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Bajinka O, Ouedraogo SY, Golubnitschaja O, Li N, Zhan X. Energy metabolism as the hub of advanced non-small cell lung cancer management: a comprehensive view in the framework of predictive, preventive, and personalized medicine. EPMA J 2024; 15:289-319. [PMID: 38841622 PMCID: PMC11147999 DOI: 10.1007/s13167-024-00357-5] [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: 03/08/2024] [Accepted: 03/20/2024] [Indexed: 06/07/2024]
Abstract
Energy metabolism is a hub of governing all processes at cellular and organismal levels such as, on one hand, reparable vs. irreparable cell damage, cell fate (proliferation, survival, apoptosis, malignant transformation etc.), and, on the other hand, carcinogenesis, tumor development, progression and metastazing versus anti-cancer protection and cure. The orchestrator is the mitochondria who produce, store and invest energy, conduct intracellular and systemically relevant signals decisive for internal and environmental stress adaptation, and coordinate corresponding processes at cellular and organismal levels. Consequently, the quality of mitochondrial health and homeostasis is a reliable target for health risk assessment at the stage of reversible damage to the health followed by cost-effective personalized protection against health-to-disease transition as well as for targeted protection against the disease progression (secondary care of cancer patients against growing primary tumors and metastatic disease). The energy reprogramming of non-small cell lung cancer (NSCLC) attracts particular attention as clinically relevant and instrumental for the paradigm change from reactive medical services to predictive, preventive and personalized medicine (3PM). This article provides a detailed overview towards mechanisms and biological pathways involving metabolic reprogramming (MR) with respect to inhibiting the synthesis of biomolecules and blocking common NSCLC metabolic pathways as anti-NSCLC therapeutic strategies. For instance, mitophagy recycles macromolecules to yield mitochondrial substrates for energy homeostasis and nucleotide synthesis. Histone modification and DNA methylation can predict the onset of diseases, and plasma C7 analysis is an efficient medical service potentially resulting in an optimized healthcare economy in corresponding areas. The MEMP scoring provides the guidance for immunotherapy, prognostic assessment, and anti-cancer drug development. Metabolite sensing mechanisms of nutrients and their derivatives are potential MR-related therapy in NSCLC. Moreover, miR-495-3p reprogramming of sphingolipid rheostat by targeting Sphk1, 22/FOXM1 axis regulation, and A2 receptor antagonist are highly promising therapy strategies. TFEB as a biomarker in predicting immune checkpoint blockade and redox-related lncRNA prognostic signature (redox-LPS) are considered reliable predictive approaches. Finally, exemplified in this article metabolic phenotyping is instrumental for innovative population screening, health risk assessment, predictive multi-level diagnostics, targeted prevention, and treatment algorithms tailored to personalized patient profiles-all are essential pillars in the paradigm change from reactive medical services to 3PM approach in overall management of lung cancers. This article highlights the 3PM relevant innovation focused on energy metabolism as the hub to advance NSCLC management benefiting vulnerable subpopulations, affected patients, and healthcare at large. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-024-00357-5.
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Affiliation(s)
- Ousman Bajinka
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Serge Yannick Ouedraogo
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Olga Golubnitschaja
- Predictive, Preventive and Personalised (3P) Medicine, University Hospital Bonn, Venusberg Campus 1, Rheinische Friedrich-Wilhelms-University of Bonn, 53127 Bonn, Germany
| | - Na Li
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Xianquan Zhan
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
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Grambozov B, Kalantari F, Beheshti M, Stana M, Karner J, Ruznic E, Zellinger B, Sedlmayer F, Rinnerthaler G, Zehentmayr F. Pretreatment 18-FDG-PET/CT parameters can serve as prognostic imaging biomarkers in recurrent NSCLC patients treated with reirradiation-chemoimmunotherapy. Radiother Oncol 2023; 185:109728. [PMID: 37301259 DOI: 10.1016/j.radonc.2023.109728] [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: 02/20/2023] [Revised: 05/02/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND PURPOSE Our study aimed to assess whether quantitative pretreatment 18F-FDG-PET/CT parameters could predict prognostic clinical outcome of recurrent NSCLC patients who may benefit from ablative reirradiation. MATERIALS AND METHODS Forty-eight patients with recurrent NSCLC of all UICC stages who underwent ablative thoracic reirradiation were analyzed. Twenty-nine (60%) patients received immunotherapy with or without chemotherapy in addition to reirradiation. Twelve patients (25%) received reirradiation only and seven (15%) received chemotherapy and reirradiation. Pretreatment 18-FDG-PET/CT was mandatory in initial diagnosis and recurrence, based on which volumetric and intensity quantitative parameters were measured before reirradiation and their impact on overall survival, progression-free survival, and locoregional control was assessed. RESULTS With a median follow-up time of 16.7 months, the median OS was 21.8 months (95%-CI: 16.2-27.3). On multivariate analysis, OS and PFS were significantly influenced by MTV (p < 0.001 for OS; p = 0.006 for PFS), TLG (p < 0.001 for OS; p = 0.001 for PFS) and SUL peak (p = 0.0024 for OS; p = 0.02 for PFS) of the tumor and MTV (p = 0.004 for OS; p < 0.001 for PFS) as well as TLG (p = 0.007 for OS; p = 0.015 for PFS) of the metastatic lymph nodes. SUL peak of the tumor (p = 0.05) and the MTV of the lymph nodes (p = 0.003) were only PET quantitative parameters that significantly impacted LRC. CONCLUSION Pretreatment tumor and metastastic lymph node MTV, TLG and tumor SUL peak significantly correlated with clinical outcome in recurrent NSCLC patients treated with reirradiation-chemoimmunotherapy.
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Affiliation(s)
- Brane Grambozov
- Department of Radiation Oncology, Paracelsus Medical University, SALK, Salzburg, Austria.
