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Kong C, Yin X, Zou J, Ma C, Liu K. The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung. BMC Cancer 2024; 24:454. [PMID: 38605303 PMCID: PMC11010275 DOI: 10.1186/s12885-024-12158-0] [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: 01/11/2024] [Accepted: 03/21/2024] [Indexed: 04/13/2024] Open
Abstract
OBJECTIVE To explore the value of six machine learning models based on PET/CT radiomics combined with EGFR in predicting brain metastases of lung adenocarcinoma. METHODS Retrospectively collected 204 patients with lung adenocarcinoma who underwent PET/CT examination and EGFR gene detection before treatment from Cancer Hospital Affiliated to Shandong First Medical University in 2020. Using univariate analysis and multivariate logistic regression analysis to find the independent risk factors for brain metastasis. Based on PET/CT imaging combined with EGFR and PET metabolic indexes, established six machine learning models to predict brain metastases of lung adenocarcinoma. Finally, using ten-fold cross-validation to evaluate the predictive effectiveness. RESULTS In univariate analysis, patients with N2-3, EGFR mutation-positive, LYM%≤20, and elevated tumor markers(P<0.05) were more likely to develop brain metastases. In multivariate Logistic regression analysis, PET metabolic indices revealed that SUVmax, SUVpeak, Volume, and TLG were risk factors for lung adenocarcinoma brain metastasis(P<0.05). The SVM model was the most efficient predictor of brain metastasis with an AUC of 0.82 (PET/CT group),0.70 (CT group),0.76 (PET group). CONCLUSIONS Radiomics combined with EGFR machine learning model as a new method have higher accuracy than EGFR mutation alone. SVM model is the most effective method for predicting brain metastases of lung adenocarcinoma, and the prediction efficiency of PET/CT group is better than PET group and CT group.
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Affiliation(s)
- Chao Kong
- Department of Graduate, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Xiaoyan Yin
- Department of Graduate, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Jingmin Zou
- Department of Graduate, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Changsheng Ma
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China.
| | - Kai Liu
- Department of Head and Neck Comprehensive Radiotherapy, Affiliated Tumor Hospital of Xinjiang Medical University, 830000, Urumqi, China.
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Shang J, Tang Y, Ran B, Wu B, Li Y, Cheng Y, Guo B, Gong J, Wang L, Ling X, Xu H. Predictive value of metabolic parameters derived from preoperative 18F-FDG positron emission tomography/computed tomography for brain metastases in patients with surgically resected non-small cell lung cancer. Quant Imaging Med Surg 2023; 13:8545-8556. [PMID: 38106281 PMCID: PMC10722012 DOI: 10.21037/qims-23-385] [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] [Received: 04/02/2023] [Accepted: 10/16/2023] [Indexed: 12/19/2023]
Abstract
Background Brain metastases (BMs) are common complications in patients with non-small cell lung cancer (NSCLC). The purpose of this study was to investigate whether the metabolic parameters derived from preoperative 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) can predict BM development in patients with surgically resected NSCLC. Methods We retrospectively reviewed 128 consecutive patients with stage I-IIIA NSCLC who underwent 18F-FDG PET/CT before curative surgery at The First Affiliated Hospital of Jinan University between November 2012 and October 2021. By drawing a volume of interest (VOI), the maximum standardized uptake values (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary tumor as well as the mean SUV (SUVmean) of the liver and arterial blood were measured. The tumor-to-liver SUV ratio (TLR) and tumor-to-blood SUV ratio (TBR) were also calculated. Receiver operating characteristic curve analysis was used to determine the best cut-off values for positron emission tomography (PET) parameters to predict BM-free survival, and Cox proportional hazards regression analysis was used to assess the predictive value of clinical variables and PET parameters. Results The median follow-up duration for survival patients was 23.4 months, and 15 patients (11.7%) experienced BM as the initial relapse site. The cumulative rates of BM over the course of 1, 2, and 5 years were 4.5%, 10.5%, and 17.5%, respectively. The optimal cut-off values for the prediction of BM-free survival were 7.7, 4.9, and 4.5 for SUVmax, TLR, and TBR, and 5.5 mL and 16.1 for MTV and TLG, respectively. In the Cox proportional hazards model, the risk of BM was significantly associated with TLR [hazard ratio (HR) =10.712; 95% confidence interval (CI): 2.958-38.801; P<0.001] and MTV (HR =3.150; 95% CI: 0.964-10.293; P=0.020) after adjusting for tumor stage, clinicopathological factors, and other PET parameters. Conclusions Preoperative TLR and MTV of the primary tumor may be helpful in predicting BM development in patients with surgically resected NSCLC. Tumor metabolic parameters may potentially be used to stratify the risk of BM and determine individualized surveillance strategies.
