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Qi Q, Zhu M, Li P, Mi Q, Xie Y, Li J, Wang C. Systematic analysis of PANoptosis-related genes identifies XIAP as a functional oncogene in breast cancer. Gene 2024; 912:148355. [PMID: 38467314 DOI: 10.1016/j.gene.2024.148355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 03/02/2024] [Accepted: 03/07/2024] [Indexed: 03/13/2024]
Abstract
BACKGROUND Breast cancer (BC) is the most prevalent malignant disease affecting women globally. PANoptosis, a novel form of cell death combining features of pyroptosis, apoptosis, and necroptosis, has recently gained attention. However, its precise function in BC and the predictive values of PANoptosis-related genes remain unclear. METHODS We used the expression data and clinical information of BC tissues or normal breast tissues from public databases, and then successfully developed and verified a BC PANoptosis-related risk model through a combination of univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and Kaplan-Meier (KM) analysis. A nomogram was constructed to estimate survival probability, and its accuracy was assessed using calibration curves. RESULTS Among 37 PANoptosis-related genes, we identified 4 differentially expressed genes related to overall survival (OS). Next, a risk model incorporating these four PANoptosis-related genes was established. Patients were stratified into low/high-risk groups based on the median risk score, with the low-risk group showing better prognoses and higher levels of immune infiltration. Utilizing the risk score and clinical features, we developed a nomogram to predict 1-, 3- and 5-year survival probability. X-linked inhibitor of apoptosis protein (XIAP) emerged as a potentially risky factor with the highest hazard ratio. In vitro experiments demonstrated that XIAP inhibition enhances the antitumor effect of doxorubicin through the PANoptosis pathway. CONCLUSION PANoptosis holds an important role in BC prognosis and treatment.
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Affiliation(s)
- Qiuchen Qi
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan 250033, PR China; Shandong Engineering & Technology Research Center for Tumor Marker Detection, Jinan 250033, PR China
| | - Mengqian Zhu
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan 250033, PR China
| | - Peilong Li
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan 250033, PR China; Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan 250033, PR China
| | - Qi Mi
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan 250033, PR China
| | - Yan Xie
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan 250033, PR China
| | - Juan Li
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan 250033, PR China; Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan 250033, PR China.
| | - Chuanxin Wang
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan 250033, PR China; Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan 250033, PR China; Shandong Provincial Key Laboratory of Innovation Technology in Laboratory Medicine, Jinan 250033, PR China.
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Xi Y, Dong H, Wang M, Chen S, Han J, Liu M, Jiang F, Ding Z. Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics. Eur J Radiol Open 2024; 12:100543. [PMID: 38235439 PMCID: PMC10793089 DOI: 10.1016/j.ejro.2023.100543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/13/2023] [Accepted: 12/26/2023] [Indexed: 01/19/2024] Open
Abstract
Purpose The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC). Methods In a retrospective analysis, we included clinical and MRI data from 313 patients diagnosed with primary NPC. Patient classification into progressive and non-progressive categories relied on the occurrence of recurrence or distant metastasis within a 5-year timeframe. Initial screening comprised clinical features and statistically significant image semantic features. Subsequently, MRI radiomics features were extracted from all patients, and optimal features were selected to formulate the Rad-Score.Combining Rad-Score, image semantic features, and clinical features to establish a combined model Evaluation of predictive efficacy was conducted using ROC curves and nomogram specific to NPC progression. Lastly, employing the optimal ROC cutoff value from the combined model, patients were dichotomized into high-risk and low-risk groups, facilitating a comparison of 10-year overall survival (OS) between the groups. Results The combined model showcased superior predictive performance for NPC progression, reflected by AUC values of 0.84, an accuracy rate of 81.60%, sensitivity at 0.77, and specificity at 0.81 within the training group. In the test set, the AUC value reached 0.81, with an accuracy of 74.6%, sensitivity at 0.82, and specificity at 0.66. Conclusion The amalgamation of Rad-Score, clinical, and imaging semantic features from multi-parameter MRI exhibited significant promise in prognosticating 5-year PFS for non-distant metastatic NPC patients. The combined model provided quantifiable data for informed and personalized diagnosis and treatment planning.
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Affiliation(s)
- Yuzhen Xi
- Department of Radiology, 903th RD Hospital of PLA, Hangzhou, China
| | - Hao Dong
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China
| | - Mengze Wang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Shiyu Chen
- Department of Radiology, 903th RD Hospital of PLA, Hangzhou, China
| | - Jing Han
- Department of Radiology, Zhejiang KangJing Hospital, Hangzhou, China
| | - Miao Liu
- Department of Radiology, 903th RD Hospital of PLA, Hangzhou, China
| | - Feng Jiang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Cancer Center, Hangzhou, China
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Liu P, Chen YW, Liu C, Wu YT, Zhao WC, Zhu JY, An Y, Xia NX. Development and validation of a nomogram model for predicting the risk of gallstone recurrence after gallbladder-preserving surgery. Hepatobiliary Pancreat Dis Int 2024; 23:288-292. [PMID: 36443144 DOI: 10.1016/j.hbpd.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 10/25/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND The high incidence of gallstone recurrence was a major concern for laparoscopic gallbladder-preserving surgery. This study aimed to investigate the risk factors for gallstone recurrence after gallbladder-preserving surgery and to establish an individualized nomogram model to predict the risk of gallstone recurrence. METHODS The clinicopathological and follow-up data of 183 patients who were initially diagnosed with gallstones and treated with gallbladder-preserving surgery at our hospital from January 2012 to January 2019 were retrospectively collected. The independent predictive factors for gallstone recurrence following gallbladder-preserving surgery were identified by multivariate logistic regression analysis. A nomogram model for the prediction of gallstone recurrence was constructed based on the selected variables. The C-index, receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive power of the nomogram model for gallstone recurrence. RESULTS During the follow-up period, a total of 65 patients experienced gallstone recurrence, and the recurrence rate was 35.5%. Multivariate logistic regression analysis revealed that the course of gallstones > 2 years [odds ratio (OR) = 2.567, 95% confidence interval (CI): 1.270-5.187, P = 0.009], symptomatic gallstones (OR = 2.589, 95% CI: 1.059-6.329, P = 0.037), multiple gallstones (OR = 2.436, 95% CI: 1.133-5.237, P = 0.023), history of acute cholecystitis (OR = 2.778, 95% CI: 1.178-6.549, P = 0.020) and a greasy diet (OR = 2.319, 95% CI: 1.186-4.535, P = 0.014) were independent risk factors for gallstone recurrence after gallbladder-preserving surgery. A nomogram model for predicting the recurrence of gallstones was established based on the above five variables. The results showed that the C-index of the nomogram model was 0.692, suggesting it was valuable to predict gallstone recurrence. Moreover, the calibration curve showed good consistency between the predicted probability and actual probability. CONCLUSIONS The nomogram model for the prediction of gallstone recurrence might help clinicians develop a proper treatment strategy for patients with gallstones. Gallbladder-preserving surgery should be cautiously considered for patients with high recurrence risks.
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Affiliation(s)
- Peng Liu
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Yong-Wei Chen
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Che Liu
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Yin-Tao Wu
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Wen-Chao Zhao
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Jian-Yong Zhu
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Yang An
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Nian-Xin Xia
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China.
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You H, Zhang D, Liu Y, Zhao Y, Xiao Y, Li X, You S, Wang T, Tian T, Xu H, Zhang R, Liu D, Li J, Yuan J, Yang W. Development and validation of a risk score nomogram model to predict the risk of 5-year all-cause mortality in diabetic patients with hypertension: A study based on NHANES data. Int J Cardiol Cardiovasc Risk Prev 2024; 21:200265. [PMID: 38577011 PMCID: PMC10992723 DOI: 10.1016/j.ijcrp.2024.200265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/14/2024] [Accepted: 03/20/2024] [Indexed: 04/06/2024]
Abstract
Background The present study aimed to develop and validate a prediction nomogram model for 5-year all-cause mortality in diabetic patients with hypertension. Methods Data were extracted from the National Health and Nutrition Examination Survey (NHANES). A total of 3291 diabetic patients with hypertension in the NHANES cycles for 1999-2014 were selected and randomly assigned at a ratio of 8:2 to the training cohort (n = 2633) and validation cohort (n = 658). Multivariable Cox regression was conducted to establish a visual nomogram model for predicting the risk of 5-year all-cause mortality. Receiver operating characteristic curves and C-indexes were used to evaluate the discriminant ability of the prediction nomogram model for all-cause mortality. Survival curves were created using the Kaplan-Meier method and compared by the log-rank test. Results The nomogram model included eight independent predictors: age, sex, education status, marital status, smoking, serum albumin, blood urea nitrogen, and previous cardiovascular disease. The C-indexes for the model in the training and validation cohorts were 0.76 (95% confidence interval: 0.73-0.79, p < 0.001) and 0.75 (95% confidence interval: 0.69-0.81, p < 0.001), respectively. The calibration curves indicated that the model had satisfactory consistency in the two cohorts. The risk of all-cause mortality gradually increased as the tertiles of the nomogram model score increased (log-rank test, p < 0.001). Conclusion The newly developed nomogram model, a readily useable and efficient tool to predict the risk of 5-year all-cause mortality in diabetic patients with hypertension, provides a novel risk stratification method for individualized intervention.
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Affiliation(s)
- Hongzhao You
- Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, National Clinical Research Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Endocrinology Centre, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Internal Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dingyue Zhang
- Department of Internal Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yilu Liu
- Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, National Clinical Research Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanyan Zhao
- Medical Research and Biometrics Centre, National Centre for Cardiovascular Diseases, Beijing, China
| | - Ying Xiao
- Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, National Clinical Research Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaojue Li
- Endocrinology Centre, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shijie You
- Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, National Clinical Research Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tianjie Wang
- Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, National Clinical Research Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tao Tian
- Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, National Clinical Research Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haobo Xu
- Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, National Clinical Research Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Rui Zhang
- Endocrinology Centre, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dong Liu
- Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, National Clinical Research Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jing Li
- Department of Internal Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiansong Yuan
- Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, National Clinical Research Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weixian Yang
- Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, National Clinical Research Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Chang ZY, Gao WX, Zhang Y, Zhao W, Wu D, Chen L. Establishment and evaluation of a prognostic model for patients with unresectable gastric cancer liver metastases. World J Clin Cases 2024; 12:2182-2193. [DOI: 10.12998/wjcc.v12.i13.2182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/08/2024] [Accepted: 03/28/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Liver metastases (LM) is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer (GC). The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis, thereby enhancing the ability to evaluate patient outcomes.
AIM To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis, thereby enhancing patient outcome assessment.
METHODS Retrospective analysis was conducted on clinical data pertaining to GCLM (type III), admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018. The dataset was divided into a development cohort and validation cohort in a ratio of 2:1. In the development cohort, we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients. Subsequently, we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis, calibration curves, and clinical decision curves. A nomogram was created to visually represent the prediction model, which was then externally validated using the validation cohort.
RESULTS A total of 372 patients were included in this study, comprising 248 individuals in the development cohort and 124 individuals in the validation cohort. Based on Cox analysis results, our final prediction model incorporated five independent risk factors including albumin levels, primary tumor size, presence of extrahepatic metastases, surgical treatment status, and chemotherapy administration. The 1-, 3-, and 5-years Area Under the Curve values in the development cohort are 0.753, 0.859, and 0.909, respectively; whereas in the validation cohort, they are observed to be 0.772, 0.848, and 0.923. Furthermore, the calibration curves demonstrated excellent consistency between observed values and actual values. Finally, the decision curve analysis curve indicated substantial net clinical benefit.
CONCLUSION Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model, demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.
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Affiliation(s)
- Zheng-Yao Chang
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Wen-Xing Gao
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Yue Zhang
- Department of Endocrinology, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Wen Zhao
- School of Medicine, Nankai University, Tianjin 300071, China
| | - Di Wu
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Lin Chen
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
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Deng Z, Liang B, Li T, Liu Q, Wang X, Sun X, Ou Z, Zhao L, Xu C, Liu H, Li J. Development and validation of a risk prediction model for valve regurgitation in Behçet's disease. Clin Rheumatol 2024; 43:1711-1721. [PMID: 38536517 DOI: 10.1007/s10067-024-06897-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND In Behçet's disease (BD), mild-to-severe valvular regurgitation (VR) poses a serious complication that contributes significantly to heart failure and eventually death. The accurate prediction of VR is crucial in the early stages of BD subjects for improved prognosis. Accordingly, this study aimed to develop a nomogram that can detect VR early in the course of BD. METHODS One hundred seventy-two patients diagnosed with Behçet's disease (BD) were conducted to assess cardiac valve regurgitation as the primary outcome. The severity of regurgitation was classified as mild, moderate, or severe. The parameters related to the diagnostic criteria were used to develop model 1. The combination of stepAIC, best subset, and random forest approaches was employed to identify the independent predictors of VR and thus establish model 2 and create a nomogram for predicting the probability of VR in BD. Receiver operating characteristics (ROC) and decision curve analysis (DCA) were used to evaluate the model performance. RESULTS Thirty-four patients experienced mild-to-severe VR events. Model 2 was established using five variables, including arterial involvement, sex, age at hospitalization, mean arterial pressure, and skin lesions. In comparison with model 1 (0.635, 95% CI: 0.512-0.757), the ROC of model 2 (0.879, 95% CI: 0.793-0.966) was improved significantly. DCA suggested that model 2 was more feasible and clinically applicable than model 1. CONCLUSION A predictive model and a nomogram for predicting the VR of patients with Behçet's disease were developed. The good performance of this model can help us identify potential high-risk groups for heart failure. Key Points • In this study, the predictors of VR in BD were evaluated, and a risk prediction model was developed for the early prediction of the occurrence of VR in patients with BD. • The VR prediction model included the following indexes: arterial involvement, sex, age at hospitalization, mean arterial pressure, and skin lesions. • The risk model that we developed was better and more optimized than the models built with diagnostic criteria parameters, and visualizing and personalizing the model, a nomogram, provided clinicians with an easy and intuitive tool for practical prediction.
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Affiliation(s)
- Zixian Deng
- Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People's Hospital (The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Jinan University), 1017 Dongmen North Road, Shenzhen, Guangdong, China
| | - Benhui Liang
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, China
| | - Tangzhiming Li
- Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People's Hospital (The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Jinan University), 1017 Dongmen North Road, Shenzhen, Guangdong, China
| | - Qiyun Liu
- Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People's Hospital (The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Jinan University), 1017 Dongmen North Road, Shenzhen, Guangdong, China
| | - Xiaoyu Wang
- Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People's Hospital (The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Jinan University), 1017 Dongmen North Road, Shenzhen, Guangdong, China
| | - Xin Sun
- Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People's Hospital (The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Jinan University), 1017 Dongmen North Road, Shenzhen, Guangdong, China
| | - Ziwei Ou
- Department of Cardiology, Xiangya Third Hospital, Central South University, Changsha, China
| | - Lin Zhao
- Department of Cardiology, Xiangya Third Hospital, Central South University, Changsha, China
| | - Cong Xu
- Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People's Hospital (The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Jinan University), 1017 Dongmen North Road, Shenzhen, Guangdong, China
| | - Huadong Liu
- Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People's Hospital (The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Jinan University), 1017 Dongmen North Road, Shenzhen, Guangdong, China.
| | - Jianghua Li
- Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People's Hospital (The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Jinan University), 1017 Dongmen North Road, Shenzhen, Guangdong, China.
