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Xu T, Liu X, Liu C, Chen Z, Ma F, Fan D. Development and validation of a nomogram for predicting the overall survival in non-small cell lung cancer patients with liver metastasis. Transl Cancer Res 2023; 12:3061-3073. [PMID: 38130305 PMCID: PMC10731345 DOI: 10.21037/tcr-23-899] [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: 05/25/2023] [Accepted: 09/28/2023] [Indexed: 12/23/2023]
Abstract
Background Among all metastatic lesions in non-small cell lung cancer (NSCLC), liver metastasis (LM) is the most lethal site with a median survival of less than 5 months. Few studies exclusively report on prognostic factors for these unique patients. We aimed to construct and validate a practical model to predict the prognosis of NSCLC patients with LM. Methods Cases of NSCLC with LM diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database, and were randomly split into training and validation cohort (7:3). The overall survival (OS) was measured from diagnosis until date of death or last follow-up. Cox regression analyses were performed to identify potential predictors of the model. A nomogram incorporating those independent factors was constructed and validated by the concordance index (C-index) and calibration plots. The decision curve analysis (DCA) and a risk stratification system were used to evaluate its clinical value. Results A total of 2,367 cases were selected for analysis and randomized to the training cohort (n=1,677) and the validation cohort (n=690). The patients were mainly male (59.3%), married (83.1%) and White (77.3%). Apart from LM, 54.2%, 26.7%, and 36.7% of patients also present with bone, brain, and lung metastases, respectively. The median follow-up was 4.0 months for all patients and 23 months for alive cases. The median OS was 5 months [interquartile range (IQR), 2-11 months]. Sex, age, race, grade, T stage, bone metastasis, brain metastasis, surgery, and chemotherapy were identified as the independent risk factors of the OS and used to develop the nomogram. The calibration curves exhibited excellent agreement between the predicted and actual survival in both the training and validation set, with a C-index of 0.700 [95% confidence interval (CI): 0.684-0.716] and 0.677 (95% CI: 0.653-0.701), respectively. The DCA and the risk classification system further supported that the prediction model was clinically effective. Conclusions This is the first study to build a prediction model for NSCLC patients with LM. It aids in treatment decisions, focused care, and physician-patient communication. The global prospective data is needed to further improve this model.
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Affiliation(s)
- Tian Xu
- Department of Oncology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xianling Liu
- Department of Oncology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Chaoyuan Liu
- Department of Oncology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Zui Chen
- Department of Oncology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Fang Ma
- Department of Oncology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Dan Fan
- Department of Oncology, The Second Xiangya Hospital of Central South University, Changsha, China
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Ren Y, Qian S, Xu G, Cai Z, Zhang N, Wang Z. Predicting survival of patients with bone metastasis of unknown origin. Front Endocrinol (Lausanne) 2023; 14:1193318. [PMID: 38027105 PMCID: PMC10658782 DOI: 10.3389/fendo.2023.1193318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Purpose Bone metastasis of unknown origin is a rare and challenging situation, which is infrequently reported. Therefore, the current study was performed to analyze the clinicopathologic features and risk factors of survival among patients with bone metastasis of unknown origin. Patients and methods We retrospectively analyzed the clinical data for patients with bone metastasis of unknown origin between 2010 and 2016 based on the Surveillance, Epidemiology, and End Results (SEER) database. Overall survival (OS) and cancer-specific survival (CSS) were first analyzed by applying univariable Cox regression analysis. Then, we performed multivariable analysis to confirm independent survival predictors. Results In total, we identified 1224 patients with bone metastasis of unknown origin for survival analysis, of which 704 males (57.5%) and 520 females (42.5%). Patients with bone metastasis of unknown origin had a 1-year OS rate of 14.50% and CSS rate of 15.90%, respectively. Race, brain metastasis, liver metastasis, radiotherapy, and chemotherapy were significant risk factors of OS on both univariable and multivariable analyses (p <0.05). As for CSS, both univariable and multivariable analyses revealed that no brain metastasis, no liver metastasis, radiotherapy, and chemotherapy were associated with increased survival (p <0.05). Conclusion Patients with bone metastasis of unknown origin experienced an extremely poor prognosis. Radiotherapy and chemotherapy were beneficial for prolonging the survival of those patients.
