1
|
He Y, Huang L, Deng J, Zhong Y, Chen T, She Y, Jiang L, Zhao D, Xie D, Jiang G, Bongiolatti S, Antonoff MB, Petersen RH, Chen C. Predicting complication risks after sleeve lobectomy for non-small cell lung cancer. Transl Lung Cancer Res 2024; 13:1318-1330. [PMID: 38973957 PMCID: PMC11225058 DOI: 10.21037/tlcr-24-325] [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: 04/11/2024] [Accepted: 06/21/2024] [Indexed: 07/09/2024]
Abstract
Background Sleeve lobectomy is a challenging procedure with a high risk of postoperative complications. To facilitate surgical decision-making and optimize perioperative treatment, we developed risk stratification models to quantify the probability of postoperative complications after sleeve lobectomy. Methods We retrospectively analyzed the clinical features of 691 non-small cell lung cancer (NSCLC) patients who underwent sleeve lobectomy between July 2016 and December 2019. Logistic regression models were trained and validated in the cohort to predict overall complications, major complications, and specific minor complications. The impact of specific complications in prognostic stratification was explored via the Kaplan-Meier method. Results Of 691 included patients, 232 (33.5%) developed complications, including 35 (5.1%) and 197 (28.5%) patients with major and minor complications, respectively. The models showed robust discrimination, yielding an area under the receiver operating characteristic (ROC) curve (AUC) of 0.853 [95% confidence interval (CI): 0.705-0.885] for predicting overall postoperative complication risk and 0.751 (95% CI: 0.727-0.762) specifically for major complication risks. Models predicting minor complications also achieved good performance, with AUCs ranging from 0.78 to 0.89. Survival analyses revealed a significant association between postoperative complications and poor prognosis. Conclusions Risk stratification models could accurately predict the probability and severity of complications in NSCLC patients following sleeve lobectomy, which may inform clinical decision-making for future patients.
Collapse
Affiliation(s)
- Yiming He
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lin Huang
- Department of Cardiothoracic Surgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Jiajun Deng
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yifan Zhong
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Tao Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lei Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Deping Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | | | - Mara B. Antonoff
- Department of Thoracic & Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - René Horsleben Petersen
- Department of Cardiothoracic Surgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| |
Collapse
|
2
|
Qian H, Yang Z, Cai L, Chen H. Conditional survival of elderly primary central nervous system lymphoma. J Cancer Res Clin Oncol 2023; 149:13391-13401. [PMID: 37491638 DOI: 10.1007/s00432-023-05200-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 07/21/2023] [Indexed: 07/27/2023]
Abstract
BACKGROUND Recent studies have reported that overall survival of elderly patients with primary central nervous system lymphoma (PCNSL), who have the highest incidence of this disease, had failed to benefit from the advancements in treatment strategies over the past decades. This highlights the necessity for intensified research to guide treatment decisions for this specific patient population. METHODS The Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute (NCI) was used to extract data of elderly PCNSL patients (age ≥ 60) who were divided into training and validation groups at the ratio of 7:3, for our analysis. Conditional survival [CS(y|x)] was defined as the probability at survival additional y years given that the patient had not died of PCNSL at a specified period of time (x years) after initial diagnosis. The CS pattern of elderly PCNSL patients was analyzed. The least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were applied to develop a novel CS-based nomogram. RESULTS A total of 3315 elderly patients diagnosed with CNS lymphoma between 2000 and 2019 were extracted from the SEER database, of whom 2320 patients were divided into the training group and 995 into the internal validation group. CS analysis revealed a noteworthy escalation in the 5-year survival rate among elderly PCNSL patients for every additional year of survival. The rates progressed from an initial 21-49%, 63%, and 75%, culminating in an impressive 88% and the survival improvement over time was nonlinear. The LASSO regression identified nine predictors and multivariate Cox regression was used to successfully construct the CS-based nomogram model with favorable prediction performance. CONCLUSION CS of elderly PCNSL patients was dynamic and increased over time. Our newly-established CS-based nomogram can provide a real-time dynamic survival estimation, allowing clinicians to better guide treatment decision for these patients.
