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Sujit SJ, Aminu M, Karpinets TV, Chen P, Saad MB, Salehjahromi M, Boom JD, Qayati M, George JM, Allen H, Antonoff MB, Hong L, Hu X, Heeke S, Tran HT, Le X, Elamin YY, Altan M, Vokes NI, Sheshadri A, Lin J, Zhang J, Lu Y, Behrens C, Godoy MCB, Wu CC, Chang JY, Chung C, Jaffray DA, Wistuba II, Lee JJ, Vaporciyan AA, Gibbons DL, Heymach J, Zhang J, Cascone T, Wu J. Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights. Nat Commun 2024; 15:3152. [PMID: 38605064 PMCID: PMC11009351 DOI: 10.1038/s41467-024-47512-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 03/21/2024] [Indexed: 04/13/2024] Open
Abstract
While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.
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Affiliation(s)
- Sheeba J Sujit
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Muhammad Aminu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tatiana V Karpinets
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Pingjun Chen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Maliazurina B Saad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Morteza Salehjahromi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John D Boom
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Mohamed Qayati
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James M George
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Haley Allen
- Natural Sciences, Rice University, Houston, TX, USA
| | - Mara B Antonoff
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lingzhi Hong
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xin Hu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Simon Heeke
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hai T Tran
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yasir Y Elamin
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mehmet Altan
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Natalie I Vokes
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Julie Lin
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianhua Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yang Lu
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carmen Behrens
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Myrna C B Godoy
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carol C Wu
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joe Y Chang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Institute of Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David A Jaffray
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Institute of Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ara A Vaporciyan
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianjun Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Lung Cancer Genomics Program, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Lung Cancer Interception Program, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Institute of Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Salehjahromi M, Karpinets TV, Sujit SJ, Qayati M, Chen P, Aminu M, Saad MB, Bandyopadhyay R, Hong L, Sheshadri A, Lin J, Antonoff MB, Sepesi B, Ostrin EJ, Toumazis I, Huang P, Cheng C, Cascone T, Vokes NI, Behrens C, Siewerdsen JH, Hazle JD, Chang JY, Zhang J, Lu Y, Godoy MCB, Chung C, Jaffray D, Wistuba I, Lee JJ, Vaporciyan AA, Gibbons DL, Gladish G, Heymach JV, Wu CC, Zhang J, Wu J. Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept. Cell Rep Med 2024; 5:101463. [PMID: 38471502 PMCID: PMC10983039 DOI: 10.1016/j.xcrm.2024.101463] [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: 02/01/2023] [Revised: 09/07/2023] [Accepted: 02/15/2024] [Indexed: 03/14/2024]
Abstract
[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.
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Affiliation(s)
| | | | - Sheeba J Sujit
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Mohamed Qayati
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Pingjun Chen
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Muhammad Aminu
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Maliazurina B Saad
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | | | - Lingzhi Hong
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX USA
| | - Julie Lin
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX USA
| | - Mara B Antonoff
- Department of Thoracic and Cardiovascular Surgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Boris Sepesi
- Department of Thoracic and Cardiovascular Surgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Edwin J Ostrin
- Department of General Internal Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | - Iakovos Toumazis
- Department of Health Services Research, MD Anderson Cancer Center, Houston, TX, USA
| | - Peng Huang
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Natalie I Vokes
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Carmen Behrens
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Jeffrey H Siewerdsen
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - John D Hazle
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Joe Y Chang
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Jianhua Zhang
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | - Yang Lu
- Department of Nuclear Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | - Myrna C B Godoy
- Department of Thoracic Imaging, MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - David Jaffray
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio Wistuba
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, USA
| | - J Jack Lee
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
| | - Ara A Vaporciyan
- Department of Thoracic and Cardiovascular Surgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Gregory Gladish
- Department of Thoracic Imaging, MD Anderson Cancer Center, Houston, TX, USA
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Carol C Wu
- Department of Thoracic Imaging, MD Anderson Cancer Center, Houston, TX, USA
| | - Jianjun Zhang
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA; Lung Cancer Genomics Program, MD Anderson Cancer Center, Houston, TX, USA; Lung Cancer Interception Program, MD Anderson Cancer Center, Houston, TX, USA
| | - Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA.
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Zschaeck S, Klinger B, van den Hoff J, Cegla P, Apostolova I, Kreissl MC, Cholewiński W, Kukuk E, Strobel H, Amthauer H, Blüthgen N, Zips D, Hofheinz F. Combination of tumor asphericity and an extracellular matrix-related prognostic gene signature in non-small cell lung cancer patients. Sci Rep 2023; 13:20840. [PMID: 38012155 PMCID: PMC10681996 DOI: 10.1038/s41598-023-46405-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/31/2023] [Indexed: 11/29/2023] Open
Abstract
One important aim of precision oncology is a personalized treatment of patients. This can be achieved by various biomarkers, especially imaging parameters and gene expression signatures are commonly used. So far, combination approaches are sparse. The aim of the study was to independently validate the prognostic value of the novel positron emission tomography (PET) parameter tumor asphericity (ASP) in non small cell lung cancer (NSCLC) patients and to investigate associations between published gene expression profiles and ASP. This was a retrospective evaluation of PET imaging and gene expression data from three public databases and two institutional datasets. The whole cohort comprised 253 NSCLC patients, all treated with curative intent surgery. Clinical parameters, standard PET parameters and ASP were evaluated in all patients. Additional gene expression data were available for 120 patients. Univariate Cox regression and Kaplan-Meier analysis was performed for the primary endpoint progression-free survival (PFS) and additional endpoints. Furthermore, multivariate cox regression testing was performed including clinically significant parameters, ASP, and the extracellular matrix-related prognostic gene signature (EPPI). In the whole cohort, a significant association with PFS was observed for ASP (p < 0.001) and EPPI (p = 0.012). Upon multivariate testing, EPPI remained significantly associated with PFS (p = 0.018) in the subgroup of patients with additional gene expression data, while ASP was significantly associated with PFS in the whole cohort (p = 0.012). In stage II patients, ASP was significantly associated with PFS (p = 0.009), and a previously published cutoff value for ASP (19.5%) was successfully validated (p = 0.008). In patients with additional gene expression data, EPPI showed a significant association with PFS, too (p = 0.033). The exploratory combination of ASP and EPPI showed that the combinatory approach has potential to further improve patient stratification compared to the use of only one parameter. We report the first successful validation of EPPI and ASP in stage II NSCLC patients. The combination of both parameters seems to be a very promising approach for improvement of risk stratification in a group of patients with urgent need for a more personalized treatment approach.
