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Gao T, Chang Y, Yue H. Association of log odds of positive lymph nodes with survival in patients with small cell lung cancer: Results from the SEER database. Clinics (Sao Paulo) 2024; 79:100369. [PMID: 38696974 PMCID: PMC11070598 DOI: 10.1016/j.clinsp.2024.100369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/25/2024] [Accepted: 04/14/2024] [Indexed: 05/04/2024] Open
Abstract
OBJECTIVES The prognosis of patients with Small Cell Lung Cancer (SCLC) can be predicted by their Lymph Node (LN) status. The authors aimed to assess the correlations between SCLC survival and number of LN Ratio (LNR), positive LN (pLNs), and Logarithmic Odds of positive LN (LODDS). METHODS This cohort study retrospectively included 1,762 patients with SCLC from the SEER database 2004‒2015. The X-tile software was used to determine the cutoff values for pLNs, LNR, and LODDS. The correlations between pLNs, LNR, and LODDS with Overall Survival (OS) and Cancer-Specific Survival (CSS) were explored using Cox regression analysis. The study used the C-index to assess the predictive value of LNR, pLNs, and LODDS on survival. RESULTS Among these 1,762 patients, 121 (6.87%) were alive, 1,641 (93.13%) died, and 1,532 (86.95%) died of SCLC. In univariable COX analysis, LNR, pLNs, and LODDS all showed a correlation with CSS and OS (p < 0.05). In multivariable COX analysis, only patients with LODDS (> 0.3 vs. ≤ 0.3) were related to both worse OS (HR = 1.28, 95% CI 1.10‒1.50) and CSS (HR = 1.29, 95% CI 1.10‒1.51), but no correction was observed between LNR and pLNs and survival (p > 0.05). The C-indices for predicting OS for LODDS were 0.552 (95% CI 0.541‒0.563), for LNR 0.504 (95% CI 0.501‒0.507), and for pLNs 0.527 (95% CI 0.514‒0.540). Moreover, the association between LODDS and prognosis in SCLC patients was significant only in patients with LN stage N1 and N2, but not in stage N3. CONCLUSION LODDS may be better than other LN assessment tools at predicting survival in SCLC patients.
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Affiliation(s)
- Ting Gao
- The First Clinical Medical College of Lanzhou University, Gansu, P.R. China; Department of Respiratory and Critical Care Medicine, Xianyang Central Hospital, Shaanxi, P.R. China
| | - Yingxuan Chang
- The First Clinical Medical College of Lanzhou University, Gansu, P.R. China
| | - Hongmei Yue
- The First Clinical Medical College of Lanzhou University, Gansu, P.R. China.
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He C, Ni M, Liu J, Teng X, Ke L, Matsuura Y, Okuda K, Sakairi Y, Cheng J, Yu L, Lv W, Hu J. A survival nomogram model for patients with resectable non-small cell lung cancer and lymph node metastasis (N1 or N2) based on the Surveillance, Epidemiology, and End Results Database and single-center data. Transl Lung Cancer Res 2024; 13:573-586. [PMID: 38601448 PMCID: PMC11002513 DOI: 10.21037/tlcr-24-119] [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: 02/01/2024] [Accepted: 03/01/2024] [Indexed: 04/12/2024]
Abstract
Background The ability to predict survival in patients with lymph node metastasis has long been elusive. After surgery, the basis for decision-making on the combination treatment of patients is not clear. The purpose of this study was thus to build a survival nomogram model to effectively predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC) and lymph node metastasis. The number of dissected lymph nodes (NDLN), number of positive lymph nodes (NPLN), lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS) were included in this study to determine the risk factors in patients with advanced NSCLC. Methods The data of 5,132 patients with NSCLC and lymph node metastasis (N1 or N2) were extracted from the Surveillance, Epidemiology, and End Results (SEER) database according to inclusion and exclusion criteria and used as the training cohort. We enrolled 117 patients from the First Affiliated Hospital, Zhejiang University School of Medicine as the external validation cohort. Receiver operating characteristic (ROC) analyses were performed to determine the best cutoff values for predicting the prognosis of patients with NSCLC. Based on the risk factors affecting prognosis, a nomogram was constructed using univariate and multivariate Cox proportional hazard regression models. The discrimination ability of the nomogram was evaluated with the concordance index (C-index) and calibration curves. For the independent risk factors, survival curves were drawn using Kaplan-Meier analysis. Results ROC curve analysis showed that the optimal NPLN cut-off value was 4, LNR was 0.26, and LODDS was -0.25, respectively. However, LNR was nonsignificant in multivariate analysis, with a P value of 0.274. The novel survival nomogram model included seven independent risk factors, among which were NPLN, LODDS, and chemotherapy. Model 4, which included N stage, NPLN, and LODDS, had a higher likelihood ratio (LR) and C-index than did the other models. The C-index was 0.648 [95% confidence interval (CI): 0.636-0.659] in the training cohort and 0.807 (95% CI: 0.751-0.863) in the external validation cohort, showing good prognostic accuracy and discrimination ability. According to the median risk score, the patients in the training cohort and external validation cohort were divided into high-risk and low-risk groups, between which significant differences in OS were found. In the training cohort, age, sex, T stage, N stage, NPLN, LODDS, and chemotherapy were significantly associated with OS (P<0.001). In the external validation cohort, T stage, NPLN, LODDS, and chemotherapy were found to be correlated with OS. Conclusions The NPLN and LODDS nomogram is an accurate survival prediction tool for patients with N1 or N2 NSCLC. Patients with lymph node metastasis can benefit from chemotherapy, but no evidence shows that radiotherapy is necessary for patients with resectable NSCLC.