Collapse
Affiliation(s)
- Hui Qian
- Department of Neurosurgery, The Central Hospital Affiliated to Shaoxing University, Zhejiang Province, Shaoxing City, China
| | - Zhihao Yang
- Department of Neurosurgery, The Central Hospital Affiliated to Shaoxing University, Zhejiang Province, Shaoxing City, China
| | - Linqiang Cai
- Department of Neurosurgery, The Central Hospital Affiliated to Shaoxing University, Zhejiang Province, Shaoxing City, China
| | - Huawei Chen
- Department of Neurosurgery, The Central Hospital Affiliated to Shaoxing University, Zhejiang Province, Shaoxing City, China.
| |
Collapse
|
3
|
Yu X, Wang F, Yang L, Ma K, Guo X, Wang L, Du L, Yu X, Lin S, Xiao H, Sui Z, Zhang L, Yu Z. Development and validation of web-based dynamic nomograms predictive of disease-free and overall survival in patients who underwent pneumonectomy for primary lung cancer. PeerJ 2023; 11:e15938. [PMID: 37637160 PMCID: PMC10448881 DOI: 10.7717/peerj.15938] [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: 05/25/2023] [Accepted: 07/31/2023] [Indexed: 08/29/2023] Open
Abstract
Background The tumour-node-metastasis (TNM) staging system is insufficient to precisely distinguish the long-term survival of patients who underwent pneumonectomy for primary lung cancer. Therefore, this study sought to identify determinants of disease-free (DFS) and overall survival (OS) for incorporation into web-based dynamic nomograms. Methods The clinicopathological variables, surgical methods and follow-up information of 1,261 consecutive patients who underwent pneumonectomy for primary lung cancer between January 2008 and December 2018 at Sun Yat-sen University Cancer Center were collected. Nomograms for predicting DFS and OS were built based on the significantly independent predictors identified in the training cohort (n = 1,009) and then were tested on the validation cohort (n = 252). The concordance index (C-index) and time-independent area under the receiver-operator characteristic curve (AUC) assessed the nomogram's discrimination accuracy. Decision curve analysis (DCA) was applied to evaluate the clinical utility. Results During a median follow-up time of 40.5 months, disease recurrence and death were observed in 446 (35.4%) and 665 (52.7%) patients in the whole cohort, respectively. In the training cohort, a higher C-reactive protein to albumin ratio, intrapericardial pulmonary artery ligation, lymph node metastasis, and adjuvant therapy were significantly correlated with a higher risk for disease recurrence; similarly, the independent predictors for worse OS were intrapericardial pulmonary artery and vein ligation, higher T stage, lymph node metastasis, and no adjuvant therapy. In the validation cohort, the integrated DFS and OS nomograms showed well-fitted calibration curves and yielded good discrimination powers with C-index of 0.667 (95% confidence intervals CIs [0.610-0.724]) and 0.697 (95% CIs [0.649-0.745]), respectively. Moreover, the AUCs for 1-year, 3-year, and 5-year DFS were 0.655, 0.726, and 0.735, respectively, and those for 3-year, 5-year, and 10-year OS were 0.741, 0.765, and 0.709, respectively. DCA demonstrated that our nomograms could bring more net benefit than the TNM staging system. Conclusions Although pneumonectomy for primary lung cancer has brought encouraging long-term outcomes, the constructed prediction models could assist in precisely identifying patients at high risk and developing personalized treatment strategies to further improve survival.