| | - Forough Kalantari
- Department of Nuclear Medicine, Rasoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran; Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Markus Stana
- Department of Radiation Oncology, Paracelsus Medical University, SALK, Salzburg, Austria
| | - Josef Karner
- Department of Radiation Oncology, Paracelsus Medical University, SALK, Salzburg, Austria
| | - Elvis Ruznic
- Department of Radiation Oncology, Paracelsus Medical University, SALK, Salzburg, Austria
| | - Barbara Zellinger
- Institute of Pathology, Paracelsus Medical University, SALK, Salzburg, Austria
| | - Felix Sedlmayer
- Department of Radiation Oncology, Paracelsus Medical University, SALK, Salzburg, Austria; radART - Institute for Research and Development on Advanced Radiation Technologies, Paracelsus Medical University, Salzburg, Austria
| | - Gabriel Rinnerthaler
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center, Salzburg Cancer Research Institute-Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR), Paracelsus Medical University Salzburg, 5020 Salzburg, Austria; Cancer Cluster Salzburg, 5020 Salzburg, Austria
| | - Franz Zehentmayr
- Department of Radiation Oncology, Paracelsus Medical University, SALK, Salzburg, Austria; radART - Institute for Research and Development on Advanced Radiation Technologies, Paracelsus Medical University, Salzburg, Austria
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Adelsmayr G, Janisch M, Müller H, Holzinger A, Talakic E, Janek E, Streit S, Fuchsjäger M, Schöllnast H. Three dimensional computed tomography texture analysis of pulmonary lesions: Does radiomics allow differentiation between carcinoma, neuroendocrine tumor and organizing pneumonia? Eur J Radiol 2023; 165:110931. [PMID: 37399666 DOI: 10.1016/j.ejrad.2023.110931] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 05/22/2023] [Accepted: 06/15/2023] [Indexed: 07/05/2023]
Abstract
PURPOSE To investigate whether CT texture analysis allows differentiation between adenocarcinomas, squamous cell carcinomas, carcinoids, small cell lung cancers and organizing pneumonia and between carcinomas and neuroendocrine tumors. METHOD This retrospective study included patients 133 patients (30 patients with organizing pneumonia, 30 patients with adenocarcinoma, 30 patients with squamous cell carcinoma, 23 patients with small cell lung cancer, 20 patients with carcinoid), who underwent CT-guided biopsy of the lung and had a corresponding histopathologic diagnosis. Pulmonary lesions were segmented in consensus by two radiologists with and without a threshold of -50HU in three dimensions. Groupwise comparisons were performed to assess for differences between all five above-listed entities and between carcinomas and neuroendocrine tumors. RESULTS Pairwise comparisons of the five entities revealed 53 statistically significant texture features when using no HU-threshold and 6 statistically significant features with a threshold of -50HU. The largest AUC (0.818 [95%CI 0.706-0.930]) was found for the feature wavelet-HHH_glszm_SmallAreaEmphasis for discrimination of carcinoid from the other entities when using no HU-threshold. In differentiating neuroendocrine tumors from carcinomas, 173 parameters proved statistically significant when using no HU threshold versus 52 parameters when using a -50HU-threshold. The largest AUC (0.810 [95%CI 0.728-0,893]) was found for the parameter original_glcm_Correlation for discrimination of neuroendocrine tumors from carcinomas when using no HU-threshold. CONCLUSIONS CT texture analysis revealed features that differed significantly between malignant pulmonary lesions and organizing pneumonia and between carcinomas and neuroendocrine tumors of the lung. Applying a HU-threshold for segmentation substantially influenced the results of texture analysis.
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Affiliation(s)
- Gabriel Adelsmayr
- Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036 Graz, Austria
| | - Michael Janisch
- Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036 Graz, Austria
| | - Heimo Müller
- Diagnostic and Research Center for Molecular BioMedicine, Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria
| | - Andreas Holzinger
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2/9/V, 8036 Graz, Austria
| | - Emina Talakic
- Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036 Graz, Austria
| | - Elmar Janek
- Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036 Graz, Austria
| | - Simon Streit
- Diagnostic and Research Center for Molecular BioMedicine, Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria
| | - Michael Fuchsjäger
- Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036 Graz, Austria.
| | - Helmut Schöllnast
- Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036 Graz, Austria; Institute of Radiology, LKH Graz II, Göstinger Strasse 22, 8020 Graz, Austria
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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:jcm12010255. [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.
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Anne-Leen D, Machaba S, Alex M, Bart DS, Laurence B, Mike S, Hans P, Van de Wiele C. Principal component analysis of texture features derived from FDG PET images of melanoma lesions. EJNMMI Phys 2022; 9:64. [PMID: 36107331 PMCID: PMC9478000 DOI: 10.1186/s40658-022-00491-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 09/01/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The clinical utility of radiomics is hampered by a high correlation between the large number of features analysed which may result in the “bouncing beta” phenomenon which could in part explain why in a similar patient population texture features identified and/or cut-off values of prognostic significance differ from one study to another. Principal component analysis (PCA) is a technique for reducing the dimensionality of large datasets containing highly correlated variables, such as texture feature datasets derived from FDG PET images, increasing data interpretability whilst at the same time minimizing information loss by creating new uncorrelated variables that successively maximize variance. Here, we report on PCA of a texture feature dataset derived from 123 malignant melanoma lesions with a significant range in lesion size using the freely available LIFEx software.
Results
Thirty-eight features were derived from all lesions. All features were standardized. The statistical assumptions for carrying out PCA analysis were met. Seven principal components with an eigenvalue > 1 were identified. Based on the “elbow sign” of the Scree plot, only the first five were retained. The contribution to the total variance of these components derived using Varimax rotation was, respectively, 30.6%, 23.6%, 16.1%, 7.4% and 4.1%. The components provided summarized information on the locoregional FDG distribution with an emphasis on high FDG uptake regions, contrast in FDG uptake values (steepness), tumour volume, locoregional FDG distribution with an emphasis on low FDG uptake regions and on the rapidity of changes in SUV intensity between different regions.
Conclusions
PCA allowed to reduce the dataset of 38 features to a set of 5 uncorrelated new variables explaining approximately 82% of the total variance contained within the dataset. These principal components may prove more useful for multiple regression analysis considering the relatively low numbers of patients usually included in clinical trials on FDG PET texture analysis. Studies assessing the superior differential diagnostic, predictive or prognostic value of principal components derived using PCA as opposed to the initial texture features in clinical relevant settings are warranted.
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Wolsztynski E, O'Sullivan F, Eary JF. Spatially coherent modeling of 3D FDG-PET data for assessment of intratumoral heterogeneity and uptake gradients. J Med Imaging (Bellingham) 2022; 9:045003. [PMID: 35915767 PMCID: PMC9334646 DOI: 10.1117/1.jmi.9.4.045003] [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: 09/10/2021] [Accepted: 06/28/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: Radiomics have become invaluable for non-invasive cancer patient risk prediction, and the community now turns to exogenous assessment, e.g., from genomics, for interpretability of these agnostic analyses. Yet, some opportunities for clinically interpretable modeling of positron emission tomography (PET) imaging data remain unexplored, that could facilitate insightful characterization at voxel level. Approach: Here, we present a novel deformable tubular representation of the distribution of tracer uptake within a volume of interest, and derive interpretable prognostic summaries from it. This data-adaptive strategy yields a 3D-coherent and smooth model fit, and a profile curve describing tracer uptake as a function of voxel location within the volume. Local trends in uptake rates are assessed at each voxel via the calculation of gradients derived from this curve. Intratumoral heterogeneity can also be assessed directly from it. Results: We illustrate the added value of this approach over previous strategies, in terms of volume rendering and coherence of the structural representation of the data. We further demonstrate consistency of the implementation via simulations, and prognostic potential of heterogeneity and statistical summaries of the uptake gradients derived from the model on a clinical cohort of 158 sarcoma patients imaged with F 18 -fluorodeoxyglucose-PET, in multivariate prognostic models of patient survival. Conclusions: The proposed approach captures uptake characteristics consistently at any location, and yields a description of variations in uptake that holds prognostic value complementarily to structural heterogeneity. This creates opportunities for monitoring of local areas of greater interest within a tumor, e.g., to assess therapeutic response in avid locations.
