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Liu Z, Yin R, Ma W, Li Z, Guo Y, Wu H, Lin Y, Chekhonin VP, Peltzer K, Li H, Mao M, Jian X, Zhang C. Bone metastasis prediction in non-small-cell lung cancer: primary CT-based radiomics signature and clinical feature. BMC Med Imaging 2024; 24:203. [PMID: 39103775 DOI: 10.1186/s12880-024-01383-5] [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: 03/28/2024] [Accepted: 07/29/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Radiomics provided opportunities to quantify the tumor phenotype non-invasively. This study extracted contrast-enhanced computed tomography (CECT) radiomic signatures and evaluated clinical features of bone metastasis in non-small-cell lung cancer (NSCLC). With the combination of the revealed radiomics and clinical features, the predictive modeling on bone metastasis in NSCLC was established. METHODS A total of 318 patients with NSCLC at the Tianjin Medical University Cancer Institute & Hospital was enrolled between January 2009 and December 2019, which included a feature-learning cohort (n = 223) and a validation cohort (n = 95). We trained a radiomics model in 318 CECT images from feature-learning cohort to extract the radiomics features of bone metastasis in NSCLC. The Kruskal-Wallis and the least absolute shrinkage and selection operator regression (LASSO) were used to select bone metastasis-related features and construct the CT radiomics score (Rad-score). Multivariate logistic regression was performed with the combination of the Rad-score and clinical data. A predictive nomogram was subsequently developed. RESULTS Radiomics models using CECT scans were significant on bone metastasis prediction in NSCLC. Model performance was enhanced with each information into the model. The radiomics nomogram achieved an AUC of 0.745 (95% confidence interval [CI]: 0.68,0.80) on predicting bone metastasis in the training set and an AUC of 0.808(95% confidence interval [CI]: 0.71,0.88) in the validation set. CONCLUSION The revealed invisible image features were of significance on guiding bone metastasis prediction in NSCLC. Based on the combination of the image features and clinical characteristics, the predictive nomogram was established. Such nomogram can be used for the auxiliary screening of bone metastasis in NSCLC.
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Affiliation(s)
- Zheng Liu
- Department of Bone and Soft Tissue Tumor, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
- The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China
- Department of Orthopedics, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong province, China
| | - Rui Yin
- The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China
- School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin, China
| | - Wenjuan Ma
- Department of Bone and Soft Tissue Tumor, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
- The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China
| | - Zhijun Li
- Department of Bone and Soft Tissue Tumor, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
- The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China
| | - Yijun Guo
- Department of Bone and Soft Tissue Tumor, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
- The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China
| | - Haixiao Wu
- Department of Bone and Soft Tissue Tumor, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
- The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China
| | - Yile Lin
- The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China
- Department of Orthopedics, Tianjin Medical University General Hospital, Tianjin, China
| | - Vladimir P Chekhonin
- The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China
- Department of Basic and Applied Neurobiology, Federal Medical Research Center for Psychiatry and Narcology, Moscow, Russian Federation
| | - Karl Peltzer
- The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China
- Department of Psychology, University of the Free State, Turfloop, South Africa
| | - Huiyang Li
- The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China
| | - Min Mao
- Department of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Xiqi Jian
- School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin, China.
| | - Chao Zhang
- Department of Bone and Soft Tissue Tumor, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
- The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China.
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Teng X, Han K, Jin W, Ma L, Wei L, Min D, Chen L, Du Y. Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism. EClinicalMedicine 2024; 72:102617. [PMID: 38707910 PMCID: PMC11066529 DOI: 10.1016/j.eclinm.2024.102617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
Background Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients. Methods The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019-2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019-2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan-Meier survival curves. Findings Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95-0.99) for internal validation. External validation further confirmed the RF model's effectiveness, yielding an AUC of 0.97 (95% CI 0.94-0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27 ± 3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients. Interpretation This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies. Funding This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).
