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Hicks RJ. The value of the Standardized Uptake Value (SUV) and Metabolic Tumor Volume (MTV) in lung cancer. Semin Nucl Med 2022; 52:734-744. [PMID: 35624032 DOI: 10.1053/j.semnuclmed.2022.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 04/21/2022] [Indexed: 11/11/2022]
Abstract
The diagnosis, staging and therapeutic monitoring of lung cancer were amongst the first applications for which the utility of FDG PET was documented and FDG PET/CT is now a routine diagnostic tool for clinical decision-making. As well as having high sensitivity for detection of disease sites, which provides critical information about stage, the intensity of uptake provides deeper biological characterization, while the burden of disease also has potential clinical significance. These disease characteristics can easily be quantified on delayed whole-body imaging as the maximum standardized uptake value (SUVmax) and metabolic tumor volume (MTV), respectively. There have been significant efforts to harmonize the measurement of these features, particularly within the context of clinical trials. Nevertheless, however calculated, in general, a high SUVmax and large MTV have been shown to have an adverse prognostic significance. Nevertheless, the use of these parameters in the interpretation and reporting of clinical scans remains inconsistent and somewhat controversial. This review details the current status of semi-quantitative FDG PET/CT in the evaluation of lung cancer.
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Affiliation(s)
- Rodney J Hicks
- Department of Medicine, St Vincent's Medical School, University of Melbourne, Melbourne Academic Centre for Health, University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Central Clinical School, Alfred Hospital, Monash University, Melbourne VIC, Australia.
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Jiang A, Liu N, Zhao R, Liu S, Gao H, Wang J, Zheng X, Ren M, Fu X, Liang X, Tian T, Ruan Z, Yao Y. Construction and Validation of a Novel Nomogram to Predict the Overall Survival of Patients With Combined Small Cell Lung Cancer: A Surveillance, Epidemiology, and End Results Population-Based Study. Cancer Control 2021; 28:10732748211051228. [PMID: 34632799 PMCID: PMC8512214 DOI: 10.1177/10732748211051228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
INTRODUCTION Combined small cell lung cancer (C-SCLC) represents a rare subtype of all small cell lung cancer cases, with limited studies investigated its prognostic factors. The aim of this study was to construct a novel nomogram to predict the overall survival (OS) of patients with C-SCLC. METHODS In this retrospective study, a total of 588 C-SCLC patients were selected from the Surveillance, Epidemiology, and End Results database. The univariate and multivariate Cox analyses were performed to identify optimal prognostic variables and construct the nomogram, with concordance index (C-index), receiver operating characteristic curves, and calibration curves being used to evaluate its discrimination and calibration abilities. Furthermore, decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification index (NRI) were also adopted to assess its clinical utility and predictive ability compared with the classic TNM staging system. RESULTS Seven independent predictive factors were identified to construct the nomogram, including T stage, N stage, M stage, brain metastasis, liver metastasis, surgery, and chemotherapy. We observed a higher C-index in both the training (.751) and validation cohorts (.736). The nomogram has higher area under the curve in predicting 6-, 12-, 18-, 24-, and 36-month survival probability of patients with C-SCLC. Meanwhile, the calibration curves also revealed high consistencies between the actual and predicted OS. DCA revealed that the nomogram could provide greater clinical net benefits to these patients. We found that the NRI for 6- and 12-month OS were .196 and .225, and the IDI for 6- and 12-month OS were .217 and .156 in the training group, suggesting that the nomogram can predict a more accurate survival probability. Similar results were also observed in the validation cohort. CONCLUSION We developed and verified a novel nomogram that can help clinicians recognize high-risk patients with C-SCLC and predict their OS.
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Affiliation(s)
- Aimin Jiang
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Na Liu
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Rui Zhao
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, 540681Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Shihan Liu
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Huan Gao
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Jingjing Wang
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Xiaoqiang Zheng
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Mengdi Ren
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Xiao Fu
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Xuan Liang
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Tao Tian
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Zhiping Ruan
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Yu Yao
- Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
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Ning J, Ge T, Jiang M, Jia K, Wang L, Li W, Chen B, Liu Y, Wang H, Zhao S, He Y. Early diagnosis of lung cancer: which is the optimal choice? Aging (Albany NY) 2021; 13:6214-6227. [PMID: 33591942 PMCID: PMC7950268 DOI: 10.18632/aging.202504] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 12/18/2020] [Indexed: 02/06/2023]
Abstract
The prognosis of lung cancer patients with different clinical stages is significantly different. The 5-year survival of stage IA groups can exceed 90%, while patients with stage IV can be less than 10%. Therefore, early diagnosis is extremely important for lung cancer patients. This research focused on various diagnosis methods of early lung cancer, including imaging screening, bronchoscopy, and emerging potential liquid biopsies, as well as volatile organic compounds, autoantibodies, aiming to improve the early diagnosis rate and explore feasible and effective early diagnosis strategies.
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Affiliation(s)
- Jing Ning
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai 200433, People's Republic of China.,Tongji University, Shanghai 200433, People's Republic of China
| | - Tao Ge
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai 200433, People's Republic of China
| | - Minlin Jiang
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai 200433, People's Republic of China.,Tongji University, Shanghai 200433, People's Republic of China
| | - Keyi Jia
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai 200433, People's Republic of China.,Tongji University, Shanghai 200433, People's Republic of China
| | - Lei Wang
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai 200433, People's Republic of China
| | - Wei Li
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai 200433, People's Republic of China
| | - Bin Chen
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai 200433, People's Republic of China
| | - Yu Liu
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai 200433, People's Republic of China.,Tongji University, Shanghai 200433, People's Republic of China
| | - Hao Wang
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai 200433, People's Republic of China.,Tongji University, Shanghai 200433, People's Republic of China
| | - Sha Zhao
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai 200433, People's Republic of China
| | - Yayi He
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai 200433, People's Republic of China
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