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Affiliation(s)
- Eric Wolsztynski
- University College Cork, Statistics Department, Cork, Ireland.,Insight SFI Research Centre for Data Analytics, Cork, Ireland
| | - Finbarr O'Sullivan
- University College Cork, Statistics Department, Cork, Ireland.,Insight SFI Research Centre for Data Analytics, Cork, Ireland
| | - Janet F Eary
- National Cancer Institute, Bethesda, Maryland, United States
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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:diagnostics12061329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [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.
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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
- Correspondence:
| | - 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.)
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Zukotynski KA, Hasan OK, Lubanovic M, Gerbaudo VH. Update on Molecular Imaging and Precision Medicine in Lung Cancer. Radiol Clin North Am 2021; 59:693-703. [PMID: 34392913 DOI: 10.1016/j.rcl.2021.05.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Precision medicine integrates molecular pathobiology, genetic make-up, and clinical manifestations of disease in order to classify patients into subgroups for the purposes of predicting treatment response and suggesting outcome. By identifying those patients who are most likely to benefit from a given therapy, interventions can be tailored to avoid the expense and toxicity of futile treatment. Ultimately, the goal is to offer the right treatment, to the right patient, at the right time. Lung cancer is a heterogeneous disease both functionally and morphologically. Further, over time, clonal proliferations of cells may evolve, becoming resistant to specific therapies. PET is a sensitive imaging technique with an important role in the precision medicine algorithm of lung cancer patients. It provides anatomo-functional insight during diagnosis, staging, and restaging of the disease. It is a prognostic biomarker in lung cancer patients that characterizes tumoral heterogeneity, helps predict early response to therapy, and may direct the selection of appropriate treatment.
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Affiliation(s)
- Katherine A Zukotynski
- Department of Medicine, McMaster University, 1200 Main Street West, Hamilton, Ontario L9G 4X5, Canada; Department of Radiology, McMaster University, 1200 Main Street West, Hamilton, Ontario L9G 4X5, Canada
| | - Olfat Kamel Hasan
- Department of Medicine, McMaster University, 1200 Main Street West, Hamilton, Ontario L9G 4X5, Canada; Department of Radiology, McMaster University, 1200 Main Street West, Hamilton, Ontario L9G 4X5, Canada
| | - Matthew Lubanovic
- Department of Radiology, McMaster University, 1200 Main Street West, Hamilton, Ontario L9G 4X5, Canada
| | - Victor H Gerbaudo
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02492, USA.
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Guberina M, Pöttgen C, Metzenmacher M, Wiesweg M, Schuler M, Aigner C, Ploenes T, Umutlu L, Gauler T, Darwiche K, Stamatis G, Theegarten D, Hautzel H, Jentzen W, Guberina N, Herrmann K, Eberhardt WE, Stuschke M. PROGNOSTIC VALUE OF POST-INDUCTION CHEMOTHERAPY VOLUMETRIC PET/CT PARAMETERS FOR STAGE IIIA/B NON-SMALL CELL LUNG CANCER PATIENTS RECEIVING DEFINITIVE CHEMORADIOTHERAPY. J Nucl Med 2021; 62:jnumed.120.260646. [PMID: 34016730 PMCID: PMC8612197 DOI: 10.2967/jnumed.120.260646] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/26/2021] [Accepted: 03/26/2021] [Indexed: 11/30/2022] Open
Abstract
Purpose/Objective(s): The aim of this follow-up analysis of the ESPATUE phase-3 trial was to explore the prognostic value of post-induction chemotherapy PET metrics in patients with stage III non-small cell lung cancer (NSCLC) who were assigned to receive definitive chemoradiotherapy. Materials/Methods: All eligible patients stage IIIA (cN2) and stage IIIB of the trial received induction chemotherapy consisting of 3 cycles of cisplatin/paclitaxel and chemoradiotherapy up to 45 Gy/1.5 Gy per fraction twice-a-day, followed by a radiation-boost with 2 Gy once per day with concurrent cisplatin/vinorelbine. The protocol definition prescribed a total dose of 65-71 Gy. 18F-FDG-PET/CT (PETpre) was performed at study entry and before concurrent chemoradiotherapy (interim-PET; PETpost). Interim PETpost metrics and known prognostic clinical parameters were correlated in uni- and multivariable survival analyses. Leave-one-out cross-validation was used to show internal validity. Results: Ninety-two patients who underwent 18F-FDG-PET/CT after induction chemotherapy were enrolled. Median MTVpost value was 5.9 ml. Altogether 85 patients completed the whole chemoradiation with the planned total dose of 60-71 Gy. In univariable proportional hazard analysis, each of the parameters MTVpost, SUVmax(post) and TLGmax(post) was associated with overall survival (P < 0.05). Multivariable survival analysis, including clinical and post-induction PET parameters, found TLGmax(post) (hazard ratio: 1.032 (95%-CI: 1.013-1.052) per 100 ml increase) and total radiation dose (hazard ratio: 0.930 (0.902-0.959) per Gray increase) significantly related with overall survival in the whole group of patients, and also in patients receiving a total dose ≥ 60 Gy. The best leave-one-out cross-validated 2 parameter classifier contained TLGmax(post) and total radiation dose. TLGmax(post) was associated with time to distant metastases (P = 0.0018), and SUVmax(post) with time to loco-regional relapse (P = 0.039) in multivariable analysis of patients receiving a total dose ≥ 60 Gy. Conclusion: Post-induction chemotherapy PET parameters demonstrated prognostic significance. Therefore, an interim 18F-FDG-PET/CT is a promising diagnostic modality for guiding individualized treatment intensification.