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Affiliation(s)
- Ying Ren
- Department of Nursing, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Orthopedics Research Institute of Zhejiang University, Hangzhou, China
- Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, China
- Zhejiang Provincial Clinical Medical Research Center for Motor System Diseases, Hangzhou, China
- International Chinese Musculoskeletal Research Society, Hangzhou, China
| | - Shengjun Qian
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Orthopedics Research Institute of Zhejiang University, Hangzhou, China
- Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, China
- Zhejiang Provincial Clinical Medical Research Center for Motor System Diseases, Hangzhou, China
- International Chinese Musculoskeletal Research Society, Hangzhou, China
| | - Guoping Xu
- Department of Nursing, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Orthopedics Research Institute of Zhejiang University, Hangzhou, China
- Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, China
- Zhejiang Provincial Clinical Medical Research Center for Motor System Diseases, Hangzhou, China
- International Chinese Musculoskeletal Research Society, Hangzhou, China
| | - Zhenhai Cai
- Department of Orthopedics Surgery, The Second Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Ning Zhang
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Orthopedics Research Institute of Zhejiang University, Hangzhou, China
- Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, China
- Zhejiang Provincial Clinical Medical Research Center for Motor System Diseases, Hangzhou, China
- International Chinese Musculoskeletal Research Society, Hangzhou, China
| | - Zhan Wang
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Orthopedics Research Institute of Zhejiang University, Hangzhou, China
- Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, China
- Zhejiang Provincial Clinical Medical Research Center for Motor System Diseases, Hangzhou, China
- International Chinese Musculoskeletal Research Society, Hangzhou, China
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Jiang L, Tong Y, Jiang J, Zhao D. Individualized assessment predictive models for risk and overall survival in elderly patients of primary kidney cancer with bone metastases: A large population-based study. Front Med (Lausanne) 2023; 10:1127625. [PMID: 37181371 PMCID: PMC10167023 DOI: 10.3389/fmed.2023.1127625] [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: 12/19/2022] [Accepted: 04/06/2023] [Indexed: 05/16/2023] Open
Abstract
Background Elderly people are at high risk of metastatic kidney cancer (KC), and, the bone is one of the most common metastatic sites for metastatic KC. However, studies on diagnostic and prognostic prediction models for bone metastases (BM) in elderly KC patients are still vacant. Therefore, it is necessary to establish new diagnostic and prognostic nomograms. Methods We downloaded the data of all KC patients aged more than 65 years during 2010-2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses were used to study independent risk factors of BM in elderly KC patients. Univariate and multivariate Cox regression analysis for the study of independent prognostic factors in elderly KCBM patients. Survival differences were studied using Kaplan-Meier (K-M) survival analysis. The predictive efficacy and clinical utility of nomograms were assessed by receiver operating characteristic (ROC) curve, the area under curve (AUC), calibration curve, and decision curve analysis (DCA). Results A final total of 17,404 elderly KC patients (training set: n = 12,184, validation set: n = 5,220) were included to study the risk of BM. 394 elderly KCBM patients (training set: n = 278, validation set: n = 116) were included to study the overall survival (OS). Age, histological type, tumor size, grade, T/N stage and brain/liver/lung metastasis were identified as independent risk factors for developing BM in elderly KC patients. Surgery, lung/liver metastasis and T stage were identified as independent prognostic factors in elderly KCBM patients. The diagnostic nomogram had AUCs of 0.859 and 0.850 in the training and validation sets, respectively. The AUCs of the prognostic nomogram in predicting OS at 12, 24 and 36 months were: training set (0.742, 0.775, 0.787), and validation set (0.721, 0.827, 0.799), respectively. The calibration curve and DCA also showed excellent clinical utility of the two nomograms. Conclusion Two new nomograms were constructed and validated to predict the risk of developing BM in elderly KC patients and 12-, 24-, and 36-months OS in elderly KCBM patients. These models can help surgeons provide more comprehensive and personalized clinical management programs for this population.
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Affiliation(s)
| | | | | | - Dongxu Zhao
- Department of Orthopedics, The China-Japan Union Hospital of Jilin University, Changchun, Jilin, 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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