Collapse
Affiliation(s)
- Xiangyang Yu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | - Feng Wang
- Department of Minimally Invasive Surgery, Beijing Chest Hospital, Capital Medical University; Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, Beijing, China
| | - Longjun Yang
- Department of Thoracic Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Kai Ma
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | - Xiaotong Guo
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | - Lixu Wang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | - Longde Du
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | - Xin Yu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | - Shengcheng Lin
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | - Hua Xiao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | - Zhilin Sui
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | - Lanjun Zhang
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Zhentao Yu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| |
Collapse
|
4
|
Xu C, Pei D, Liu Y, Guo J, Liu N, Wang Q, Yu Y, Kang Z. Clinical characteristics and prostate-cancer-specific mortality of competitive risk nomogram in the second primary prostate cancer. Front Oncol 2023; 13:918324. [PMID: 37260974 PMCID: PMC10229042 DOI: 10.3389/fonc.2023.918324] [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: 04/12/2022] [Accepted: 03/09/2023] [Indexed: 06/02/2023] Open
Abstract
Background With the development of early diagnosis and treatment, the second primary malignancy (SPM) attracts increasing attention. The second primary prostate cancer (spPCa) is an important class of SPM, but remains poorly understood. Methods We retrospectively analyzed 3,322 patients with spPCa diagnosed between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Chi-square test was applied to compare demographic and clinical variables and analyze causes of death. Multivariate competitive risk regression model was used to identify risk factors associated with prostate-cancer-specific mortality (PCSM), and these factors were enrolled to build a nomogram of competitive risk. The C-index, calibration curve, and decision curve analysis (DCA) were employed to evaluate the discrimination ability of our nomogram. Results The median follow-up (interquartile range, IQR) time was 47 (24-75) months, and the median (IQR) diagnosis interval between the first primary cancer (FPC) and spPCa was 32 (16-57) months. We found that the three most common sites of SPM were the urinary system, digestive system, and skin. Through multivariate competitive risk analysis, we enrolled race (p < 0.05), tumor-node-metastasis (TNM) stage (p < 0.001), Gleason score (p < 0.05), surgery (p = 0.002), and radiotherapy (p = 0.032) to construct the model to predict the outcomes of spPCa. The C-index was 0.856 (95% CI, 0.813-0.899) and 0.905 (95% CI, 0.941-0.868) in the training and validation set, respectively. Moreover, both the calibration curve and DCA illustrated that our nomogram performed well in predicting PCSM. Conclusion In conclusion, we identified four risk factors associated with the prognosis of spPCa and construct a competing risk nomogram, which performed well in predicting the 3-, 5-, and 10-year PCSM.
Collapse
|
5
|
Cheng M, Lin R, Bai N, Zhang Y, Wang H, Guo M, Duan X, Zheng J, Qiu Z, Zhao Y. Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer. Clin Radiol 2023; 78:e377-e385. [PMID: 36914457 DOI: 10.1016/j.crad.2022.12.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/14/2022] [Accepted: 12/20/2022] [Indexed: 01/15/2023]
Abstract
AIM To develop and validate a nomogram model that combines computed tomography (CT)-based radiological factors extracted from deep-learning and clinical factors for the early predictions of immune checkpoint inhibitor-related pneumonitis (ICI-P). MATERIALS AND METHODS Forty ICI-P patients and 101 patients without ICI-P were divided randomly into the training (n=113) and test (n=28) sets. The convolution neural network (CNN) algorithm was used to extract the CT-based radiological features of predictable ICI-P and calculated the CT score of each patient. A nomogram model to predict the risk of ICI-P was developed by logistic regression. RESULTS CT score was calculated from five radiological features extracted by the residual neural network-50-V2 with feature pyramid networks. Four predictors of ICI-P in the nomogram model included a clinical feature (pre-existing lung diseases), two serum markers (absolute lymphocyte count and lactate dehydrogenase), and a CT score. The area under curve of the nomogram model in the training (0.910 versus 0.871 versus 0.778) and test (0.900 versus 0.856 versus 0.869) sets was better than the radiological and clinical models. The nomogram model showed good consistency and better clinical practicability. CONCLUSION The nomogram model that combined CT-based radiological factors and clinical factors can be used as a new non-invasive tool for the early prediction of ICI-P in lung cancer patients after immunotherapy with low cost and low manual input.