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Affiliation(s)
- Maja Guberina
- Department for Radiotherapy, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany
| | - Christoph Pöttgen
- Department for Radiotherapy, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany
| | - Martin Metzenmacher
- Department of Medical Oncology, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany
- Division of Thoracic Oncology, West German Cancer Center, University Medicine Essen–Ruhrlandklinik, University Duisburg–Essen, Essen, Germany
| | - Marcel Wiesweg
- Department of Medical Oncology, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany
- Division of Thoracic Oncology, West German Cancer Center, University Medicine Essen–Ruhrlandklinik, University Duisburg–Essen, Essen, Germany
| | | | - Clemens Aigner
- Department of Thoracic Surgery and Thoracic Endoscopy, West German Lung Center, University Medicine Essen–Ruhrlandklinik, University Duisburg–Essen, Essen, Germany
| | - Till Ploenes
- Department of Thoracic Surgery and Thoracic Endoscopy, West German Lung Center, University Medicine Essen–Ruhrlandklinik, University Duisburg–Essen, Essen, Germany
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg–Essen, Essen, Germany
| | - Thomas Gauler
- Department for Radiotherapy, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany
| | - Kaid Darwiche
- Section of Interventional Pneumology, Department of Pulmonary Medicine, West German Cancer Center, University Medicine Essen–Ruhrlandklinik, University Duisburg–Essen, Essen, Germany
| | - Georgios Stamatis
- Department of Thoracic Surgery and Thoracic Endoscopy, West German Lung Center, University Medicine Essen–Ruhrlandklinik, University Duisburg–Essen, Essen, Germany
| | - Dirk Theegarten
- Institute of Pathology, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany; and
| | - Hubertus Hautzel
- Department for Nuclear Medicine, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany
| | - Walter Jentzen
- Department for Nuclear Medicine, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany
| | - Nika Guberina
- Department for Radiotherapy, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany
| | - Ken Herrmann
- German Cancer Consortium, Partner Site University Hospital Essen, Essen
- Department for Nuclear Medicine, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany
| | - Wilfried E.E. Eberhardt
- Department of Medical Oncology, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany
- Division of Thoracic Oncology, West German Cancer Center, University Medicine Essen–Ruhrlandklinik, University Duisburg–Essen, Essen, Germany
| | - Martin Stuschke
- Department for Radiotherapy, West German Cancer Center, University Hospital Essen, University Duisburg–Essen, Essen, Germany
- German Cancer Consortium, Partner Site University Hospital Essen, Essen
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10
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Jin J, Wu K, Li X, Yu Y, Wang X, Sun H. Relationship between tumor heterogeneity and volume in cervical cancer: Evidence from integrated fluorodeoxyglucose 18 PET/MR texture analysis. Nucl Med Commun 2021; 42:545-552. [PMID: 33323868 DOI: 10.1097/mnm.0000000000001354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
OBJECTIVE The aim of this study was to evaluate the effect of cervical cancer volume on PET/magnetic resonance (MR) texture heterogeneity. MATERIALS AND METHODS We retrospectively analyzed the PET/MR images of 138 patients with pathologically diagnosed cervical squamous cell carcinoma, including 50 patients undergoing surgery and 88 patients receiving concurrent chemoradiotherapy. Fluorodeoxyglucose 18 (18FDG)-PET/MR examination were performed for each patient before treatment, and the PET and MR texture analysis were undertaken. The texture features of the tumor based on gray-level co-occurrence matrices were extracted, and the correlation between tumor texture features and volume parameters was analyzed using Spearman's rank correlation coefficient. Finally, the variation trend of tumor texture heterogeneity was analyzed as tumor volumes increased. RESULTS PET texture features were highly correlated with metabolic tumor volume (MTV), including entropy-log2, entropy-log10, energy, homogeneity, dissimilarity, contrast, correlation, and the correlation coefficients (rs) were 0.955, 0.955, -0.897, 0.883, -0.881, -0.876, and 0.847 (P < 0.001), respectively. In the range of smaller MTV, the texture heterogeneity of energy, entropy-log2, and entropy-log10 increases with an increase in tumor volume, whereas the texture heterogeneity of homogeneity, dissimilarity, contrast, and correlation decreases with an increase in tumor volume. Only homogeneity, contrast, correlation, and dissimilarity had high correlation with tumor volume on MRI. The correlation coefficients (rs) were 0.76, -0.737, 0.644, and -0.739 (P < 0.001), respectively. The texture heterogeneity of MRI features that are highly correlated with tumor volume decreases with increasing tumor volume. CONCLUSION In the small tumor volume range, the heterogeneity variation trend of PET texture features is inconsistent as the tumor volume increases, but the variation trend of MRI texture heterogeneity is consistent, and MRI texture heterogeneity decreases as tumor volume increases. These results suggest that MRI is a better imaging modality when compared with PET in determining tumor texture heterogeneity in the small tumor volume range.
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Affiliation(s)
- Junjie Jin
- Department of Radiology, Shengjing Hospital of China Medical University
- Liaoning Provincial Key Laboratory of Medical Imaging
| | - Ke Wu
- Department of Radiology, Shengjing Hospital of China Medical University
| | - Xiaoran Li
- Department of Radiology, Shengjing Hospital of China Medical University
| | - Yang Yu
- Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xinghao Wang
- Department of Radiology, Shengjing Hospital of China Medical University
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University
- Liaoning Provincial Key Laboratory of Medical Imaging
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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: 31] [Impact Index Per Article: 10.3] [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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12
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Houseni M, Mahmoud MA, Saad S, ElHussiny F, Shihab M. Advanced intra-tumoural structural characterisation of hepatocellular carcinoma utilising FDG-PET/CT: a comparative study of radiomics and metabolic features in 3D and 2D. Pol J Radiol 2021; 86:e64-e73. [PMID: 33708274 PMCID: PMC7934742 DOI: 10.5114/pjr.2021.103239] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 08/12/2020] [Indexed: 12/18/2022] Open
Abstract
PURPOSE The aim of our work is to evaluate the correlation of two-dimensional (2D) and three-dimensional (3D) radiomics and metabolic features of hepatocellular carcinoma (HCC) with tumour diameter, staging, and metabolic tumour volume (MTV). MATERIAL AND METHODS Thirty-three patients with HCC were studied using 18F-fluorodeoxyglucose positron-emission tomography with computed tomography (18F [FDG] PET/CT). The tumours were segmented from the PET images after CT correction. Metabolic parameters and 35 radiomics features were compared using 2D and 3D modes. The metabolic parameters and tumour morphology were compared using 2 different types of software. Tumour heterogeneity was studied in both metabolic parameters and radiomics features. Finally, the correlation between the metabolic and radiomics features in 3D mode, as well as tumour morphology and staging according to the American Joint Committee on Cancer (AJCC) staging were studied. RESULTS Most of the metabolic parameters and radiomics features are statically stable through the 2D and 3D modes. Most of the 3D mode features show a correlation with metabolic parameters; the total lesion glycolysis (TLG) shows the highest correlation, with a Spearman correlation coefficient (rs) of 0.9776. Also, the grey level run length matrix/run length non-uniformity (GLRLM_RLNU) from radiomics features exhibits a correlation with a Spearman correlation coefficient of 0.9733. Maximum tumour diameter is correlated with TLG and GLRLM_RLNU, with rs equal to 0.7461 and 0.7143, respectively. Regarding AJCC staging, some features show a medium but prognostic correlation. In the case of 2D-mode features, all metabolic and radiomics features show no significant correlation with MTV, AJCC staging, and tumour maximum diameter. CONCLUSIONS Most of the normal metabolic parameters and radiomics features are statistically stable through the 3D and 2D modes. 3D radiomics features are significantly correlated with tumour volume, maximum diameter, and staging. Conversely, 2D features have negligible correlation with the same parameters. Therefore, 3D mode features are preferable and can accurately evaluate tumour heterogeneity.