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Li E, Ni H. Prognostic nomogram for early-stage cervical cancer in the elderly: A SEER database analysis. Prev Med Rep 2024; 41:102700. [PMID: 38638679 PMCID: PMC11024999 DOI: 10.1016/j.pmedr.2024.102700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 04/20/2024] Open
Abstract
Background To identify key clinical factors affecting the survival of elderly patients with early-stage cervical cancer and to construct a nomogram for predicting their prognosis. Methods Patients (aged ≥ 65 years old) diagnosed with cervical cancer between 2004 and 2015 at clinical stages IA to IIA were included in this study. Diagnosis was confirmed via pathological examination, and the cases were randomly divided into a training or a validation group in a 7:3 ratio. Univariate and multivariable Cox regression analyses were performed to identify independent factors affecting the prognosis of elderly early-stage cervical cancer patients, based on which a nomogram was constructed to predict their 12-, 24- and 36-month overall survival (OS). The nomogram's performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) curves. Results A total of 686 patients were identified as eligible and assessed. Multivariable Cox proportional hazard regression analysis revealed that age, tumor diameter, marital status and surgical intervention were independent prognostic factors for elderly individuals with early-stage cervical cancer, which were then used to construct the nomogram. The calibration curves showed a strong correlation between predicted and observed survival rates, and Kaplan-Meier survival curves for different risk subgroups demonstrated significant survival differences (P < 0.001). DCA confirmed the nomogram's clinical utility in predicting the prognosis of elderly patients with early-stage cervical cancer. Conclusion The prognostic model developed in this study can accurately predict the OS of elderly patients with early-stage cervical cancer, showing high concordance with actual clinical outcomes.
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Affiliation(s)
- Ernan Li
- Dependent of Obstetrics and Gynecology, Northern Theater Command General Hospital, Shengyang, China
| | - Huanjuan Ni
- Dependent of Obstetrics and Gynecology, Northern Theater Command General Hospital, Shengyang, China
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Frego N, Contieri R, Fasulo V, Maffei D, Avolio PP, Arena P, Beatrici E, Sordelli F, De Carne F, Lazzeri M, Saita A, Hurle R, Buffi NM, Casale P, Lughezzani G. Development of a microultrasound-based nomogram to predict extra-prostatic extension in patients with prostate cancer undergoing robot-assisted radical prostatectomy. Urol Oncol 2024; 42:159.e9-159.e16. [PMID: 38423852 DOI: 10.1016/j.urolonc.2024.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/12/2024] [Accepted: 01/26/2024] [Indexed: 03/02/2024]
Abstract
OBJECTIVES To develop a microultrasound-based nomogram including clinicopathological parameters and microultrasound findings to predict the presence of extra-prostatic extension and guide the grade of nerve-sparing. MATERIAL AND METHODS All patients underwent microultrasound the day before robot-assisted radical prostatectomy. Variables significantly associated with extra-prostatic extension at univariable analysis were used to build the multivariable logistic model, and the regression coefficients were used to develop the nomogram. The model was subjected to 1000 bootstrap resamples for internal validation. The performance of the microultrasound-based model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). RESULTS Overall, 122/295 (41.4%) patients had a diagnosis of extra-prostatic extension on definitive pathology. Microultrasound correctly identify extra-prostatic extension in 84/122 (68.9%) cases showing a sensitivity and a specificity of 68.9% and 84.4%, with an AUC of 76.6%. After 1000 bootstrap resamples, the predictive accuracy of the microultrasound-based model was 85.9%. The calibration plot showed a satisfactory concordance between predicted probabilities and observed frequencies of extra-prostatic extension. The DCA showed a higher clinical net-benefit compared to the model including only clinical parameters. Considering a 4% cut-off, nerve-sparing was recommended in 173 (58.6%) patients and extra-prostatic extension was detected in 32 (18.5%) of them. CONCLUSION We developed a microultrasound-based nomogram for the prediction of extra-prostatic extension that could aid in the decision whether to preserve or not neurovascular bundles. External validation and a direct comparison with mpMRI-based nomogram is crucial to corroborate our results.
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Affiliation(s)
- Nicola Frego
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Roberto Contieri
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Vittorio Fasulo
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Davide Maffei
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Pier Paolo Avolio
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Paola Arena
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Edoardo Beatrici
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Federica Sordelli
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Fabio De Carne
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Massimo Lazzeri
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy
| | - Alberto Saita
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy
| | - Rodolfo Hurle
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy
| | - Nicolò Maria Buffi
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy.
| | - Paolo Casale
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy
| | - Giovanni Lughezzani
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
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Shi S, Zhu X, Cheang I, Liao S, Yin T, Lu X, Yao W, Zhang H, Li X, Zhou Y. Development and validation of a diagnostic nomogram in pulmonary hypertension due to left heart disease. Heart Lung 2024; 65:11-18. [PMID: 38364358 DOI: 10.1016/j.hrtlng.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Pulmonary hypertension (pH) due to left heart disease (pH-LHD) is the most common form of pH in clinical practice. OBJECTIVES The purpose of the study is to develop a diagnostic nomogram predictive model combining conventional noninvasive examination and detection indicators. METHODS Our study retrospectively included 361 patients with left heart disease (LHD) who underwent right heart catheterization between 2013 and 2020. All patients were randomly divided into a training cohort (253, 70 %) and a validation cohort (108, 30 %). pH was defined as resting mean pulmonary arterial pressure (mPAP) ≥25 mmHg measured by RHC examination. Data dimension reduction and feature selection were used by Lasso regression model. The nomogram was constructed based on multivariable logistic regression. RESULTS A total of 175 patients with LHD were diagnosed with pH during their hospitalization, representing 48.5 % of the cohort. The mean age of the overall group was 55.6 years, with 76.7 % being male patients. Excessive resting heart rate, elevated New York Heart Association functional class, increased red blood cell distribution width, right ventricular end-diastolic diameter, and pulmonary artery systolic pressure measured by echocardiography were independently associated with the prevalence of pH-LHD. The inclusion of these 5 variables in the nomogram showed good discrimination (AUC = 0.866 [95 % CI, 0.820-0.911]) and optimal calibration (Hosmer-Lemeshow test, P = 0.791) for the validation cohort. CONCLUSIONS The noninvasive nomogram of pH-LHD developed in this study has excellent diagnostic value and clinical applicability, and can more accurately evaluate the presence risk of pH in patients with LHD.
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Affiliation(s)
- Shi Shi
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China; Department of Cardiology, Hai'an People's Hospital, Nantong 226600, China
| | - Xu Zhu
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Iokfai Cheang
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Shengen Liao
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Ting Yin
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Xinyi Lu
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Wenming Yao
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Haifeng Zhang
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China; Department of Cardiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215002, China
| | - Xinli Li
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Yanli Zhou
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
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Li M, Tang J, Pan X, Zhang D. Predicting the Survival Benefit of Radiotherapy in Elderly Breast Cancer Patients: A Population-Based Analysis. J Surg Res 2024; 297:26-40. [PMID: 38428261 DOI: 10.1016/j.jss.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/30/2023] [Accepted: 02/05/2024] [Indexed: 03/03/2024]
Abstract
INTRODUCTION This study aimed to establish two prediction tools predicting cancer-specific survival (CSS) and overall survival (OS) in elderly breast cancer patients with or without radiotherapy. METHODS Clinicopathological data of breast cancer patients aged more than 70 y from 2010 to 2018 were retrospectively collected from the Surveillance, Epidemiology, and End Results database. Patients were randomly divided into the training and validation cohorts at 7:3, and the Cox proportional risk model was used to construct the nomograms. The concordance index, the area under the receiver operating characteristic curve, and the calibration plot are used to evaluate the discrimination and accuracy of the nomograms. RESULTS One lakh twenty eight thousand two hundred twenty three elderly breast cancer patients were enrolled, including 57,915 who received radiotherapy. The Cox regression model was used to identify independent factors. These independent influencing factors are used to construct the prediction models. The calibration plots reflect the excellent consistency between the predicted and actual survival rates. The concordance index of nomograms for CSS and OS was more than 0.7 in both the radiotherapy group and the nonradiotherapy group, and similar results are also shown in area under the receiver operating characteristic curve. Decision curve analysis showed that the prognostication accuracy of the model was much higher than that of the traditional tumor, node, metastasis staging. CONCLUSIONS Radiotherapy can benefit elderly breast cancer patients significantly. The two prediction tools provide a personalized survival scale for evaluating the CSS and OS of elderly breast cancer patients, which can better provide clinicians with better-individualized management for these patients.
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Affiliation(s)
- Maoxian Li
- Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
| | - Jie Tang
- Department of Biostatistics and Epidemiology, Public Health School, Shenyang Medical College, Shenyang, China
| | - Xiudan Pan
- Department of Biostatistics and Epidemiology, Public Health School, Shenyang Medical College, Shenyang, China
| | - Dianlong Zhang
- Women and Children's Hospital, Qingdao University, Qingdao, China.
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Guo Q, Gao Y, Lin Y, Li W, Zhang Z, Mao Y, Xu X. A nomogram of preoperative indicators predicting lymph vascular space invasion in cervical cancer. Arch Gynecol Obstet 2024; 309:2079-2087. [PMID: 38358484 DOI: 10.1007/s00404-024-07385-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 01/08/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE To develop predictive nomograms of lymph vascular space invasion (LVSI) in patients with early-stage cervical cancer. METHODS We identified 403 patients with cervical cancer from the Affiliated Hospital of Jiangnan University from January 2015 to December 2019. Patients were divided into the training set (n = 242) and the validation set (n = 161), with patients in the training set subdivided into LVSI (+) and LVSI (-) groups according to postoperative pathology. Preoperative hematologic indexes were compared between the two subgroups. Univariate and multivariate logistic regression analyses were used to analyze the independent risk factors for LVSI, from which a nomogram was constructed using the R package. RESULTS LVSI (+) was present in 94 out of 242 patients in the training set, accompanied by a significant increase in the preoperative squamous cell carcinoma antigen (SCC), white blood cells (WBC), neutrophil (NE), platelet (PLT), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic inflammation index (SII), and tumor size (P < 0.05). Univariate analysis showed that SCC, WBC, NE, NLR, PLR, SII, and tumor size were correlated with LVSI (P < 0.05), and multivariate analysis showed that tumor size, SCC, WBC, and NLR were independent risk factors for LVSI (P < 0.05). A nomogram was correspondingly established with good performance in predicting LVSI [training: ROC-AUC = 0.845 (95% CI: 0.731-0.843) and external validation: ROC-AUC = 0.704 (95% CI: 0.683-0.835)] and high accuracy (training: C-index = 0.787; external validation: C-index = 0.759). CONCLUSION The nomogram based on preoperative tumor size, SCC, WBC, and NLR had excellent accuracy and discriminative capability to assess the risk of LVSI in early-stage cervical cancer patients.
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Affiliation(s)
- Qu Guo
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Yufeng Gao
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
- Wuxi Medical College, Jiangnan University, Wuxi, China
| | - Yaying Lin
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
- Wuxi Medical College, Jiangnan University, Wuxi, China
| | - Weimin Li
- Ultrasonography Department, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Zhenyu Zhang
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Yurong Mao
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xizhong Xu
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China.
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Hu Y, Ye Z, Obore N, Guo X, Yu H. Non-invasive prediction model of histologic chorioamnionitis with preterm prelabour rupture of membranes. Eur J Obstet Gynecol Reprod Biol 2024; 296:299-306. [PMID: 38508104 DOI: 10.1016/j.ejogrb.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 03/01/2024] [Accepted: 03/07/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND The aim of this study is to identify risk factors associated with histological chorioamnionitis (HCA) and develop a predictive model for antepartum assessment of the risk of PPROM with HCA. METHODS This study retrospectively analyzed pregnant women who experienced PPROM between 25 + 0 and 35 + 0 weeks of gestational age. The women were divided into two groups based on the presence or absence of HCA. Univariate and multivariate logistic regression analyses were conducted to identify maternal risk factors and develop a clinical prediction model for HCA. The model's discrimination and consistency were evaluated using receiver operating characteristic (ROC) and calibration curves. RESULTS Seventeen thousand one hundred forty-six (17,146) pregnant women were screened, and 726 (4.23 %) had PPROM. Out of the 286 subjects with PPROM, 160 developed HCA. The maternal age of these subjects ranged from 18 to 43 years (30.0 ± 5.4), while their gestational age (GA) ranged from 25 + 0 to 35 + 0 weeks (31.6 ± 2.0). The average GA at delivery was 32.2 ± 2.0 (weeks).Compared with the non-HCA group, the expectant time > 48 h, GA at delivery > 32 weeks, twin pregnancy, HGB (<110 g/Lg/L), degree of LGB (IIb-III), and WBC (>9.5 × 109 /L) were significantly more than in the PPROM with HCA group. The results show that the best model was obtained by leave-one-out logistic regression (AUC = 0.785, CA = 0.741, F1 = 0.739, Precision = 0.740, Recall = 0.741). In the validation set, logistic regression also achieved good results (AUC = 0.710, CA = 0.671, F1 = 0.654, Precision = 0.683, Recall = 0.671). Combining the previous analysis, we found that the prognostic model constructed using the core six features had the best predictive effect. CONCLUSIONS Six features were associated with the occurrence of chorioamnionitis. These features were used to construct a diagnostic model that can accurately predict the probability of chorioamnionitis occurrence and provide a beneficial tool for the prevention and management of PPROM with HCA.
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Affiliation(s)
- Yan Hu
- Department of Obstetrics and Gynecology, Zhongda Hospital Affiliated to Southeast University, Nanjing 210009, China.
| | - Zheng Ye
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210006, China
| | - Nathan Obore
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Xiaojun Guo
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Hong Yu
- Department of Obstetrics and Gynecology, Zhongda Hospital Affiliated to Southeast University, Nanjing 210009, China.