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Affiliation(s)
- Sebastian Zschaeck
- Department of Radiation Oncology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin Institute of Health (BIH), 10178, Berlin, Germany
| | - Bertram Klinger
- Berlin Institute of Health (BIH), 10178, Berlin, Germany
- Computational Modelling in Medicine, Instiute of Pathology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany
| | - Jörg van den Hoff
- Helmholtz-Zentrum Dresden-Rossendorf, PET Center, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | - Paulina Cegla
- Department of Nuclear Medicine, Greater Poland Cancer Centre, Poznan, Poland
| | - Ivayla Apostolova
- Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, Otto Von Guericke University, Magdeburg, Germany
| | - Michael C Kreissl
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, Otto Von Guericke University, Magdeburg, Germany
| | - Witold Cholewiński
- Department of Nuclear Medicine, Greater Poland Cancer Centre, Poznan, Poland
| | - Emily Kukuk
- Department of Radiation Oncology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Helen Strobel
- Department of Radiation Oncology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Holger Amthauer
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, Otto Von Guericke University, Magdeburg, Germany
- Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Nils Blüthgen
- Berlin Institute of Health (BIH), 10178, Berlin, Germany
- Computational Modelling in Medicine, Instiute of Pathology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany
| | - Daniel Zips
- Department of Radiation Oncology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany
| | - Frank Hofheinz
- Helmholtz-Zentrum Dresden-Rossendorf, PET Center, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany.
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Chacon-Barahona JA, MacKeigan JP, Lanning NJ. Unique Metabolic Contexts Sensitize Cancer Cells and Discriminate between Glycolytic Tumor Types. Cancers (Basel) 2023; 15:cancers15041158. [PMID: 36831501 PMCID: PMC9953999 DOI: 10.3390/cancers15041158] [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: 01/12/2023] [Revised: 02/06/2023] [Accepted: 02/08/2023] [Indexed: 02/15/2023] Open
Abstract
Cancer cells utilize variable metabolic programs in order to maintain homeostasis in response to environmental challenges. To interrogate cancer cell reliance on glycolytic programs under different nutrient availabilities, we analyzed a gene panel containing all glycolytic genes as well as pathways associated with glycolysis. Using this gene panel, we analyzed the impact of an siRNA library on cellular viability in cells containing only glucose or only pyruvate as the major bioenergetic nutrient source. From these panels, we aimed to identify genes that elicited conserved and glycolysis-dependent changes in cellular bioenergetics across glycolysis-promoting and OXPHOS-promoting conditions. To further characterize gene sets within this panel and identify similarities and differences amongst glycolytic tumor RNA-seq profiles across a pan-cancer cohort, we then used unsupervised statistical classification of RNA-seq profiles for glycolytic cancers and non-glycolytic cancer types. Here, Kidney renal clear cell carcinoma (KIRC); Head and Neck squamous cell carcinoma (HNSC); and Lung squamous cell carcinoma (LUSC) defined the glycolytic cancer group, while Prostate adenocarcinoma (PRAD), Thyroid carcinoma (THCA), and Thymoma (THYM) defined the non-glycolytic cancer group. These groups were defined based on glycolysis scoring from previous studies, where KIRC, HNSC, and LUSC had the highest glycolysis scores, meanwhile, PRAD, THCA, and THYM had the lowest. Collectively, these results aimed to identify multi-omic profiles across cancer types with demonstrated variably glycolytic rates. Our analyses provide further support for strategies aiming to classify tumors by metabolic phenotypes in order to therapeutically target tumor-specific vulnerabilities.
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Affiliation(s)
| | - Jeffrey P. MacKeigan
- Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA
- Department of Cell Biology, Van Andel Research Institute, Grand Rapids, MI 49503, USA
- Correspondence: (J.P.M.); (N.J.L.)
| | - Nathan J. Lanning
- Department of Biological Sciences, California State University, Los Angeles, CA 90032, USA
- Correspondence: (J.P.M.); (N.J.L.)
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Su Y, Zhou H, Huang W, Li L, Wang J. The value of preoperative positron emission tomography/computed tomography in differentiating the invasive degree of hypometabolic lung adenocarcinoma. BMC Med Imaging 2023; 23:31. [PMID: 36765284 PMCID: PMC9912592 DOI: 10.1186/s12880-023-00986-8] [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] [Accepted: 02/03/2023] [Indexed: 02/12/2023] Open
Abstract
OBJECTIVES To investigate the value of preoperative positron emission tomography/computed tomography (PET/CT) in differentiating the invasive degree of hypometabolic lung adenocarcinoma. METHODS We retrospectively analyzed the data of patients who underwent PET/CT examination, high-resolution computed tomography, and surgical resection for low-metabolism lung adenocarcinoma in our hospital between June 2016 and December 2021. We also investigated the relationship between the preoperative PET/CT findings and the pathological subtype of hypometabolic lung adenocarcinoma. RESULTS A total of 128 lesions were found in 113 patients who underwent resection for lung adenocarcinoma, including 20 minimally invasive adenocarcinomas (MIA) and 108 invasive adenocarcinomas (IAC), whose preoperative PET/CT showed low metabolism. There were significant differences in the largest diameter (Dmax), lesion type, maximum standard uptake value (SUVmax), SUVindex (the ratio of SUVmax of lesion to SUVmax of contralateral normal lung paranchyma), fasting blood glucose, lobulation, spiculation, and pleura indentation between the MIA and IAC groups (p < 0.05). Multivariate logistic regression analysis showed that the Dmax (odds ratio (OR) = 1.413, 95% confidence interval (CI: 1.155-1.729, p = 0.001)) and SUVmax (OR = 12.137, 95% CI: 1.068-137.900, p = 0.044) were independent risk factors for predicting the hypometabolic IAC (p < 0.05). Receiver operating characteristic (ROC) curve analysis showed that the Dmax ≥ 10.5 mm and SUVmax ≥ 0.85 were the cut-off values for differentiating MIA from IAC, with high sensitivity (84.3% and 75.9%, respectively) and specificity (84.5% and 85.0%, respectively), the Combined Diagnosis showed higher sensitivity (91.7%) and specificity (85.0%). CONCLUSIONS The PET/CT findings correlated with the subtype of hypometabolic lung adenocarcinoma. The parameters Dmax and SUVmax were independent risk factors for predicting IAC, and the sensitivity of Combined Diagnosis prediction is better.