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Affiliation(s)
- Xiaoyan Teng
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Kun Han
- Department of Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Wei Jin
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Liru Ma
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Lirong Wei
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Daliu Min
- Department of Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Libo Chen
- Department of Nuclear Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Yuzhen Du
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
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Li W, Li J, Cai J. Development of a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit: a retrospective analysis based on the MIMIC IV database. J Cancer Res Clin Oncol 2024; 150:164. [PMID: 38546896 PMCID: PMC10978606 DOI: 10.1007/s00432-024-05667-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 02/24/2024] [Indexed: 04/01/2024]
Abstract
PURPOSE The present study aimed to develop a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit to facilitate risk stratification and treatment planning. METHODS We used the MIMIC IV 2.0 (the Medical Information Mart for Intensive Care IV) to retrieve patients with secondary bone tumors as a study cohort. To evaluate the predictive ability of each characteristic on patient mortality, stepwise Cox regression was used to screen variables, and the selected variables were included in the final Cox proportional hazard model. Finally, the performance of the model was tested using the decision curve, calibration curve, and receiver operating characteristic (ROC) curve. RESULTS A total of 1028 patients were enrolled after excluding cases with missing information. In the training cohort, albumin, APSIII (Acute Physiology Score III), chemotherapy, lactate, chloride, hepatic metastases, respiratory failure, SAPSII (Simplified Acute Physiology Score II), and total protein were identified as independent risk factors for patient death and then incorporated into the final model. The model showed good and robust prediction performance. CONCLUSION We developed a nomogram prognostic model for patients with secondary bone tumors in the intensive care unit, which provides effective survival prediction information.
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Affiliation(s)
- Weikang Li
- Department of Orthopedics, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, 430074, China
| | - Jinliang Li
- Department of Orthopedics, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, 430074, China
| | - Jinkui Cai
- Department of Orthopedics, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, 430074, China.
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Zhang Y, Xiao L, LYu L, Zhang L. Construction of a predictive model for bone metastasis from first primary lung adenocarcinoma within 3 cm based on machine learning algorithm: a retrospective study. PeerJ 2024; 12:e17098. [PMID: 38495760 PMCID: PMC10944632 DOI: 10.7717/peerj.17098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 02/21/2024] [Indexed: 03/19/2024] Open
Abstract
Background Adenocarcinoma, the most prevalent histological subtype of non-small cell lung cancer, is associated with a significantly higher likelihood of bone metastasis compared to other subtypes. The presence of bone metastasis has a profound adverse impact on patient prognosis. However, to date, there is a lack of accurate bone metastasis prediction models. As a result, this study aims to employ machine learning algorithms for predicting the risk of bone metastasis in patients. Method We collected a dataset comprising 19,454 cases of solitary, primary lung adenocarcinoma with pulmonary nodules measuring less than 3 cm. These cases were diagnosed between 2010 and 2015 and were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. Utilizing clinical feature indicators, we developed predictive models using seven machine learning algorithms, namely extreme gradient boosting (XGBoost), logistic regression (LR), light gradient boosting machine (LightGBM), Adaptive Boosting (AdaBoost), Gaussian Naive Bayes (GNB), multilayer perceptron (MLP) and support vector machine (SVM). Results The results demonstrated that XGBoost exhibited superior performance among the four algorithms (training set: AUC: 0.913; test set: AUC: 0.853). Furthermore, for convenient application, we created an online scoring system accessible at the following URL: https://www.xsmartanalysis.com/model/predict/?mid=731symbol=7Fr16wX56AR9Mk233917, which is based on the highest performing model. Conclusion XGBoost proves to be an effective algorithm for predicting the occurrence of bone metastasis in patients with solitary, primary lung adenocarcinoma featuring pulmonary nodules below 3 cm in size. Moreover, its robust clinical applicability enhances its potential utility.