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Keskin ET, Can O, Özdemir H, Savun M, Şam Özdemir M, Tataroğlu ÖD, Şimşek A. A New Nephrometry Score for Predicting Positive Surgical Margin After Laparoscopic Partial Nephrectomy. Ann Surg Oncol 2024; 31:3523-3530. [PMID: 38294613 DOI: 10.1245/s10434-024-14970-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 01/11/2024] [Indexed: 02/01/2024]
Abstract
PURPOSE To introduce the KESKIN ratio as a novel predictor of positive surgical margin (PSM) after laparoscopic partial nephrectomy (PN) and to evaluate other clinical characteristics and nephrometry scores (including RENAL, PADUA, and C-index) for predicting PSM. METHODS We retrospectively analyzed 95 patients who underwent laparoscopic PN between June 2020 and April 2023. The KESKIN ratio was defined for all patients. The KESKIN ratio, tumor and patient-related paramaters, and nephrometry scores were analyzed to predict PSM. RESULTS Positive surgical margin was found in 12 of 95 patients (12.6%). There was no statistical difference between the PSM and negative surgical margin (NSM) groups in RENAL, PADUA, and C-index scores. Only the KESKIN ratio was found to be a statistically significant predictor of PSM in both univariate and multivariate regression analysis (p = 0.007 and p = 0.043, respectively). Mean endophytic diameter and endophytic percentage were found to be statistically significant predictors of PSM in only univariate analysis (p = 0.005 and p = 0.01, respectively). The value of 0.5 was determined as the cut-off value for the KESKIN ratio. Values higher than 0.5 indicate an increase in PSM. CONCLUSIONS The KESKIN ratio is a novel, easily measurable, and calculable image-based parameter that can be used to predict PSM after laparascopic PN. If externally validated in a larger patient population, the KESKIN ratio may be used in future versions of the current nephrometry scoring systems for predicting the PSM.
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Affiliation(s)
- Emin Taha Keskin
- Department of Urology, Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey.
| | - Osman Can
- Department of Urology, Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey
| | - Harun Özdemir
- Department of Urology, Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey
| | - Metin Savun
- Department of Urology, Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey
| | - Merve Şam Özdemir
- Department of Radiology, Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey
| | | | - Abdülmuttalip Şimşek
- Department of Urology, Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey
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Miao G, Cai Z, He X, Yang J, Zhang Y, Ma A, Zhao X, Tan M. Development of a predictive nomogram for 28-day mortality risk in non-traumatic or post-traumatic subarachnoid hemorrhage patients. Neurol Sci 2024; 45:2149-2163. [PMID: 37994964 DOI: 10.1007/s10072-023-07199-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 11/08/2023] [Indexed: 11/24/2023]
Abstract
OBJECTIVE Subarachnoid hemorrhage (SAH) is associated with high rates of mortality and permanent disability. At present, there are few definite clinical tools to predict prognosis in SAH patients. The current study aims to develop and assess a predictive nomogram model for estimating the 28-day mortality risk in both non-traumatic or post-traumatic SAH patients. METHODS The MIMIC-III database was searched to select patients with SAH based on ICD-9 codes. Patients were separated into non-traumatic and post-traumatic SAH groups. Using LASSO regression analysis, we identified independent risk factors associated with 28-day mortality and incorporated them into nomogram models. The performance of each nomogram was assessed by calculating various metrics, including the area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). RESULTS The study included 999 patients with SAH, with 631 in the non-traumatic group and 368 in the post-traumatic group. Logistic regression analysis revealed critical independent risk factors for 28-day mortality in non-traumatic SAH patients, including gender, age, glucose, platelet, sodium, BUN, WBC, PTT, urine output, SpO2, and heart rate and age, glucose, PTT, urine output, and body temperature for post-traumatic SAH patients. The prognostic nomograms outperformed the commonly used SAPSII and APSIII systems, as evidenced by superior AUC, NRI, IDI, and DCA results. CONCLUSION The study identified independent risk factors associated with the 28-day mortality risk and developed predictive nomogram models for both non-traumatic and post-traumatic SAH patients. The nomogram holds promise in guiding prognosis improvement strategies for patients with SAH.
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Affiliation(s)
- Guiqiang Miao
- Department of Orthopedics, Foshan Fosun Chancheng Hospital, Foshan, 528010, China
| | - Zhenbin Cai
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Xin He
- Clinical Laboratory Center, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Jie Yang
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Yunlong Zhang
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Ao Ma
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Xiaodong Zhao
- Department of Orthopedics, Foshan Fosun Chancheng Hospital, Foshan, 528010, China.
| | - Minghui Tan
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
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He D, Zhang Y, He S, Zhang Y, Dai K, Xu C, Huang Y. Predictive progression outcomes and risk stratification in patients with recurrent or metastatic nasopharyngeal carcinoma who received first-line immunochemotherapy. Clin Transl Oncol 2024; 26:1209-1219. [PMID: 38070050 DOI: 10.1007/s12094-023-03344-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 10/31/2023] [Indexed: 02/29/2024]
Abstract
PURPOSE Progression after first-line immunochemotherapy (ICT) for recurrent or metastatic nasopharyngeal carcinoma (R/M NPC) is a clinical concern due to subsequent limited treatment options. This study firstly predicted the progress outcome. METHODS A cohort of 186 R/M NPC cases that received first-line ICT was included for developing a Cox regression model for progression-free survival (PFS) and risk stratification, which was verified by cross-validation. Discrimination and calibration were evaluated. Progression sites in risk groups was shown with a Sankey diagram. RESULTS Baseline predictors including liver metastasis, trend of plasma Epstein-Barr virus DNA copies, lymphocyte-to-monocyte ratio, and level of platelet and lactate dehydrogenase were identified for model construction, which stratify the cohort into low, middle, and high-risk groups. The overall concordance index (C-index) was 0.67 (95% CI 0.62-0.73). The area under the curve (AUC) was 0.68 (95% CI 0.60-0.76), 0.74 (95% CI 0.66-0.82), 0.75 (95% CI 0.65-0.84) at predicting 12, 18, and 24 months PFS, indicating a moderate accuracy. Cross-validation showed the model performance was robust. Compared with the low-risk group (median PFS: 24.4 months, 95% CI 18.4 months to not reached), the high-risk group (median PFS: 7.1 months, 95% CI 6.4-10.1 months; hazard risk: 7.4, 95% CI 4.4-12.4, p < 0.001) progressed with more liver metastasis after ICT resistance. CONCLUSION It was the first study that described the risk factors and progression characteristics in R/M NPC patients who received first-line ICT, investigating the progression patterns, which was helpful to identify patients with different risks and help guide personalized interventions.
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Affiliation(s)
- Danjie He
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Yudong Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510062, People's Republic of China
| | - Shuiqing He
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Yuzhuo Zhang
- Guangzhou Medical University, Guangzhou, Guangdong, 511436, People's Republic of China
| | - Keyao Dai
- Guangzhou Medical University, Guangzhou, Guangdong, 511436, People's Republic of China
| | - Cheng Xu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
| | - Ying Huang
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
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Xie R, Mao Z, Xu X, Sun T. Epidemiological features and a survival nomogram for primary lymphoma of the male genital tract. Ann Hematol 2024; 103:1687-1695. [PMID: 38424302 DOI: 10.1007/s00277-024-05668-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 02/14/2024] [Indexed: 03/02/2024]
Abstract
Primary lymphoma of the male genital tract (PLMGT) is rare, and data on its epidemiology and prognosis are lacking. Our study aimed to estimate the incidence and develop a predictive nomogram for PLMGT. We pooled the incidence and survival data of PLMGT over the last 20 years from the Surveillance, Epidemiology, and End Results (SEER) database. Incidence rates were calculated by year of diagnosis, age, race, and histology. Independent prognostic factors selected by Cox regression analysis were used to develop a nomogram for predicting overall survival (OS). Our study enrolled 1312 patients with PLMGT. The overall incidence rate of PLMGT was 0.437/1,000,000 during 2000-2019. OS was associated with age, marital status, histological subtype, Ann Arbor stage, and therapeutic strategy, which were used to construct nomograms to predict 1-, 3-, and 5-year OS rates. Receiver operating characteristic curves, calibration plots, and decision curve analysis showed good performance of the nomogram. Based on the total score of each patient from the nomogram, the patients were clustered into three risk groups, and the risk stratification model was more successful in predicting clinical outcomes than the traditional Ann Arbor staging system. The incidence rate of PLMGT has remained relatively stable over the past two decades. For the OS of patients with PLMGT, we established a novel predictive nomogram involving all independent risk factors obtained from the SEER database and developed a corresponding risk classification system that showed better predictive performance than the Ann Arbor staging system.
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Affiliation(s)
- Rongli Xie
- Department of Hematology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Zekai Mao
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Xiaojun Xu
- Department of Hematology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Tiantian Sun
- Department of Hematology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
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Pan Y, Han X, Tu Y, Zhang P, Yu H, Bao Y. Nomogram for Predicting Remission of Metabolic Syndrome 1 Year after Sleeve Gastrectomy Surgery in Chinese Patients with Obesity. Obes Surg 2024; 34:1590-1599. [PMID: 38478194 DOI: 10.1007/s11695-024-07156-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 04/20/2024]
Abstract
PURPOSE Sleeve gastrectomy (SG) is a widely used and effective treatment for patients with obesity and comorbid metabolic abnormalities. No specialized tool is available to predict metabolic syndrome (MS) remission after SG. We presented a nomogram that evaluated the probability of MS remission in obese patients 1 year after SG. MATERIALS AND METHODS Patients with preoperative MS who underwent SG were enrolled in this retrospective study. They were divided into a training set and a validation set. Multivariate logistic regression analysis was performed to identify independent predictors of MS remission, and these predictors were included in the nomogram. Receiver operating characteristic curve was used to evaluate discrimination. Calibration was performed with the Hosmer-Lemeshow goodness-of-fit test. The net benefits of the nomogram were evaluated using decision curve analysis (DCA). RESULTS Three hundred and eighteen patients with a median age of 34.0 years were analyzed. A training set and a validation set with 159 individuals each were established. A combination of age, preoperative high-density lipoprotein cholesterol, elevated triglycerides and glycated hemoglobin level independently and accurately predicted MS remission. The nomogram included these factors. The discriminative ability was moderate in training and validation sets (Area under curve 0.800 and 0.727, respectively). The Hosmer-Lemeshow X2 value of the nomogram was 8.477 (P = 0.388) for the training set and 5.361 (P = 0.718) for the validation set, indicating good calibration. DCA showed the nomogram had clinical benefits in both datasets. CONCLUSION Our nomogram could accurately predict MS remission in Chinese patients with obesity 1 year after SG.
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Affiliation(s)
- Yunhui Pan
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai, 200233, China
| | - Xiaodong Han
- Department of General Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Yinfang Tu
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai, 200233, China
| | - Pin Zhang
- Department of General Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Haoyong Yu
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai, 200233, China
| | - Yuqian Bao
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai, 200233, China.
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Liu Y, Sun BJT, Zhang C, Li B, Yu XX, Du Y. Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study. World J Gastroenterol 2024; 30:2233-2248. [DOI: 10.3748/wjg.v30.i16.2233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/08/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Perineural invasion (PNI) has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer (RC). Preoperative prediction of PNI status is helpful for individualized treatment of RC. Recently, several radiomics studies have been used to predict the PNI status in RC, demonstrating a good predictive effect, but the results lacked generalizability. The preoperative prediction of PNI status is still challenging and needs further study.
AIM To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients.
METHODS This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers. The patients underwent pre-operative high-resolution magnetic resonance imaging (MRI) between May 2019 and August 2022. Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging (T2WI) and contrast-enhanced T1WI (T1CE) sequences. The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared (T2WI, T1CE and T2WI + T1CE fusion sequences). A clinical-radiomics (CR) model was established by combining the radiomics features and clinical risk factors. The internal and external validation groups were used to validate the proposed models. The area under the receiver operating characteristic curve (AUC), DeLong test, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) were used to evaluate the model performance.
RESULTS Among the radiomics models, the T2WI + T1CE fusion sequences model showed the best predictive performance, in the training and internal validation groups, the AUCs of the fusion sequence model were 0.839 [95% confidence interval (CI): 0.757-0.921] and 0.787 (95%CI: 0.650-0.923), which were higher than those of the T2WI and T1CE sequence models. The CR model constructed by combining clinical risk factors had the best predictive performance. In the training and internal and external validation groups, the AUCs of the CR model were 0.889 (95%CI: 0.824-0.954), 0.889 (95%CI: 0.803-0.976) and 0.894 (95%CI: 0.814-0.974). Delong test, NRI, and IDI showed that the CR model had significant differences from other models (P < 0.05). Calibration curves demonstrated good agreement, and DCA revealed significant benefits of the CR model.
CONCLUSION The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively, which facilitates individualized treatment of RC patients.
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Affiliation(s)
- Yan Liu
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Bai-Jin-Tao Sun
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Chuan Zhang
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Bing Li
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Xuan Yu
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Yong Du
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China.
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Tang XW, Ren WS, Huang S, Zou K, Xu H, Shi XM, Zhang W, Shi L, Lü MH. Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis. World J Hepatol 2024; 16:625-639. [DOI: 10.4254/wjh.v16.i4.625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/26/2024] [Accepted: 03/18/2024] [Indexed: 04/24/2024] Open
Abstract
BACKGROUND Liver cirrhosis patients admitted to intensive care unit (ICU) have a high mortality rate.
AIM To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.
METHODS We extracted demographic, etiological, vital sign, laboratory test, comorbidity, complication, treatment, and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic ICU (eICU) collaborative research database (eICU-CRD). Predictor selection and model building were based on the MIMIC-IV dataset. The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors. The final predictors were included in the multivariate logistic regression model, which was used to construct a nomogram. Finally, we conducted external validation using the eICU-CRD. The area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were used to assess the efficacy of the models.
RESULTS Risk factors, including the mean respiratory rate, mean systolic blood pressure, mean heart rate, white blood cells, international normalized ratio, total bilirubin, age, invasive ventilation, vasopressor use, maximum stage of acute kidney injury, and sequential organ failure assessment score, were included in the multivariate logistic regression. The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases, respectively. The calibration curve also confirmed the predictive ability of the model, while the decision curve confirmed its clinical value.
CONCLUSION The nomogram has high accuracy in predicting in-hospital mortality. Improving the included predictors may help improve the prognosis of patients.
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Affiliation(s)
- Xiao-Wei Tang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
- Nuclear Medicine and Molecular Imaging Key Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
| | - Wen-Sen Ren
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
- Nuclear Medicine and Molecular Imaging Key Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
| | - Shu Huang
- Department of Gastroenterology, Lianshui People’ Hospital of Kangda College Affiliated to Nanjing Medical University, Huaian 223499, Jiangsu Province, China
| | - Kang Zou
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
- Nuclear Medicine and Molecular Imaging Key Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
| | - Huan Xu
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
- Nuclear Medicine and Molecular Imaging Key Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
| | - Xiao-Min Shi
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
- Nuclear Medicine and Molecular Imaging Key Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
| | - Wei Zhang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
- Nuclear Medicine and Molecular Imaging Key Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
| | - Lei Shi
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
- Nuclear Medicine and Molecular Imaging Key Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
| | - Mu-Han Lü
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
- Nuclear Medicine and Molecular Imaging Key Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
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Yang XL, Zeng Z, Wang C, Sheng YL, Wang GY, Zhang FQ, Lian X. Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms. J Mol Neurosci 2024; 74:48. [PMID: 38662286 DOI: 10.1007/s12031-024-02218-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/31/2024] [Indexed: 04/26/2024]
Abstract
We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCGA database were assigned to training and validation cohort, respectively. The differences in expression of immune checkpoint genes (ICGs) and immune infiltration landscape were compared between LTS and short time survivor (STS) (OS<1.5 years). The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify the genes differentially expressed between LTS and STS. Three different machine learning algorithms were employed to select the predictive genes from the overlapping region of DEGs and WGCNA to construct the nomogram. The comparison between LTS and STS revealed that STS exhibited an immune-resistant status, with higher expression of ICGs (P<0.05) and greater infiltration of immune suppression cells compared to LTS (P<0.05). Four genes, namely, OSMR, FMOD, CXCL14, and TIMP1, were identified and incorporated into the nomogram, which possessed good potential in predicting LTS probability among GB patients both in the training (C-index, 0.791; 0.772-0.817) and validation cohort (C-index, 0.770; 0.751-0.806). STS was found to be more likely to exhibit an immune-cold phenotype. The identified predictive genes were used to construct the nomogram with potential to identify LTS among GB patients.