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Shang J, You H, Dong C, Li Y, Cheng Y, Tang Y, Guo B, Gong J, Ling X, Xu H. Predictive value of baseline metabolic tumor burden on 18F-FDG PET/CT for brain metastases in patients with locally advanced non-small-cell lung cancer. Front Oncol 2022; 12:1029684. [PMID: 36387169 PMCID: PMC9643834 DOI: 10.3389/fonc.2022.1029684] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/12/2022] [Indexed: 11/23/2023] Open
Abstract
OBJECTIVES Brain metastases (BMs) are a major cause leading to the failure of treatment management for non-small-cell lung cancer (NSCLC) patients. The purpose of this study was to evaluate the predictive value of baseline metabolic tumor burden on 18F-FDG PET/CT measured with metabolic tumor volume (MTV) and total lesion glycolysis (TLG) for brain metastases (BMs) development in patients with locally advanced non-small-cell lung cancer (NSCLC) after treatment. METHODS Forty-seven patients with stage IIB-IIIC NSCLC who underwent baseline 18F-FDG PET/CT examinations were retrospectively reviewed. The maximum standardized uptake value (SUVmax), MTV, and TLG of the primary tumor (SUVmaxT, MTVT, and TLGT), metastatic lymph nodes (SUVmaxN, MTVN, and TLGN), and whole-body tumors (SUVmaxWB, MTVWB, and TLGWB) were measured. The optimal cut-off values of PET parameters to predict brain metastasis-free survival were obtained using Receiver operating characteristic (ROC) analysis, and the predictive value of clinical variables and PET parameters were evaluated using Cox proportional hazards regression analysis. RESULTS The median follow-up duration was 25.0 months for surviving patients, and 13 patients (27.7%) developed BM. The optimal cut-off values were 21.1 mL and 150.0 g for MTVT and TLGT, 20.0, 10.9 mL and 55.6 g for SUVmaxN, MTVN and TLGN, and 27.9, 27.4 mL and 161.0 g for SUVmaxWB, MTVWB and TLGWB, respectively. In the Cox proportional hazards models, the risk of BM was significantly associated with MTVN and MTVWB or TLGN and TLGWB after adjusting for histological cell type, N stage, SUVmaxN, and SUVmaxWB. CONCLUSIONS Baseline metabolic tumor burden (MTV and TLG) evaluated from the level of metastatic lymph nodes and whole-body tumors are significant predictive factors for BM development in patients with locally advanced NSCLC.
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Affiliation(s)
- Jingjie Shang
- Department of Nuclear Medicine and Positron Emission Tomography (PET)/Computed Tomography (CT)-Magnetic Resonance Imaging (MRI) Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Huimin You
- Department of Endocrinology, The Fifth Affiliated Hospital of GuangZhou Medical University, Guangzhou, China
| | - Chenchen Dong
- Department of Nuclear Medicine and Positron Emission Tomography (PET)/Computed Tomography (CT)-Magnetic Resonance Imaging (MRI) Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yingxin Li
- Department of Nuclear Medicine and Positron Emission Tomography (PET)/Computed Tomography (CT)-Magnetic Resonance Imaging (MRI) Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yong Cheng
- Department of Nuclear Medicine and Positron Emission Tomography (PET)/Computed Tomography (CT)-Magnetic Resonance Imaging (MRI) Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yongjin Tang
- Department of Nuclear Medicine and Positron Emission Tomography (PET)/Computed Tomography (CT)-Magnetic Resonance Imaging (MRI) Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Bin Guo
- Department of Nuclear Medicine and Positron Emission Tomography (PET)/Computed Tomography (CT)-Magnetic Resonance Imaging (MRI) Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jian Gong
- Department of Nuclear Medicine and Positron Emission Tomography (PET)/Computed Tomography (CT)-Magnetic Resonance Imaging (MRI) Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xueying Ling
- Department of Nuclear Medicine and Positron Emission Tomography (PET)/Computed Tomography (CT)-Magnetic Resonance Imaging (MRI) Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hao Xu
- Department of Nuclear Medicine and Positron Emission Tomography (PET)/Computed Tomography (CT)-Magnetic Resonance Imaging (MRI) Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
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孙 爽, 门 玉, 惠 周. [Research Progress on Risk Factors of Brain Metastasis in Non-small Cell Lung Cancer]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2022; 25:193-200. [PMID: 35340162 PMCID: PMC8976204 DOI: 10.3779/j.issn.1009-3419.2022.101.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/16/2022] [Accepted: 01/18/2022] [Indexed: 11/05/2022]
Abstract
Brain metastasis of non-small cell lung cancer (NSCLC) is a common treatment failure mode, and the median survival time of NSCLC patients with brain metastasis is only 1 mon-2 mon. Prophylactic cranial irradiation (PCI) can delay the occurrence of brain metastasis, but the survival benefits of NSCLC patients are still controversial. It is particularly important to identify the patients who are most likely to benefit from PCI. This article reviews the high risk factors of brain metastasis in NSCLC.