Collapse
Affiliation(s)
- M Cheng
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - R Lin
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China
| | - N Bai
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China
| | - Y Zhang
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - H Wang
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - M Guo
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - X Duan
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - J Zheng
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Z Qiu
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China
| | - Y Zhao
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China.
| |
Collapse
|
6
|
Effects of Neoadjuvant Radiotherapy on Survival in Patients with Stage IIIA-N2 Non-Small-Cell Lung Cancer Following Pneumonectomy. J Clin Med 2022; 11:jcm11237188. [PMID: 36498762 PMCID: PMC9738364 DOI: 10.3390/jcm11237188] [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: 10/25/2022] [Revised: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
Background: Pneumonectomy is a drastic but sometimes inevitable treatment option for patients with non-small-cell lung cancer (NSCLC) to improve their chances for long-term survival. However, the optimal adjuvant radiotherapy used for patients with N2 NSCLC following pneumonectomy remains unclear in the literature. Methods: T1-4N0-2M0 NSCLC patients registered in the Surveillance, Epidemiology, and End Results database were retrospectively analyzed. Propensity score matching was applied to balance the assignment of patients. Cox proportional hazards models and Kaplan−Meier analyses were used to identify the factors related to overall survival rates. Restricted cubic splines were used to detect the possible nonlinear dependency of the relationship between the risk of survival and age. Results: A total of 4308 NSCLC patients were enrolled in this study. In N2 patients, the long-term outcome of the chemotherapy and postoperative radiotherapy groups was the worst (p = 0.014). Subgroup analyses showed that the influence of age on survival outcome was confined to patients who received chemotherapy and neoadjuvant radiotherapy (p = 0.004). Meanwhile, patients >65 years of age who received chemotherapy and neoadjuvant radiotherapy had significantly worse prognoses than those in the chemotherapy group (p = 0.005). Conclusions: Our results show that neoadjuvant radiotherapy may have potential benefits in patients aged ≤ 65 years who are scheduled for pneumonectomy, but not in elderly patients.
Collapse
|
7
|
Fu K, Lei M, Wu LS, Shi JC, Yang SY, Yang WQ, Xu JY, Kang YN, Yang ZY, Zhang X, Huang KN, Han C, Tian Y, Zhang Y. Triage by PAX1 and ZNF582 methylation in women with cervical intraepithelial neoplasia grade 3: a multicenter case-control study. Open Forum Infect Dis 2022; 9:ofac013. [PMID: 35402629 PMCID: PMC8988013 DOI: 10.1093/ofid/ofac013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/12/2022] [Indexed: 11/19/2022] Open
Abstract
Background The colposcopy-conization inconsistency is common in women with cervical intraepithelial neoplasia grade 3 (CIN3). No adequate method has been reported to identify the final pathology of conization. In this study, we explored the ability of PAX1 and ZNF582 methylation to predict the pathological outcome of conization in advance. Methods This was a multicenter study and included 277 histologically confirmed CIN3 women who underwent cold knife conization (CKC) from January 2019 to December 2020. The methylation levels of PAX1 (PAX1m) and ZNF582 (ZNF582m) were determined by quantitative methylation-specific polymerase chain reaction (qMSP) and expressed in ΔCp. Receiver operating characteristic curves were used to evaluate predictive accuracy. Results The final pathological results in 48 (17.33%) patients were inflammation or low-grade squamous intraepithelial lesion (LSIL), 190 (68.59%) were high-grade squamous intraepithelial lesion (HSIL), and 39 (14.08%) were squamous cervical cancer (SCC). PAX1m and ZNF582m increased as lesions progressed from inflammation/LSIL, HSIL, to SCC. PAX1 and ZNF582 methylation yielded better prediction performance compared with common screening strategies, whether individually or combined. A 4.33-fold increase in the probability of inflammation/LSIL was observed in patients with lower ZNF582 methylation levels (ΔCpZNF582 ≥ 19.18). A 6.53-fold increase in SCC risk was observed in patients with elevated ZNF582 methylation (ΔCpZNF582 < 7.09). Conclusions DNA methylation would be an alternative screening method to triage and predict the final outcome of conization in CIN3 cases.
