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Yang SS, Zhong XH, Wang HX, Min AJ, Wang WM. Nomograms for Predicting Cancer-Specific Survival of Patients with Gingiva Squamous Cell Carcinoma: A Population-Based Study. Curr Med Sci 2021; 41:953-960. [PMID: 34693495 DOI: 10.1007/s11596-021-2435-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 03/29/2021] [Indexed: 12/09/2022]
Abstract
OBJECTIVE The use of the traditional American Joint Committee on Cancer (AJCC) staging system alone has limitations in predicting the survival of gingiva squamous cell carcinoma (GSCC) patients. We aimed to establish a comprehensive prognostic nomogram with a prognostic value similar to the AJCC system. METHODS Patients were identified from SEER database. Variables were selected by a backward stepwise selection method in a Cox regression model. A nomogram was used to predict cancer-specific survival rates for 3, 5 and 10 years in patients with GSCC. Several basic features of model validation were used to evaluate the performance of the survival model: consistency index (C-index), receiver operating characteristic (ROC) curve, calibration chart, net weight classification improvement (NRI), comprehensive discriminant improvement (IDI) and decision curve analysis (DCA). RESULTS Multivariate analyses revealed that age, race, marital status, insurance, AJCC stage, pathology grade and surgery were risk factors for survival. In particular, the C-index, the area under the ROC curve (AUC) and the calibration plots showed good performance of the nomogram. Compared to the AJCC system, NRI and IDI showed that the nomogram has improved performance. Finally, the nomogram's 3-year and 5-year and 10-year DCA curves yield net benefits higher than traditional AJCC, whether training set or a validation set. CONCLUSION We developed and validated the first GSCC prognosis nomogram, which has a better prognostic value than the separate AJCC staging system. Overall, the nomogram of this study is a valuable tool for clinical practice to consult patients and understand their risk for the next 3, 5 and 10 years.
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Affiliation(s)
- Si-Si Yang
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Xiao-Huan Zhong
- Department of Orthodontics, Center of Stomatology, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Hui-Xin Wang
- Department of Orthodontics, Center of Stomatology, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - An-Jie Min
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Wei-Ming Wang
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
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Ono K, Yoshioka N, Masui M, Obata K, Kunisada Y, Okui T, Ibaragi S, Kawai H, Nagatsuka H, Sasaki A. A case of oral cancer with delayed occipital lymph node metastasis: Case report. Clin Case Rep 2020; 8:2469-2475. [PMID: 33363761 PMCID: PMC7752593 DOI: 10.1002/ccr3.3086] [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: 05/19/2020] [Accepted: 06/15/2020] [Indexed: 12/03/2022] Open
Abstract
Consideration of unexpected metastasis in patients who have undergone neck dissection with advanced tumors must be anticipated with careful follow-up.
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Affiliation(s)
- Kisho Ono
- Department of Oral and Maxillofacial SurgeryDentistry and Pharmaceutical SciencesOkayama University Graduate School of MedicineOkayamaJapan
| | - Norie Yoshioka
- Department of Oral and Maxillofacial SurgeryDentistry and Pharmaceutical SciencesOkayama University Graduate School of MedicineOkayamaJapan
| | - Masanori Masui
- Department of Oral and Maxillofacial SurgeryDentistry and Pharmaceutical SciencesOkayama University Graduate School of MedicineOkayamaJapan
| | - Kyoichi Obata
- Department of Oral and Maxillofacial SurgeryDentistry and Pharmaceutical SciencesOkayama University Graduate School of MedicineOkayamaJapan
| | - Yuki Kunisada
- Department of Oral and Maxillofacial SurgeryDentistry and Pharmaceutical SciencesOkayama University Graduate School of MedicineOkayamaJapan
| | - Tatsuo Okui
- Department of Oral and Maxillofacial SurgeryDentistry and Pharmaceutical SciencesOkayama University Graduate School of MedicineOkayamaJapan
| | - Soichiro Ibaragi
- Department of Oral and Maxillofacial SurgeryDentistry and Pharmaceutical SciencesOkayama University Graduate School of MedicineOkayamaJapan
| | - Hotaka Kawai
- Department of Oral Pathology and MedicineDentistry and Pharmaceutical SciencesOkayama University Graduate School of MedicineOkayamaJapan
| | - Hitoshi Nagatsuka
- Department of Oral Pathology and MedicineDentistry and Pharmaceutical SciencesOkayama University Graduate School of MedicineOkayamaJapan
| | - Akira Sasaki
- Department of Oral and Maxillofacial SurgeryDentistry and Pharmaceutical SciencesOkayama University Graduate School of MedicineOkayamaJapan
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Tong D, Tian Y, Zhou T, Ye Q, Li J, Ding K, Li J. Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data. BMC Med Inform Decis Mak 2020; 20:22. [PMID: 32033604 PMCID: PMC7006213 DOI: 10.1186/s12911-020-1043-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 01/31/2020] [Indexed: 12/16/2022] Open
Abstract
Background Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data. Methods In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell’s concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno’s concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates. Results Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell’s concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno’s concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively. Conclusion In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.
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Affiliation(s)
- Danyang Tong
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Tianshu Zhou
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Qiancheng Ye
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Jun Li
- Department of Surgical Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Hangzhou, 31009, Zhejiang Province, China
| | - Kefeng Ding
- Department of Surgical Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Hangzhou, 31009, Zhejiang Province, China
| | - Jingsong Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China. .,Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
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