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Affiliation(s)
- Yuling Su
- Department of Nuclear Medicine, Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China.
| | - Hui Zhou
- grid.452930.90000 0004 1757 8087Department of Nuclear Medicine, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Wenshan Huang
- grid.452930.90000 0004 1757 8087Department of Nuclear Medicine, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Lei Li
- grid.452930.90000 0004 1757 8087Department of Nuclear Medicine, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Jinyu Wang
- grid.452930.90000 0004 1757 8087Department of Nuclear Medicine, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
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Meng Y, Sun J, Zhang G, Yu T, Piao H. Imaging glucose metabolism to reveal tumor progression. Front Physiol 2023; 14:1103354. [PMID: 36818450 PMCID: PMC9932271 DOI: 10.3389/fphys.2023.1103354] [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: 11/20/2022] [Accepted: 01/20/2023] [Indexed: 02/05/2023] Open
Abstract
Purpose: To analyze and review the progress of glucose metabolism-based molecular imaging in detecting tumors to guide clinicians for new management strategies. Summary: When metabolic abnormalities occur, termed the Warburg effect, it simultaneously enables excessive cell proliferation and inhibits cell apoptosis. Molecular imaging technology combines molecular biology and cell probe technology to visualize, characterize, and quantify processes at cellular and subcellular levels in vivo. Modern instruments, including molecular biochemistry, data processing, nanotechnology, and image processing, use molecular probes to perform real-time, non-invasive imaging of molecular and cellular events in living organisms. Conclusion: Molecular imaging is a non-invasive method for live detection, dynamic observation, and quantitative assessment of tumor glucose metabolism. It enables in-depth examination of the connection between the tumor microenvironment and tumor growth, providing a reliable assessment technique for scientific and clinical research. This new technique will facilitate the translation of fundamental research into clinical practice.
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Affiliation(s)
- Yiming Meng
- Central Laboratory, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Jing Sun
- Central Laboratory, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Guirong Zhang
- Central Laboratory, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Tao Yu
- Department of Medical Image, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, China,*Correspondence: Tao Yu, ; Haozhe Piao,
| | - Haozhe Piao
- Department of Neurosurgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, China,*Correspondence: Tao Yu, ; Haozhe Piao,
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Heuser C, Renner K, Kreutz M, Gattinoni L. Targeting lactate metabolism for cancer immunotherapy - a matter of precision. Semin Cancer Biol 2023; 88:32-45. [PMID: 36496155 DOI: 10.1016/j.semcancer.2022.12.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 11/29/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022]
Abstract
Immune checkpoint inhibitors and adoptive T cell therapies have been valuable additions to the toolbox in the fight against cancer. These treatments have profoundly increased the number of patients with a realistic perspective toward a return to a cancer-free life. Yet, in a number of patients and tumor entities, cancer immunotherapies have been ineffective so far. In solid tumors, immune exclusion and the immunosuppressive tumor microenvironment represent substantial roadblocks to successful therapeutic outcomes. A major contributing factor to the depressed anti-tumor activity of immune cells in tumors is the harsh metabolic environment. Hypoxia, nutrient competition with tumor and stromal cells, and accumulating noxious waste products, including lactic acid, pose massive constraints to anti-tumor immune cells. Numerous strategies are being developed to exploit the metabolic vulnerabilities of tumor cells in the hope that these would also alleviate metabolism-inflicted immune suppression. While promising in principle, especially in combination with immunotherapies, these strategies need to be scrutinized for their effect on tumor-fighting immune cells, which share some of their key metabolic properties with tumor cells. Here, we provide an overview of strategies that seek to tackle lactate metabolism in tumor or immune cells to unleash anti-tumor immune responses, thereby opening therapeutic options for patients whose tumors are currently not treatable.
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Affiliation(s)
- Christoph Heuser
- Division of Functional Immune Cell Modulation, Leibniz Institute for Immunotherapy (LIT), 93053 Regensburg, Germany.
| | - Kathrin Renner
- Department of Internal Medicine III, University Hospital Regensburg, 93053 Regensburg, Germany; Department of Otorhinolaryngology, University Hospital Regensburg, 93053 Regensburg, Germany
| | - Marina Kreutz
- Department of Internal Medicine III, University Hospital Regensburg, 93053 Regensburg, Germany; Clinical Cooperation Group Immunometabolomics, Leibniz Institute for Immunotherapy (LIT), 93053 Regensburg, Germany; Center for Immunomedicine in Transplantation and Oncology (CITO), University Hospital Regensburg, 93053 Regensburg, Germany
| | - Luca Gattinoni
- Division of Functional Immune Cell Modulation, Leibniz Institute for Immunotherapy (LIT), 93053 Regensburg, Germany; Center for Immunomedicine in Transplantation and Oncology (CITO), University Hospital Regensburg, 93053 Regensburg, Germany; University of Regensburg, 93053 Regensburg, Germany.