Collapse
Affiliation(s)
- Kun Fu
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha, China
- Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Ming Lei
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha, China
- Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Li-Sha Wu
- Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Institute of medical sciences, Xiangya Hospital, Central South University, Changsha, China
| | - Jing-Cheng Shi
- Department of Epidemiology and Biostatistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Si-Yu Yang
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha, China
- Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Wen-Qing Yang
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha, China
- Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jin-Yun Xu
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha, China
- Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Ya-Nan Kang
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhen-Ying Yang
- Department of Gynecology, The Central Hospital of Yongzhou, University of South China, Yongzhou, China
| | - Xuan Zhang
- Department of Gynecology, Chenzhou No.1 People’s Hospital, Xiangnan University, Chenzhou, China
| | - Kang-Ni Huang
- Department of Gynecology, Yiyang Central Hospital, Yiyang, China
| | - Chi Han
- Department of Gynecology, Xiangtan Central Hospital, Xiangtan, China
| | - Yan Tian
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha, China
- Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yu Zhang
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha, China
- Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|
8
|
Wang Z, Hu F, Chang R, Yu X, Xu C, Liu Y, Wang R, Chen H, Liu S, Xia D, Chen Y, Ge X, Zhou T, Zhang S, Pang H, Fang X, Zhang Y, Li J, Hu K, Cai Y. Development and Validation of a Prognostic Model to Predict Overall Survival for Lung Adenocarcinoma: A Population-Based Study From the SEER Database and the Chinese Multicenter Lung Cancer Database. Technol Cancer Res Treat 2022; 21:15330338221133222. [PMID: 36412085 PMCID: PMC9706045 DOI: 10.1177/15330338221133222] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/15/2022] [Accepted: 09/29/2022] [Indexed: 10/31/2023] Open
Abstract
Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer (NSCLC). The aim of our study was to determine prognostic risk factors and establish a novel nomogram for lung adenocarcinoma patients. Methods: This retrospective cohort study is based on the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese multicenter lung cancer database. We selected 22,368 eligible LUAD patients diagnosed between 2010 and 2015 from the SEER database and screened them based on the inclusion and exclusion criteria. Subsequently, the patients were randomly divided into the training cohort (n = 15,657) and the testing cohort (n = 6711), with a ratio of 7:3. Meanwhile, 736 eligible LUAD patients from the Chinese multicenter lung cancer database diagnosed between 2011 and 2021 were considered as the validation cohort. Results: We established a nomogram based on each independent prognostic factor analysis for 1-, 3-, and 5-year overall survival (OS) . For the training cohort, the area under the curves (AUCs) for predicting the 1-, 3-, and 5-year OS were 0.806, 0.856, and 0.886. For the testing cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.804, 0.849, and 0.873. For the validation cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.86, 0.874, and 0.861. The calibration curves were observed to be closer to the ideal 45° dotted line with regard to 1-, 3-, and 5-year OS in the training cohort, the testing cohort, and the validation cohort. The decision curve analysis (DCA) plots indicated that the established nomogram had greater net benefits in comparison with the Tumor-Node-Metastasis (TNM) staging system for predicting 1-, 3-, and 5-year OS of lung adenocarcinoma patients. The Kaplan-Meier curves indicated that patients' survival in the low-risk group was better than that in the high-risk group (P < .001). Conclusion: The nomogram performed very well with excellent predictive ability in both the US population and the Chinese population.
Collapse
Affiliation(s)
- Zhiqiang Wang
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Fan Hu
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Ruijie Chang
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Xiaoyue Yu
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Chen Xu
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Yujie Liu
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Rongxi Wang
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Hui Chen
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Shangbin Liu
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Danni Xia
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Yingjie Chen
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Xin Ge
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Tian Zhou
- Dongfang
Hospital, Beijing University of Chinese
Medicine, Beijing, China
| | - Shuixiu Zhang
- Dongfang
Hospital, Beijing University of Chinese
Medicine, Beijing, China
| | - Haoyue Pang
- Dongfang
Hospital, Beijing University of Chinese
Medicine, Beijing, China
| | - Xueni Fang
- Dongfang
Hospital, Beijing University of Chinese
Medicine, Beijing, China
| | - Yushuang Zhang
- The Fourth
Hospital of Hebei Medical University,
Shijiazhuang, China
| | - Jin Li
- The Fourth
Hospital of Hebei Medical University,
Shijiazhuang, China
| | - Kaiwen Hu
- Dongfang
Hospital, Beijing University of Chinese
Medicine, Beijing, China
| | - Yong Cai
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| |
Collapse
|