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Affiliation(s)
- Cheng He
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Miaoqi Ni
- Echocardiography and Vascular Ultrasound Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiacong Liu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Teng
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lei Ke
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yosuke Matsuura
- Department of Thoracic Surgical Oncology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Katsuhiro Okuda
- Department of Thoracic and Pediatric Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Yuichi Sakairi
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Jun Cheng
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Li Yu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wang Lv
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jian Hu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Clinical Evaluation Technology for Medical Device of Zhejiang Province, Hangzhou, China
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Xie W, Lin P, Li Z, Wan H, Liang F, Fan J, Deng L, Huang X. The prognostic value of lymphatic metastatic size in head and neck squamous cell carcinoma. Eur Arch Otorhinolaryngol 2024; 281:387-395. [PMID: 37682351 DOI: 10.1007/s00405-023-08199-z] [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: 06/26/2023] [Accepted: 08/16/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND Lymphatic metastatic size was proved to predict prognosis in different types of carcinomas, except in head and neck squamous cell carcinoma (HNSCC) located in hypopharynx, oropharynx and supraglottic region et al. The aim of this study is to evaluate the prognostic value of lymphatic metastatic size in HNSCC, which may guide clinical decision-making in practice. METHODS From 2008 to 2022, 171 patients, who were diagnosed as HNSCC in our center, were included. The demographic data, clinicopathological characteristics and lymphatic metastatic size were recorded and analyzed using the Kaplan-Meier method and Cox regression analysis. RESULTS Among 171 patients, 107 cases were hypopharyngeal cancer, 38 cases supraglottic cancer and 26 cases oropharyngeal cancer. The median of lymphatic metastatic size was 8 mm (range 0-46). According to lymphatic metastatic size, the patients were assigned to three subgroups: Group I (0 mm), Group II ( ≤ 10 mm) and Group III (> 10 mm). Kaplan-Meier analysis with log rank test revealed that Group I and Group II had similar locoregional control rate, distant metastasis free probability, disease-free survival and overall survival (all p > 0.05), whereas Group III had significant worse prognosis. Adjusted for demographic and other clinicopathological characteristics, lymphatic metastatic size was an independent predictor of disease-free survival and overall survival in HNSCC. CONCLUSIONS Lymphatic metastatic size was an independently prognostic factor in HNSCC, which may assist in postoperative adjuvant treatment decisions.
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Affiliation(s)
- Wenqian Xie
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
- Department of Otolaryngology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
| | - Peiliang Lin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
- Department of Otolaryngology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
| | - Zhijuan Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
- Pathology Department, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
| | - Huan Wan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
- Cellular and Molecular Diagnostics Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
| | - Faya Liang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
- Department of Otolaryngology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
| | - Jianming Fan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
- Department of Otolaryngology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
| | - Lanlan Deng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
- Department of Otolaryngology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China
| | - Xiaoming Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China.
- Department of Otolaryngology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Yingfeng Road, Guangzhou, 510289, Guangdong, China.