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Affiliation(s)
- Yu Zhang
- Department of Thoracic Surgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Lixia Xiao
- Department of Thoracic Surgery, Feicheng Hospital Affiliated to Shandong First Medical University, Taian, Shandong, China
| | - Lan LYu
- Department of Plastic Surgery, Feicheng Hospital Affiliated to Shandong First Medical University, Taian, Shandong, China
| | - Liwei Zhang
- Department of Thoracic Surgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
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Li L, Xu Y, Wang Y, Zhang Q, Wang Y, Xu C. The Diagnostic and Prognostic Value of the Combination of Tumor M2-Pyruvate Kinase, Carcinoembryonic Antigen, and Cytokeratin 19 Fragment in Non-Small Cell Lung Cancer. Technol Cancer Res Treat 2024; 23:15330338241265983. [PMID: 39043046 PMCID: PMC11271166 DOI: 10.1177/15330338241265983] [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] [Indexed: 07/25/2024] Open
Abstract
Objective: Finding biomarkers related to non-small cell lung cancer (NSCLC) is helpful for the diagnosis and precise treatment of lung cancer. The relationship between serum tumor M2-pyruvate kinase (TuM2-PK), carcinoembryonic antigen (CEA), and cytokeratin 19 fragment (CYFRA21-1) and NSCLC was analyzed. Methods: The serum levels of TuM2-PK, CEA, and CYFRA21-1 in 184 patients with the NSCLC group, 60 patients with the benign lung disease (BLD) group, and 90 healthy controls (HC) group were detected. The levels of TuM2-PK were measured by using an enzyme-linked immunosorbent assay. The detection methods of CEA and CYFRA21-1 were electrochemiluminescence. The receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic value of TuM2-PK, CEA, and CYFRA21-1 on NSCLC. The Kaplan-Meier survival curve was drawn to evaluate the survival status in NSCLC patients with different serum levels of TuM2-PK, CEA, and CYFRA21-1. Results: Serum levels of TuM2-PK, CEA, and CYFRA21-1 in the NSCLC group were significantly higher than those in the BLD group and the HC group (P < .01). Serum levels of TuM2-PK, CEA, and CYFRA21-1 in NSCLC patients were associated with the tumor lymph node metastasis stage (P < .05), lymph node metastasis (P < .05), and distant metastasis (P < .05). The ROC curve showed that the area under the curve of serum levels of TuM2-PK, CEA, and CYFRA21-1 was 0.814, 0.638, and 0.719, respectively, and that the combination of the above 3 was 0.918. The Kaplan-Meier survival curve showed that the 1-, 3- and 5-year survival rate in NSCLC patients with positive TuM2-PK, CEA, and CYFRA21-1 was significantly lower than that in NSCLC patients with negative TuM2-PK, CEA, and CYFRA21-1, respectively (P < .05). Conclusions: Serum TuM2-PK, CEA, and CYFRA21-1 levels have high clinical values in the diagnosis of NSCLC, and can effectively judge the prognosis of patients.
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Affiliation(s)
- Li Li
- Department of Respiratory Medicine, Affiliated to Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Respiratory Medicine, Affiliated to Nanjing Chest Hospital, Southeast University, Nanjing, Jiangsu, China
| | - Yihan Xu
- Nanjing Ninghai High School, Nanjing, Jiangsu, China
| | - Yuchao Wang
- Department of Respiratory Medicine, Affiliated to Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Respiratory Medicine, Affiliated to Nanjing Chest Hospital, Southeast University, Nanjing, Jiangsu, China
| | - Qian Zhang
- Department of Respiratory Medicine, Affiliated to Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Respiratory Medicine, Affiliated to Nanjing Chest Hospital, Southeast University, Nanjing, Jiangsu, China
| | - Yan Wang
- Medical Imaging Department II, Affiliated to Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chunhua Xu
- Department of Respiratory Medicine, Affiliated to Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Respiratory Medicine, Affiliated to Nanjing Chest Hospital, Southeast University, Nanjing, Jiangsu, China
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Mobasseri M, Tarverdizadeh N, Mirghafourvand M, Salehi-Pourmehr H, Ostadrahimi A, Farshbaf-Khalili A. The role of bone turnover markers in screening low bone mineral density and their relationship with fracture risk in the postmenopausal period. JOURNAL OF RESEARCH IN MEDICAL SCIENCES : THE OFFICIAL JOURNAL OF ISFAHAN UNIVERSITY OF MEDICAL SCIENCES 2023; 28:54. [PMID: 37496649 PMCID: PMC10366982 DOI: 10.4103/jrms.jrms_612_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 03/09/2023] [Accepted: 03/22/2023] [Indexed: 07/28/2023]
Abstract
Background Using bone turnover marker (BTM) monitoring to identify "quick losers" who may develop osteoporosis in the coming years is one of the main challenges in clinical practice. This study was implemented to examine the association of BTMs with bone mineral density (BMD) as well as to determine their relationship with the fracture risk assessment tool (FRAX) in women in the postmenopausal period. Materials and Methods This study was observational cross-sectional research that was done on women between the ages of 50 and 65 who were in the postmenopausal period. A dual-energy X-ray absorptiometry was applied to select 120 eligible women with normal BMD and 120 women without normal BMD. BTMs were assessed using enzyme-linked immunosorbent assay. Osteoporosis's Odds Ratio (OR) was estimated using a confounder-adjusted logistic regression model. The area under curve was calculated for the differentiation of low BMD in the postmenopausal period through receiver-operator characteristic (ROC) curves. To assess the probability of major osteoporotic fracture and hip fracture for the future 10 years, FRAX was applied. Results Higher serum osteocalcin (OC) (OR: 1.134, 95% confidence interval [CI]: 1.086-1.184), osteopontin (OP) (OR: 1.180; 95%CI: 1.105-1.261), and alkaline phosphatase (ALP) (OR: 1.007; 95%CI: 1.001-1.144) concentrations were potential risk factors for developing low BMD in women after menopause. The area under curve (AUC) (95%CI) for OC, OP, and ALP was 0.75 (0.668-0.8130), 0.75 (0.685-0.812), and 0.602 (0.524-0.670), respectively. ROC analysis indicated that at the cut-off point of 16.28 ng/mL, sensitivity and specificity were 70.3% and 70.9%, respectively, for OC. Furthermore, at the cut-off point of 28.85 ng/mL, the sensitivity of 70.3% and specificity of 66.6% were obtained for OP. The serum OC and OP were significantly related to hip and major osteoporotic fractures (P < 0.05). Conclusion The higher serum concentration of OC, OP, and ALP had significant associations with lower BMD. These BTMs can be complementary tools and helpful in the postmenopausal period as measures for screening of bone loss and possible bone fracture.