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Affiliation(s)
- Xi-Lin Yang
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Zheng Zeng
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Chen Wang
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Yun-Long Sheng
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
- State Key Laboratory of Molecular Oncology and Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS), Peking Union Medical College (PUMC), Beijing, People's Republic of China
| | - Guang-Yu Wang
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Fu-Quan Zhang
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
| | - Xin Lian
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
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Wei Q, Lu X, Yang Z, Zhu J, Jiang J, Xu Y, Li F, Bu H, Chen Y, Tuo S, Chen R, Ye X, Geer L, Tan X, Wang J, Wu Y, Song F, Su Y. Development and validation of a risk nomogram to estimate risk of hyponatremia after spinal cord injury: A retrospective single-center study. J Spinal Cord Med 2024:1-9. [PMID: 38656250 DOI: 10.1080/10790268.2024.2329437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/26/2024] Open
Abstract
OBJECTIVE This study aimed to establish a nomogram-based assessment for predicting the risk of hyponatremia after spinal cord injury (SCI). DESIGN The study is a retrospective single-center study. PARTICIPANTS SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University. SETTING The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China. METHODS We performed a retrospective clinical study to collect SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University from 2016 to 2020. Based on their clinical scores, the SCI patients were grouped as either hyponatremic or non-hyponatremic, SCI patients in 2016-2019 were identified as the training set, and patients in 2020 were identified as the test set. A nomogram was generated, the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to validate the model. RESULTS A total of 895 SCI patients were retrieved. After excluding patients with incomplete data, 883 patients were finally included in this study and used to construct the nomograms. The indicators used in the nomogram included sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, white blood cell (WBC), albumin and serum Ca2+. These indices were determined by the least absolute shrinkage and selection operator (LASSO) regression analysis. The C-index of the model was 0.81, the area under the curve (AUC) of the training set was 0.82(Cl:0.79-0.85), and the validation set was 0.79(Cl:0.73-0.85). CONCLUSIONS Nomogram has good predictive ability, sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, WBC, albumin and serum Ca2+ were predictors of hyponatremia after SCI.
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Affiliation(s)
- Qian Wei
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Xuefeng Lu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Zihong Yang
- Graduate School of Guangxi Medical University, Nanning, People's Republic of China
| | - Jichong Zhu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Jie Jiang
- The Second Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Yaobin Xu
- Graduate School of Guangxi Medical University, Nanning, People's Republic of China
| | - Fengxin Li
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Haifeng Bu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Yikai Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Sijing Tuo
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Ruyu Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Xiaoxia Ye
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Laoyi Geer
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Xiuwei Tan
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Jiling Wang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Yanlan Wu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Fangming Song
- Graduate School of Guangxi Medical University, Nanning, People's Republic of China
- Guangxi Research Center for Regenerative Medicine, Nanning, People's Republic of China
| | - Yiji Su
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
- Guangxi Research Center for Regenerative Medicine, Nanning, People's Republic of China
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Tan B, Yang C, Hu J, Xing H, Zhang M. Prediction of early recovery of graft function after living donor liver transplantation in children. Sci Rep 2024; 14:9472. [PMID: 38658800 DOI: 10.1038/s41598-024-60211-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/19/2024] [Indexed: 04/26/2024] Open
Abstract
For end-stage liver disease in children, living donor liver transplantation (LDLT) is often the important standard curative treatment. However, there is a lack of research on early recovery of graft function after pediatric LDLT. This is a single-center, ambispective cohort study. We collected the demographic and clinicopathological data of donors and recipients, and determined the risk factors of postoperative delayed recovery of hepatic function (DRHF) by univariate and multivariate Logistic analyses. 181 cases were included in the retrospective cohort and 50 cases in the prospective cohort. The incidence of DRHF after LDLT in children was 29.4%, and DRHF could well evaluate the early recovery of graft function after LDLT. Through Logistic analyses and AIC score, preoperative liver function of donors, ischemia duration level of the liver graft, Ln (Cr of recipients before operation) and Ln (TB of recipients on the 3rd day after operation) were predictive indicators for DRHF after LDLT in children. Using the above factors, we constructed a predictive model to evaluate the incidence of postoperative DRHF. Self-verification and prospective internal verification showed that this prediction model had good accuracy and clinical applicability. In conclusion, we pointed many risk factors for early delayed recovery of graft function after LDLT in children, and developed a visual and personalized predictive model for them, offering valuable insights for clinical management.
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Affiliation(s)
- Bingqian Tan
- Department of Hepatobiliary Surgery Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400000, China
| | - Chenyu Yang
- Department of Hepatobiliary Surgery Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400000, China
| | - Jiqiang Hu
- Department of Hepatobiliary Surgery Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400000, China
| | - Huiwu Xing
- Department of Hepatobiliary Surgery Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400000, China.
- Department of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, Henan, China.
| | - Mingman Zhang
- Department of Hepatobiliary Surgery Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400000, China.
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Zheng G, Cai J, Deng H, Yang H, Xiong W, Chen E, Bai H, He J. Development of a risk prediction model for subsequent infection after colonization with carbapenem-resistant Enterobacterales: a retrospective cohort study. Antimicrob Resist Infect Control 2024; 13:46. [PMID: 38659068 DOI: 10.1186/s13756-024-01394-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 03/31/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Colonization of carbapenem-resistant Enterobacterale (CRE) is considered as one of vital preconditions for infection, with corresponding high morbidity and mortality. It is important to construct a reliable prediction model for those CRE carriers with high risk of infection. METHODS A retrospective cohort study was conducted in two Chinese tertiary hospitals for patients with CRE colonization from 2011 to 2021. Univariable analysis and the Fine-Gray sub-distribution hazard model were utilized to identify potential predictors for CRE-colonized infection, while death was the competing event. A nomogram was established to predict 30-day and 60-day risk of CRE-colonized infection. RESULTS 879 eligible patients were enrolled in our study and divided into training (n = 761) and validation (n = 118) group, respectively. There were 196 (25.8%) patients suffered from subsequent CRE infection. The median duration of subsequent infection after identification of CRE colonization was 20 (interquartile range [IQR], 14-32) days. Multisite colonization, polymicrobial colonization, catheterization and receiving albumin after colonization, concomitant respiratory diseases, receiving carbapenems and antimicrobial combination therapy before CRE colonization within 90 days were included in final model. Model discrimination and calibration were acceptable for predicting the probability of 60-day CRE-colonized infection in both training (area under the curve [AUC], 74.7) and validation dataset (AUC, 81.1). Decision-curve analysis revealed a significantly better net benefit in current model. Our prediction model is freely available online at https://ken-zheng.shinyapps.io/PredictingModelofCREcolonizedInfection/ . CONCLUSIONS Our nomogram has a good predictive performance and could contribute to early identification of CRE carriers with a high-risk of subsequent infection, although external validation would be required.
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Affiliation(s)
- Guanhao Zheng
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
| | - Jiaqi Cai
- Department of Clinical Laboratory, Kunshan Hospital, Nanjing University of Chinese Medicine, Kunshan, China
- School of Medicine, Jiangsu University, Zhenjiang, China
| | - Han Deng
- Department of International Medical Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
| | - Haoyu Yang
- Department of Pharmacy, Handan First Hospital, Handan, China
| | - Wenling Xiong
- Department of Infection Management, Chongqing University Cancer Hospital, Chongqing, China
| | - Erzhen Chen
- Department of Emergency Intensive Care Unit, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Hao Bai
- Department of Pharmacy, Chongqing University Cancer Hospital, Chongqing, China.
| | - Juan He
- Department of Pharmacy, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Zhang D, Zhou W, Lu WW, Qin XC, Zhang XY, Wang JL, Wu J, Luo YH, Duan YY, Zhang CX. Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study. Acad Radiol 2024:S1076-6332(24)00217-4. [PMID: 38658211 DOI: 10.1016/j.acra.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 03/21/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
RATIONALE AND OBJECTIVES The aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC). MATERIALS AND METHODS In this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets. Deep learning and handcrafted radiomics features reflecting tumor phenotypes on BMUS and CDFI images were extracted. The BMUS score and CDFI score were calculated after radiomics feature selection. Subsequently, a DLRN was developed based on the scores and independent clinic-ultrasonic risk variables. The performance of the DLRN was evaluated for calibration, discrimination, and clinical usefulness. RESULTS The DLRN predicted the LVI with accuracy, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI 0.90-0.95), 0.91 (95% CI 0.87-0.95), and 0.91 (95% CI 0.86-0.94) in the training, internal test, and external test sets, respectively, with good calibration. The DLRN demonstrated superior performance compared to the clinical model and single scores across all three sets (p < 0.05). Decision curve analysis and clinical impact curve confirmed the clinical utility of the model. Furthermore, significant enhancements in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indicated that the two scores could serve as highly valuable biomarkers for assessing LVI. CONCLUSION The DLRN exhibited strong predictive value for LVI in IBC, providing valuable information for individualized treatment decisions.
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Affiliation(s)
- Di Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.)
| | - Wang Zhou
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.)
| | - Wen-Wu Lu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.)
| | - Xia-Chuan Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.); Department of Ultrasound, Beijing Anzhen Hospital Nanchong Hospital, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nan Chong, Sichuan 637000, China (X.C.Q.)
| | - Xian-Ya Zhang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China (X.Y.Z.)
| | - Jun-Li Wang
- Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People's Hospital, WuHu), Wuhu, Anhui 241001, China (J.L.W.)
| | - Jun Wu
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China (J.W.)
| | - Yan-Hong Luo
- The Third Affiliated Hospital of Anhui Medical University, Hefei First People's Hospital, Hefei, Anhui 230061, China (Y.H.L.)
| | - Ya-Yang Duan
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.)
| | - Chao-Xue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.).
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Yuan Y, Xu M, Zhang X, Tang X, Zhang Y, Yang X, Xia G. Development and validation of a nomogram model for predicting the risk of MAFLD in the young population. Sci Rep 2024; 14:9376. [PMID: 38654043 DOI: 10.1038/s41598-024-60100-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 04/18/2024] [Indexed: 04/25/2024] Open
Abstract
This study aimed to develop and validate a nomogram model that includes clinical and laboratory indicators to predict the risk of metabolic-associated fatty liver disease (MAFLD) in young Chinese individuals. This study retrospectively analyzed a cohort of young population who underwent health examination from November 2018 to December 2021 at The Affiliated Hospital of Southwest Medical University in Luzhou City, Sichuan Province, China. We extracted the clinical and laboratory data of 43,040 subjects and randomized participants into the training and validation groups (7:3). Univariate logistic regression analysis, the least absolute shrinkage and selection operator regression, and multivariate logistic regression models identified significant variables independently associated with MAFLD. The predictive accuracy of the model was analyzed in the training and validation sets using area under the receiver operating characteristic (AUROC), calibration curves, and decision curve analysis. In this study, we identified nine predictors from 31 variables, including age, gender, body mass index, waist-to-hip ratio, alanine aminotransferase, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, uric acid, and smoking. The AUROC for the subjects in the training and validation groups was 0.874 and 0.875, respectively. The calibration curves show excellent accuracy of the nomogram. This nomogram which was based on demographic characteristics, lifestyle habits, anthropometrics, and laboratory data can visually and individually predict the risk of developing MAFLD. This nomogram is a quick and effective screening tool for assessing the risk of MAFLD in younger populations and identifying individuals at high risk of MAFLD, thereby contributing to the improvement of MAFLD management.
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Affiliation(s)
- Yi Yuan
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Muying Xu
- The People's Hospital Of Luzhou, Luzhou, 646000, Sichuan, China
| | - Xuefei Zhang
- Department of Health Management Center, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Xiaowei Tang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Yanlang Zhang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Xin Yang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Guodong Xia
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
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Li F, Liu L, Feng Q, Wang X, Liu F, Yang L, Miao L, Wang W, Ji G, Yu C. Prognostic and predictive value of tumor deposits in advanced signet ring cell colorectal cancer: SEER database analysis and multicenter validation. World J Surg Oncol 2024; 22:107. [PMID: 38644507 PMCID: PMC11034099 DOI: 10.1186/s12957-024-03362-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/11/2024] [Indexed: 04/23/2024] Open
Abstract
BACKGROUND Colorectal signet-ring cell carcinoma (SRCC) is a rare cancer with a bleak prognosis. The relationship between its clinicopathological features and survival remains incompletely elucidated. Tumor deposits (TD) have been utilized to guide the N staging in the 8th edition of American Joint Committee on Cancer (AJCC) staging manual, but their prognostic significance remains to be established in colorectal SRCC. PATIENTS AND METHODS The subjects of this study were patients with stage III/IV colorectal SRCC who underwent surgical treatment. The research comprised two cohorts: a training cohort and a validation cohort. The training cohort consisted of 631 qualified patients from the SEER database, while the validation cohort included 135 eligible patients from four independent hospitals in China. The study assessed the impact of TD on Cancer-Specific Survival (CSS) and Overall Survival (OS) using Kaplan-Meier survival curves and Cox regression models. Additionally, a prognostic nomogram model was constructed for further evaluation. RESULTS In both cohorts, TD-positive patients were typically in the stage IV and exhibited the presence of perineural invasion (PNI) (P < 0.05). Compared to the TD-negative group, the TD-positive group showed significantly poorer CSS (the training cohort: HR, 1.87; 95% CI, 1.52-2.31; the validation cohort: HR, 2.43; 95% CI, 1.55-3.81; all P values < 0.001). This association was significant in stage III but not in stage IV. In the multivariate model, after adjusting for covariates, TD maintained an independent prognostic value (P < 0.05). A nomogram model including TD, N stage, T stage, TNM stage, CEA, and chemotherapy was constructed. Through internal and external validation, the model demonstrated good calibration and accuracy. Further survival curve analysis based on individual scores from the model showed good discrimination. CONCLUSION TD positivity is an independent factor of poor prognosis in colorectal SRCC patients, and it is more effective to predict the prognosis of colorectal SRCC by building a model with TD and other clinically related variables.