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8
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Gao Y, Wu C, Chen X, Ma L, Zhang X, Chen J, Liao X, Liu M. PET/CT molecular imaging in the era of immune-checkpoint inhibitors therapy. Front Immunol 2022; 13:1049043. [PMID: 36341331 PMCID: PMC9630646 DOI: 10.3389/fimmu.2022.1049043] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/10/2022] [Indexed: 04/24/2024] Open
Abstract
Cancer immunotherapy, especially immune-checkpoint inhibitors (ICIs), has paved a new way for the treatment of many types of malignancies, particularly advanced-stage cancers. Accumulating evidence suggests that as a molecular imaging modality, positron emission tomography/computed tomography (PET/CT) can play a vital role in the management of ICIs therapy by using different molecular probes and metabolic parameters. In this review, we will provide a comprehensive overview of the clinical data to support the importance of 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) imaging in the treatment of ICIs, including the evaluation of the tumor microenvironment, discovery of immune-related adverse events, evaluation of therapeutic efficacy, and prediction of therapeutic prognosis. We also discuss perspectives on the development direction of 18F-FDG PET/CT imaging, with a particular emphasis on possible challenges in the future. In addition, we summarize the researches on novel PET molecular probes that are expected to potentially promote the precise application of ICIs.
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9
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Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study. Cancers (Basel) 2022; 14:cancers14153656. [PMID: 35954318 PMCID: PMC9367613 DOI: 10.3390/cancers14153656] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/23/2022] [Accepted: 07/25/2022] [Indexed: 02/04/2023] Open
Abstract
The tumour immune microenvironment influences the efficacy of immune checkpoint inhibitors. Within this microenvironment are CD8-expressing tumour-infiltrating lymphocytes (CD8+ TILs), which are an important mediator and marker of anti-tumour response. In practice, the assessment of CD8+ TILs via tissue sampling involves logistical challenges. Radiomics, the high-throughput extraction of features from medical images, may offer a novel and non-invasive alternative. We performed a systematic review of the available literature reporting radiomic signatures associated with CD8+ TILs. We also aimed to evaluate the methodological quality of the identified studies using the Radiomics Quality Score (RQS) tool, and the risk of bias and applicability with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Articles were searched from inception until 31 December 2021, in three electronic databases, and screened against eligibility criteria. Twenty-seven articles were included. A wide variety of cancers have been studied. The reported radiomic signatures were heterogeneous, with very limited reproducibility between studies of the same cancer group. The overall quality of studies was found to be less than desirable (mean RQS = 33.3%), indicating a need for technical maturation. Some potential avenues for further investigation are also discussed.
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10
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Gao Y, Yuan L, Zeng J, Li F, Li X, Tan F, Liu X, Wan H, Kui X, Liu X, Ke C, Pei Z. eIF6 is potential diagnostic and prognostic biomarker that associated with 18F-FDG PET/CT features and immune signatures in esophageal carcinoma. Lab Invest 2022; 20:303. [PMID: 35794622 PMCID: PMC9258187 DOI: 10.1186/s12967-022-03503-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 06/24/2022] [Indexed: 11/25/2022]
Abstract
Background Although eukaryotic initiation factor 6 (eIF6) is a novel therapeutic target, data on its importance in the development of esophageal carcinoma (ESCA) remains limited. This study evaluated the correlation between eIF6 expression and metabolic analysis using fluorine-18 fluorodeoxyglucose (18F-FDG) -Positron emission tomography (PET) and immune gene signatures in ESCA. Methods This study employed The Cancer Genome Atlas (TCGA) to analyze the expression and prognostic value of eIF6, as well as its relationship with the immune gene signatures in ESCA patients. The qRT-PCR and Western blot analyses were used to profile the expression of eIF6 in ESCA tissues and different ESCA cell lines. The expression of tumor eIF6 and glucose transporter 1 (GLUT1) was examined using immunohistochemical tools in fifty-two ESCA patients undergoing routine 18F-FDG PET/CT before surgery. In addition, the cellular responses to eIF6 knockdown in human ESCA cells were assessed via the MTS, EdU, flow cytometry and wound healing assays. Results Our data demonstrated that compared with the normal esophageal tissues, eIF6 expression was upregulated in ESCA tumor tissues and showed a high diagnostic value with an area under curve of 0.825 for predicting ESCA. High eIF6 expression was significantly correlated with shorter overall survival of patients with esophagus adenocarcinoma (p = 0.038), but not in squamous cell carcinoma of the esophagus (p = 0.078). In addition, tumor eIF6 was significantly associated with 18F-FDG PET/CT parameters: maximal and mean standardized uptake values (SUVmax and SUVmean) and total lesion glycolysis (TLG) (rho = 0.458, 0.460, and 0.300, respectively, p < 0.01) as well as GLUT1 expression (rho = 0.453, p < 0.001). A SUVmax cutoff of 18.2 led to prediction of tumor eIF6 expression with an accuracy of 0.755. Functional analysis studies demonstrated that knockdown of eIF6 inhibited ESCA cell growth and migration, and fueled cell apoptosis. Moreover, the Bulk RNA gene analysis revealed a significant inverse association between eIF6 and the tumor-infiltrating immune cells (macrophages, T cells, or Th1 cells) and immunomodulators in the ESCA microenvironment. Conclusion Our study suggested that eIF6 might serve as a potential prognostic biomarker associated with metabolic variability and immune gene signatures in ESCA tumor microenvironment.
