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Pan Y, Chen HY, Chen JY, Wang XJ, Zhou JP, Shi L, Yu RS. Clinical and CT Quantitative Features for Predicting Liver Metastases in Patients with Pancreatic Neuroendocrine Tumors: A Study with Prospective/External Validation. Acad Radiol 2024:S1076-6332(24)00071-0. [PMID: 38490841 DOI: 10.1016/j.acra.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/29/2024] [Accepted: 02/01/2024] [Indexed: 03/17/2024]
Abstract
RATIONALE AND OBJECTIVES We aimed to evaluate clinical characteristics and quantitative CT imaging features for the prediction of liver metastases (LMs) in patients with pancreatic neuroendocrine tumors (PNETs). METHODS Patients diagnosed with pathologically confirmed PNETs were included, 133 patients were in the training group, 22 patients in the prospective internal validation group, and 28 patients in the external validation group. Clinical information and quantitative features were collected. The independent variables for predicting LMs were confirmed through the implementation of univariate and multivariate logistic analyses. The diagnostic performance was evaluated by conducting receiver operating characteristic curves for predicting LMs in the training and validation groups. RESULTS PNETs with LMs demonstrated significantly larger diameter and lower arterial/portal tumor-parenchymal enhancement ratio, arterial/portal absolute enhancement value (AAE/PAE value) (p < 0.05). After multivariate analyses, A high level of tumor marker (odds ratio (OR): 5.32; 95% CI, 1.54-18.35), maximum diameter larger than 24.6 mm (OR: 7.46; 95% CI, 1.70-32.72), and AAE value ≤ 51 HU (OR: 4.99; 95% CI, 0.93-26.95) were independent positive predictors of LMs in patients with PNETs, with area under curve (AUC) of 0.852 (95%CI, 0.781-0.907). The AUCs for prospective internal and external validation groups were 0.883 (95% CI, 0.686-0.977) and 0.789 (95% CI, 0.602-0.916), respectively. CONCLUSION Tumor marker, maximum diameter and absolute enhancement value in arterial phase were independent predictors with good predictive performance for the prediction of LMs in patients with PNETs. Combining clinical and quantitative features may facilitate the attainment of good predictive precision in predicting LMs.
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Affiliation(s)
- Yao Pan
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
| | - Hai-Yan Chen
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Jie-Yu Chen
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Xiao-Jie Wang
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
| | - Jia-Ping Zhou
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Ri-Sheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China.
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Wei S, Li L, Yi T, Su L, Gao Q, Wu L, OuYang Z. Epidemiologic characteristics and a prognostic nomogram for patients with vulvar cancer: results from the Surveillance, Epidemiology, and End Results (SEER) program in the United States, 1975 to 2016. J Gynecol Oncol 2023; 34:e81. [PMID: 37477104 PMCID: PMC10627757 DOI: 10.3802/jgo.2023.34.e81] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/29/2023] [Accepted: 06/24/2023] [Indexed: 07/22/2023] Open
Abstract
OBJECTIVE To elucidate clinical characteristics and build a prognostic nomogram for patients with vulvar cancer. METHODS The study population was drawn from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly assigned to training and validation sets. Cox proportional hazards model and competing risk model were used to identify the prognostic parameters of overall survival (OS) and cancer-specific survival (CSS) to construct a nomogram. The nomogram was assessed by concordance index (C-index), area under the curve (AUC), calibration plot, and decision curve analysis (DCA). RESULTS A total of 20,716 patients were included in epidemiological analysis, of whom 7,025 patients were selected in survival analysis, including 4,215 and 2,810 in training and validation sets, respectively. The multivariate Cox model showed that the predictors for OS were age, marital status, histopathology, differentiation and tumor node metastasis (TNM) stages, whether to undergo surgery and chemotherapy. However, the predictors for CSS were age, race, differentiation and TNM stages, whether to undergo surgery and radiation. The C-index for OS and CSS in the training set were 0.76 and 0.80. The AUC in the training set for 1-, 3- and 5-year OS and CSS were 0.84, 0.81, 0.80 and 0.88, 0.85, 0.83, respectively, which was similar in the validation set. The calibration curves showed good agreement between prediction and actual observations. DCA revealed that the nomogram had a better discrimination than TNM stages. CONCLUSIONS The nomogram showed accurate prognostic prediction in OS and CSS for vulvar cancer, which could provide guidance to clinical practice.