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Affiliation(s)
- Mohamed Houseni
- Department of Medical Imaging, National Liver Institute, Menoufia University, Egypt
| | - Menna Allah Mahmoud
- Department of Medical Imaging, National Liver Institute, Menoufia University, Egypt
| | - Salwa Saad
- Department of Physics, Faculty of Science, Tanta University, Tanta, Egypt
| | - Fathi ElHussiny
- Department of Physics, Faculty of Science, Tanta University, Tanta, Egypt
| | - Mohammed Shihab
- Department of Physics, Faculty of Science, Tanta University, Tanta, Egypt
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Vaugier L, Ferrer L, Mengue L, Jouglar E. Radiomics for radiation oncologists: are we ready to go? BJR Open 2020; 2:20190046. [PMID: 33178967 PMCID: PMC7594896 DOI: 10.1259/bjro.20190046] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 03/06/2020] [Accepted: 03/09/2020] [Indexed: 12/19/2022] Open
Abstract
Radiomics have emerged as an exciting field of research over the past few years, with very wide potential applications in personalised and precision medicine of the future. Radiomics-based approaches are still however limited in daily clinical practice in oncology. This review focus on how radiomics could be incorporated into the radiation therapy pipeline, and globally help the radiation oncologist, from the tumour diagnosis to follow-up after treatment. Radiomics could impact on all steps of the treatment pipeline, once the limitations in terms of robustness and reproducibility are overcome. Major ongoing efforts should be made to collect and share data in the most standardised manner possible.
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Affiliation(s)
- Loïg Vaugier
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Ludovic Ferrer
- Department of Medical Physics, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Laurence Mengue
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Emmanuel Jouglar
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
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Liu H, Li H, Habes M, Li Y, Boimel P, Janopaul-Naylor J, Xiao Y, Ben-Josef E, Fan Y. Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis. IEEE Trans Biomed Eng 2020; 67:2735-2744. [PMID: 31995474 DOI: 10.1109/tbme.2020.2969839] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Feature dimensionality reduction plays an important role in radiomic studies with a large number of features. However, conventional radiomic approaches may suffer from noise, and feature dimensionality reduction techniques are not equipped to utilize latent supervision information of patient data under study, such as differences in patients, to learn discriminative low dimensional representations. To achieve robustness to noise and feature dimensionality reduction with improved discriminative power, we develop a robust collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively under adaptive sparse regularization. Our method is built upon matrix tri-factorization enhanced by adaptive sparsity regularization for simultaneous feature dimensionality reduction and denoising. Particularly, latent grouping information of patients with distinct radiomic features is learned and utilized as supervision information to guide the feature dimensionality reduction, and noise in radiomic features is adaptively isolated in a Bayesian framework under a general assumption of Laplacian distributions of transform-domain coefficients. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and evaluation results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.
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15
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Karacavus S, Yılmaz B, Tasdemir A, Kayaaltı Ö, Kaya E, İçer S, Ayyıldız O. Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC? J Digit Imaging 2019; 31:210-223. [PMID: 28685320 DOI: 10.1007/s10278-017-9992-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
We investigated the association between the textural features obtained from 18F-FDG images, metabolic parameters (SUVmax, SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.
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Affiliation(s)
- Seyhan Karacavus
- Department of Nuclear Medicine, Saglık Bilimleri University, Kayseri Training and Research Hospital, 38010, Kayseri, Turkey. .,Department of Biomedical Engineering, Erciyes University, Engineering Faculty, Kayseri, Turkey.
| | - Bülent Yılmaz
- Department of Electrical and Electronics Engineering, Abdullah Gül University, Engineering Faculty, Kayseri, Turkey
| | - Arzu Tasdemir
- Department of Pathology, Saglik Bilimleri University, Kayseri Training and Research Hospital, Kayseri, Turkey
| | - Ömer Kayaaltı
- Department of Computer Technologies, Erciyes University, Develi Hüseyin Şahin Vocational College, Kayseri, Turkey
| | - Eser Kaya
- Department of Nuclear Medicine, Acibadem University, School of Medicine, İstanbul, Turkey
| | - Semra İçer
- Department of Biomedical Engineering, Erciyes University, Engineering Faculty, Kayseri, Turkey
| | - Oguzhan Ayyıldız
- Department of Electrical and Electronics Engineering, Abdullah Gül University, Engineering Faculty, Kayseri, Turkey
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16
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Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer. Sci Rep 2019; 9:8730. [PMID: 31217441 PMCID: PMC6584670 DOI: 10.1038/s41598-019-45117-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 05/31/2019] [Indexed: 01/10/2023] Open
Abstract
Tumor growth dynamics vary substantially in non-small cell lung cancer (NSCLC). We aimed to develop biomarkers reflecting longitudinal change of radiomic features in NSCLC and evaluate their prognostic power. Fifty-three patients with advanced NSCLC were included. Three primary variables reflecting patterns of longitudinal change were extracted: area under the curve of longitudinal change (AUC1), beta value reflecting slope over time, and AUC2, a value obtained by considering the slope and area over the longitudinal change of features. We constructed models for predicting survival with multivariate cox regression, and identified the performance of these models. AUC2 exhibited an excellent correlation between patterns of longitudinal volume change and a significant difference in overall survival time. Multivariate regression analysis based on cut-off values of radiomic features extracted from baseline CT and AUC2 showed that kurtosis of positive pixel values and surface area from baseline CT, AUC2 of density, skewness of positive pixel values, and entropy at inner portion were associated with overall survival. For the prediction model, the areas under the receiver operating characteristic curve (AUROC) were 0.948 and 0.862 at 1 and 3 years of follow-up, respectively. Longitudinal change of radiomic tumor features may serve as prognostic biomarkers in patients with advanced NSCLC.
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17
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Pfaehler E, Beukinga RJ, de Jong JR, Slart RHJA, Slump CH, Dierckx RAJO, Boellaard R. Repeatability of 18 F-FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method. Med Phys 2018; 46:665-678. [PMID: 30506687 PMCID: PMC7380016 DOI: 10.1002/mp.13322] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 11/14/2018] [Accepted: 11/21/2018] [Indexed: 02/07/2023] Open
Abstract
Background 18F‐fluoro‐2‐deoxy‐D‐Glucose positron emission tomography (18F‐FDG PET) radiomics has the potential to guide the clinical decision making in cancer patients, but validation is required before radiomics can be implemented in the clinical setting. The aim of this study was to explore how feature space reduction and repeatability of 18F‐FDG PET radiomic features are affected by various sources of variation such as underlying data (e.g., object size and uptake), image reconstruction methods and settings, noise, discretization method, and delineation method. Methods The NEMA image quality phantom was scanned with various sphere‐to‐background ratios (SBR), simulating different activity uptakes, including spheres with low uptake, that is, SBR smaller than 1. Furthermore, images of a phantom containing 3D printed inserts reflecting realistic heterogeneity uptake patterns were acquired. Data were reconstructed using various matrix sizes, reconstruction algorithms, and scan durations (noise). For every specific reconstruction and noise level, ten statistically equal replicates were generated. The phantom inserts were delineated using CT and PET‐based segmentation methods. A total of 246 radiomic features was extracted from each image dataset. Images were discretized with a fixed number of 64 bins (FBN) and a fixed bin width (FBW) of 0.25 for the high and a FBW of 0.05 for the low uptake data. In terms of feature reduction, we determined the impact of these factors on the composition of feature clusters, which were defined on the basis of Spearman's correlation matrices. To assess feature repeatability, the intraclass correlation coefficient was calculated over the ten replicates. Results In general, larger spheres with high uptake resulted in better repeatability compared to smaller low uptake spheres. In terms of repeatability, features extracted from heterogeneous phantom inserts were comparable to features extracted from bigger high uptake spheres. For example, for an EARL‐compliant reconstruction, larger and smaller high uptake spheres yielded good repeatability for 32% and 30% of the features, while the heterogeneous inserts resulted in 34% repeatable features. For the low uptake spheres, this was the case for 22% and 20% of the features for bigger and smaller spheres, respectively. Images reconstructed with point‐spread‐function (PSF) resulted in the highest repeatability when compared with OSEM or time‐of‐flight, for example, 53%, 30%, and 32% of repeatable features, respectively (for unsmoothed data, discretized with FBN, 300 s scan duration). Reducing image noise (increasing scan duration and smoothing) and using CT‐based segmentation for the low uptake spheres yielded improved repeatability. FBW discretization resulted in higher repeatability than FBN discretization, for example, 89% and 35% of the features, respectively (for the EARL‐compliant reconstruction and larger high uptake spheres). Conclusion Feature space reduction and repeatability of 18F‐FDG PET radiomic features depended on all studied factors. The high sensitivity of PET radiomic features to image quality suggests that a high level of image acquisition and preprocessing standardization is required to be used as clinical imaging biomarker.