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11
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Cascone T, Fradette J, Pradhan M, Gibbons DL. Tumor Immunology and Immunotherapy of Non-Small-Cell Lung Cancer. Cold Spring Harb Perspect Med 2022; 12:a037895. [PMID: 34580079 PMCID: PMC8957639 DOI: 10.1101/cshperspect.a037895] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Historically, non-small-cell lung cancer (NSCLC) has been regarded as a nonimmunogenic tumor; however, recent studies have shown that NSCLCs are among the most responsive cancers to monoclonal antibody immune checkpoint inhibitors (ICIs). ICIs have dramatically improved clinical outcomes for a subset of patients (∼20%) with locally advanced and metastatic NSCLC, and they have also demonstrated promise as neoadjuvant therapy for early-stage resectable disease. Nevertheless, the majority of patients with NSCLC are refractory to ICIs for reasons that are poorly understood. Thus, major questions are: how do we initially identify the patients most likely to derive significant clinical benefit from these therapies; how can we increase the number of patients benefiting; what are the mechanisms of primary and acquired resistance to immune-based therapies; are there additional immune checkpoints besides PD-1/PD-L1 and CTLA-4 that can be targeted to provide greater clinical benefit to patients; and how do we best combine ICI therapy with surgery, radiotherapy, chemotherapy, and targeted therapy? To answer these questions, we need to deploy the latest technologies to study tumors and their microenvironment and how they interact with components of the innate and adaptive immune systems. There is also a need for new preclinical model systems to investigate the molecular mechanisms of resistance to treatment and identify novel therapeutic targets. Recent advances in technology are beginning to shed new light on the immune landscape of NSCLC that may uncover biomarkers of response and maximize the clinical benefit of immune-based therapies. Identification of the mechanisms of resistance should lead to the identification of novel targets and the generation of new therapeutic strategies that improve outcomes for a greater number of patients. In the sections below, we discuss the results of studies examining the immune microenvironment in NSCLC, summarize the clinical experience with immunotherapy for NSCLC, and review candidate biomarkers of response to these agents in NSCLC.
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Affiliation(s)
- Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Jared Fradette
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Monika Pradhan
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
- Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
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12
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van Genugten EAJ, Weijers JAM, Heskamp S, Kneilling M, van den Heuvel MM, Piet B, Bussink J, Hendriks LEL, Aarntzen EHJG. Imaging the Rewired Metabolism in Lung Cancer in Relation to Immune Therapy. Front Oncol 2022; 11:786089. [PMID: 35070990 PMCID: PMC8779734 DOI: 10.3389/fonc.2021.786089] [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: 09/30/2021] [Accepted: 12/10/2021] [Indexed: 12/14/2022] Open
Abstract
Metabolic reprogramming is recognized as one of the hallmarks of cancer. Alterations in the micro-environmental metabolic characteristics are recognized as important tools for cancer cells to interact with the resident and infiltrating T-cells within this tumor microenvironment. Cancer-induced metabolic changes in the micro-environment also affect treatment outcomes. In particular, immune therapy efficacy might be blunted because of somatic mutation-driven metabolic determinants of lung cancer such as acidity and oxygenation status. Based on these observations, new onco-immunological treatment strategies increasingly include drugs that interfere with metabolic pathways that consequently affect the composition of the lung cancer tumor microenvironment (TME). Positron emission tomography (PET) imaging has developed a wide array of tracers targeting metabolic pathways, originally intended to improve cancer detection and staging. Paralleling the developments in understanding metabolic reprogramming in cancer cells, as well as its effects on stromal, immune, and endothelial cells, a wave of studies with additional imaging tracers has been published. These tracers are yet underexploited in the perspective of immune therapy. In this review, we provide an overview of currently available PET tracers for clinical studies and discuss their potential roles in the development of effective immune therapeutic strategies, with a focus on lung cancer. We report on ongoing efforts that include PET/CT to understand the outcomes of interactions between cancer cells and T-cells in the lung cancer microenvironment, and we identify areas of research which are yet unchartered. Thereby, we aim to provide a starting point for molecular imaging driven studies to understand and exploit metabolic features of lung cancer to optimize immune therapy.
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Affiliation(s)
- Evelien A J van Genugten
- Department of Medical Imaging, Radboud University Medical Centre (Radboudumc), Nijmegen, Netherlands
| | - Jetty A M Weijers
- Department of Medical Imaging, Radboud University Medical Centre (Radboudumc), Nijmegen, Netherlands
| | - Sandra Heskamp
- Department of Medical Imaging, Radboud University Medical Centre (Radboudumc), Nijmegen, Netherlands
| | - Manfred Kneilling
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University, Tuebingen, Germany.,Department of Dermatology, Eberhard Karls University, Tuebingen, Germany
| | | | - Berber Piet
- Department of Respiratory Diseases, Radboudumc, Nijmegen, Netherlands
| | - Johan Bussink
- Radiotherapy and OncoImmunology Laboratory, Department of Radiation Oncology, Radboudumc, Netherlands
| | - Lizza E L Hendriks
- Department of Pulmonary Diseases, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre (UMC), Maastricht, Netherlands
| | - Erik H J G Aarntzen
- Department of Medical Imaging, Radboud University Medical Centre (Radboudumc), Nijmegen, Netherlands
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13
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Frequent EGFR Mutations and Better Prognosis in Positron Emission Tomography-Negative, Solid-Type Lung Cancer. Clin Lung Cancer 2021; 23:e60-e68. [PMID: 34750065 DOI: 10.1016/j.cllc.2021.10.003] [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: 08/14/2021] [Revised: 09/29/2021] [Accepted: 10/02/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND The differential diagnosis of a solitary solid-type lung nodule is diverse. 18F-fluorodeoxyglucose positron emission tomography (PET) has a high sensitivity in the diagnosis of solid-type lung cancers; however, PET-negative, solid-type lung cancers are rarely observed. In this study, we analyzed the clinical/genetic features and prognosis of PET-negative, solid-type lung cancers. PATIENTS AND METHODS Between January 2007 and February 2020, 709 patients with solid-type lung cancers (tumor size ≥2.0 cm) underwent pulmonary resection. Clinical, genetic, and prognostic features were evaluated in 27 patients (3.8%) with tumors showing negative PET results defined as SUVmax <2.0. RESULTS All 27 patients had lung adenocarcinoma; 23 had invasive adenocarcinomas and 4 had invasive mucinous adenocarcinomas. The PET-negative group showed high frequencies of females and never-smokers. Recurrence-free survival was significantly better in the PET-negative group compared with PET-positive counterparts extracted using propensity score matching from patients who underwent pulmonary resection during the same period (P = .0052). Furthermore, 83% of PET-negative, solid-type invasive lung adenocarcinoma patients harbored EGFR mutation, which was significantly higher than that of PET-positive, solid-type invasive lung adenocarcinoma patients (38%, n = 225) who received EGFR mutation testing in our cohort (P < .0001). PET-negative, solid-type lung adenocarcinoma patients with EGFR mutations had significantly better recurrence-free survival compared with PET-positive, solid-type lung adenocarcinoma patients with EGFR mutations extracted using propensity score matching (P = .0030). CONCLUSION PET-negative, solid-type lung cancers are characterized with a high incidence of EGFR mutation and a better prognosis compared with PET-positive, solid-type lung cancer.