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Affiliation(s)
- Majid Mobasseri
- Endocrine Reserach Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Nahid Tarverdizadeh
- Department of Midwifery, Faculty of Nursing and Midwifery, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mojgan Mirghafourvand
- Social Determinants of Health Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Hanieh Salehi-Pourmehr
- Research Center for Evidence-Based Medicine, Iranian EBM Centre: A Joanna Briggs Institute Center of Excellence, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Alireza Ostadrahimi
- Nutrition Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Azizeh Farshbaf-Khalili
- Physical Medicine and Rehabilitation Research Centre, Aging Research Institute, Tabriz University of Medical Sciences, Tabriz, Iran
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Jiang M, Chen P, Zhang X, Guo X, Gao Q, Ma L, Mei W, Zhang J, Zheng J. Metabolic phenotypes, serum tumor markers, and histopathological subtypes in predicting bone metastasis: analysis of 695 patients with lung cancer in China. Quant Imaging Med Surg 2023; 13:1642-1654. [PMID: 36915307 PMCID: PMC10006154 DOI: 10.21037/qims-22-741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 12/09/2022] [Indexed: 02/04/2023]
Abstract
Background Patients with lung cancer who develop bone metastasis (BM) generally have an adverse prognosis. Although several clinical models have been used to predict BM in patients with lung cancer, the results are unsatisfactory. In this retrospective study, we investigated the role of 18F-2-fluoro-2-deoxyglucose (FDG) metabolic activity, serum tumor markers, and histopathological subtypes in predicting BM in patients with lung cancer. Methods This study included 695 consecutive patients with lung cancer who underwent 18F-FDG positron emission tomography/computed tomography (PET/CT) and in whom serum tumor markers were detected prior to treatment. The maximum standardized uptake value of primary tumors (pSUVmax), metastatic lymph nodes (nSUVmax) and distant metastases (mSUVmax), 8 serum tumor markers [carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), squamous cell carcinoma-related antigen (SCCA), cytokeratin 19 fragment (CYFRA21-1), carbohydrate antigen (CA) 125, CA50, CA72-4, and ferritin], and histopathological subtypes were compared between patients with and without BM. Receiver operating characteristic (ROC) curve and multiple logistic regression analyses were performed to identify predictors of BM in patients with lung cancer. Results BM was identified in 133 (19.1%) patients and not in 562 (80.9%). Patients with BM had significantly higher pSUVmax, nSUVmax, and mSUVmax than did those without BM. High concentrations of 6 serum tumor markers (i.e., CEA, ferritin, NSE, CA50, CA125, and CYFRA21-1) were significantly associated with BM. There were significant differences in the proportion of histopathological subtypes between patients with and without BM (χ2=32.35; P<0.001). The area under ROC-derived curve based on metabolic parameters was 0.737 (95% CI: 0.644-0.829) and 0.884 (95% CI: 0.825-0.943) when combined with the 6 serum tumor markers and histopathological subtypes, respectively. Conclusions High pSUVmax, nSUVmax, and mSUVmax favor the presence of BM in patients with lung cancer, and serum tumor markers and histopathological subtypes are important factors for predicting BM in these patients.
