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Fine needle aspiration biopsy (FNAB) is

Aim:The aim of this study is to test the diagnostic success of strain elastography in distinguishing benign from malignant thyroid nodules.Materials and Methods:The size, echogenicity, and halo integrity of 293 thyroid nodules and the presence of microcalcification in these nodules were evaluated on gray-scale examination. Doppler characteristics and elastography patterns were also evaluated and recorded. Nodules were classified in four categories (patterns 1–4) based on elastographic examination.Results:According to the cytopathological findings, 222 nodules were benign, and 71 nodules were malignant. The risk of a nodule to be malignant was 3.8 times increased by hypoechogenicity, 7.7 times increased by the presence of microcalcification, and 11.5 times increased by the absence of halo. On Doppler patterns, the presence of central vascularity increased the malignancy risk of a nodule by 5.8 times. According to the receiver operating characteristic analysis, patterns 3 and 4 were malignant, and patterns 1 and 2 were benign. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of elastography were 100%, 80.2%, 61.7%, 100%, and 85%, respectively.Conclusion:Strain elastography can be used as a noninvasive method in distinguishing benign from malignant thyroid nodules and in identifying the patients who would undergo surgery.

Objective To assess the composition of lumbar multifidus muscle, in patients with unilateral lumbar disc herniation causing nerve compression, using quantitative and qualitative magnetic resonance imaging (MRI) measurement methods. Methods Two radiologists retrospectively measured MRI signal intensity of the multifidus muscle, as high intensity represents more fat, and visually graded the fat content using a 5-point grading system in patients with unilateral subarticular lumbar disc herniation. Findings from the herniated and contralateral sides were compared. The association between fat content and severity of nerve compression and symptom duration were also evaluated. Results Ninety patients (aged 24–70 years) were included. Signal intensity of the affected multifidus muscle was significantly higher versus the contralateral muscle for quantitative measurements and qualitative scoring for both investigators. Significant correlations were observed between the severity of nerve compression and symptom duration and the degree of fat content in the affected multifidus muscle. Conclusions Higher fat composition was observed in the multifidus muscle ipsilateral to the lumbar disc herniation versus the contralateral side. Straightforward visual grading of muscle composition regarding fat infiltration appeared to be as useful as quantitative measurement.

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The aim of this study was to determine the sensitivity, specificity, and diagnostic accuracy of helical computed tomography (CT) without oral, intravenous, or rectal administration of contrast material in confirming the diagnosis of acute appendicitis in patients with suggestive clinical and laboratory findings. One hundred and thirty patients with suspected acute appendicitis underwent an unenhanced helical CT scan. Scans were obtained in a single breath-hold from the level of umbilicus to the pubic symphysis using a 5-mm collimation. Oral, intravenous, or rectal contrast materials were not used. The criteria for diagnosis of acute appendicitis included an enlarged diameter of appendix more than 6 mm with associated periappendiceal inflammation. The results yielded a sensitivity of 94.7%, a specificity of 91.7%, an accuracy of 93.8%, a positive predictive value of 96.7%, and a negative predictive value of 86.8%. Unenhanced helical CT accurately diagnoses acute appendicitis, and it protects the patients from unnecessary further time-consuming diagnostic procedures, the risks associated with contrast material administration, and unnecessary surgical interventions.

Aim The aim of this study is to evaluate the diagnostic value of machine learning- (ML-) based quantitative texture analysis in the differentiation of benign and malignant thyroid nodules. Materials and methods A sum of 306 quantitative textural features of 235 thyroid nodules (102 malignant, 43.4%; 133 benign, 56.4%) of a total of 198 patients were investigated using the random forest ML classifier. Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method. The diagnostic accuracy, sensitivity, specificity, and area under curve (AUC) of the proposed method were compared with the histopathological or cytopathological findings as reference methods. Results Of the 306 initial texture features, 284 (92.2%) showed good reproducibility (intraclass correlation ≥0.80). The random forest classifier accurately identified 87 out of 102 malignant thyroid nodules and 117 out of 133 benign thyroid nodules, which is a diagnostic sensitivity of 85.2%, specificity of 87.9%, and accuracy of 86.8%. The AUC of the model was 0.92. Conclusions Quantitative textural analysis of thyroid nodules using ML classification can accurately discriminate benign and malignant thyroid nodules. Our findings should be validated by multicenter prospective studies using completely independent external data.