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Nomogram to Predict Long-Term Overall Survival and Cancer-Specific Survival of Radiotherapy Patients with Nasopharyngeal Carcinoma. BIOMED RESEARCH INTERNATIONAL 2023; 2023:7126881. [PMID: 36704722 PMCID: PMC9873435 DOI: 10.1155/2023/7126881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 12/21/2022] [Accepted: 12/28/2022] [Indexed: 01/19/2023]
Abstract
Objective To establish and validate a nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with nasopharyngeal carcinoma (NPC) receiving radiotherapy by integrating multiple independent prognostic factors. Materials and Methods Data from 5663 patients with NPC who received definite radiotherapy between 2004 and 2018 were included and divided into training and validation cohorts. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors of patients with NPC after radiotherapy. Thereafter, the predictive accuracy of the nomogram model was evaluated. Results Age, race, marital status, pathological type, tumor size, T stage, N stage, M stage, American Joint Committee on Cancer stage, and chemotherapy were independent factors affecting the prognosis of patients with NPC receiving radiotherapy. Nomograms with a concordance index of 0.726 (95% confidence interval (CI): 0.675-0.777) and 0.732 (95% CI: 0.680-0.785) were able to predict OS and CSS, respectively. The area under the curve showed excellent predictive performance. Additionally, the calibration curve indicated that the predicted survival rate was consistent with the actual survival rate, and the decision curve indicated its clinical value. The established risk stratification system was able to accurately stratify patients receiving radiotherapy for NPC into three risk subgroups with significant differences in prognosis (P < 0.05). Conclusions The constructed nomogram had good prognostic performance and could be used as an effective tool to evaluate the prognosis of patients with NPC after radiotherapy. This nomogram could be further used to guide clinical decisions and personalized treatment plans.
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Jiang S, Han L, Liang L, Long L. Development and validation of an MRI-based radiomic model for predicting overall survival in nasopharyngeal carcinoma patients with local residual tumors after intensity-modulated radiotherapy. BMC Med Imaging 2022; 22:174. [PMID: 36195860 PMCID: PMC9533536 DOI: 10.1186/s12880-022-00902-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To investigate the potential value of the pretreatment MRI-based radiomic model in predicting the overall survival (OS) of nasopharyngeal carcinoma (NPC) patients with local residual tumors after intensity-modulated radiotherapy (IMRT). METHODS A total of 218 consecutive nonmetastatic NPC patients with local residual tumors after IMRT [training cohort (n = 173) and validation cohort (n = 45)] were retrospectively included in this study. Clinical and MRI data were obtained. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features from pretreatment MRI. The clinical, radiomic, and combined models for predicting OS were constructed. The models' performances were evaluated using Harrell's concordance index (C-index), calibration curve, and decision curve analysis. RESULTS The C-index of the radiomic model was higher than that of the clinical model, with the C-index of 0.788 (95% CI 0.724-0.852) versus 0.672 (95% CI 0.599-0.745) in the training cohort and 0.753 (95% CI 0.604-0.902) versus 0.634 (95% CI 0.593-0.675) in the validation cohort. Calibration curves showed good agreement between the radiomic model-predicted probability of 2- and 3-year OS and the actual observed probability in the training and validation groups. Decision curve analysis showed that the radiomic model had higher clinical usefulness than the clinical model. The discrimination of the combined model improved significantly in the training cohort (P < 0.01) but not in the validation cohort, with the C-index of 0.834 and 0.734, respectively. The radiomic model divided patients into high- and low-risk groups with a significant difference in OS in both the training and validation cohorts. CONCLUSIONS Pretreatment MRI-based radiomic model may improve OS prediction in NPC patients with local residual tumors after IMRT and may assist in clinical decision-making.
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Affiliation(s)
- Shengping Jiang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, China
| | - Lin Han
- Department of Rehabilitation Medicine, The First People's Hospital of Yulin, No. 495 Jiaoyu Road, Yulin, 537000, China
| | - Leifeng Liang
- Department of Radiation Oncology, The First People's Hospital of Yulin, No. 495 Jiaoyu Road, Yulin, 537000, China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, China. .,Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China.
