Ruyi Zhang1, Mei Xu2, Xiangxiang Liu1, Miao Wang1, Qiang Jia1, Shen Wang1, Xiangqian Zheng3, Xianghui He4, Chao Huang5, Yaguang Fan6, Heng Wu6, Ke Xu7, Dihua Li8, Zhaowei Meng9. 1. Department of Nuclear Medicine, Tianjin Medical University General Hospital, Tianjin, China. 2. Department of Pediatric, Tianjin Medical University General Hospital, Tianjin, China. 3. Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin City, Tianjin, China. 4. Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, China. 5. Hull York Medical School, University of Hull, Hull, UK. 6. Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China. 7. Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China. ke_xu@hotmail.com. 8. Tianjin Key Laboratory of Acute Abdomen Disease Associated Organ Injury and ITCWM Repair, Institute of Acute Abdominal Diseases, Tianjin Nankai Hospital, Tianjin, China. dhli2013@163.com. 9. Department of Nuclear Medicine, Tianjin Medical University General Hospital, Tianjin, China. zmeng@tmu.edu.cn.
Abstract
PURPOSE: This study aimed to develop a clinically predictive nomogram model to predict the survival probability of differentiated thyroid carcinoma patients and compare the value of this model with that of the eighth edition AJCC cancer staging system. METHODS: We selected 59,876 differentiated thyroid carcinoma patients diagnosed between 2004 and 2015 from the SEER database and separated those patients into a training set (70%) and a validation set (30%) randomly. We used Cox regression analysis to build the nomogram model (model 1) and the eighth edition AJCC cancer staging model (model 2). Then we compared the predictive accuracy, discrimination, and clinical usage of both models by calculating AUC (Area under the curve), C-index, as well as analyzing DCA (Decision Curve Analysis) performance respectively. RESULTS: AUCs of all predicted time points (12-month, 36-month, 60-month, and 120-month) of model 1 were 0.933, 0.913, 0.879, and 0.868 for the training set; 0.933, 0.926, 0.916, and 0.894 for the validation set. As for model 2, data were 0.938, 0.906, 0.866, and 0.847 for the training set; 0.924, 0.925, 0.912, and 0.867 for the validation set. C-indices of model 1 were higher than those of model 2 (0.923 vs. 0.918 for the training set, 0.938 vs. 0.930 for the validation set). DCA comparison showed that the net benefit of model 1 was bigger when comparing with that of model 2. CONCLUSIONS: Model 1 provided with both better predictive accuracy and clinical usage compared with those of model 2 and might be able to predict the survival probability of differentiated thyroid carcinoma patients visually and accurately with a higher net benefit.
PURPOSE: This study aimed to develop a clinically predictive nomogram model to predict the survival probability of differentiated thyroid carcinomapatients and compare the value of this model with that of the eighth edition AJCC cancer staging system. METHODS: We selected 59,876 differentiated thyroid carcinomapatients diagnosed between 2004 and 2015 from the SEER database and separated those patients into a training set (70%) and a validation set (30%) randomly. We used Cox regression analysis to build the nomogram model (model 1) and the eighth edition AJCC cancer staging model (model 2). Then we compared the predictive accuracy, discrimination, and clinical usage of both models by calculating AUC (Area under the curve), C-index, as well as analyzing DCA (Decision Curve Analysis) performance respectively. RESULTS: AUCs of all predicted time points (12-month, 36-month, 60-month, and 120-month) of model 1 were 0.933, 0.913, 0.879, and 0.868 for the training set; 0.933, 0.926, 0.916, and 0.894 for the validation set. As for model 2, data were 0.938, 0.906, 0.866, and 0.847 for the training set; 0.924, 0.925, 0.912, and 0.867 for the validation set. C-indices of model 1 were higher than those of model 2 (0.923 vs. 0.918 for the training set, 0.938 vs. 0.930 for the validation set). DCA comparison showed that the net benefit of model 1 was bigger when comparing with that of model 2. CONCLUSIONS: Model 1 provided with both better predictive accuracy and clinical usage compared with those of model 2 and might be able to predict the survival probability of differentiated thyroid carcinomapatients visually and accurately with a higher net benefit.
Authors: Yi Ho Lee; Yu Mi Lee; Tae Yon Sung; Jong Ho Yoon; Dong Eun Song; Tae Yong Kim; Jung Hwan Baek; Jin Suk Ryu; Ki Wook Chung; Suck Joon Hong Journal: Ann Surg Oncol Date: 2017-01-27 Impact factor: 5.344
Authors: J Jonklaas; G Nogueras-Gonzalez; M Munsell; D Litofsky; K B Ain; S T Bigos; J D Brierley; D S Cooper; B R Haugen; P W Ladenson; J Magner; J Robbins; D S Ross; M C Skarulis; D L Steward; H R Maxon; S I Sherman Journal: J Clin Endocrinol Metab Date: 2012-04-10 Impact factor: 5.958
Authors: Nicole A Cipriani; Sapna Nagar; Sharone P Kaplan; Michael G White; Tatjana Antic; Peter M Sadow; Briseis Aschebrook-Kilfoy; Peter Angelos; Edwin L Kaplan; Raymon H Grogan Journal: Thyroid Date: 2015-10-26 Impact factor: 6.568