3T magnetic resonance spectroscopy as a powerful diagnostic modality for assessment of thyroid nodules

Application of dual-source computed tomography in the diagnosis of thyroid cancer and evaluation of biological behaviors

OBJECTIVE

Thyroid cancer (TC) is an extremely prevailing malignant endocrine tumor. Therefore, effective diagnostic tools are necessary. This study explored the application value of dual-source computed tomography (DSCT) in TC diagnosis and biological behavior assessment.

METHODS

This study retrospectively selected 68 TC patients and another 74 benign patients with thyroid adenoma, nodular goiter, or adenomatous hyperplasia. All patients were confirmed by pathological examination and underwent DSCT examination. The iodine concentration (IC) obtained from plain computed tomography (CT) scanning and normalized iodine concentration (NIC) in the arterial phase and venous phase were recorded. The positive expression rates of estrogen receptor alpha (ERα), estrogen receptors beta (ERβ), and Ki67 in pathological tissues were determined by immunohistochemistry, and their correlation with IC in plain CT was assessed by Pearson correlation analysis, respectively. The diagnostic values of IC in plain CT and venous phase NIC in TC patients were evaluated using the receiver operating characteristic curve.

RESULTS

Malignant patients had lower IC in plain DSCT scanning, venous phase NIC, and ERβ, and higher ERα and Ki67 than benign patients. IC level in plain DSCT scanning was inversely-correlated with ERα and Ki-67 positive expression rates, but positively-related to ERβ to different degrees. For the diagnosis of TC patients, the AUC of IC level in plain DSCT was 0.771, with a cut-off value of 1.250 (97.06% sensitivity and 41.89% specificity), and the AUC of venous phase NIC was 0.738, with a cut-off value of 0.825 (100% sensitivity and 43.24% specificity).

CONCLUSION

The IC level obtained from DSCT scanning could assist in the differential diagnosis of malignant and benign thyroid nodules and evaluation of biological behaviors.

Malignant and benign thyroid nodule differentiation through the analysis of blood plasma with terahertz spectroscopy

The liquid and lyophilized blood plasma of patients with benign or malignant thyroid nodules and healthy individuals were studied by terahertz (THz) time-domain spectroscopy and machine learning. The blood plasma samples from malignant nodule patients were shown to have higher absorption. The glucose concentration and miRNA-146b level were correlated with the sample's absorption at 1 THz. A two-stage ensemble algorithm was proposed for the THz spectra analysis. The first stage was based on the Support Vector Machine with a linear kernel to separate healthy and thyroid nodule participants. The second stage included additional data preprocessing by Ornstein-Uhlenbeck kernel Principal Component Analysis to separate benign and malignant thyroid nodule participants. Thus, the distinction of malignant and benign thyroid nodule patients through their lyophilized blood plasma analysis by terahertz time-domain spectroscopy and machine learning was demonstrated.