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14
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Shao X, Shao X, Niu R, Jiang Z, Xu M, Wang Y. Investigating the association between ground-glass nodules glucose metabolism and the invasive growth pattern of early lung adenocarcinoma. Quant Imaging Med Surg 2021; 11:3506-3517. [PMID: 34341727 DOI: 10.21037/qims-20-1189] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 03/26/2021] [Indexed: 01/11/2023]
Abstract
Background To explore the association between the glucose metabolism level of lung ground-glass nodules (GGNs), as revealed by 18F-flurodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging, and the invasive pathological growth pattern of early lung adenocarcinoma. Methods We retrospectively analyzed patients who underwent PET/CT examination and surgical resection due to persistent GGNs, which were confirmed to be early lung adenocarcinoma by postoperative pathology examination. After adjusting for confounding factors and performing stratified analysis, we explored the association between the maximum standard uptake value of PET (SUVmax) and the invasive pathological growth pattern of early stage lung adenocarcinoma. Results The proportions of invasive adenocarcinoma (INV) in the SUVmax of Tertile 1, Tertile 2, and Tertile 3 were 52.7%, 73.3%, and 87.1%, respectively. After adjusting for potential confounding factors, the risk of INV gradually increased as the GGN SUVmax increased [odds ratio (OR): 1.520, 95% confidence interval (CI): 1.044-2.213, P=0.029]. This trend was statistically significant (OR: 1.678, 95% CI: 1.064-2.647, P=0.026), especially in Tertile 3 vs. Tertile 1 (OR: 4.879, 95% CI: 1.349-17.648, P=0.016). Curve fitting showed that the SUVmax and INV risk were linearly and positively associated. The association was consistent in different subgroups based on GGN number, type, shape, edge, bronchial sign, vacuole sign, pleural depression sign, diameters, and consolidation-to-tumor ratio, suggesting that there was no significant interaction between different grouping parameters and the association (P for interaction range = 0.129-0.909). Conclusions In FDG PET, the glucose metabolism level (SUVmax) of lung GGNs is independently associated with INV risk, and this association is linear and positive.
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Affiliation(s)
- Xiaoliang Shao
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Xiaonan Shao
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Rong Niu
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Zhenxing Jiang
- Department of Radiology, the Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Mei Xu
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Yuetao Wang
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
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15
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Blumenthaler AN, Hofstetter WL, Mehran RJ, Rajaram R, Rice DC, Roth JA, Sepesi B, Swisher SG, Vaporciyan AA, Walsh GL, Strange CD, Antonoff MB. Preoperative Maximum Standardized Uptake Value Associated with Recurrence Risk In Early Lung Cancer. Ann Thorac Surg 2021; 113:1835-1844. [PMID: 34252403 DOI: 10.1016/j.athoracsur.2021.06.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 05/07/2021] [Accepted: 06/01/2021] [Indexed: 11/01/2022]
Abstract
BACKGROUND We aimed to investigate the maximum standardized uptake value (SUVmax) as a predictor of recurrence and timing of recurrence after resection of early-stage non-small cell lung cancer. METHODS We retrospectively reviewed patients from a single institution who underwent lobectomy for stage I-IIa non-small cell lung cancer from 2013-2018. Exclusion criteria included preoperative therapy and neuroendocrine histology. We collected recurrence and follow-up data, as well as preoperative SUVmax. A receiver operator characteristic curve was used to identify the optimal SUVmax for predicting recurrence. Kaplan-Meier curves and Cox Regression analyses were used to identify predictors of freedom from recurrence (FFR). RESULTS The study included 238 patients, 30(12.6%) of whom developed recurrence. The receiver operator characteristic curve had an area-under-the-curve of 0.671 and identified 4.93 as the optimal SUVmax cut-off. Patients were stratified into groups based on this value; each group included 119 patients. High SUVmax was associated with larger tumor size, poor differentiation, lymphovascular invasion, and shorter FFR. The proportion of patients without recurrence at 5 years in the low- and high-SUVmax groups were 92.4% and 73.4%, respectively (p<0.001). On univariate analysis, poor differentiation (HR:2.35, 95%CI:1.04-5.31; p=0.04), lymphovascular invasion (HR:3.19;95%CI:1.37-7.44;p=0.007), visceral pleural invasion (HR:2.33;95%CI:1.05-5.20;p=0.04), and SUVmax≥4.93 (HR:4.51;95%CI:1.84-11.03;p=0.001) predicted FFR. On multivariable analysis, only SUVmax≥4.93 remained significant (HR:5.36, 95%CI:1.50-19.17; p=0.01). CONCLUSIONS SUVmax is independently associated with risk of recurrence after resection of early-stage lung cancer. SUVmax may be a valuable tool in stratifying patients with early-stage lung cancer for adjuvant therapy and surveillance frequency.