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Affiliation(s)
- 爽 孙
- 100021 北京,国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院,北京协和医学院肿瘤医院放疗科Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - 玉 门
- 100021 北京,国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院,北京协和医学院肿瘤医院放疗科Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
- 100021 北京,国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院,特需医疗部Department of VIP Medical Services, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - 周光 惠
- 100021 北京,国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院,北京协和医学院肿瘤医院放疗科Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
- 100021 北京,国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院,特需医疗部Department of VIP Medical Services, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Sun S, Men Y, Kang J, Sun X, Yuan M, Yang X, Bao Y, Wang J, Deng L, Wang W, Zhai Y, Liu W, Zhang T, Wang X, Bi N, Lv J, Liang J, Feng Q, Chen D, Xiao Z, Zhou Z, Wang L, Hui Z. A Nomogram for Predicting Brain Metastasis in IIIA-N2 Non-Small Cell Lung Cancer After Complete Resection: A Competing Risk Analysis. Front Oncol 2021; 11:781340. [PMID: 34966684 PMCID: PMC8710765 DOI: 10.3389/fonc.2021.781340] [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] [Received: 09/22/2021] [Accepted: 11/16/2021] [Indexed: 11/16/2022] Open
Abstract
Background Brain metastasis (BM) is one of the most common failure patterns of pIIIA-N2 non-small cell lung cancer (NSCLC) after complete resection. Prophylactic cranial irradiation (PCI) can improve intracranial control but not overall survival. Thus, it is particularly important to identify the risk factors that are associated with BM and subsequently provide instructions for selecting patients who will optimally benefit from PCI. Methods and Materials Between 2011 and 2014, patients with pIIIA-N2 NSCLC who underwent complete resection in our institution were reviewed and enrolled in the study. Clinical characteristics, pathological parameters, treatment mode, BM time, and overall survival were analyzed. A nomogram was built based on the corresponding parameters by Fine and Gray’s competing risk analysis to predict the 1-, 3-, and 5-year probabilities of BM. Receiver operating characteristic curves and calibration curves were chosen for validation. A statistically significant difference was set as P <0.05. Results A total of 517 patients were enrolled in our retrospective study. The median follow-up time for surviving patients was 53.2 months (range, 0.50–123.17 months). The median age was 57 (range, 25–80) years. Of the 517 patients, 122 (23.6%) had squamous cell carcinoma, 391 (75.6%) received adjuvant chemotherapy, and 144 (27.3%) received post-operative radiotherapy. The 1-, 3-, and 5-year survival rates were 94.0, 72.9, and 66.0%, respectively. The 1-, 3-, and 5-year BM rates were 5.4, 15.7, and 22.2%, respectively. According to the univariate analysis, female, non-smokers, patients with non-squamous cell carcinoma, bronchial invasion, perineural invasion, and patients who received adjuvant chemotherapy were more likely to develop BM. In a multivariate analysis, non-squamous cell carcinoma (subdistribution hazard ratios, SHR: 3.968; 95% confidence interval, CI: 1.743–9.040; P = 0.0010), bronchial invasion (SHR: 2.039, 95% CI: 1.325–3.139; P = 0.0012), perineural invasion (SHR: 2.514, 95% CI: 1.058–5.976; P = 0.0370), and adjuvant chemotherapy (SHR: 2.821, 95% CI: 1.424–5.589; P = 0.0030) were independent risk factors for BM. A nomogram model was established based on the final multivariable analysis result. The area under the curve was 0.767 (95% CI, 0.758–0.777). Conclusions For patients with IIIA-N2 NSCLC after complete resection, a nomogram was established based on clinicopathological factors and treatment patterns for predicting the BM. Based on this nomogram, patients with a high risk of BM who may benefit from PCI can be screened.