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Affiliation(s)
- Elisabeth Pfaehler
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Roelof J Beukinga
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands
| | - Johan R de Jong
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Riemer H J A Slart
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands
| | - Cornelis H Slump
- MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Location VUMC, Amsterdam, The Netherlands
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18
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Hughes NM, Mou T, O’Regan KN, Murphy P, O’Sullivan JN, Wolsztynski E, Huang J, Kennedy MP, Eary JF, O’Sullivan F. Tumor heterogeneity measurement using [18F] FDG PET/CT shows prognostic value in patients with non-small cell lung cancer. Eur J Hybrid Imaging 2018. [DOI: 10.1186/s41824-018-0043-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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19
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Tumor heterogeneity, hypoxia, and immune markers in surgically resected non-small-cell lung cancer. Nucl Med Commun 2018; 39:636-644. [PMID: 29608508 DOI: 10.1097/mnm.0000000000000832] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
OBJECTIVES This study aimed to determine the prognostic role of textural features and their association with metabolic parameters, hypoxia, and cancer-related immune markers in non-small-cell lung cancer (NSCLC) patients. PATIENTS AND METHODS The trial was registered at http://www.clinicaltrials.gov (NCT02519062). From January 2010 to May 2014, 44 patients (male : female=33 : 11; median age: 69.5 years), referred to our Institution for NSCLC resection, were enrolled. Tumor specimens were assessed for HIF-1α, CD68-TAMs, CD8-TILs, PD-1-TILs, and PD-L1 expressions. All patients underwent fluorine-18-fluorodeoxyglucose (F-FDG) PET before surgery. Semiquantitative parameters included maximum standardized uptake value (SUVmax), SUVpeak, SUVmean, metabolic tumor volume, and total lesion glycolysis, whereas for heterogeneity, we considered tumor sphericity, skewness, kurtosis, entropy, and energy. Parameters were correlated with disease-free survival (DFS) considering a median follow-up of 22.7 months. RESULTS SUVmax (cutoff: 7.9; P=0.015), SUVpeak (cutoff: 6.7; P=0.013), SUVmean (cutoff: 5.5; P=0.028), metabolic tumor volume (cutoff: 3.6 cm; P=0.027), and entropy (cutoff: 1.89; P=0.045) showed a statistically significant association with DFS. Also, a high expression of cytoplasmic HIF-1α (score 3) was associated with DFS (hazard ratio: 0.09; P=0.003). All F-FDG PET variables differed significantly in tumors with high or low entropy (≤1.89). Also, a significantly higher level of mean CD8-TILs was observed in tumors with higher entropy (P=0.041).Using identified prognostic factors, we developed a scoring system, which was confirmed to be associated with DFS (P<0.004). On receiver operating characteristics analysis, a score above 3 was defined as the optimal cutoff point. CONCLUSION Tumor heterogeneity, metabolic parameters, and high expression of hypoxia were found to be prognostic factors in NSCLC patients who were candidates for surgery. Higher levels of entropy appear to be associated with increased density of CD8-TILs. The combination of investigated prognostic factors enabled the development of a potential scoring system associated with DFS.
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Heterogeneity analysis of 18F-FDG PET imaging in oncology: clinical indications and perspectives. Clin Transl Imaging 2018. [DOI: 10.1007/s40336-018-0299-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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21
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Lee JW, Lee SM. Radiomics in Oncological PET/CT: Clinical Applications. Nucl Med Mol Imaging 2018; 52:170-189. [PMID: 29942396 PMCID: PMC5995782 DOI: 10.1007/s13139-017-0500-y] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 09/22/2017] [Accepted: 09/29/2017] [Indexed: 12/11/2022] Open
Abstract
18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.
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Affiliation(s)
- Jeong Won Lee
- Department of Nuclear Medicine, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, 25, Simgok-ro 100 Gil 25, Seo-gu, Incheon, 22711 South Korea
- Institute for Integrative Medicine, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, Incheon, South Korea
| | - Sang Mi Lee
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, South Korea
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Liu S, Zheng H, Pan X, Chen L, Shi M, Guan Y, Ge Y, He J, Zhou Z. Texture analysis of CT imaging for assessment of esophageal squamous cancer aggressiveness. J Thorac Dis 2017; 9:4724-4732. [PMID: 29268543 DOI: 10.21037/jtd.2017.06.46] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background To explore the role of texture analysis of computed tomography (CT) images in preoperative assessment of esophageal squamous cell carcinoma (ESCC) aggressiveness. Methods Seventy-three patients with pathologically confirmed ESCC underwent unenhanced and contrast enhanced CT imaging preoperatively. Texture analysis was performed on unenhanced and contrast enhanced CT images, respectively. Six CT texture parameters were obtained. One-way analysis of variance or independent-samples t-test (normality), independent-samples Kruskal-Wallis test or Mann-Whitney U test (non-normality), binary Logistic regression analysis (multivariable), Spearman correlation test, receiver operating characteristic (ROC) curve analysis and intraclass correlation coefficient (ICC) were used for statistical analyses. Results Kurtosis was an independent predictor for T stages (T1-2 vs. T3-4) as well as overall stages (I-II vs. III-IV) based on unenhanced CT images, while entropy was an independent predictor for T stages (T1-2 vs. T3-4), lymph node metastasis (N- vs. N+) and overall stages (I/II vs. III/IV). Skew and kurtosis based on unenhanced CT images showed significant differences among N stages (N0, N1, N2 and N3) as well as 90th percentile based on contrast enhanced CT images. In correlation with T stage of ESCC, kurtosis and entropy significantly correlated with T stage both on unenhanced and contrast enhanced CT images. Reversely, entropy and 90th percentile based on contrast enhanced CT images showed significant correlations with N stage (r: 0.526, 0.265; both P<0.05), as well as overall stage (r: 0.562, 0.315; both P<0.05). For identifying ESCC with different T stages (T1-2 vs. T3-4), lymph node metastasis (N- vs. N+) and overall stages (I/II vs. III/IV), entropy based on contrast enhanced CT images, showed good performance with area under ROC curve area under curve (AUC) of 0.637, 0.815 and 0.778, respectively. Conclusions Texture analysis of CT images held great potential in differentiating different T, N and overall stages of ESCC preoperatively, while failed to assess the differentiation degrees.