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Affiliation(s)
- Alisa N Blumenthaler
- Departments of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wayne L Hofstetter
- Departments of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Reza J Mehran
- Departments of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ravi Rajaram
- Departments of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David C Rice
- Departments of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jack A Roth
- Departments of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Boris Sepesi
- Departments of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Stephen G Swisher
- Departments of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ara A Vaporciyan
- Departments of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Garrett L Walsh
- Departments of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Chad D Strange
- Departments of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mara B Antonoff
- Departments of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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16
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Guo H, Li W, Qian L, Cui J. Clinical challenges in neoadjuvant immunotherapy for non-small cell lung cancer. Chin J Cancer Res 2021; 33:203-215. [PMID: 34158740 PMCID: PMC8181868 DOI: 10.21147/j.issn.1000-9604.2021.02.08] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 04/07/2021] [Indexed: 12/25/2022] Open
Abstract
Immune checkpoint inhibitors (ICIs), a type of immunotherapy, have become one of the most important therapeutic options for first- and second-line treatment of advanced non-small cell lung cancer (NSCLC). Recent clinical studies have shown that immunotherapy can offer substantial survival benefits to patients with early-stage or resectable advanced NSCLC. However, considering the importance of timing when using ICIs and their associated adverse events (AEs), the advantages and disadvantages of using these agents need to be weighed carefully when deciding the use of a combined treatment. In addition, the inconsistency between imaging assessment and pathological results poses further challenges to the evaluation of efficacy of neoadjuvant immunotherapy. It is also important to develop new methodologies and discover suitable biomarkers that can be used to evaluate survival outcomes of immunotherapy and identify patients who would benefit the most from this treatment. In this review, we aimed to summarize previous results of ongoing clinical trials on neoadjuvant immunotherapy for lung cancer and discuss the challenges and future perspectives of this therapeutic approach in the treatment of resectable NSCLC.
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Affiliation(s)
- Hanfei Guo
- Cancer Center, the First Hospital of Jilin University, Changchun 130021, China
| | - Wenqian Li
- Cancer Center, the First Hospital of Jilin University, Changchun 130021, China
| | - Lei Qian
- Cancer Center, the First Hospital of Jilin University, Changchun 130021, China
| | - Jiuwei Cui
- Cancer Center, the First Hospital of Jilin University, Changchun 130021, China
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17
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Obesity is a risk factor for intrahepatic cholangiocarcinoma progression associated with alterations of metabolic activity and immune status. Sci Rep 2021; 11:5845. [PMID: 33712681 PMCID: PMC7955092 DOI: 10.1038/s41598-021-85186-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 02/26/2021] [Indexed: 12/12/2022] Open
Abstract
Body mass index (BMI) is well known to be associated with poor prognosis in several cancers. The relationship between BMI and the long-term outcomes of patients with intrahepatic cholangiocarcinoma (ICC) is incompletely understood. This study investigated the relationships of BMI with clinicopathological characteristics and patient outcomes, focusing on metabolic activity and immune status. The relationship between BMI and the maximum standardized uptake value (SUVmax) on fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) was analyzed. In addition, immunohistochemistry was performed for programmed cell death-ligand 1 (PD-L1), cluster of differentiation 8 (CD8), and forkhead box protein P3 (Foxp3). Seventy-four patients with ICC were classified into normal weight (BMI < 25.0 kg/m2, n = 48) and obesity groups (BMI ≥ 25.0 kg/m2, n = 26), respectively. Serum carbohydrate antigen 19–9 levels were higher in the obesity group than in the normal weight group. Tumor size and the intrahepatic metastasis rate were significantly larger in the obesity group. Patients in the obesity group had significantly worse prognoses than those in the normal weight group. Moreover, BMI displayed a positive correlation with SUVmax on 18F-FDG PET/CT (n = 46, r = 0.5152). Patients with high 18F-FDG uptake had a significantly higher rate of PD-L1 expression, lower CD8 + tumor-infiltrating lymphocyte (TIL) counts, and higher Foxp3 + TIL counts. The elevated BMI might predict the outcomes of patients with ICC. Obesity might be associated with ICC progression, possibly through alterations in metabolic activity and the immune status.
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18
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Pradhan M, Chocry M, Gibbons DL, Sepesi B, Cascone T. Emerging biomarkers for neoadjuvant immune checkpoint inhibitors in operable non-small cell lung cancer. Transl Lung Cancer Res 2021; 10:590-606. [PMID: 33569339 PMCID: PMC7867746 DOI: 10.21037/tlcr-20-573] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The advent of immune checkpoint inhibitors (ICIs) has dramatically changed the treatment of patients with locally advanced unresectable and metastatic non-small cell lung cancer (NSCLC). Now, ICIs are undergoing evaluation as neoadjuvant therapy in patients with early-stage, resectable NSCLC using candidate surrogate endpoints of clinical efficacy, i.e., major pathologic response (MPR, ≤10% viable tumor cells in resected tumors). The initial results from early, small-scale trials are encouraging; however, they also reveal that a substantial number of patients with operable disease may not benefit from neoadjuvant ICIs. Consequently, much investigative effort is currently directed toward identifying mechanisms of resistance to ICI therapy in resectable NSCLC. There is also an urgent need for biomarkers that could be used to guide the clinical decision-making process and maximize the clinical benefit of ICIs in patients with early-stage, resectable NSCLC. Here, we summarize the initial results from the trials of neoadjuvant ICIs in patients with early-stage and locally advanced operable NSCLC and review the findings of studies investigating emerging biomarkers associated with those trials.