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Affiliation(s)
- Shiyuan Wei
- Department of Gynecology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Lu Li
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Tingting Yi
- Department of Hematology, The First Affiliated Hospital of Shaoyang University, Shaoyang, China
| | - Licong Su
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qi Gao
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Liangzhi Wu
- Department of Gynecology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Zhenbo OuYang
- Department of Gynecology, Guangdong Second Provincial General Hospital, Guangzhou, China.
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Tang Q, Lu J, Wu W, Liu Z, Zhao S, Li C, Chen G, Lu J. Risk prediction model of polypharmacy for community-dwelling elderly patients: An assessment tool for early detection. Front Pharmacol 2022; 13:977492. [DOI: 10.3389/fphar.2022.977492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/31/2022] [Indexed: 11/11/2022] Open
Abstract
Background: Polypharmacy has become a major and growing public health issue, with significant implications for health outcomes and expenditure on healthcare resources. In this study, a risk prediction model of polypharmacy represented by a nomogram for community-dwelling elderly patients based on the Chinese population was constructed.Methods: A cross-sectional study was conducted in Shanghai, China. The variables data affecting polypharmacy were fetched from the information system database of health government departments in Shanghai. The Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the predictor variables, and multivariate logistic regression was used to establish the prediction model. A visual tool of the nomogram was established for predicting the risk of polypharmacy in the elderly population. In addition, the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to estimate the performance of the model.Results: A total of 80,012 elderly patients were included in this study. Eight variables, containing age, residential area, preferred medical institutions, number of visits to tertiary hospitals, number of visits to secondary hospitals, number of visits to community health centers, number of diagnoses, and main types of disease, were included in the risk prediction model of nomogram. The area under the curve (AUC) of the nomogram was 0.782 in both sets, demonstrating that the model has a good discriminant ability. The calibration chart shows that the prediction model fits well with the validation set. DCA results displayed that the threshold probabilities of the two sets in the prediction model reached up to 90%, implying that the model had a preferable application value.Conclusion: This study explored the risk factors for polypharmacy among the elderly in Shanghai, China, and applied the nomogram to establish a predictive model via eight variables, which provided an effective tool for early screening and timely prevention of polypharmacy. Family physicians or pharmacists could scientifically use the tool to closely observe community-dwelling elderly patients, decreasing the adverse health effects caused by medication for the elderly.
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Yang D, Su Y, Zhao F, Chen C, Zhao K, Xiong X, Ding Y. A Practical Nomogram and Risk Stratification System Predicting Cancer-Specific Survival for Hepatocellular Carcinoma Patients With Severe Liver Fibrosis. Front Surg 2022; 9:920589. [PMID: 35784933 PMCID: PMC9243509 DOI: 10.3389/fsurg.2022.920589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide. This study aims to construct a novel practical nomogram and risk stratification system to predict cancer-specific survival (CSS) in HCC patients with severe liver fibrosis. Methods Data on 1,878 HCC patients with severe liver fibrosis in the period 1975 to 2017 were extracted from the Surveillance, Epidemiology, and End Results database (SEER). Patients were block-randomized (1,316 training cohort, 562 validation cohort) by setting random seed. Univariate and multivariate COX regression analyses were employed to select variables for the nomogram. The consistency index (C-index), the area under time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration curves were used to evaluate the performance of the nomogram. Decision curve analysis (DCA), the C-index, the net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to compare the nomogram with the AJCC tumor staging system. We also compared the risk stratification of the nomogram with the American Joint Committee on Cancer (AJCC) staging system. Results Seven variables were selected to establish the nomogram. The C-index (training cohort: 0.781, 95%CI: 0.767–0.793; validation cohort: 0.793, 95%CI = 95%CI: 0.779–0.798) and the time-dependent AUCs (the training cohort: the values of 1-, 3-, and 5 years were 0.845, 0.835, and 0.842, respectively; the validation cohort: the values of 1-, 3-, and 5 years were 0.861, 0.870, and 0.876, respectively) showed satisfactory discrimination. The calibration plots also revealed that the nomogram was consistent with the actual observations. NRI (training cohort: 1-, 2-, and 3-year CSS: 0.42, 0.61, and 0.67; validation cohort: 1-, 2-, and 3-year CSS: 0.26, 0.52, and 0.72) and IDI (training cohort: 1-, 3-, and 5-year CSS:0.16, 0.20, and 0.22; validation cohort: 1-, 3-, and 5-year CSS: 0.17, 0.26, and 0.30) indicated that the established nomogram significantly outperformed the AJCC staging system (P < 0.001). Moreover, DCA also showed that the nomogram was more practical and had better recognition. Conclusion A nomogram for predicting CSS for HCC patients with severe liver fibrosis was established and validated, which provided a new system of risk stratification as a practical tool for individualized treatment and management.
