A new computational model for human thyroid cancer enhances the preoperative diagnostic efficacy.
Considering the high rate of missed diagnosis and delayed treatments for thyroid cancer, an effective systematic model for the differential diagnosis is highly needed. Thus we analyzed the data on the clinicopathological characteristics, routine laboratory tests and imaging examinations in a cohort of 13,980 patients with thyroid cancer to establish a new diagnostic model for differentiating thyroid cancer in clinical practice. Here, we randomly selected two-thirds of the population to develop the thyroid malignancy risk scoring system (TMRS) for preoperative differentiation between thyroid cancer and benignant thyroid diseases, and then validated its differential diagnostic power in the rest one-third population. The 18 predictors finally enrolled in the TMRS included male gender, clinical manifestations (fever, neck sore, neck lump, palpitations or sweating), laboratory findings (TSH>1.56mIU/L, FT3>5.85pmol/L, TPOAb>14.97IU/ml, TgAb>48.00IU/ml, Tg>34.59μg/L, Ct>64.00ng/L, and CEA>0.41μg/L), and ultrasound features (tumor number≤ 23mm, site, size, echo texture, margins, and shape of neck lymphnodes). The TMRS is validated to be well-calibrated (P = 0.437) and excellently discriminated (AUC = 0.93, 95% CI [0.92, 0.94]), with an accuracy of 83.2%, a sensitivity of 89.3%, a specificity of 81.5%, positive and negative predictive values of 56.8% and 96.6%, positive and negative likelihood ratios of 4.83 and 0.13 in the development cohort, respectively. The TMRS highlights that this differential diagnostic system could help provide accurate preoperative risk stratification for thyroid cancer, and avoid unnecessary over- and under-treatment for such patients.
Department of Endocrinology, Changzheng Hospital, Second Military Medical University, Shanghai, P. R. Chinaa.|Endocrine Laboratory, Changzheng Hospital, Second Military Medical University, P. R. China.
Tuo
T
Li
Department of Ultrasonography, Changzheng Hospital, Second Military Medical University, P. R. China.
Jianguo
J
Sheng
Department of Pathology, Changzheng Hospital, Second Military Medical University, P. R. China.
Weiqin
W
Li
Department of General Surgery, Changzheng Hospital, Second Military Medical University, P. R. China.
Xin
X
Zhang
Department of Pathology, Changzheng Hospital, Second Military Medical University, P. R. China.
Hongyu
H
Yu
Department of General Surgery, Changzheng Hospital, Second Military Medical University, P. R. China.
Xueyun
X
Chen
Department of Ultrasonography, Changzheng Hospital, Second Military Medical University, P. R. China.
Jianquan
J
Zhang
Center for Clinical Epidemiology and Evidence-based Medicine, Second Military Medical University, Shanghai, P. R. China.
Quancai
Q
Cai
Department of Endocrinology, Changzheng Hospital, Second Military Medical University, Shanghai, P. R. Chinaa.|Endocrine Laboratory, Changzheng Hospital, Second Military Medical University, P. R. China.
Yongquan
Y
Shi
Department of Endocrinology, Changzheng Hospital, Second Military Medical University, Shanghai, P. R. Chinaa.|Endocrine Laboratory, Changzheng Hospital, Second Military Medical University, P. R. China.
Zhimin
Z
Liu
D000328:Adult; D015331:Cohort Studies; D019295:Computational Biology / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015999:Multivariate Analysis; D057234:Preoperative Period; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data; D012307:Risk Factors; D012737:Sex Factors; D013959:Thyroid Diseases / Q000175:diagnosis*; D013964:Thyroid Neoplasms / Q000175:diagnosis*
computational model; differential diagnosis; thyroid cancer