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Affiliation(s)
- Monika Pradhan
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mathieu Chocry
- Aix-Marseille Université, Institut de Neurophysiopathologie (INP), CNRS, Marseille, France
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Boris Sepesi
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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19
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Wang L, Ruan M, Lei B, Yan H, Sun X, Chang C, Liu L, Xie W. The potential of 18F-FDG PET/CT in predicting PDL1 expression status in pulmonary lesions of untreated stage IIIB-IV non-small-cell lung cancer. Lung Cancer 2020; 150:44-52. [PMID: 33065462 DOI: 10.1016/j.lungcan.2020.10.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 09/20/2020] [Accepted: 10/05/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVES To investigate the potential of 2-deoxy-2(18F)fluoro-d-glucose (18F-FDG) combined positron emission tomography and computed tomography (PET/CT) in predicting programmed cell death ligand-1 (PDL1) expression status in pulmonary lesions of advanced non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS This retrospective study includes 133 untreated stage IIIB-IV NSCLC patients who underwent pulmonary lesion biopsy for PDL1 immunochemistry 1-4 weeks after 18F-FDG PET/CT scanning, randomly assigned to cohorts for modelling and validation of PDL1 expression predictors. Mean and maximum standard uptake values (pSUVmean and pSUVmax), metabolic tumour volume (pMTV), and total lesion glycolysis (pTLG) of primary lesions were determined. PDL1 expression in pulmonary lesions (pPDL1) was determined using tumour proportion score (TPS), and pPDL1 TPS < 1%, 1-49 %, and ≥ 50 % were considered as pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively. RESULTS pSUVmean and pSUVmax values were increased with the increase of pPDL1 levels, whereas pMTV and pTLG values were not associated with pPDL1 levels. In the modelling cohort, we found that pSUVmax rather than pSUVmean was an independent predictor for pPDL1-negative, pPDL1-moderate, and pPDL1-strong, whereas pSUVmax < 14.4, 14.4-17.5, and > 17.5 were suggested as predictors for pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively (odds ratio: 4.82, 3.92, and 4.45, respectively; P = 0.002, 0.021, and 0.020, respectively). In the validation cohort, pSUVmax < 14.4, 14.4-17.5, and > 17.5 showed significantly high probabilities of being pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively (P = 0.006). The accuracies of pSUVmax < 14.4, 14.4-17.5, and > 17.5 predicting pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively, in validation cohort, were 66.7 %, 75.8 %, and 84.8 %, respectively. CONCLUSION pSUVmax on 18F-FDG PET/CT is a potential biomarker for pPDL1 TPS < 1%, 1-49 %, and ≥ 50 % in untreated stage IIIB-IV NSCLC, and therefore may be helpful for determining immunotherapeutic strategy for advanced NSCLC.
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Affiliation(s)
- Lihua Wang
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 Huaihai West Road, Shanghai 200030, China; Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, 241 Huaihai West Road, Shanghai 200030, China; Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Road, Shanghai 201318, China
| | - Maomei Ruan
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 Huaihai West Road, Shanghai 200030, China; Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, 241 Huaihai West Road, Shanghai 200030, China
| | - Bei Lei
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 Huaihai West Road, Shanghai 200030, China; Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, 241 Huaihai West Road, Shanghai 200030, China
| | - Hui Yan
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 Huaihai West Road, Shanghai 200030, China; Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, 241 Huaihai West Road, Shanghai 200030, China
| | - Xiaoyan Sun
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 Huaihai West Road, Shanghai 200030, China; Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, 241 Huaihai West Road, Shanghai 200030, China
| | - Cheng Chang
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 Huaihai West Road, Shanghai 200030, China; Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, 241 Huaihai West Road, Shanghai 200030, China
| | - Liu Liu
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 Huaihai West Road, Shanghai 200030, China; Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, 241 Huaihai West Road, Shanghai 200030, China; Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Road, Shanghai 201318, China.
| | - Wenhui Xie
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 Huaihai West Road, Shanghai 200030, China; Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, 241 Huaihai West Road, Shanghai 200030, China; Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Road, Shanghai 201318, China.
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Recent and Current Advances in FDG-PET Imaging within the Field of Clinical Oncology in NSCLC: A Review of the Literature. Diagnostics (Basel) 2020; 10:diagnostics10080561. [PMID: 32764429 PMCID: PMC7459495 DOI: 10.3390/diagnostics10080561] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/29/2020] [Accepted: 08/03/2020] [Indexed: 02/07/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths around the world, the most common type of which is non-small-cell lung cancer (NSCLC). Computed tomography (CT) is required for patients with NSCLC, but often involves diagnostic issues and large intra- and interobserver variability. The anatomic data obtained using CT can be supplemented by the metabolic data obtained using fluorodeoxyglucose F 18 (FDG) positron emission tomography (PET); therefore, the use of FDG-PET/CT for staging NSCLC is recommended, as it provides more accuracy than either modality alone. Furthermore, FDG-PET/magnetic resonance imaging (MRI) provides useful information on metabolic activity and tumor cellularity, and has become increasingly popular. A number of studies have described FDG-PET/MRI as having a high diagnostic performance in NSCLC staging. Therefore, multidimensional functional imaging using FDG-PET/MRI is promising for evaluating the activity of the intratumoral environment. Radiomics is the quantitative extraction of imaging features from medical scans. The chief advantages of FDG-PET/CT radiomics are the ability to capture information beyond the capabilities of the human eye, non-invasiveness, the (virtually) real-time response, and full-field analysis of the lesion. This review summarizes the recent advances in FDG-PET imaging within the field of clinical oncology in NSCLC, with a focus on surgery and prognostication, and investigates the site-specific strengths and limitations of FDG-PET/CT. Overall, the goal of treatment for NSCLC is to provide the best opportunity for long-term survival; therefore, FDG-PET/CT is expected to play an increasingly important role in deciding the appropriate treatment for such patients.
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Heterogeneity of Glucose Transport in Lung Cancer. Biomolecules 2020; 10:biom10060868. [PMID: 32517099 PMCID: PMC7356687 DOI: 10.3390/biom10060868] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 05/28/2020] [Accepted: 05/29/2020] [Indexed: 02/06/2023] Open
Abstract
Increased glucose uptake is a known hallmark of cancer. Cancer cells need glucose for energy production via glycolysis and the tricarboxylic acid cycle, and also to fuel the pentose phosphate pathway, the serine biosynthetic pathway, lipogenesis, and the hexosamine pathway. For this reason, glucose transport inhibition is an emerging new treatment for different malignancies, including lung cancer. However, studies both in animal models and in humans have shown high levels of heterogeneity in the utilization of glucose and other metabolites in cancer, unveiling a complexity that is difficult to target therapeutically. Here, we present an overview of different levels of heterogeneity in glucose uptake and utilization in lung cancer, with diagnostic and therapeutic implications.
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