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Affiliation(s)
- Fuchao Li
- Department of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China
- Department of Geriatrics, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China
| | - Lei Liu
- Medical Centre for Digestive Diseases, The Second Affiliated Hospital of Nanjing Medical University, 121 Jiangjiayuan Road, Nanjing, 210046, China
- Department of Gastroenterology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, Jiangsu, 214200, China
| | - Qingzhao Feng
- Department of General Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China
| | - Xiaohong Wang
- Department of Gastroenterology, Xuzhou Central Hospital, Xuzhou, Jiangsu Province, 221009, China
| | - Fang Liu
- Department of Gastroenterology, Xuzhou Central Hospital, Xuzhou, Jiangsu Province, 221009, China
| | - Li Yang
- Department of Geriatrics, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China
| | - Lin Miao
- Medical Centre for Digestive Diseases, The Second Affiliated Hospital of Nanjing Medical University, 121 Jiangjiayuan Road, Nanjing, 210046, China.
| | - Weiming Wang
- Department of Oncology, Yixing Hospital Affiliated to Medical College of Yangzhou University, Yixing, Jiangsu Province, 214200, China.
| | - Guozhong Ji
- Medical Centre for Digestive Diseases, The Second Affiliated Hospital of Nanjing Medical University, 121 Jiangjiayuan Road, Nanjing, 210046, China.
| | - Chenggong Yu
- Department of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China.
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Wang Y, Feng W, Peng J, Ye F, Song J, Bao X, Li C. Development and validation of a risk prediction model for aspiration in patients with acute ischemic stroke. J Clin Neurosci 2024; 124:60-66. [PMID: 38652929 DOI: 10.1016/j.jocn.2024.04.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 03/22/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Aspiration is a frequently observed complication in individuals diagnosed with acute ischemic stroke, leading to potentially severe consequences. However, the availability of predictive tools for assessing aspiration probabilities remains limited. Hence, our study aimed to develop and validate a nomogram for accurately predicting aspiration probability in patients with acute ischemic stroke. METHODS We analyzed 30 potential risk factors associated with aspiration in 359 adult patients diagnosed with acute ischemic stroke. Advanced statistical techniques, such as Least absolute shrinkage and selection operator (LASSO) and Multivariate Logistic regression, were employed to identify independent predictors. Subsequently, we developed a nomogram prediction model based on these predictors, which underwent internal validation through 1000 bootstrap resampling. Two additional cohorts (Cohort A n = 64; Cohort B, n = 105) were included for external validation. The discriminatory power and calibration performance of the nomogram were assessed using rigorous methods, including the Hosmer-Lemeshow test, area under the receiver operating characteristic curve (AUC), calibration curve analyses, and decision curve analyses (DCA). RESULTS The nomogram was established based on four variables: sputum suction, brain stem infarction, temporal lobe infarction, and Barthel Index score. The predictive model exhibited satisfactory discriminative ability, with an area under the receiver operating characteristic curve of 0.853 (95 % confidence interval, 0.795-0.910), which remained consistent at 0.852 (95 % confidence interval, 0.794-0.912) during the internal validation. The Hosmer-Lemeshow test (P = 0.394) and calibration curve demonstrated favorable consistency between the predicted and observed outcomes in the development cohort. The AUC was 0.872 (95 % confidence interval, 0.783-0.962) in validation cohort A and 0.877 (95 % confidence interval, 0.764-0.989) in validation cohort B, demonstrating sustained accuracy. DCA showed a good net clinical benefit of the nomogram. CONCLUSIONS A nomogram for predicting the probability of aspiration in patients with acute ischemia has been successfully developed and validated.
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Affiliation(s)
- Yina Wang
- Department of Neurology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, China; Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, China
| | - Weijiao Feng
- Department of Neurology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, China
| | - Jie Peng
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, China
| | - Fen Ye
- Department of Neurology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, China
| | - Jun Song
- Department of Otolaryngology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, China
| | - Xiaoyan Bao
- Department of Nephrology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, China
| | - Chaosheng Li
- Department of Neurology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, China.
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Lim A, Edderkaoui M, Zhang Y, Wang Q, Wang R, Pandol SJ, Ou Y. Designing a predictive Framework: Immune-Related Gene-Based nomogram and prognostic model for kidney renal papillary cell carcinoma. Int Immunopharmacol 2024; 131:111878. [PMID: 38493693 DOI: 10.1016/j.intimp.2024.111878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Kidney renal papillary cell carcinoma (KIRP) is frequently associated with an unfavorable prognosis for affected individuals. Unfortunately, there has been insufficient exploration in search for a reliable prognosis signature and predictive indicators to forecast outcomes for KIRP patients. AIM The aim of this study is to employ a comprehensive analysis of data for the identification of prognosis genes, leading to the development of a nomogram with strong predictive capabilities. The objective is to provide a valuable statistical tool that, when implemented in a clinical setting, can offer patients an early opportunity for treatment and enhance their chances of ultimate recovery from this life-threatening disease. METHODS Different packages in R were used to analyze RNA-seq data from the TCGA data portal. Multivariate Cox regression analysis and Kaplan-Meier analysis were also used to investigate the prognostic values of immune-related genes and construct the predictive model and nomogram. A p-value < 0.05 was considered to be significant. RESULTS A total of 368 immune-related genes and 60 TFs were identified as differentially expressed in KIRP tissues compared with normal tissues. Of the 368, 23 were found to be related to overall survival. GO and KEGG analysis suggested that these prognostic immune-related genes mainly participated in the ERK1 and ERK2 cascades, Rap1 signaling pathway, and the PI3K-Akt signaling pathway. 9 genes were identified from Cox regression to be statistically significant prognostic-related genes. Survival analysis showed that a model based on these 9 prognostic-related genes has high predictive performance. Immunohistochemistry results show that APOH, BIRC5, CCL19, and GRN were significantly increased in kidney cancer. B cells and CD4 + T cells were positively correlated with risk score model. CONCLUSION A prognostic model was successfully created based on 9 immune-related genes correlated with overall survival in KIRP. This work aims to provide some insight into therapeutic approaches and prognostic predictors of KIRP.
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Affiliation(s)
- Adrian Lim
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Mouad Edderkaoui
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; University of California at Los Angeles, California
| | - Yi Zhang
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Qiang Wang
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Ruoxiang Wang
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Stephen J Pandol
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; University of California at Los Angeles, California
| | - Yan Ou
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
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Wang L, Feng B, Liang M, Li D, Cong R, Chen Z, Wang S, Ma X, Zhao X. Prognostic performance of MRI LI-RADS version 2018 features and clinical-pathological factors in alpha-fetoprotein-negative hepatocellular carcinoma. Abdom Radiol (NY) 2024:10.1007/s00261-024-04278-9. [PMID: 38642093 DOI: 10.1007/s00261-024-04278-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 04/22/2024]
Abstract
PURPOSE To evaluate the role of the magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) version 2018 features and clinical-pathological factors for predicting the prognosis of alpha-fetoprotein (AFP)-negative (≤ 20 ng/ml) hepatocellular carcinoma (HCC) patients, and to compare with other traditional staging systems. METHODS We retrospectively enrolled 169 patients with AFP-negative HCC who received preoperative MRI and hepatectomy between January 2015 and August 2020 (derivation dataset:validation dataset = 118:51). A prognostic model was constructed using the risk factors identified via Cox regression analysis. Predictive performance and discrimination capability were evaluated and compared with those of two traditional staging systems. RESULTS Six risk factors, namely the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade, were associated with recurrence-free survival. The prognostic model constructed using these factors achieved C-index of 0.705 and 0.674 in the derivation and validation datasets, respectively. Furthermore, the model performed better in predicting patient prognosis than traditional staging systems. The model effectively stratified patients with AFP-negative HCC into high- and low-risk groups with significantly different outcomes (p < 0.05). CONCLUSION A prognostic model integrating the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade may serve as a valuable tool for refining risk stratification in patients with AFP-negative HCC.
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Affiliation(s)
- Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Dengfeng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Rong Cong
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Zhaowei Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing, 100176, China
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, 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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Xie K, Cui C, Li X, Yuan Y, Wang Z, Zeng L. MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses. Acad Radiol 2024:S1076-6332(24)00207-1. [PMID: 38644089 DOI: 10.1016/j.acra.2024.03.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/29/2024] [Accepted: 03/30/2024] [Indexed: 04/23/2024]
Abstract
RATIONALE AND OBJECTIVES Pulmonary nodules or masses are highly prevalent worldwide, and differential diagnosis of benign and malignant lesions remains difficult. Magnetic resonance imaging (MRI) can provide functional and metabolic information of pulmonary lesions. This study aimed to establish a nomogram model based on clinical features, imaging features, and multi-sequence MRI radiomics to identify benign and malignant solid pulmonary nodules or masses. MATERIALS AND METHODS A total of 145 eligible patients (76 male; mean age, 58.4 years ± 13.7 [SD]) with solid pulmonary nodules or masses were retrospectively analyzed. The patients were randomized into two groups (training cohort, n = 102; validation cohort, n = 43). The nomogram was used for predicting malignant pulmonary lesions. The diagnostic performance of different models was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS Of these patients, 95 patients were diagnosed with benign lesions and 50 with malignant lesions. Multivariate analysis showed that age, DWI value, LSR value, and ADC value were independent predictors of malignant lesions. Among the radiomics models, the multi-sequence MRI-based model (T1WI+T2WI+ADC) achieved the best diagnosis performance with AUCs of 0.858 (95%CI: 0.775, 0.919) and 0.774 (95%CI: 0.621, 0.887) for the training and validation cohorts, respectively. Combining multi-sequence radiomics, clinical and imaging features, the predictive efficacy of the clinical-imaging-radiomics model was significantly better than the clinical model, imaging model and radiomics model (all P < 0.05). CONCLUSION The MRI-based clinical-imaging-radiomics model is helpful to differentiate benign and malignant solid pulmonary nodules or masses, and may be useful for precision medicine of pulmonary diseases.
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Affiliation(s)
- Kexin Xie
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Can Cui
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Xiaoqing Li
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Yongfeng Yuan
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Liang Zeng
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China.
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Zeng H, Xue X, Chen D, Zheng B, Liang B, Que Z, Xu D, Wang X, Lin S. Conditional survival analysis and real-time prognosis prediction in stage III T3-T4 colon cancer patients after surgical resection: a SEER database analysis. Int J Colorectal Dis 2024; 39:54. [PMID: 38639915 PMCID: PMC11031473 DOI: 10.1007/s00384-024-04614-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/15/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Conditional survival (CS) takes into consideration the duration of survival post-surgery and can provide valuable additional insights. The aim of this study was to investigate the risk factors associated with reduced one-year postoperative conditional survival in patients diagnosed with stage III T3-T4 colon cancer and real-time prognosis prediction. Furthermore, we aim to develop pertinent nomograms and predictive models. METHODS Clinical data and survival outcomes of patients diagnosed with stage III T3-T4 colon cancer were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2019. Patients were divided into training and validation cohorts at a ratio of 7:3. The training set consisted of a total of 11,386 patients for conditional overall survival (cOS) and 11,800 patients for conditional cancer-specific survival (cCSS), while the validation set comprised 4876 patients for cOS and 5055 patients for cCSS. Univariate and multivariate Cox regression analyses were employed to identify independent risk factors influencing one-year postoperative cOS and cCSS. Subsequently, predictive nomograms for cOS and cCSS at 2-year, 3-year, 4-year, and 5-year intervals were constructed based on the identified prognostic factors. The performance of these nomograms was rigorously assessed through metrics including the concordance index (C-index), calibration curves, and the area under curve (AUC) derived from the receiver operating characteristic (ROC) analysis. Clinical utility was further evaluated using decision curve analysis (DCA). RESULTS A total of 18,190 patients diagnosed with stage III T3-T4 colon cancer were included in this study. Independent risk factors for one-year postoperative cOS and cCSS included age, pT stage, pN stage, pretreatment carcinoembryonic antigen (CEA) levels, receipt of chemotherapy, perineural invasion (PNI), presence of tumor deposits, the number of harvested lymph nodes, and marital status. Sex and tumor site were significantly associated with one-year postoperative cOS, while radiation therapy was notably associated with one-year postoperative cCSS. In the training cohort, the developed nomogram demonstrated a C-index of 0.701 (95% CI, 0.711-0.691) for predicting one-year postoperative cOS and 0.701 (95% CI, 0.713-0.689) for one-year postoperative cCSS. Following validation, the C-index remained robust at 0.707 (95% CI, 0.721-0.693) for one-year postoperative cOS and 0.700 (95% CI, 0.716-0.684) for one-year postoperative cCSS. ROC and calibration curves provided evidence of the model's stability and reliability. Furthermore, DCA underscored the nomogram's superior clinical utility. CONCLUSIONS Our study developed nomograms and predictive models for postoperative stage III survival in T3-T4 colon cancer with the aim of accurately estimating conditional survival. Survival bias in our analyses may lead to overestimation of survival outcomes, which may limit the applicability of our findings.
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Affiliation(s)
- Hao Zeng
- Department of Gastroenterology and Anorectal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Xueyi Xue
- Department of Gastroenterology and Anorectal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Dongbo Chen
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Biaohui Zheng
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Baofeng Liang
- Department of Gastroenterology and Anorectal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
- Department of Surgery II, Shanghang County Hospital, Longyan City, Fujian Province, China
| | - Zhipeng Que
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Dongbo Xu
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Xiaojie Wang
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China.
| | - Shuangming Lin
- Department of Gastroenterology and Anorectal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China.
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Wang L, Zhang L, Wang D, Chen J, Su W, Sun L, Jiang J, Wang J, Zhou Q. Predicting central cervical lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis: a practical nomogram based on retrospective study. PeerJ 2024; 12:e17108. [PMID: 38650652 PMCID: PMC11034492 DOI: 10.7717/peerj.17108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 02/22/2024] [Indexed: 04/25/2024] Open
Abstract
Background In papillary thyroid carcinoma (PTC) patients with Hashimoto's thyroiditis (HT), preoperative ultrasonography frequently reveals the presence of enlarged lymph nodes in the central neck region. These nodes pose a diagnostic challenge due to their potential resemblance to metastatic lymph nodes, thereby impacting the surgical decision-making process for clinicians in terms of determining the appropriate surgical extent. Methods Logistic regression analysis was conducted to identify independent risk factors associated with central lymph node metastasis (CLNM) in PTC patients with HT. Then a prediction model was developed and visualized using a nomogram. The stability of the model was assessed using ten-fold cross-validation. The performance of the model was further evaluated through the use of ROC curve, calibration curve, and decision curve analysis. Results A total of 376 HT PTC patients were included in this study, comprising 162 patients with CLNM and 214 patients without CLNM. The results of the multivariate logistic regression analysis revealed that age, Tg-Ab level, tumor size, punctate echogenic foci, and blood flow grade were identified as independent risk factors associated with the development of CLNM in HT PTC. The area under the curve (AUC) of this model was 0.76 (95% CI [0.71-0.80]). The sensitivity, specificity, accuracy, and positive predictive value of the model were determined to be 88%, 51%, 67%, and 57%, respectively. Conclusions The proposed clinic-ultrasound-based nomogram in this study demonstrated a favorable performance in predicting CLNM in HT PTCs. This predictive tool has the potential to assist clinicians in making well-informed decisions regarding the appropriate extent of surgical intervention for patients.