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Shi J, Peng B, Wang C, Zhou X, Lu T, Xu R, Chang X, Shen Z, Wang K, Xu C, Zhang L. Development and validation of a nomogram for predicting overall survival of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy. J Cancer Res Clin Oncol 2023; 149:11779-11790. [PMID: 37407846 DOI: 10.1007/s00432-023-05073-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 06/29/2023] [Indexed: 07/07/2023]
Abstract
INTRODUCTION Currently, the prognosis of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy is poor. The goal of this research was to develop and validate a novel nomogram for exactly predicting the overall survival (OS) of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy. METHODS The data applied in our research were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. We divided selected data into a training cohort and a validation cohort using R software, with a ratio of 7:3. Univariate Cox regression and multivariate Cox regression were utilized to select significant variables to build the nomogram. To validate our nomogram, calibration curves, receiver operating characteristic curves (ROC), decision curve analysis (DCA), and Kaplan-Meier survival curves were employed. The nomogram model was also compared with the tumor-node-metastasis (TNM) staging system by utilizing net reclassification index (NRI) and integrated discrimination improvement (IDI). RESULTS Eight variables-age, sex, operative type, LN removed number, chemotherapy, AJCC stage, M stage, histology-were statistically significant in the multivariate Cox regression analysis and were selected to develop our nomogram. Based on ROC curves, calibration curves, and DCA analysis, our novel nomogram demonstrated good predictive accuracy and clinical utility. Using Kaplan-Meier (KM) survival curves and log-rank tests, the risk stratification system was able to stratify patients based on their estimated mortality risk. The nomogram performed better than the TNM staging system based on the NRI and IDI indexes. CONCLUSIONS We developed and validated a nomogram to predict prognosis of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy. Using this nomogram, clinicians may find this nomogram useful in predicting OS of targeted patients and making more appropriate treatment decisions.
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Affiliation(s)
- Jiaxin Shi
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China
| | - Bo Peng
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China
| | - Chenghao Wang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China
| | - Xiang Zhou
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China
| | - Tong Lu
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China
| | - Ran Xu
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China
| | - Xiaoyan Chang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China
| | - Zhiping Shen
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China
| | - Kaiyu Wang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China
| | - Chengyu Xu
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China
| | - Linyou Zhang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China.
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Luo L, Tan Y, Zhao S, Yang M, Che Y, Li K, Liu J, Luo H, Jiang W, Li Y, Wang W. The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer. BMC Cancer 2023; 23:496. [PMID: 37264319 DOI: 10.1186/s12885-023-10990-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/21/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis. METHODS Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve. RESULTS From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients. CONCLUSIONS This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.
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Affiliation(s)
- Liping Luo
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yubo Tan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shixuan Zhao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Man Yang
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yurou Che
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Kezhen Li
- School of Medicine, Southwest Medical University, Luzhou, China
| | - Jieke Liu
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Huaichao Luo
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Wenjun Jiang
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yongjie Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Weidong Wang
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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Wang S, Wei J, Guo Y, Xu Q, Lv X, Yu Y, Liu M. Construction and validation of nomograms based on the log odds of positive lymph nodes to predict the prognosis of lung neuroendocrine tumors. Front Immunol 2022; 13:987881. [PMID: 36211370 PMCID: PMC9539638 DOI: 10.3389/fimmu.2022.987881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/11/2022] [Indexed: 11/18/2022] Open
Abstract
Background This research aimed to investigate the predictive performance of log odds of positive lymph nodes (LODDS) for the long-term prognosis of patients with node-positive lung neuroendocrine tumors (LNETs). Methods We collected 506 eligible patients with resected N1/N2 classification LNETs from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The study cohort was split into derivation cohort (n=300) and external validation cohort (n=206) based on different geographic regions. Nomograms were constructed based on the derivation cohort and validated using the external validation cohort to predict the 1-, 3-, and 5-year cancer-specific survival (CSS) and overall survival (OS) of patients with LNETs. The accuracy and clinical practicability of nomograms were tested by Harrell’s concordance index (C-index), integrated discrimination improvement (IDI), net reclassification improvement (NRI), calibration plots, and decision curve analyses. Results The Cox proportional-hazards model showed the high LODDS group (-0.79≤LODDS) had significantly higher mortality compared to those in the low LODDS group (LODDS<-0.79) for both CSS and OS. In addition, age at diagnosis, sex, histotype, type of surgery, radiotherapy, and chemotherapy were also chosen as predictors in Cox regression analyses using stepwise Akaike information criterion method and included in the nomograms. The values of C-index, NRI, and IDI proved that the established nomograms were better than the conventional eighth edition of the TNM staging system. The calibration plots for predictions of the 1-, 3-, and 5-year CSS/OS were in excellent agreement. Decision curve analyses showed that the nomograms had value in terms of clinical application. Conclusions We created visualized nomograms for CSS and OS of LNET patients, facilitating clinicians to bring individually tailored risk assessment and therapy.
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Affiliation(s)
- Suyu Wang
- Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Juan Wei
- Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yibin Guo
- Department of Health Statistics, Naval Medical University, Shanghai, China
| | - Qiumeng Xu
- Department of Orthopaedics, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Xin Lv
- Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yue Yu
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
- *Correspondence: Meiyun Liu, ; Yue Yu,
| | - Meiyun Liu
- Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Meiyun Liu, ; Yue Yu,
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