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Affiliation(s)
- Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Huanhuan Zheng
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Xia Pan
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Ling Chen
- Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Minke Shi
- Department of Thoracic and Cardiovascular Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Yue Guan
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210046, China
| | - Yun Ge
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210046, China
| | - Jian He
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
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23
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Phillips I, Ajaz M, Ezhil V, Prakash V, Alobaidli S, McQuaid SJ, South C, Scuffham J, Nisbet A, Evans P. Clinical applications of textural analysis in non-small cell lung cancer. Br J Radiol 2017; 91:20170267. [PMID: 28869399 DOI: 10.1259/bjr.20170267] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed "radiomics" and includes semantic and agnostic approaches. Textural analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue subtypes of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarize the current published data and understand the potential future role of TA in managing lung cancer.
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Affiliation(s)
- Iain Phillips
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Mazhar Ajaz
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK.,2 Surrey Clinical Research Centre, University of Surrey, Guildford, UK
| | - Veni Ezhil
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Vineet Prakash
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Sheaka Alobaidli
- 3 Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
| | | | | | - James Scuffham
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Andrew Nisbet
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Philip Evans
- 3 Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
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24
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Sollini M, Cozzi L, Antunovic L, Chiti A, Kirienko M. PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Sci Rep 2017; 7:358. [PMID: 28336974 PMCID: PMC5428425 DOI: 10.1038/s41598-017-00426-y] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 02/23/2017] [Indexed: 12/21/2022] Open
Abstract
Imaging with positron emission tomography (PET)/computed tomography (CT) is crucial in the management of cancer because of its value in tumor staging, response assessment, restaging, prognosis and treatment responsiveness prediction. In the last years, interest has grown in texture analysis which provides an "in-vivo" lesion characterization, and predictive information in several malignances including NSCLC; however several drawbacks and limitations affect these studies, especially because of lack of standardization in features calculation, definitions and methodology reporting. The present paper provides a comprehensive review of literature describing the state-of-the-art of FDG-PET/CT texture analysis in NSCLC, suggesting a proposal for harmonization of methodology.
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Affiliation(s)
- M Sollini
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy.
| | - L Cozzi
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy
- Radiotherapy and Radiosurgery Unit, Humanitas Clinical and Research Center, via Manzoni, 56-20089, Rozzano, (Milan), Italy
| | - L Antunovic
- Nuclear Medicine Unit, Humanitas Clinical and Research Center, via Manzoni, 56-20089, Rozzano, (Milan), Italy
| | - A Chiti
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy
- Nuclear Medicine Unit, Humanitas Clinical and Research Center, via Manzoni, 56-20089, Rozzano, (Milan), Italy
| | - M Kirienko
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy
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25
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Gerbaudo VH, Kim CK. PET Imaging-Based Phenotyping as a Predictive Biomarker of Response to Tyrosine Kinase Inhibitor Therapy in Non-small Cell Lung Cancer: Are We There Yet? Nucl Med Mol Imaging 2016; 51:3-10. [PMID: 28250852 DOI: 10.1007/s13139-016-0453-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Revised: 08/27/2016] [Accepted: 09/15/2016] [Indexed: 12/22/2022] Open
Abstract
The increased understanding of the molecular pathology of different malignancies, especially lung cancer, has directed investigational efforts to center on the identification of different molecular targets and on the development of targeted therapies against these targets. A good representative is the epidermal growth factor receptor (EGFR); a major driver of non-small cell lung cancer tumorigenesis. Today, tumor growth inhibition is possible after treating lung tumors expressing somatic mutations of the EGFR gene with tyrosine kinase inhibitors (TKI). This opened the doors to biomarker-directed precision or personalized treatments for lung cancer patients. The success of these targeted anticancer therapies depends in part on being able to identify biomarkers and their patho-molecular make-up in order to select patients that could respond to specific therapeutic agents. While the identification of reliable biomarkers is crucial to predict response to treatment before it begins, it is also essential to be able to monitor treatment early during therapy to avoid the toxicity and morbidity of futile treatment in non-responding patients. In this context, we share our perspective on the role of PET imaging-based phenotyping in the personalized care of lung cancer patients to non-invasively direct and monitor the treatment efficacy of TKIs in clinical practice.
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Affiliation(s)
- Victor H Gerbaudo
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02492 USA
| | - Chun K Kim
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02492 USA
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26
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Abstract
Precision medicine allows tailoring of preventive or therapeutic interventions to avoid the expense and toxicity of futile treatment given to those who will not respond. Lung cancer is a heterogeneous disease functionally and morphologically. PET is a sensitive molecular imaging technique with a major role in the precision medicine algorithm of patients with lung cancer. It contributes to the precision medicine of lung neoplasia by interrogating tumor heterogeneity throughout the body. It provides anatomofunctional insight during diagnosis, staging, and restaging of the disease. It is a biomarker of tumoral heterogeneity that helps direct selection of the most appropriate treatment, the prediction of early response to cytotoxic and cytostatic therapies, and is a prognostic biomarker in patients with lung cancer.
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Affiliation(s)
- Katherine A Zukotynski
- Division of Nuclear Medicine and Molecular Imaging, Department of Medicine, McMaster University, 1200 Main Street West, Hamilton, Ontario L9G 4X5, Canada; Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, McMaster University, 1200 Main Street West, Hamilton, Ontario L9G 4X5, Canada
| | - Victor H Gerbaudo
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.
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27
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Giganti F, Antunes S, Salerno A, Ambrosi A, Marra P, Nicoletti R, Orsenigo E, Chiari D, Albarello L, Staudacher C, Esposito A, Del Maschio A, De Cobelli F. Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol 2016; 27:1831-1839. [PMID: 27553932 DOI: 10.1007/s00330-016-4540-y] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 06/17/2016] [Accepted: 08/01/2016] [Indexed: 12/12/2022]
Abstract
OBJECTIVES To investigate the association between preoperative texture analysis from multidetector computed tomography (MDCT) and overall survival in patients with gastric cancer. METHODS Institutional review board approval and informed consent were obtained. Fifty-six patients with biopsy-proved gastric cancer were examined by MDCT and treated with surgery. Image features from texture analysis were quantified, with and without filters for fine to coarse textures. The association with survival time was assessed using Kaplan-Meier and Cox analysis. RESULTS The following parameters were significantly associated with a negative prognosis, according to different thresholds: energy [no filter] - Logarithm of relative risk (Log RR): 3.25; p = 0.046; entropy [no filter] (Log RR: 5.96; p = 0.002); entropy [filter 1.5] (Log RR: 3.54; p = 0.027); maximum Hounsfield unit value [filter 1.5] (Log RR: 3.44; p = 0.027); skewness [filter 2] (Log RR: 5.83; p = 0.004); root mean square [filter 1] (Log RR: - 2.66; p = 0.024) and mean absolute deviation [filter 2] (Log RR: - 4.22; p = 0.007). CONCLUSIONS Texture analysis could increase the performance of a multivariate prognostic model for risk stratification in gastric cancer. Further evaluations are warranted to clarify the clinical role of texture analysis from MDCT. KEY POINTS • Textural analysis from computed tomography can be applied in gastric cancer. • Preoperative non-invasive texture features are related to prognosis in gastric cancer. • Texture analysis could help to evaluate the aggressiveness of this tumour.