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Affiliation(s)
- Lirong Wang
- Department of Ultrasound, the Second Affiliated Hospital of Xi ’an Jiaotong University, Xi ’an, Shannxi, China
| | - Lin Zhang
- Department of Ultrasound, the Second Affiliated Hospital of Xi ’an Jiaotong University, Xi ’an, Shannxi, China
| | - Dan Wang
- Department of Ultrasound, the Second Affiliated Hospital of Xi ’an Jiaotong University, Xi ’an, Shannxi, China
| | - Jiawen Chen
- Department of Otolaryngology-Head and Neck Surgery, the Second Affiliated Hospital of Xi ’an Jiaotong University, Xi ’an, Shannxi, China
| | - Wenxiu Su
- Department of Pathology, the Second Affiliated Hospital of Xi ’an Jiaotong University, Xi ’an, Shannxi, China
| | - Lei Sun
- Department of Ultrasound, the Second Affiliated Hospital of Xi ’an Jiaotong University, Xi ’an, Shannxi, China
| | - Jue Jiang
- Department of Ultrasound, the Second Affiliated Hospital of Xi ’an Jiaotong University, Xi ’an, Shannxi, China
| | - Juan Wang
- Department of Ultrasound, the Second Affiliated Hospital of Xi ’an Jiaotong University, Xi ’an, Shannxi, China
| | - Qi Zhou
- Department of Ultrasound, the Second Affiliated Hospital of Xi ’an Jiaotong University, Xi ’an, Shannxi, China
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Li M, Hu X, Li Y, Chen G, Ding CG, Tian X, Tian P, Xiang H, Pan X, Ding X, Xue W, Zheng J, Ding C. Development and validation of a novel nomogram model for predicting delayed graft function in deceased donor kidney transplantation based on pre-transplant biopsies. BMC Nephrol 2024; 25:138. [PMID: 38641807 PMCID: PMC11031976 DOI: 10.1186/s12882-024-03557-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 03/21/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Delayed graft function (DGF) is an important complication after kidney transplantation surgery. The present study aimed to develop and validate a nomogram for preoperative prediction of DGF on the basis of clinical and histological risk factors. METHODS The prediction model was constructed in a development cohort comprising 492 kidney transplant recipients from May 2018 to December 2019. Data regarding donor and recipient characteristics, pre-transplantation biopsy results, and machine perfusion parameters were collected, and univariate analysis was performed. The least absolute shrinkage and selection operator regression model was used for variable selection. The prediction model was developed by multivariate logistic regression analysis and presented as a nomogram. An external validation cohort comprising 105 transplantation cases from January 2020 to April 2020 was included in the analysis. RESULTS 266 donors were included in the development cohort, 458 kidneys (93.1%) were preserved by hypothermic machine perfusion (HMP), 96 (19.51%) of 492 recipients developed DGF. Twenty-eight variables measured before transplantation surgery were included in the LASSO regression model. The nomogram consisted of 12 variables from donor characteristics, pre-transplantation biopsy results and machine perfusion parameters. Internal and external validation showed good discrimination and calibration of the nomogram, with Area Under Curve (AUC) 0.83 (95%CI, 0.78-0.88) and 0.87 (95%CI, 0.80-0.94). Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSION A DGF predicting nomogram was developed that incorporated donor characteristics, pre-transplantation biopsy results, and machine perfusion parameters. This nomogram can be conveniently used for preoperative individualized prediction of DGF in kidney transplant recipients.
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Affiliation(s)
- Meihe Li
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Xiaojun Hu
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Yang Li
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Guozhen Chen
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Chen-Guang Ding
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Xiaohui Tian
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Puxun Tian
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Heli Xiang
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Xiaoming Pan
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Xiaoming Ding
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Wujun Xue
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China.
| | - Jin Zheng
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China.
| | - Chenguang Ding
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
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Wang Q, Shen K, Fei B, Wei M, Ge X, Xie Z. Development and validation of a nomogram to predict cancer-specific survival of elderly patients with unresected gastric cancer who received chemotherapy. Sci Rep 2024; 14:9008. [PMID: 38637579 PMCID: PMC11026516 DOI: 10.1038/s41598-024-59516-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/11/2024] [Indexed: 04/20/2024] Open
Abstract
This investigation aimed to explore the prognostic factors in elderly patients with unresected gastric cancer (GC) who have received chemotherapy and to develop a nomogram for predicting their cancer-specific survival (CSS). Elderly gastric cancer patients who have received chemotherapy but no surgery in the Surveillance, Epidemiology, and End Results Database between 2004 and 2015 were included in this study. Cox analyses were conducted to identify prognostic factors, leading to the formulation of a nomogram. The nomogram was validated using receiver operating characteristic (ROC) and calibration curves. The findings elucidated six prognostic factors encompassing grade, histology, M stage, radiotherapy, tumor size, and T stage, culminating in the development of a nomogram. The ROC curve indicated that the area under curve of the nomogram used to predict CSS for 3, 4, and 5 years in the training queue as 0.689, 0.708, and 0.731, and in the validation queue, as 0.666, 0.693, and 0.708. The calibration curve indicated a high degree of consistency between actual and predicted CSS for 3, 4, and 5 years. This nomogram created to predict the CSS of elderly patients with unresected GC who have received chemotherapy could significantly enhance treatment accuracy.
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Affiliation(s)
- Qi Wang
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Kexin Shen
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Bingyuan Fei
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Mengqiang Wei
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Xinbin Ge
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Zhongshi Xie
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China.
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Gu Z, Yang C, Zhang K, Wu H. Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer. BMC Cancer 2024; 24:492. [PMID: 38637740 PMCID: PMC11025233 DOI: 10.1186/s12885-024-12258-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
OBJECTIVE Cancer-related fatigue (CRF) has been considered the biggest influencing factor for cancer patients after surgery. This study aimed to develop and validate a nomogram for severe cancer-related fatigue (CRF) patients with cervical cancer (CC). METHODS A cross-sectional study was conducted to develop and validate a nomogram (building set = 196; validation set = 88) in the Department of Obstetrics and Gynecology of a Class III hospital in Shenyang, Liaoning Province. We adopted the questionnaire method, including the Cancer Fatigue Scale (CFS), Medical Uncertainty in Illness Scale (MUIS), Medical Coping Modes Questionnaire (MCMQ), Multidimensional Scale of Perceived Social Support (MSPSS), and Sense of Coherence-13 (SOC-13). Binary logistic regression was used to test the risk factors of CRF. The R4.1.2 software was used to develop and validate the nomogram, including Bootstrap resampling method, the ability of Area Under Curve (AUC), Concordance Index (C-Index), Hosmer Lemeshow goodness of fit test, Receiver Operating Characteristic (ROC) curve, Calibration calibration curve, and Decision Curve Analysis curve (DCA). RESULTS The regression equation was Logit(P) = 1.276-0.947 Monthly income + 0.989 Long-term passive smoking - 0.952 Physical exercise + 1.512 Diagnosis type + 1.040 Coping style - 0.726 Perceived Social Support - 2.350 Sense of Coherence. The C-Index of the nomogram was 0.921 (95% CI: 0.877∼0.958). The ROC curve showed the sensitivity of the nomogram was 0.821, the specificity was 0.900, and the accuracy was 0.857. AUC was 0.916 (95% CI: 0.876∼0.957). The calibration showed that the predicted probability of the nomogram fitted well with the actual probability. The DCA curve showed when the prediction probability was greater than about 10%, the benefit of the nomogram was positive. The results in the validation group were similar. CONCLUSION This nomogram had good identifiability, accuracy and clinical practicality, and could be used as a prediction and evaluation tool for severe cases of clinical patients with CC.
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Affiliation(s)
- ZhiHui Gu
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China
| | - ChenXin Yang
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China
| | - Ke Zhang
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China
| | - Hui Wu
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China.
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Yan Y, An X, Ren H, Luo B, Jin S, Liu L, Di Y, Li T, Huang Y. Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms. Neurosurg Rev 2024; 47:169. [PMID: 38635054 DOI: 10.1007/s10143-024-02408-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/30/2024] [Accepted: 04/09/2024] [Indexed: 04/19/2024]
Abstract
Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations. Geometric and hemodynamic parameters were calculated. A stepwise Cox proportional hazards regression analysis was employed to construct a nomogram. IAs were stratified into low-, intermediate-, and high-risk groups based on the total points from the nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA) and Kaplan-Meier curves were evaluated for internal validation. In total, 30 untreated saccular IAs were grown (31.3%; 95%CI 21.8%-40.7%). The PHASES, ELAPSS, and UIATS performed poorly in distinguishing growth status. Hypertension (hazard ratio [HR] 4.26, 95%CI 1.61-11.28; P = 0.004), nonsphericity index (95%CI 4.10-25.26; P = 0.003), max relative residence time (HR 1.01, 95%CI 1.00-1.01; P = 0.032) were independently related to the growth status. A nomogram was constructed with the above predictors and achieved a satisfactory prediction in the validation cohort. The log-rank test showed significant discrimination among the Kaplan-Meier curves of different risk groups in the training and validation cohorts. A nomogram consisting of geometric and hemodynamic parameters presented an accurate prediction for the growth status of small IAs and achieved risk stratification. It showed higher predictive efficacy than the assessment tools.
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Affiliation(s)
- Yujia Yan
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China
| | - Xingwei An
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Hecheng Ren
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China
| | - Bin Luo
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China
| | - Song Jin
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Li Liu
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Yang Di
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Tingting Li
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Ying Huang
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China.
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Gao T, Yang L, Zhou J, Zhang Y, Wang L, Wang Y, Wang T. Development and validation of a nomogram prediction model for ADHD in children based on individual, family, and social factors. J Affect Disord 2024; 356:483-491. [PMID: 38640979 DOI: 10.1016/j.jad.2024.04.069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 04/14/2024] [Accepted: 04/16/2024] [Indexed: 04/21/2024]
Abstract
OBJECTIVES A reliable, user-friendly, and multidimensional prediction tool can help to identify children at high risk for ADHD and facilitate early recognition and family management of ADHD. We aimed to develop and validate a risk nomogram for ADHD in children aged 3-17 years in the United States based on clinical manifestations and complex environments. METHODS A total of 141,356 cases were collected for the prediction model. Another 54,444 cases from a new data set were utilized for performing independent external validation. The LASSO regression was used to control possible variables. A final risk nomogram for ADHD was established based on logistic regression, and the discrimination and calibration of the established nomogram were evaluated by bootstrapping with 1000 resamples. RESULTS A final risk nomogram for ADHD was established based on 13 independent predictors, including behavioral problems, learning disabilities, age, intellectual disabilities, anxiety symptoms, gender, premature birth, maternal age at childbirth, parent-child interaction patterns, etc. The C-index of this model was 0.887 in the training set, and 0.862 in the validation set. Internal and external validation proved that the model was reliable. CONCLUSIONS A nomogram, a statistical prediction tool that assesses individualized ADHD risk for children is helpful for the early identification of children at high risk for ADHD and the construction of a conceptual model of society-family-school collaborative diagnosis, treatment, and management of ADHD.
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Affiliation(s)
- Ting Gao
- Department of Rehabilitation, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou 510623, China
| | - Lan Yang
- Nanfang Hospital, Southern Medical University, Guangzhou 510282, China
| | - Jiayu Zhou
- Department of Neonatology, National Children's Medical Center / Children's Hospital of Fudan University, Shanghai 201102, China
| | - Yu Zhang
- Department of Rehabilitation, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou 510623, China; School of Physical Education and Health, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Laishuan Wang
- Department of Neonatology, National Children's Medical Center / Children's Hospital of Fudan University, Shanghai 201102, China
| | - Yan Wang
- Department of Neurology, Xi 'an Children's Hospital, Shaanxi 710021, China.
| | - Tianwei Wang
- Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
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Meng X, Hao F, Wang N, Qin P, Ju Z, Sun D. Log odds of positive lymph nodes (LODDS)-based novel nomogram for survival estimation in patients with invasive micropapillary carcinoma of the breast. BMC Med Res Methodol 2024; 24:90. [PMID: 38637725 PMCID: PMC11025266 DOI: 10.1186/s12874-024-02218-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Invasive micropapillary carcinoma (IMPC) of the breast is known for its high propensity for lymph node (LN) invasion. Inadequate LN dissection may compromise the precision of prognostic assessments. This study introduces a log odds of positive lymph nodes (LODDS) method to address this issue and develops a novel LODDS-based nomogram to provide accurate prognostic information. METHODS The study analyzed data from 1,901 patients with breast IMPC from the Surveillance, Epidemiology, and End Results database. It assessed the relationships between LODDS and the number of excised LN (eLN), positive LN (pLN), and the pLN ratio (pLNR), identifying an optimal threshold value using a restricted cubic spline method. Predictive factors were identified by the Cox least absolute shrinkage and selection operator (Cox-LASSO) regression and validated through multivariate Cox regression to construct a nomogram. The model's accuracy, discrimination, and utility were assessed. The study also explored the consequences of excluding LODDS from the nomogram and compared its effectiveness with the tumor-node-metastasis (TNM) staging system. RESULTS LODDS improved N status classification by identifying heterogeneity in patients with pLN ratios of 0% (pLN =0) or 100% (pLN =eLN) and setting -1.08 as the ideal cutoff. Five independent prognostic factors for breast cancer-specific survival (BCSS) were identified: tumor size, N status, LODDS, progesterone receptor status, and histological grade. The LODDS-based nomogram achieved a strong concordance index of 0.802 (95% CI: 0.741-0.863), surpassing both the version without LODDS and the conventional TNM staging in all tests. CONCLUSIONS For breast IMPC, LODDS served as an independent prognostic factor, its effectiveness unaffected by the anatomical LN count, enhancing the accuracy of N staging. The LODDS-based nomogram showed promise in offering more personalized prognostic information.
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Affiliation(s)
- Xiangdi Meng
- Department of Radiation Oncology, Weifang People's Hospital, No. 151 Guangwen Street, Kuiwen District, Weifang, 261041, Shandong, China
- Graduate School of Medicine, Gunma University, Maebashi, Japan
| | - Furong Hao
- Department of Radiation Oncology, Weifang People's Hospital, No. 151 Guangwen Street, Kuiwen District, Weifang, 261041, Shandong, China
| | - Nan Wang
- Department of Radiation Oncology, Weifang People's Hospital, No. 151 Guangwen Street, Kuiwen District, Weifang, 261041, Shandong, China
| | - Peiyan Qin
- Department of Radiation Oncology, Weifang People's Hospital, No. 151 Guangwen Street, Kuiwen District, Weifang, 261041, Shandong, China
| | - Zhuojun Ju
- Graduate School of Medicine, Gunma University, Maebashi, Japan
| | - Daqing Sun
- Department of Radiation Oncology, Weifang People's Hospital, No. 151 Guangwen Street, Kuiwen District, Weifang, 261041, Shandong, China.