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Affiliation(s)
- Maoqing Jiang
- Department of Radiology and PET/CT Center, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China.,Department of Nuclear Medicine, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Ping Chen
- Department of Nephrology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Xiaohui Zhang
- Department of Radiology and PET/CT Center, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Xiuyu Guo
- Department of Radiology and PET/CT Center, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Qiaoling Gao
- Department of Radiology and PET/CT Center, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Lijuan Ma
- Department of Radiology and PET/CT Center, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Weiqi Mei
- Department of Nuclear Medicine, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Jingfeng Zhang
- Department of Radiology and PET/CT Center, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Jianjun Zheng
- Department of Radiology and PET/CT Center, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
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Li W, Guo Z, Zou Z, Alswadeh M, Wang H, Liu X, Li X. Development and validation of a prognostic nomogram for bone metastasis from lung cancer: A large population-based study. Front Oncol 2022; 12:1005668. [PMID: 36249042 PMCID: PMC9561801 DOI: 10.3389/fonc.2022.1005668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/16/2022] [Indexed: 11/25/2022] Open
Abstract
Background Bone is one of the most common metastatic sites of advanced lung cancer, and the median survival time is significantly shorter than that of patients without metastasis. This study aimed to identify prognostic factors associated with survival and construct a practical nomogram to predict overall survival (OS) in lung cancer patients with bone metastasis (BM). Methods We extracted the patients with BM from lung cancer between 2011 and 2015 from the Surveillance, Epidemiology, and End Result (SEER) database. Univariate and multivariate Cox regressions were performed to identify independent prognostic factors for OS. The variables screened by multivariate Cox regression analysis were used to construct the prognostic nomogram. The performance of the nomogram was assessed by receiver operating characteristic (ROC) curve, concordance index (C-index), and calibration curves, and decision curve analysis (DCA) was used to assess its clinical applicability. Results A total of 7861 patients were included in this study and were randomly divided into training (n=5505) and validation (n=2356) cohorts using R software in a ratio of 7:3. Cox regression analysis showed that age, sex, race, grade, tumor size, histological type, T stage, N stage, surgery, brain metastasis, liver metastasis, chemotherapy and radiotherapy were independent prognostic factors for OS. The C-index was 0.723 (95% CI: 0.697-0.749) in the training cohorts and 0.738 (95% CI: 0.698-0.778) in the validation cohorts. The AUC of both the training cohorts and the validation cohorts at 3-month (0.842 vs 0.859), 6-month (0.793 vs 0.814), and 1-year (0.776 vs 0.788) showed good predictive performance, and the calibration curves also demonstrated the reliability and stability of the model. Conclusions The nomogram associated with the prognosis of BM from lung cancer was a reliable and practical tool, which could provide risk assessment and clinical decision-making for individualized treatment of patients.
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Affiliation(s)
- Weihua Li
- Department of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Artificial Joints Engineering and Technology Research Center of Jiangxi Province, Nanchang, China
| | - Zixiang Guo
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zehui Zou
- Department of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Artificial Joints Engineering and Technology Research Center of Jiangxi Province, Nanchang, China
| | - Momen Alswadeh
- Department of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Artificial Joints Engineering and Technology Research Center of Jiangxi Province, Nanchang, China
| | - Heng Wang
- Department of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Artificial Joints Engineering and Technology Research Center of Jiangxi Province, Nanchang, China
| | - Xuqiang Liu
- Department of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Artificial Joints Engineering and Technology Research Center of Jiangxi Province, Nanchang, China
- *Correspondence: Xuqiang Liu, ; Xiaofeng Li,
| | - Xiaofeng Li
- Department of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Artificial Joints Engineering and Technology Research Center of Jiangxi Province, Nanchang, China
- *Correspondence: Xuqiang Liu, ; Xiaofeng Li,
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Rodriguez-Merchan EC, Peleteiro-Pensado M. Newly Released Advances in the Molecular Mechanisms of Osseous Metastasis and Potential Therapeutic Strategies. THE ARCHIVES OF BONE AND JOINT SURGERY 2022; 10:741-755. [PMID: 36246026 PMCID: PMC9527427 DOI: 10.22038/abjs.2022.57856.2865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 02/15/2022] [Indexed: 01/24/2023]
Abstract
The appearance of bone metastases (BM) in individuals with advanced solid cancers (breast, prostate, lung) often worsens their quality of life and prognosis. Although none have been fully effective, several strategies have been used to combat BM. Hence, the need for new data that could be useful for treating bone metastasis. To this end, we reviewed the recent literature on the subject. About patients with prostate cancer, treatments with PIP5K1α inhibitors have been found to inhibit tumor invasion and metastasis, and G protein-coupled receptor class C group 5 member A (GPRC5A) could be a future therapeutic target. Regarding patients with breast cancer, we found the following: Asperolide A could be another curative drug; targeting transforming growth factor-beta (TGFβ) and bone morphogenetic protein (BMP) signaling pathways, along with osteoclast activity, could be a favorable therapeutic approach in the preclusion of osteolytic bone destruction; TRAF6 inhibitors such as 6877002 appear promising; aiming the BMP4-SMAD7 signaling axis is an innovative therapeutic approach; there is favorable proof for the plausible therapeutic utilization of bone aiming immunostimulatory MOF (BT-isMOF) nanoparticles, and inhibition of IL4R and macrophages could have therapeutic benefits. For lung cancer, the function of LIGHT in osteolytic osseous illness instigated by metastatic non-small cell lung cancer should be highlighted.