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Affiliation(s)
- Francesco Giganti
- Department of Radiology and Centre for Experimental Imaging San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.
- San Raffaele Vita-Salute University, Milan, Italy.
| | - Sofia Antunes
- Centre for Experimental Imaging, San Raffaele Scientific Institute, Milan, Italy
| | - Annalaura Salerno
- Department of Radiology and Centre for Experimental Imaging San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- San Raffaele Vita-Salute University, Milan, Italy
| | | | - Paolo Marra
- Department of Radiology and Centre for Experimental Imaging San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- San Raffaele Vita-Salute University, Milan, Italy
| | - Roberto Nicoletti
- Department of Radiology and Centre for Experimental Imaging San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Elena Orsenigo
- Department of Surgery, San Raffaele Scientific Institute, Milan, Italy
| | - Damiano Chiari
- San Raffaele Vita-Salute University, Milan, Italy
- Department of Surgery, San Raffaele Scientific Institute, Milan, Italy
| | - Luca Albarello
- Pathology Unit, San Raffaele Scientific Institute, Milan, Italy
| | - Carlo Staudacher
- San Raffaele Vita-Salute University, Milan, Italy
- Department of Surgery, San Raffaele Scientific Institute, Milan, Italy
| | - Antonio Esposito
- Department of Radiology and Centre for Experimental Imaging San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- San Raffaele Vita-Salute University, Milan, Italy
| | - Alessandro Del Maschio
- Department of Radiology and Centre for Experimental Imaging San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- San Raffaele Vita-Salute University, Milan, Italy
| | - Francesco De Cobelli
- Department of Radiology and Centre for Experimental Imaging San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- San Raffaele Vita-Salute University, Milan, Italy
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28
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Mena E, Yanamadala A, Cheng G, Subramaniam RM. The Current and Evolving Role of PET in Personalized Management of Lung Cancer. PET Clin 2016; 11:243-59. [DOI: 10.1016/j.cpet.2016.02.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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29
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Fried DV, Mawlawi O, Zhang L, Fave X, Zhou S, Ibbott G, Liao Z, Court LE. Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors. Radiology 2015; 278:214-22. [PMID: 26176655 DOI: 10.1148/radiol.2015142920] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE To determine whether quantitative imaging features from pretreatment positron emission tomography (PET) can enhance patient overall survival risk stratification beyond what can be achieved with conventional prognostic factors in patients with stage III non-small cell lung cancer (NSCLC). MATERIALS AND METHODS The institutional review board approved this retrospective chart review study and waived the requirement to obtain informed consent. The authors retrospectively identified 195 patients with stage III NSCLC treated definitively with radiation therapy between January 2008 and January 2013. All patients underwent pretreatment PET/computed tomography before treatment. Conventional PET metrics, along with histogram, shape and volume, and co-occurrence matrix features, were extracted. Linear predictors of overall survival were developed from leave-one-out cross-validation. Predictive Kaplan-Meier curves were used to compare the linear predictors with both quantitative imaging features and conventional prognostic factors to those generated with conventional prognostic factors alone. The Harrell concordance index was used to quantify the discriminatory power of the linear predictors for survival differences of at least 0, 6, 12, 18, and 24 months. Models were generated with features present in more than 50% of the cross-validation folds. RESULTS Linear predictors of overall survival generated with both quantitative imaging features and conventional prognostic factors demonstrated improved risk stratification compared with those generated with conventional prognostic factors alone in terms of log-rank statistic (P = .18 vs P = .0001, respectively) and concordance index (0.62 vs 0.58, respectively). The use of quantitative imaging features selected during cross-validation improved the model using conventional prognostic factors alone (P = .007). Disease solidity and primary tumor energy from the co-occurrence matrix were found to be selected in all folds of cross-validation. CONCLUSION Pretreatment PET features were associated with overall survival when adjusting for conventional prognostic factors in patients with stage III NSCLC.
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Affiliation(s)
- David V Fried
- From the Departments of Radiation Physics (D.V.F., O.M., L.Z., X.F., G.I., L.E.C.), Imaging Physics (O.M.), Biostatistics (S.Z.), and Radiation Oncology (Z.L.), the University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Tex (D.V.F., X.F., G.I., L.E.C.)
| | - Osama Mawlawi
- From the Departments of Radiation Physics (D.V.F., O.M., L.Z., X.F., G.I., L.E.C.), Imaging Physics (O.M.), Biostatistics (S.Z.), and Radiation Oncology (Z.L.), the University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Tex (D.V.F., X.F., G.I., L.E.C.)
| | - Lifei Zhang
- From the Departments of Radiation Physics (D.V.F., O.M., L.Z., X.F., G.I., L.E.C.), Imaging Physics (O.M.), Biostatistics (S.Z.), and Radiation Oncology (Z.L.), the University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Tex (D.V.F., X.F., G.I., L.E.C.)
| | - Xenia Fave
- From the Departments of Radiation Physics (D.V.F., O.M., L.Z., X.F., G.I., L.E.C.), Imaging Physics (O.M.), Biostatistics (S.Z.), and Radiation Oncology (Z.L.), the University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Tex (D.V.F., X.F., G.I., L.E.C.)
| | - Shouhao Zhou
- From the Departments of Radiation Physics (D.V.F., O.M., L.Z., X.F., G.I., L.E.C.), Imaging Physics (O.M.), Biostatistics (S.Z.), and Radiation Oncology (Z.L.), the University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Tex (D.V.F., X.F., G.I., L.E.C.)
| | - Geoffrey Ibbott
- From the Departments of Radiation Physics (D.V.F., O.M., L.Z., X.F., G.I., L.E.C.), Imaging Physics (O.M.), Biostatistics (S.Z.), and Radiation Oncology (Z.L.), the University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Tex (D.V.F., X.F., G.I., L.E.C.)
| | - Zhongxing Liao
- From the Departments of Radiation Physics (D.V.F., O.M., L.Z., X.F., G.I., L.E.C.), Imaging Physics (O.M.), Biostatistics (S.Z.), and Radiation Oncology (Z.L.), the University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Tex (D.V.F., X.F., G.I., L.E.C.)
| | - Laurence E Court
- From the Departments of Radiation Physics (D.V.F., O.M., L.Z., X.F., G.I., L.E.C.), Imaging Physics (O.M.), Biostatistics (S.Z.), and Radiation Oncology (Z.L.), the University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Tex (D.V.F., X.F., G.I., L.E.C.)
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