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Ding CW, Liu C, Zhang ZP, Cheng CY, Pei GS, Jing ZC, Qiu JY. Development and external validation of a nomogram for predicting short-term prognosis in patients with acute pulmonary embolism. Int J Cardiol 2024:132065. [PMID: 38642720 DOI: 10.1016/j.ijcard.2024.132065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 04/17/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Accurate assessment and timely intervention play a crucial role in ameliorating poor short-term prognosis of acute pulmonary embolism (APE) patients. The currently employed scoring models exhibit a degree of complexity, and some models may not comprehensively incorporate relevant indicators, thereby imposing limitations on the evaluative efficacy. Our study aimed to construct and externally validate a nomogram that predicts 30-day all-cause mortality risk in APE patients. METHODS Clinical data from APE patients in Intensive Care-IV database was included as a training cohort. Additionally, we utilized our hospital's APE database as an external validation cohort. The nomogram was developed, and its predictive ability was evaluated using receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis. RESULTS A collective of 1332 patients and 336 patients were respectively enrolled as the training cohort and the validation cohort in this study. Five variables including age, malignancy, oxygen saturation, blood glucose, and the use of vasopressor, were identified based on the results of the multivariate Cox regression model. The ROC value for the nomogram in the training cohort yielded 0.765, whereas in the validation group, it reached 0.907. Notably, these values surpassed the corresponding ROC values for the Pulmonary Embolism Severity Index, which were 0.713 in the training cohort and 0.754 in the validation cohort. CONCLUSIONS The nomogram including five indicators had a good performance in predicting short-term prognosis in patients with APE, which was easier to apply and provided better recommendations for clinical decision-making.
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Affiliation(s)
- Chao-Wei Ding
- Department of Respiratory and Critical Care Medicine, Fujian Medical University Xiamen Humanity Hospital, Xiamen 361000, China; Department of Respiratory and Critical Care Medicine, the Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Chao Liu
- Department of Cardiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China
| | - Zi-Ping Zhang
- Department of Respiratory and Critical Care Medicine, the Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Chun-Yan Cheng
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China
| | - Guang-Sheng Pei
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Zhi-Cheng Jing
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China.
| | - Jia-Yong Qiu
- Department of Respiratory and Critical Care Medicine, the Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China; Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China; Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China.
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Mitrovic M, Pantic N, Bukumiric Z, Sabljic N, Virijevic M, Pravdic Z, Cvetkovic M, Ilic N, Rajic J, Todorovic-Balint M, Vidovic A, Suvajdzic-Vukovic N, Thachil J, Antic D. Venous thromboembolism in patients with acute myeloid leukemia: development of a predictive model. Thromb J 2024; 22:37. [PMID: 38632595 PMCID: PMC11022429 DOI: 10.1186/s12959-024-00607-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 04/12/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Patients with acute myeloid leukemia (AML) are at increased risk of venous thromboembolic events (VTE). However, thromboprophylaxis is largely underused. OBJECTIVES This study aimed to determine possible VTE development risk factors and to develop a novel predictive model. METHODS We conducted a retrospective cohort study of adult patients with newly diagnosed AML. We used univariate and multivariable logistic regression to estimate binary outcomes and identify potential predictors. Based on our final model, a dynamic nomogram was constructed with the goal of facilitating VTE probability calculation. RESULTS Out of 626 eligible patients with AML, 72 (11.5%) developed VTE during 6 months of follow-up. Six parameters were independent predictors: male sex (odds ratio [OR] 1.82, 95% confidence interval [CI]: 1.077-2.065), prior history of thrombotic events (OR 2.27, 95% CI: 1.4-4.96), international normalized ratio (OR 0.21, 95% CI: 0.05-0.95), Eastern Cooperative Oncology Group performance status (OR 0.71, 95% CI: 0.53-0.94), and intensive therapy (OR 2.05, 95% CI: 1.07-3.91). The C statistics for the model was 0.68. The model was adequately calibrated and internally validated. The decision-curve analysis suggested the use of thromboprophylaxis in patients with VTE risks between 8 and 20%. CONCLUSION We developed a novel and convenient tool that may assist clinicians in identifying patients whose VTE risk is high enough to warrant thromboprophylaxis.
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Affiliation(s)
- Mirjana Mitrovic
- Clinic of Hematology, University Clinical Center of Serbia, Belgrade, Serbia.
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
| | - Nikola Pantic
- Clinic of Hematology, University Clinical Center of Serbia, Belgrade, Serbia
| | - Zoran Bukumiric
- Faculty of Medicine, Institute for medical statistics and informatics, University of Belgrade, Belgrade, Serbia
| | - Nikica Sabljic
- Clinic of Hematology, University Clinical Center of Serbia, Belgrade, Serbia
| | - Marijana Virijevic
- Clinic of Hematology, University Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Zlatko Pravdic
- Clinic of Hematology, University Clinical Center of Serbia, Belgrade, Serbia
| | - Mirjana Cvetkovic
- Clinic of Hematology, University Clinical Center of Serbia, Belgrade, Serbia
| | - Nikola Ilic
- Faculty of Medicine, Center for Information and Communication Technologies, University of Belgrade, Belgrade, Serbia
| | - Jovan Rajic
- Clinic of Hematology, University Clinical Center of Serbia, Belgrade, Serbia
| | - Milena Todorovic-Balint
- Clinic of Hematology, University Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Ana Vidovic
- Clinic of Hematology, University Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Nada Suvajdzic-Vukovic
- Clinic of Hematology, University Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Jecko Thachil
- Manchester University NHS, Manchester, Great Britain
| | - Darko Antic
- Clinic of Hematology, University Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
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Li Y, Wang JP, Zhu X. Construction of a nomogram for predicting compensated cirrhosis with Wilson's disease based on non-invasive indicators. BMC Med Imaging 2024; 24:90. [PMID: 38627672 PMCID: PMC11020316 DOI: 10.1186/s12880-024-01265-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 03/29/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Wilson's disease (WD) often leads to liver fibrosis and cirrhosis, and early diagnosis of WD cirrhosis is essential. Currently, there are few non-invasive prediction models for WD cirrhosis. The purpose of this study is to non-invasively predict the occurrence risk of compensated WD cirrhosis based on ultrasound imaging features and clinical characteristics. METHODS A retrospective analysis of the clinical characteristics and ultrasound examination data of 102 WD patients from November 2018 to November 2020 was conducted. According to the staging system for WD liver involvement, the patients were divided into a cirrhosis group (n = 43) and a non-cirrhosis group (n = 59). Multivariable logistic regression analysis was used to identify independent influencing factors for WD cirrhosis. A nomogram for predicting WD cirrhosis was constructed using R analysis software, and validation of the model's discrimination, calibration, and clinical applicability was completed. Due to the low incidence of WD and the small sample size, bootstrap internal sampling with 500 iterations was adopted for validation to prevent overfitting of the model. RESULTS Acoustic Radiation Force Impulse (ARFI), portal vein diameter (PVD), and serum albumin (ALB) are independent factors affecting WD cirrhosis. A nomogram for WD cirrhosis was constructed based on these factors. The area under the ROC curve (AUC) of the model's predictive ability is 0.927 (95% CI: 0.88-0.978). As demonstrated by 500 Bootstrap internal sampling validations, the model has high discrimination and calibration. Clinical decision curve analysis shows that the model has high clinical practical value. ROC curve analysis of the model's rationality indicates that the model's AUC is greater than the AUC of using ALB, ARFI, and PVD alone. CONCLUSION The nomogram model constructed based on ARFI, PVD, and ALB can serve as a non-invasive tool to effectively predict the risk of developing WD cirrhosis.
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Affiliation(s)
- Yan Li
- Department of Ultrasound, The first affiliated hospital of Anhui University of Traditional Chinese Medicine, MeiShan Road, 230031, Anhui, P.R. China.
| | - Jing Ping Wang
- Department of Ultrasound, The first affiliated hospital of Anhui University of Traditional Chinese Medicine, MeiShan Road, 230031, Anhui, P.R. China
| | - Xiaoli Zhu
- Department of Intervention, The First Affiliated Hospital of Soochow University, 899, The Pinghai Road, 215006, Jiangsu, P.R. China
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Tao W, Zhan S, Shen Y, Zhao T, Li F, Gao M, Yang T, Yu J. Nomogram for predicting early hypophosphatemia in term infants. BMC Pediatr 2024; 24:255. [PMID: 38627752 PMCID: PMC11020330 DOI: 10.1186/s12887-024-04737-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Physiological processes rely on phosphate, which is an essential component of adenosine triphosphate (ATP). Hypophosphatasia can affect nearly every organ system in the body. It is crucial to monitor newborns with risk factors for hypophosphatemia and provide them with the proper supplements. We aimed to evaluate the risk factors and develop a nomogram for early hypophosphatemia in term infants. METHODS We conducted a retrospective study involving 416 term infants measured serum phosphorus within three days of birth. The study included 82 term infants with hypophosphatemia (HP group) and 334 term infants without hypophosphatemia (NHP group). We collected data on the characteristics of mothers, newborn babies, and childbirth. Furthermore, univariate and multivariate logistic regression analyses were performed to identify independent risk factors for hypophosphatemia in term infants, and a nomogram was developed and validated based on the final independent risk factors. RESULTS According to our analysis, the multivariate logistic regression analysis showed that male, maternal diabetes, cesarean delivery, lower serum magnesium, and lower birth weight were independent risk factors for early hypophosphatemia in term infants. In addition, the C-index of the developed nomogram was 0.732 (95% CI = 0.668-0.796). Moreover, the calibration curve indicated good consistency between the hypophosphatemia diagnosis and the predicted probability, and a decision curve analysis (DCA) confirmed the clinical utility of the nomogram. CONCLUSIONS The analysis revealed that we successfully developed and validated a nomogram for predicting early hypophosphatemia in term infants.
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Affiliation(s)
- Wan Tao
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Shina Zhan
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Yingjie Shen
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Tianjiao Zhao
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Feitian Li
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Miao Gao
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Tingting Yang
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Jinqian Yu
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China.
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Cai H, Lin Y, Wu Y, Wang Y, Li S, Zhang Y, Zhuang J, Liu X, Guan G. The prognostic model and immune landscape based on cancer-associated fibroblast features for patients with locally advanced rectal cancer. Heliyon 2024; 10:e28673. [PMID: 38590874 PMCID: PMC11000021 DOI: 10.1016/j.heliyon.2024.e28673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 03/13/2024] [Accepted: 03/21/2024] [Indexed: 04/10/2024] Open
Abstract
Background This study aimed to construct a nomogram based on CAF features to predict the cancer-specific survival (CSS) rates of locally advanced rectal cancer (LARC) patients. Methods The EPIC algorithm was employed to calculate the proportion of CAFs. based on the differentially expressed genes between the high and low CAF proportion subgroups, prognostic genes were identified via LASSO and Cox regression analyses. They were then used to construct a prognostic risk signature. Moreover, the GSE39582 and GGSE38832 datasets were used for external validation. Lastly, the level of immune infiltration was evaluated using ssGSEA, ESTIMATE, CIBERSORTx, and TIMER. Results A higher level of CAF infiltration was associated with a worse prognosis. Additionally, the number of metastasized lymph nodes and distant metastases, as well as the level of immune infiltration were higher in the high CAF proportion subgroup. Five prognostic genes (SMOC2, TUBAL3, C2CD4A, MAP1B, BMP8A) were identified and subsequently incorporated into the prognostic risk signature to predict the 1-, 3-, and 5-year CSS rates in the training and validation sets. Differences in survival rates were also determined in the external validation cohort. Furthermore, independent prognostic factors, including TNM stage and risk score, were combined to established a nomogram. Notably, our results revealed that the proportions of macrophages and neutrophils and the levels of cytokines secreted by M2 macrophages were higher in the high-risk subgroup. Finally, the prognostic genes were significantly associated with the level of immune cell infiltration. Conclusion Herein, a nomogram based on CAF features was developed to predict the CSS rate of LARC patients. The risk model was capable of reflecting differences in the level of immune cell infiltration.
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Affiliation(s)
- Huajun Cai
- Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yijuan Lin
- Department of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yong Wu
- Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Ye Wang
- Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Shoufeng Li
- Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yiyi Zhang
- Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Jinfu Zhuang
- Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Xing Liu
- Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Guoxian Guan
- Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- Department of Colorectal Surgery, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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Mao J, Tian Y, Luo N. An ion channel-based prognostic model identified TRPV2 and GJB3 as immunotherapy determinants in pancreatic cancer. Heliyon 2024; 10:e27301. [PMID: 38560261 PMCID: PMC10979059 DOI: 10.1016/j.heliyon.2024.e27301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
Background Less than 10% of people who have pancreatic ductal adenocarcinoma (PDAC) will survive the malignancy for five years. The ion channel genes-related biomarker and predictive model were needed for exploitation. Methods Differentially expressed ion channel genes (DEICGs) were detected in PDAC patients. GO and KEGG enrichment analysis was conducted on DEICGs. The prognostic genes were found using Cox regression analysis. After that, a risk model was created and examined. A nomogram was created based on independent predictive analysis. The molecular functions of two risk groups were explored. Immune checkpoint molecule expression was compared in two risk groups. We evaluated the possible cancer immunotherapy response in two risk groups using the TIDE method. We further examined how TRPV2 functions in PDAC as a potent oncogene and regulates the activity of macrophages by in vitro validation, including CCK8, EdU, and Transwell assays. Results A total of twenty-four DEICGs were found. Next, we discovered that two DEICGs (TRPV2 and GJB3) were connected to PDAC patients' overall survival (OS). The risk model was created and validated, and a nomogram was used to forecast the overall survival of PDAC patients. The high-risk group considerably accumulated oncogenic pathways. Furthermore, we discovered a correlation between the expression of critical immunological checkpoints and the risk score. Furthermore, patients in the high-risk category had a lower chance of benefiting from immune therapy. The HPA database confirmed that TRPV2 is expressed as a protein. Lastly, TRPV2 controls macrophage activity and acts as a potent oncogene in PDAC. Conclusion Altogether, this study suggested that two ion channel genes, TRPV2 and GJB3, were potential biomarkers for the prognosis of PDAC and immunotherapy targets, and the research will be crucial for creating novel PDAC treatment targets and predictive molecular indicators.