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Chai X, Yinwang E, Wang Z, Wang Z, Xue Y, Li B, Zhou H, Zhang W, Wang S, Zhang Y, Li H, Mou H, Sun L, Qu H, Wang F, Zhang Z, Chen T, Ye Z. Predictive and Prognostic Biomarkers for Lung Cancer Bone Metastasis and Their Therapeutic Value. Front Oncol 2021; 11:692788. [PMID: 34722241 PMCID: PMC8552022 DOI: 10.3389/fonc.2021.692788] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 08/30/2021] [Indexed: 12/25/2022] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide. Bone metastasis, which usually accompanies severe skeletal-related events, is the most common site for tumor distant dissemination and detected in more than one-third of patients with advanced lung cancer. Biopsy and imaging play critical roles in the diagnosis of bone metastasis; however, these approaches are characterized by evident limitations. Recently, studies regarding potential biomarkers in the serum, urine, and tumor tissue, were performed to predict the bone metastases and prognosis in patients with lung cancer. In this review, we summarize the findings of recent clinical research studies on biomarkers detected in samples obtained from patients with lung cancer bone metastasis. These markers include the following: (1) bone resorption-associated markers, such as N-terminal telopeptide (NTx)/C-terminal telopeptide (CTx), C-terminal telopeptide of type I collagen (CTx-I), tartrate-resistant acid phosphatase isoform 5b (TRACP-5b), pyridinoline (PYD), and parathyroid hormone related peptide (PTHrP); (2) bone formation-associated markers, including total serum alkaline phosphatase (ALP)/bone specific alkaline phosphatase(BAP), osteopontin (OP), osteocalcin (OS), amino-terminal extension propeptide of type I procollagen/carboxy-terminal extension propeptide of type I procollagen (PICP/PINP); (3) signaling markers, including epidermal growth factor receptor/Kirsten rat sarcoma/anaplastic lymphoma kinase (EGFR/KRAS/ALK), receptor activator of nuclear factor κB ligand/receptor activator of nuclear factor κB/osteoprotegerin (RANKL/RANK/OPG), C-X-C motif chemokine ligand 12/C-X-C motif chemokine receptor 4 (CXCL12/CXCR4), complement component 5a receptor (C5AR); and (4) other potential markers, such as calcium sensing receptor (CASR), bone sialoprotein (BSP), bone morphogenetic protein 2 (BMP2), cytokeratin 19 fragment/carcinoembryonic antigen (CYFRA/CEA), tissue factor, cell-free DNA, long non-coding RNA, and microRNA. The prognostic value of these markers is also investigated. Furthermore, we listed some clinical trials targeting hotspot biomarkers in advanced lung cancer referring for their therapeutic effects.
