Oncotarget

Clinical Research Papers:

A new computational model for human thyroid cancer enhances the preoperative diagnostic efficacy

Tuo Li, Jianguo Sheng, Weiqin Li, Xin Zhang, Hongyu Yu, Xueyun Chen, Jianquan Zhang, Quancai Cai, Yongquan Shi and Zhimin Liu _

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Oncotarget. 2015; 6:28463-28477. https://doi.org/10.18632/oncotarget.4691

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Abstract

Tuo Li1,2,*, Jianguo Sheng3, Weiqin Li4, Xin Zhang5, Hongyu Yu4, Xueyun Chen5, Jianquan Zhang3, Quancai Cai6, Yongquan Shi1,2 and Zhimin Liu1,2

1 Department of Endocrinology, Changzheng Hospital, Second Military Medical University, Shanghai, P. R. Chinaa

2 Endocrine laboratory, Changzheng Hospital, Second Military Medical University, P. R. China

3 Department of Ultrasonography, Changzheng Hospital, Second Military Medical University, P. R. China

4 Department of Pathology, Changzheng Hospital, Second Military Medical University, P. R. China

5 Department of General Surgery, Changzheng Hospital, Second Military Medical University, P. R. China

6 Center for Clinical Epidemiology and Evidence-based Medicine, Second Military Medical University, Shanghai, P. R. China

* This is the first author of this article

Correspondence to:

Zhimin Liu, email:

Yongquan Shi, email:

Quancai Cai, email:

Jianquan Zhang, email:

Keywords: thyroid cancer, differential diagnosis, computational model

Received: April 02, 2015 Accepted: June 10, 2015 Published: June 29, 2015

Abstract

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.


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