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Affiliation(s)
- Jiakai Mao
- Department of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yu Tian
- Department of Vascular Surgery, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Nan Luo
- Department of Infection, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China
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Wang L, Chu X, Yu X, Su C. Identification of nomogram associated with durable clinical benefit gene for advanced non-small cell lung cancer with sensitivity to responsive to immunotherapy. Heliyon 2024; 10:e27801. [PMID: 38560208 PMCID: PMC10981036 DOI: 10.1016/j.heliyon.2024.e27801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 02/19/2024] [Accepted: 03/06/2024] [Indexed: 04/04/2024] Open
Abstract
Background Immunotherapy has become the standard treatment for advanced non-small cell lung cancer (NSCLC). However, a subset of the most advanced NSCLC patients fails to respond adequately to Immune checkpoint inhibitors (ICIs). Developing new nomograms and integrating prognostic factors are crucial for improving the clinical predictability of NSCLC patients undergoing ICIs. Methods Clinical information and genomic data of NSCLC patients undergoing ICIs were retrieved from cBioPortal. Gene alterations associated with durable clinical benefit (DCB) were compared to those linked to no durable benefit (NDB). The Kaplan-Meier plot method was employed for survival analysis, and a novel nomogram was formulated by selecting pertinent clinical variables. Results For the NSCLC patients receiving immunotherapy, three subgroups were identified based on the treatment regimen, including anti-PD-1 monotherapy, anti-PD-1 combination with anti-CTLA-4, and first-line treatment. The mutation status of TP53, PGR, PTPRT, RELN, MUC19, LRP1B, and FAT3 was found to be associated with progression-free survival (PFS). Using the clinicopathological parameters and genomic data of the patients, we developed three novel nomograms to predict the prognosis of ICI treatment in different subgroups. Conclusion Our study revealed that PGR, PTPRD, RELN, MUC19, LRP1B, and FAT3 mutation could serve as predictive biomarkers. Our systematic nomograms demonstrate significant potential in predicting the prognosis for NSCLC patients with sensitivity to different ICI treatment strategies.
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Affiliation(s)
- Li Wang
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, PR China
| | - Xiangling Chu
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, PR China
| | - Xin Yu
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, PR China
| | - Chunxia Su
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, PR China
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Lin M, Zhang Y, Wang H, Wang Y, Wang Y, Feng N, He Q. Multivariate analyses on male factors and construction of a nomogram for predicting low in vitro fertilization rate. Heliyon 2024; 10:e29271. [PMID: 38623219 PMCID: PMC11016707 DOI: 10.1016/j.heliyon.2024.e29271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/30/2024] [Accepted: 04/03/2024] [Indexed: 04/17/2024] Open
Abstract
Low fertilization rate (LFR) and total fertilization failure (TFF) are often encountered in routine in vitro fertilization (IVF) procedure. To solve this problem, multivariate analyses on the relationship between male factors and in vitro fertilization rate were performed, and a nomogram for prediction of LFR was constructed. This retrospective study contained 2011 couples who received IVF treatment from January 2017 to December 2021. Man factors and in vitro fertilization rate were collected. Among these couples, 1347 cases had in vitro fertilization rates ≥30 % (control group), and 664 cases had in vitro fertilization rates <30 % (LFR group). Univariate analyses of male factors found that between the two groups there were significant differences (p < 0.05) in sperm progressive motility (SPR), sperm concentration (SC), total sperm number, normal sperm morphology rate (NSMR), DNA fragmentation index (DFI), sperm acrosin activity (SAA) and the clinical diagnosis of primary or secondary infertility. Multivariate logistic regression analyses showed that SPR, SAA, and SC were independent risk factors for LFR. An algorithm and a correspondent nomogram for predicting high LFR risk were constructed using data from the training cohort. The LFR nomogram exhibited an excellent discrimination power and a high fitting degree in both the training cohort (AUC = 0.90, 95 % CI: 0.88-0.92), (H-L: x2 = 5.43, p = 0.71) and validation cohort (AUC = 0.89, 95 % CI:0.87-0.92), (H-L: x2 = 7.85, p = 0.45), respectively. The decision curve analysis (DCA) demonstrated a high efficiency of the LFR nomogram for clinical utility. SPR, SAA, and SC are independent risk factors for LFR. The LFR nomogram established based on these factors could be a useful tool to predict high risk of LFR, and patients with high risk of LFR can be guided to direct ICSI procedure. Clinical application of the LFR nomogram may increase the in vitro fertilization rate by facilitating the decision making in IVF service.
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Affiliation(s)
- Mengyuan Lin
- Center of Reproductive Medicine, Women's Hospital of Jiangnan University, Wuxi, Jiangsu, China
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Yuwei Zhang
- Medical School of Nantong University, Nantong, China
| | - Honghua Wang
- Center of Reproductive Medicine, Women's Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Yan Wang
- Center of Reproductive Medicine, Women's Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Yang Wang
- Department of Urology, Jiangnan University Medical Center, Wuxi, China
| | - Ninghan Feng
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
- Department of Urology, Jiangnan University Medical Center, Wuxi, China
| | - Qingwen He
- Department of Public Health, Women's Hospital of Jiangnan University, Wuxi, Jiangsu, China
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Nie GL, Yan J, Li Y, Zhang HL, Xie DN, Zhu XW, Li X. Predictive model for non-malignant portal vein thrombosis associated with cirrhosis based on inflammatory biomarkers. World J Gastrointest Oncol 2024; 16:1213-1226. [PMID: 38660630 PMCID: PMC11037040 DOI: 10.4251/wjgo.v16.i4.1213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 01/18/2024] [Accepted: 02/23/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Portal vein thrombosis (PVT), a complication of liver cirrhosis, is a major public health concern. PVT prediction is the most effective method for PVT diagnosis and treatment. AIM To develop and validate a nomogram and network calculator based on clinical indicators to predict PVT in patients with cirrhosis. METHODS Patients with cirrhosis hospitalized between January 2016 and December 2021 at the First Hospital of Lanzhou University were screened and 643 patients with cirrhosis who met the eligibility criteria were retrieved. Following a 1:1 propensity score matching 572 patients with cirrhosis were screened, and relevant clinical data were collected. PVT risk factors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. Variance inflation factors and correlation matrix plots were used to analyze multicollinearity among the variables. A nomogram was constructed to predict the probability of PVT based on independent risk factors for PVT, and its predictive performance was verified using a receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Finally, a network calculator was constructed based on the nomograms. RESULTS This study enrolled 286 cirrhosis patients with PVT and 286 without PVT. LASSO analysis revealed 13 variables as strongly associated with PVT occurrence. Multivariate logistic regression analysis revealed nine indicators as independent PVT risk factors, including etiology, ascites, gastroesophageal varices, platelet count, D-dimer, portal vein diameter, portal vein velocity, aspartate transaminase to neutrophil ratio index, and platelet-to-lymphocyte ratio. LASSO and correlation matrix plot results revealed no significant multicollinearity or correlation among the variables. A nomogram was constructed based on the screened independent risk factors. The nomogram had excellent predictive performance, with an area under the ROC curve of 0.821 and 0.829 in the training and testing groups, respectively. Calibration curves and DCA revealed its good clinical performance. Finally, the optimal cutoff value for the total nomogram score was 0.513. The sensitivity and specificity of the optimal cutoff values were 0.822 and 0.706, respectively. CONCLUSION A nomogram for predicting PVT occurrence was successfully developed and validated, and a network calculator was constructed. This can enable clinicians to rapidly and easily identify high PVT risk groups.
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Affiliation(s)
- Guo-Le Nie
- The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Jun Yan
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Ying Li
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Hong-Long Zhang
- The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Dan-Na Xie
- The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Xing-Wang Zhu
- The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Xun Li
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
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Shi Y, Zhang YX, Jiao MF, Ren XJ, Hu BJ, Liu AH, Li XR. Construction and validation of a neovascular glaucoma nomogram in patients with diabetic retinopathy after pars plana vitrectomy. World J Diabetes 2024; 15:654-663. [DOI: 10.4239/wjd.v15.i4.654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 12/30/2023] [Accepted: 02/06/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Neovascular glaucoma (NVG) is likely to occur after pars plana vitrectomy (PPV) for diabetic retinopathy (DR) in some patients, thus reducing the expected benefit. Understanding the risk factors for NVG occurrence and building effective risk prediction models are currently required for clinical research.
AIM To develop a visual risk profile model to explore factors influencing DR after surgery.
METHODS We retrospectively selected 151 patients with DR undergoing PPV. The patients were divided into the NVG (NVG occurrence) and No-NVG (No NVG occurrence) groups according to the occurrence of NVG within 6 months after surgery. Independent risk factors for postoperative NVG were screened by logistic regression. A nomogram prediction model was established using R software, and the model’s prediction accuracy was verified internally and externally, involving the receiver operator characteristic curve and correction curve.
RESULTS After importing the data into a logistic regression model, we concluded that a posterior capsular defect, preoperative vascular endothelial growth factor ≥ 302.90 pg/mL, glycosylated hemoglobin ≥ 9.05%, aqueous fluid interleukin 6 (IL-6) ≥ 53.27 pg/mL, and aqueous fluid IL-10 ≥ 9.11 pg/mL were independent risk factors for postoperative NVG in patients with DR (P < 0.05). A nomogram model was established based on the aforementioned independent risk factors, and a computer simulation repeated sampling method was used to internally and externally verify the nomogram model. The area under the curve (AUC), sensitivity, and specificity of the model were 0.962 [95% confidence interval (95%CI): 0.932-0.991], 91.5%, and 82.3%, respectively. The AUC, sensitivity, and specificity of the external validation were 0.878 (95%CI: 0.746-0.982), 66.7%, and 95.7%, respectively.
CONCLUSION A nomogram constructed based on the risk factors for postoperative NVG in patients with DR has a high prediction accuracy. This study can help formulate relevant preventive and treatment measures.
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Affiliation(s)
- Yi Shi
- Surgical Retina, Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Yan-Xin Zhang
- Glaucoma, Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Ming-Fei Jiao
- Surgical Retina, Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Xin-Jun Ren
- Ocular Trauma, Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Bo-Jie Hu
- Surgical Retina, Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Ai-Hua Liu
- Glaucoma, Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Xiao-Rong Li
- Surgical Retina, Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
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Shang JR, Xu CY, Zhai XX, Xu Z, Qian J. Risk factors, prognostic factors, and nomograms for distant metastasis in patients with diagnosed duodenal cancer: A population-based study. World J Gastrointest Oncol 2024; 16:1384-1420. [PMID: 38660656 PMCID: PMC11037036 DOI: 10.4251/wjgo.v16.i4.1384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/24/2023] [Accepted: 02/04/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Duodenal cancer is one of the most common subtypes of small intestinal cancer, and distant metastasis (DM) in this type of cancer still leads to poor prognosis. Although nomograms have recently been used in tumor areas, no studies have focused on the diagnostic and prognostic evaluation of DM in patients with primary duodenal cancer. AIM To develop and evaluate nomograms for predicting the risk of DM and personalized prognosis in patients with duodenal cancer. METHODS Data on duodenal cancer patients diagnosed between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for DM in patients with duodenal cancer, and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors in duodenal cancer patients with DM. Two novel nomograms were established, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS A total of 2603 patients with duodenal cancer were included, of whom 457 cases (17.56%) had DM at the time of diagnosis. Logistic analysis revealed independent risk factors for DM in duodenal cancer patients, including gender, grade, tumor size, T stage, and N stage (P < 0.05). Univariate and multivariate COX analyses further identified independent prognostic factors for duodenal cancer patients with DM, including age, histological type, T stage, tumor grade, tumor size, bone metastasis, chemotherapy, and surgery (P < 0.05). The accuracy of the nomograms was validated in the training set, validation set, and expanded testing set using ROC curves, calibration curves, and DCA curves. The results of Kaplan-Meier survival curves (P < 0.001) indicated that both nomograms accurately predicted the occurrence and prognosis of DM in patients with duodenal cancer. CONCLUSION The two nomograms are expected as effective tools for predicting DM risk in duodenal cancer patients and offering personalized prognosis predictions for those with DM, potentially enhancing clinical decision-making.
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Affiliation(s)
- Jia-Rong Shang
- Department of Oncology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210000, Jiangsu Province, China
| | - Chen-Yi Xu
- Department of Oncology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210000, Jiangsu Province, China
- Department of Proctology, Nanjing Hospital of Chinese Medicine, Nanjing 210000, Jiangsu Province, China
| | - Xiao-Xue Zhai
- Department of Oncology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210000, Jiangsu Province, China
| | - Zhe Xu
- Department of Digestive Cancer Surgery, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210000, Jiangsu Province, China
| | - Jun Qian
- Department of Oncology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210000, Jiangsu Province, China
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Tong YX, Ye X, Chen YQ, You YR, Zhang HJ, Chen SX, Wang LL, Xue YJ, Chen LH. A nomogram model of spectral CT quantitative parameters and clinical characteristics predicting lymphovascular invasion of gastric cancer. Heliyon 2024; 10:e29214. [PMID: 38601586 PMCID: PMC11004867 DOI: 10.1016/j.heliyon.2024.e29214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 04/02/2024] [Accepted: 04/02/2024] [Indexed: 04/12/2024] Open
Abstract
Objective The study established a nomogram based on quantitative parameters of spectral computed tomography (CT) and clinical characteristics, aiming to evaluate its predictive value for preoperative lymphovascular invasion (LVI) in gastric cancer (GC). Methods From December 2019 to December 2021, 171 patients with pathologically confirmed GC were retrospectively collected with corresponding clinical data and spectral CT quantitative data. Patients were divided into LVI-positive and LVI-negative groups based on their pathological results. The univariate and multivariate logistic regression analyses were used to identify the risk factors and construct a nomogram. The calibration curve and receiver operating characteristic (ROC) curve were adopted to evaluate the predictive accuracy of nomogram. Results Four clinical characteristics or spectral CT quantitative parameters, including Borrmann classification (P = 0.039), CA724 (P = 0.007), tumor thickness (P = 0.031), and iodine concentration in the venous phase (VIC) (P = 0.004) were identified as independent factors for LVI in GC patients. The nomogram was established based on the four factors, which had a potent predictive accuracy in the training, internal validation and external validation cohorts, with the area under the ROC curve (AUC) of 0.864 (95% CI, 0.798-0.930), 0.964 (95% CI, 0.903-1.000) and 0.877 (95% CI, 0.759-0.996), respectively. Conclusion This study constructed a comprehensive nomogram consisting spectral CT quantitative parameters and clinical characteristics of GC, which exhibited a robust efficiency in predicting LVI in GC patients.
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Affiliation(s)
- Yong-Xiu Tong
- Department of Radiology, Provincial Clinical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Xiao Ye
- Department of Radiology, Fujian Provincial Geriatric Hospital, Fuzhou, 350001, China
| | - Yong-Qin Chen
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Ya-ru You
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China
| | - Hui-Juan Zhang
- Department of Radiology, Provincial Clinical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Shu-Xiang Chen
- Department of Radiology, Provincial Clinical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Li-Li Wang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou, 350001, China
| | - Yun-Jing Xue
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou, 350001, China
| | - Li-Hong Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou, 350001, China
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