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Affiliation(s)
- Xupeng Chai
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Eloy Yinwang
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Zenan Wang
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Zhan Wang
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Yucheng Xue
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Binghao Li
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Hao Zhou
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Wenkan Zhang
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Shengdong Wang
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Yongxing Zhang
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Hengyuan Li
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Haochen Mou
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Lingling Sun
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Hao Qu
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Fangqian Wang
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Zengjie Zhang
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Tao Chen
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
| | - Zhaoming Ye
- Department of Orthopedics, Musculoskeletal Tumor Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.,Institute of Orthopedic Research, Zhejiang University, Hangzhou, China
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Xie S, Wu Z, Qi Y, Wu B, Zhu X. The metastasizing mechanisms of lung cancer: Recent advances and therapeutic challenges. Biomed Pharmacother 2021; 138:111450. [PMID: 33690088 DOI: 10.1016/j.biopha.2021.111450] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/25/2021] [Accepted: 02/27/2021] [Indexed: 12/12/2022] Open
Abstract
Lung cancer is one of the common malignant tumors that threaten human life with serious incidence and high mortality. According to the histopathological characteristics, lung cancer is mainly divided into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). NSCLC accounts for about 80-85% of lung cancers. In fact, lung cancer metastasis is a major cause of treatment failure in clinical patients. The underlying reason is that the mechanisms of lung cancer metastasis are still not fully understood. The metastasis of lung cancer cells is controlled by many factors, including the interaction of various components in the lung cancer microenvironment, epithelial-mesenchymal transition (EMT) transformation, and metastasis of cancer cells through blood vessels and lymphatics. The molecular relationships are even more intricate. Further study on the mechanisms of lung cancer metastasis and in search of effective therapeutic targets can bring more reference directions for clinical drug research and development. This paper focuses on the factors affecting lung cancer metastasis and connects with related molecular mechanisms of the lung cancer metastasis and mechanisms of lung cancer to specific organs, which mainly reviews the latest research progress of NSCLC metastasis. Besides, in this paper, experimental models of lung cancer and metastasis, mechanisms in SCLC transfer and the challenges about clinical management of lung cancer are also discussed. The review is intended to provide reference value for the future research in this field and promising treatment clues for clinical patients.
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Affiliation(s)
- Shimin Xie
- Guangdong Key Laboratory for Research and Development of Natural Drugs, The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
| | - Zhengguo Wu
- Department of Thoracic Surgery, Yantian District People's Hospital, Shenzhen, China
| | - Yi Qi
- Guangdong Key Laboratory for Research and Development of Natural Drugs, The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, China
| | - Binhua Wu
- Guangdong Key Laboratory for Research and Development of Natural Drugs, The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, China.
| | - Xiao Zhu
- Guangdong Key Laboratory for Research and Development of Natural Drugs, The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, China; The Key Lab of Zhanjiang for R&D Marine Microbial Resources in the Beibu Gulf Rim, Guangdong Medical University, Zhanjiang, China.
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12
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Wu S, Pan Y, Mao Y, Chen Y, He Y. Current progress and mechanisms of bone metastasis in lung cancer: a narrative review. Transl Lung Cancer Res 2021; 10:439-451. [PMID: 33569325 PMCID: PMC7867745 DOI: 10.21037/tlcr-20-835] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Lung cancer is a kind of malignant tumor with rapid progression and poor prognosis. Distant metastasis has been the main cause of mortality among lung cancer patients. Bone is one of the most common sites. Among all lung cancer patients with bone metastasis, most of them are osteolytic metastasis. Some serious clinical consequences like bone pain, pathological fractures, spinal instability, spinal cord compression and hypercalcemia occur as well. Since the severity of bone metastasis in lung cancer, it is undoubtedly necessary to know how lung cancer spread to bone, how can we diagnose it and how can we treat it. Here, we reviewed the process, possible mechanisms, diagnosis methods and current treatment of bone metastasis in lung cancer. We divided the process of bone metastasis in lung cancer into three steps: tumor invasion, tumor cell migration and invasion in bone tissue. It may be influenced by genetic factors, microenvironment and other adhesion-related factors. Imaging examination, laboratory examination, and pathological examination are used to diagnose lung cancer metastasis to bone. Surgery, radiotherapy, targeted therapy, bisphosphonate, radiation therapy and chemotherapy are the common clinical treatment methods currently. We also found some problems remained to be solved. For example, drugs for skeletal related events mainly target on osteoclasts at present, which increase the ratio of patients in osteoporosis and fractures in the long term. In all, this review provides the direction for future research on bone metastasis in lung cancer.
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Affiliation(s)
- Shengyu Wu
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China.,Medical School, Tongji University, Shanghai, China
| | - Yue Pan
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China.,Medical School, Tongji University, Shanghai, China
| | - Yanyu Mao
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China.,Medical School, Tongji University, Shanghai, China
| | - Yu Chen
- Spine Center, Orthopedic department, Shanghai Changzheng Hospital, Shanghai, China
| | - Yayi He
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China
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