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

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>34.59?g/L, Ct>64.00ng/L, and CEA>0.41?g/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.

SUBMITTER: Li T

PROVIDER: S-EPMC4695072 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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

Li Tuo T Sheng Jianguo J Li Weiqin W Zhang Xin X Yu Hongyu H Chen Xueyun X Zhang Jianquan J Cai Quancai Q Shi Yongquan Y Liu Zhimin Z

Oncotarget 20150901 29

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 t ...[more]

PMID: 26325368

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Project description:BACKGROUND: Dyslipidemia is an important risk factor for cardiovascular disease and type II diabetes. Lipoprotein diagnostics, such as LDL cholesterol and HDL cholesterol, help to diagnose these diseases. Lipoprotein profile measurements could improve lipoprotein diagnostics, but interpretational complexity has limited their clinical application to date. We have previously developed a computational model called Particle Profiler to interpret lipoprotein profiles. In the current study we further developed and calibrated Particle Profiler using subjects with specific genetic conditions. We subsequently performed technical validation and worked at an initial indication of clinical usefulness starting from available data on lipoprotein concentrations and metabolic fluxes. Since the model outcomes cannot be measured directly, the only available technical validation was corroboration. For an initial indication of clinical usefulness, pooled lipoprotein metabolic flux data was available from subjects with various types of dyslipidemia. Therefore we investigated how well lipoprotein metabolic ratios derived from Particle Profiler distinguished reported dyslipidemic from normolipidemic subjects. RESULTS: We found that the model could fit a range of normolipidemic and dyslipidemic subjects from fifteen out of sixteen studies equally well, with an average 8.8% ± 5.0% fit error; only one study showed a larger fit error. As initial indication of clinical usefulness, we showed that one diagnostic marker based on VLDL metabolic ratios better distinguished dyslipidemic from normolipidemic subjects than triglycerides, HDL cholesterol, or LDL cholesterol. The VLDL metabolic ratios outperformed each of the classical diagnostics separately; they also added power of distinction when included in a multivariate logistic regression model on top of the classical diagnostics. CONCLUSIONS: In this study we further developed, calibrated, and corroborated the Particle Profiler computational model using pooled lipoprotein metabolic flux data. From pooled lipoprotein metabolic flux data on dyslipidemic patients, we derived VLDL metabolic ratios that better distinguished normolipidemic from dyslipidemic subjects than standard diagnostics, including HDL cholesterol, triglycerides and LDL cholesterol. Since dyslipidemias are closely linked to cardiovascular disease and diabetes type II development, lipoprotein metabolic ratios are candidate risk markers for these diseases. These ratios can in principle be obtained by applying Particle Profiler to a single lipoprotein profile measurement, which makes clinical application feasible.

Project description:BackgroundMouse models of metastatic human cancers are important tools in preclinical studies for testing new systematic therapies and studying effectors of cancer metastasis. The major drawbacks of current mouse models for metastatic thyroid cancer are that they have low metastasis rates and do not allow in vivo tumor detection. Here, we report and characterize an in vivo detectable metastasis mouse model of human thyroid cancer using multiple thyroid cancer cell lines.MethodsHuman anaplastic thyroid cancer cell lines 8505C, C-643, SW-1736, and THJ-16T; follicular thyroid cancer cell lines FTC-133, FTC-236, and FTC-238; and Hürthle cell carcinoma cell line XTC-1 were transfected with a linearized pGL4.51[luc2/CMV/Neo] vector or transduced with lentivirus containing Luc2-eGFP reporter genes. The stably transfected cells were injected intravenously into NOD.Cg-Prkdc(scid) Il2rg(tm1Wjl)/SzJ mice. Tumors were detected with an in vivo imaging system-Xenogen IVIS. Vemurafenib, a BRAF inhibitor, was used to treat lung metastases generated from 8505C-Luc2 cells with a BRAF(V600E) mutation to test the accuracy of the model to evaluate response to therapy.ResultsIntravenous injection of as few as 30,000 8505C-Luc2 cells produced lung metastases in 100% of the injected mice, and many of these mice also developed bone metastases at a later stage of the disease. Similarly, metastatic tumors also developed in all mice injected with C-643-Luc2, THJ-16T-Luc2, FTC-133-Luc2, FTC-236-Luc2, FTC-238-Luc2, and XTC-1-Luc2 cells. The metastases were easily detectable in vivo, and tumor progression could be dynamically and accurately followed and correlated with the actual tumor burden. Furthermore, disease progression could be easily controlled by adjusting the number of injected cells. The in vivo treatment of 8505C xenograft lung metastases with vemurafenib dramatically reduced the growth and signal intensity with good correlation with actual tumor burden.ConclusionsHerein we report an in vivo detectable mouse model of metastatic human thyroid cancer that is reliable and reproducible. It will serve as a useful tool in the preclinical testing of alternative systematic therapies for metastatic thyroid cancer, and for functional studies of thyroid cancer tumor biology in vivo.

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

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

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