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Affiliation(s)
- Shuang Sun
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Men
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Very Important Person (VIP) Medical Services, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingjing Kang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xin Sun
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meng Yuan
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xu Yang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yongxing Bao
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianyang Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lei Deng
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenqing Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yirui Zhai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenyang Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tao Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xin Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Nan Bi
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jima Lv
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jun Liang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qinfu Feng
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dongfu Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zefen Xiao
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zongmei Zhou
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Luhua Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Radiation Oncology, National Cancer Center/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Zhouguang Hui
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Very Important Person (VIP) Medical Services, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Identification of a high-risk group for brain metastases in non-small cell lung cancer patients. J Neurooncol 2021; 155:101-106. [PMID: 34546499 DOI: 10.1007/s11060-021-03849-w] [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: 07/28/2021] [Accepted: 09/16/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE Identification of a high-risk group of brain metastases (BM) in patients with non-small cell lung cancer (NSCLC) could lead to early interventions and probably better prognosis. The objective of the study was to identify this group by generating a multivariable model with recognized and accessible risk factors. METHODS A retrospective cohort from patients seen at a single center during 2010-2020, was divided into a training (TD) and validation (VD) datasets, associations with BM were measured in the TD with logit, variables significantly associated were used to generate a multivariate model. Model´s performance was measured with the AUC/C-statistic, Akaike information criterion, and Brier score. RESULTS From 570 patients with NSCLC who met the strict eligibility criteria a TD and VD were randomly assembled, no significant differences were found amid both datasets. Variables associated with BM in the multivariate logit analyses were age [P 0.001, OR 0.96 (95% CI 0.93-0.98)]; mutational status positive [P 0.027, OR 1.96 (95% CI 1.07-3.56); and carcinoembryonic antigen levels [P 0.016, OR 1.001 (95% CI 1.000-1.003). BM were diagnosed in 24% of the whole cohort. Stratification into a high-risk group after simplification of the model, displayed a frequency of BM of 63% (P < 0.001). CONCLUSION A multivariate model comprising age, carcinoembryonic antigen levels, and mutation status allowed the identification of a truly high-risk group of BM in NSCLC patients.
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Gao QP, Yang DZ, Yuan ZB, Guo YX. Prognostic factors and its predictive value in patients with metastatic spinal cancer. World J Clin Cases 2021; 9:5470-5478. [PMID: 34307601 PMCID: PMC8281408 DOI: 10.12998/wjcc.v9.i20.5470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 04/25/2021] [Accepted: 04/26/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The spine is the most common location of metastatic diseases. Treating a metastatic spinal tumor depends on many factors, including patients' overall health and life expectancy. The present study was conducted to investigate prognostic factors and clinical outcomes in patients with vertebral metastases. AIM To investigate prognostic factors and their predictive value in patients with metastatic spinal cancer. METHODS A retrospective analysis of 109 patients with metastatic spinal cancer was conducted between January 2015 and September 2017. The prognoses and survival were analyzed, and the effects of factors such as clinical features, treatment methods, primary lesions and affected spinal segments on the prognosis of patients with metastatic spinal cancer were discussed. The prognostic value of Frankel spinal cord injury functional classification scale, metastatic spinal cord compression (MSCC), spinal instability neoplastic score (SINS) and the revised Tokuhashi score for prediction of prognosis was explored in patients with metastatic spinal tumors. RESULTS Age, comorbidity of metastasis from elsewhere, treatment methods, the number of spinal tumors, patient's attitude toward tumors and Karnofsky performance scale score have an effect on the prognosis of patients (all P < 0.05). With respect to classification of spinal cord injury, before operation, the proportion of grade B and grade C was higher in the group of patients who died than in the group of patients who survived, and that of grade D and grade E was lower in the group of patients who died than in the group of patients who survived (all P < 0.05). At 1 mo after operation, the proportion of grade A, B and C was higher in the group of patients who died than in the group of patients who survived, and that of grade E was lower in patients in the group of patients who died than in the group of patients who survived (all P < 0.05). MSCC occurred in four (14.3%) patients in the survival group and 17 (21.0%) patients in the death group (P < 0.05). All patients suffered from intractable pain, dysfunction in spinal cord and even paralysis. The proportion of SINS score of 1 to 6 points was lower in the death group than in the survival group, and the proportion of SINS score of 7 to 12 points was higher in the death group than in the survival group (all P < 0.05). The proportion of revised Tokuhashi score of 0 to 8 points and 9 to 11 points were higher in the death group than in the survival group, and the proportion of revised Tokuhashi score of 12 to 15 points was lower in the death group than in the survival group (all P < 0.05). Frankel spinal cord injury functional classification scale, MSCC, SINS and revised Tokuhashi score were important factors influencing the surgical treatment of patients with metastatic spinal cancer (all P < 0.05). CONCLUSION Frankel spinal cord injury functional classification scale, MSCC, SINS and revised Tokuhashi score were helpful in predicting the prognosis of patients with metastatic spinal cancer.
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Affiliation(s)
- Qing-Peng Gao
- Department of Spine Surgery, The Second Clinical Medical College of Jinan University, Shenzhen 518020, Guangdong Province, China
| | - Da-Zhi Yang
- Department of Spine Surgery, Shenzhen People’s Hospital, 2nd Clinical Medical College of Jinan University, Shenzhen 518020, Guangdong Province, China
| | - Zheng-Bin Yuan
- Department of Spine Surgery, Shenzhen People’s Hospital, 2nd Clinical Medical College of Jinan University, Shenzhen 518020, Guangdong Province, China
| | - Yu-Xia Guo
- Department of Gynecology, Shenzhen People’s Hospital, 2nd Clinical Medical College of Jinan University, Shenzhen 518020, Guangdong Province, China
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