSCLC) patients undergoing definitive chemo-radiotherapy. METHODS 18F-FDG PET/CT scans performed for radiotherapy (RT) planning were retrieved. Auto-segmentation with visual adaption was used to define the primary tumour on PET images. Six pre-selected prognostic and reproducible PET texture -and shape-features were calculated using texture respectively shape analysis. The correlation between these RFs and metabolic active tumour volume (MTV)3, gross tumour volume (GTV)4 and maximum and mean of standardized uptake value (SUV)5 was tested with a Spearman's Rank test. The prognostic value of RFs was tested in a univariate cox regression analysis and a multivariate cox regression analysis with GTV, clinical stage and histology. P-value ≤ 0.05 were considered significant. RESULTS Image analysis was performed for 233 patients: 145 males and 88 females, mean age of 65.7 and clinical stage II-IV. Mean GTV was 129.87 cm3 (SD 130.30 cm3). Texture and shape-features correlated more strongly to MTV and GTV compared to SUV-measurements. Four RFs predicted PFS in the univariate analysis. No RFs predicted PFS in the multivariate analysis, whereas GTV and clinical stage predicted PFS (p = 0.001 and p = 0.008 respectively). CONCLUSION The pre-selected RFs were insignificant in predicting PFS in combination with GTV, clinical stage and histology. These results might be due to variations in technical parameters. However, it is relevant to question whether RFs are stable enough to provide clinically useful information.
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Affiliation(s)
- Marie Manon Krebs Krarup
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Lotte Nygård
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Ivan Richter Vogelius
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Denmark; Faculty of Health and Medical Sciences, Copenhagen University, Denmark.
| | - Flemming Littrup Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Gary Cook
- PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
| | - Vicky Goh
- PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
| | - Barbara Malene Fischer
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark; PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
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Effects of simulated dose variation on contrast-enhanced CT-based radiomic analysis for Non-Small Cell Lung Cancer. Eur J Radiol 2020; 124:108804. [PMID: 31926387 DOI: 10.1016/j.ejrad.2019.108804] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 12/05/2019] [Accepted: 12/16/2019] [Indexed: 01/18/2023]
Abstract
PURPOSE To examine the potential effect of CT dose variation on radiomic features in vivo using simulated contrast-enhanced CT dose reduction in patients with non-small lung cell cancer (NSCLC). METHODS In this retrospective study, we included 69 patients (25 females, 44 males, median age 66 years) with histologically proven NSCLC who underwent a whole contrast-enhanced body FDG-PET/CT for primary staging. To simulate different CT dose levels, we used an algorithm to simulate low-dose CT images based on a noise model derived from phantom experiments. The tumor lesions and reference regions in the paraspinal muscle were manually segmented to obtain three-dimensional regions of interest. Radiomic feature extraction was performed using the PyRadiomics toolbox. The median relative feature value deviation was assessed for each feature and each dose level. RESULTS The mean segmented tumor volume was 340 ml. T-stages of the primary tumors were primarily T3/4. For NSCLCs, the median relative feature value deviation in the lowest dose images varied for the calculated features from 52.2% to -49.5%. In general, dose-dependent deviations of feature values showed a monotonous increase or decrease with decreasing dose levels. Statistical analyses revealed significant differences between the dose levels in 91% of features. CONCLUSIONS We examined the effects of simulated CT dose reduction on the values of radiomic features in primary NSCLC and showed significant deviations of varying degrees in numerous feature classes. This is a theoretical indicator of potential influence under real conditions, which should be taken into account in clinical use.
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An appreciation from the out-going editor-in-chief. Ann Nucl Med 2019; 33:875-876. [DOI: 10.1007/s12149-019-01423-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Harmon S, Seder CW, Chen S, Traynor A, Jeraj R, Blasberg JD. Quantitative FDG PET/CT may help risk-stratify early-stage non-small cell lung cancer patients at risk for recurrence following anatomic resection. J Thorac Dis 2019; 11:1106-1116. [PMID: 31179052 PMCID: PMC6531752 DOI: 10.21037/jtd.2019.04.46] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 04/03/2019] [Indexed: 01/05/2023]
Abstract
BACKGROUND Preoperative identification of non-small cell lung cancer (NSCLC) patients at risk for disease recurrence has proven unreliable. The extraction of quantitative metrics from imaging based on tumor intensity and texture may enhanced disease characterization. This study evaluated tumor-specific 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computerized tomography (PET/CT) uptake patterns and their association with disease recurrence in early-stage NSCLC. METHODS Sixty-four stage I/II NSCLC patients who underwent anatomic resection between 2001 and 2014 were examined. Pathologically or radiographic confirmed disease recurrence within 5 years of resection comprised the study group. Quantitative imaging metrics were extracted within the primary tumor volume. Squamous cell carcinoma (SCC) (N=27) and adenocarcinoma (AC) (N=41) patients were compared using a Wilcoxon signed-rank test. Associations between imaging and clinical variables with 5-year disease-free survival (DFS) and overall survival (OS) were evaluated by Cox proportional-hazards regression. RESULTS Clinical and pathologic characteristics were similar between recurrence (N=34) and patients achieving 5-year DFS (N=30). Standardized uptake value (SUV)max and SUVmean varied significantly by histology, with SCC demonstrating higher uptake intensity and heterogeneity patterns. Entropy-grey-level co-occurrence matrix (GLCM) was a significant univariate predictor of DFS (HR =0.72, P=0.04) and OS (HR =0.65, P=0.007) independent of histology. Texture features showed higher predictive ability for DFS in SCC than AC. Pathologic node status and staging classification were the strongest clinical predictors of DFS, independent of histology. CONCLUSIONS Several imaging metrics correlate with increased risk for disease recurrence in early-stage NSCLC. The predictive ability of imaging was strongest when patients are stratified by histology. The incorporation of 18F-FDG PET/CT texture features with preoperative risk factors and tumor characteristics may improve identification of high-risk patients.
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Affiliation(s)
- Stephanie Harmon
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Christopher W. Seder
- Department of Thoracic and Cardiovascular Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Song Chen
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA
- Department of Nuclear Medicine, The 1st Hospital of China Medical University, Shenyang 110016, China
| | - Anne Traynor
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA
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