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Chen C, Chen Z, Chio CL, Zhao Y, Li Y, Liu Z, Jin Z, Wu X, Wei W, Zhao Q, Li Y. Higher Expression of WT1 With Lower CD58 Expression may be Biomarkers for Risk Stratification of Patients With Cytogenetically Normal Acute Myeloid Leukemia. Technol Cancer Res Treat 2021; 20:15330338211052152. [PMID: 34738847 PMCID: PMC8573474 DOI: 10.1177/15330338211052152] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background: Cytogenetics at diagnosis is the most important prognostic factor for adult acute myeloid leukemia (AML), but nearly 50% of AML patients who exhibit cytogenetically normal AML (CN-AML) do not undergo effective risk stratification. Therefore, the development of potential biomarkers to further define risk stratification for CN-AML patients is worth exploring. Methods: Transcriptome data from 163 cases in the GSE12417-GPL96 dataset and 104 CN-AML patient cases in the GSE71014-GPL10558 dataset were downloaded from the Gene Expression Omnibus database for overall survival (OS) analysis and validation. Results: The combination of Wilms tumor 1 (WT1) and cluster of diffraction 58 (CD58) can predict the prognosis of CN-AML patients. High expression of WT1 and low expression of CD58 were associated with poor OS in CN-AML. Notably, when WT1 and CD58 were used to concurrently predict OS, CN-AML patients were divided into three groups: low risk, WT1lowCD58high; intermediate risk, WT1highCD58high or WT1lowCD58low; and high risk, WT1highCD58low. Compared with low-risk patients, intermediate- and high-risk patients had shorter survival time and worse OS. Furthermore, a nomogram model constructed with WT1 and CD58 may personalize and reveal the 1-, 2-, 3-, 4-, and 5-year OS rate of CN-AML patients. Both time-dependent receiver operating characteristics and calibration curves suggested that the nomogram model demonstrated good performance. Conclusion: Higher expression of WT1 with lower CD58 expression may be a potential biomarker for risk stratification of CN-AML patients. Moreover, a nomogram model constructed with WT1 and CD58 may personalize and reveal the 1-, 2-, 3-, 4-, and 5-year OS rates of CN-AML patients.
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Affiliation(s)
- Cunte Chen
- Institute of Hematology, School of Medicine, 47885Jinan University, Guangzhou, China
| | - Zhuowen Chen
- 66278The First People's Hospital of Foshan City, Foshan, China
| | - Chi Leong Chio
- Institute of Hematology, School of Medicine, 47885Jinan University, Guangzhou, China
| | - Ying Zhao
- 66278The First People's Hospital of Foshan City, Foshan, China
| | - Yongsheng Li
- Guangdong Cord Blood Bank, Guangzhou, Guangdong, China.,Guangzhou Municipality Tianhe Nuoya Bio-engineering Co., Ltd, Guangzhou, Guangdong, China
| | - Zhipeng Liu
- Guangdong Cord Blood Bank, Guangzhou, Guangdong, China.,Guangzhou Municipality Tianhe Nuoya Bio-engineering Co., Ltd, Guangzhou, Guangdong, China
| | - Zhenyi Jin
- Institute of Hematology, School of Medicine, 47885Jinan University, Guangzhou, China
| | - Xiuli Wu
- Institute of Hematology, School of Medicine, 47885Jinan University, Guangzhou, China
| | - Wei Wei
- Guangdong Cord Blood Bank, Guangzhou, Guangdong, China.,Guangzhou Municipality Tianhe Nuoya Bio-engineering Co., Ltd, Guangzhou, Guangdong, China
| | - Qi Zhao
- Institute of Translational Medicine, Cancer Centre, 59193University of Macau, Taipa, Macau SPR, China
| | - Yangqiu Li
- Institute of Hematology, School of Medicine, 47885Jinan University, Guangzhou, China
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