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Affiliation(s)
- Dashuai Yang
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yang Su
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Fangrui Zhao
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chen Chen
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
- Correspondence: Youming Ding Chen Chen
| | - Kailiang Zhao
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiangyun Xiong
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Youming Ding
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
- Correspondence: Youming Ding Chen Chen
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Liao T, Su T, Huang L, Li B, Feng LH. Development and validation of a novel nomogram for predicting survival rate in pancreatic neuroendocrine neoplasms. Scand J Gastroenterol 2022; 57:85-90. [PMID: 34592854 DOI: 10.1080/00365521.2021.1984571] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Over the past decades, the incidence and prevalence of pancreatic neuroendocrine neoplasms (pNENs) have steadily increased. However, accurate prediction of the prognosis and treatment of this condition are currently challenging. This study aims to develop and validate a personalized nomogram to predict the survival of patients with pNENs. MATERIALS AND METHODS A total of 9739 patients with pNENs were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Subsequently, the patients were randomly assigned to a derivation cohort (n = 6874) and a validation cohort (n = 2865). The survival of patients was assessed using the Cox proportional hazards (PHs) regression analysis. Then, the nomogram that predicted 3-and 5-year survival rates were developed in the derivation cohort. Further, the predictive performance of the nomogram was evaluated through discrimination and calibration. RESULTS The Cox regression analysis revealed that age, differentiation, the extent of tumor, M staging, and surgery were independent prognostic predictors for pNENs. The nomogram showed superior discrimination capability than AJCC staging in both derived and validation cohorts (C-index: 0.874 versus 0.721 and 0.833 versus 0.721). The calibration curves showed that the practical and predicted survival rates effectively coincided, specifically for the 3-year survival rate. CONCLUSION Our nomogram is a valuable tool for the prediction of the survival rate for patients with pNENs; this may promote individualized prognostic evaluation and treatment.
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Affiliation(s)
- Tianbao Liao
- Department of President's Office, Youjiang Medical University for Nationalities, Baise, China.,Philippine Christian University Center for International Education, Manila City, Philippine
| | - Tingting Su
- Department of ECG Diagnostics, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Lina Huang
- Department of Comprehensive Internal Medicine, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Bixun Li
- Department of Comprehensive Internal Medicine, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Lu-Huai Feng
- Department of Comprehensive Internal Medicine, Guangxi Medical University Cancer Hospital, Nanning, China
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Wang L, Li P, Hou M, Zhang X, Cao X, Li H. Construction of a risk prediction model for Alzheimer's disease in the elderly population. BMC Neurol 2021; 21:271. [PMID: 34233656 PMCID: PMC8262052 DOI: 10.1186/s12883-021-02276-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 06/09/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Dementia is one of the greatest global health and social care challenges of the twenty-first century. The etiology and pathogenesis of Alzheimer's disease (AD) as the most common type of dementia remain unknown. In this study, a simple nomogram was drawn to predict the risk of AD in the elderly population. METHODS Nine variables affecting the risk of AD were obtained from 1099 elderly people through clinical data and questionnaires. Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the best predictor variables, and multivariate logistic regression analysis was used to construct the prediction model. In this study, a graphic tool including 9 predictor variables (nomogram-see precise definition in the text) was drawn to predict the risk of AD in the elderly population. In addition, calibration diagram, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to verify the model. RESULTS Six predictors namely sex, age, economic status, health status, lifestyle and genetic risk were identified by LASSO regression analysis of nine variables (body mass index, marital status and education level were excluded). The area under the ROC curve in the training set was 0.822, while that in the validation set was 0.801, suggesting that the model built with these 6 predictors showed moderate predictive ability. The DCA curve indicated that a nomogram could be applied clinically if the risk threshold was between 30 and 40% (30 to 42% in the validation set). CONCLUSION The inclusion of sex, age, economic status, health status, lifestyle and genetic risk into the risk prediction nomogram could improve the ability of the prediction model to predict AD risk in the elderly patients.
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Affiliation(s)
- Lingling Wang
- Department of Neurology, People's Hospital of Xinjiang Uygur Autonomous Region, NO.91 Tianchi Road, Tianshan District, Urumqi, Xinjiang, 830001, Uygur Autonomous Region, China
| | - Ping Li
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Ming Hou
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Xiumin Zhang
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Xiaolin Cao
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Hongyan Li
- Department of Neurology, People's Hospital of Xinjiang Uygur Autonomous Region, NO.91 Tianchi Road, Tianshan District, Urumqi, Xinjiang, 830001, Uygur Autonomous Region, China.
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