Shin JH, et al. Korean J Radiol. 2016 May-Jun;17(3):370-395. https://doi.org/10.3348/kjr.2016.17.3.370

Ha EJ, et al. Korean J Radiol. 2021 Dec;22(12):2094-2123. https://doi.org/10.3348/kjr.2021.0713

To explore the role of histogram analysis of apparent diffusion coefficient (ADC) maps based on entire tumor volume data in determining glioma grade and to evaluate the diagnostic performance of ADC maps at standard (1000 sec/mm(2)) and high (3000 sec/mm(2)) b values. This retrospective study was approved by the institutional review board, and informed consent was waived. Twenty-seven patients with astrocytic tumors underwent diffusion-weighted magnetic resonance imaging with b values of 1000 and 3000 sec/mm(2), and the corresponding ADC maps were calculated (ADC(1000) and ADC(3000), respectively). Regions of interest containing the lesion were drawn on every section of the ADC map containing the tumor and were summated to derive volume-based data of the entire tumor. Histogram parameters were correlated with tumor grade by using repeated measurements analysis of variance, the Tukey-Kramer test for post hoc comparisons, and an unpaired Student t test. Receiver operating characteristic (ROC) curves were constructed to determine the optimum threshold for each histogram parameter, and sensitivity and specificity were assessed. Minimum ADC(1000) and ADC(3000) both decreased with increasing tumor grade. The 50th and 75th percentiles of cumulative ADC(1000) histograms showed significant differences between grades (P = .015 and .001, respectively), while the fifth and 75th percentiles of cumulative ADC(3000) histograms showed such differences (P = .015 and .014, respectively). Minimum ADC and the fifth percentile for both ADC(1000) (P < .001 and P = .024, respectively) and ADC(3000) (P < .001 and P = .001, respectively) proved to be significant histogram parameters for differentiating high- from low-grade gliomas. The diagnostic value of the parameters derived from ADC(1000) and ADC(3000) were compared, and a significant difference (0.202, P = .014) was found between the areas under the ROC curve of the fifth percentiles for ADC(1000) and ADC(3000). Histogram analysis of ADC maps based on entire tumor volume can be a useful tool for grading gliomas. The fifth percentile of the cumulative ADC histogram obtained at a high b value was the most promising parameter for differentiating high- from low-grade gliomas.

Na DG, et al. Korean J Radiol. 2012 Mar-Apr;13(2):117-125. http://dx.doi.org/10.3348/kjr.2012.13.2.117

Background: Thyroid nodules with fine-needle aspiration (FNA) readings of nondiagnostic or atypia of undetermined significance (AUS), also referred to as follicular lesion of undetermined significance (FLUS) are problematic for their optimal management. The usefulness of performing a core-needle biopsy (CNB) to clarify whether these nodules are benign or malignant has not been established. The purpose of the present study was to determine whether CNB provides better diagnostic information than repeat FNA (rFNA) in thyroid nodules having nondiagnostic or AUS/FLUS readings. Materials and Methods: The Bethesda System for Reporting Thyroid Cytopathology was used for FNA readings and for CNB readings. The study included 225 thyroid nodules from 220 consecutive patients who previously had nondiagnostic (Group N-DIAG, n=64) or AUS/FLUS (Group AF, n=161) FNA readings. All patients simultaneously underwent rFNA and CNB of each nodule. The nondiagnostic and AUS/FLUS readings by rFNA and by CNB were compared. The diagnostic sensitivities of rFNA and CNB for malignancy in thyroid nodules were also assessed. Statistical analysis was performed using a McNemar's test. Results: In N-DIAG Group, the nondiagnostic readings for the CNBs were lower than that those for rFNAs (1.6% vs. 28.1%, p<0.001). In the AF Group, the AUS/FLUS readings for the CNBs were lower than those for the rFNAs (23.6% vs. 39.8%, p<0.001). The inconclusive diagnoses (nondiagnostic or AUS/FLUS) for the CNBs were lower than those for the rFNAs in Group N-DIAG (12.5% vs. 45.3%, p<0.001) and Group AF (26.7% vs. 49.1%, p<0.001). The sensitivity of CNB for thyroid malignancy was higher than that of rFNA in Group N-DIAG (100% vs. 71.4%, p=0.125) and Group AF (78.5% vs. 55.4%, p<0.001). Conclusion: After patients have had one FNA of a thyroid nodule yielding inconclusive diagnostic results (nondiagnostic or AUS/FLUS), CNB is more useful than rFNA for reducing the frequency of inconclusive diagnostic results. CNB will improve the diagnostic performance for malignancy more than rFNA in thyroid nodules that on the first FNA had nondiagnostic or AUS/FLUS readings.

Background: Although ultrasonography (US) has an essential role in assessing the malignancy risk of thyroid nodules, a malignancy risk-stratification system has not been established. The purpose of this study was to develop a clinically feasible US risk-stratification system—the Thyroid Imaging Reporting and Data System (TIRADS)—primarily based on the solidity and echogenicity of thyroid nodules. Methods: From January 2010 to May 2011, a total of consecutive 2000 thyroid nodules (≥1 cm) with final diagnoses were enrolled from the database of low and high cancer volume institutions (1000 nodules from each institution). For the development of TIRADS, the type and predictive value of US predictors in the groups categorized by solidity and echogenicity were analyzed, and the US predictors were integrated and categorized according to the malignancy risk. Results: The suspicious US features of microcalcification, taller than wide shape, and spiculated/microlobulated margin were independently predictive of malignancy in the solid or hypoechoic nodule group (p < 0.001, respectively). Meanwhile, only microcalcification was independently predictive of malignancy in the partially cystic nodule group (p = 0.006), and microcalcification and spiculated/microlobulated margin were independently predictive of malignancy in the iso- and hyperechoic nodule group (p = 0.002 and p = 0.015, respectively). Although the presence of any suspicious US features had a high malignancy risk in the group of solid hypoechoic nodules, it had an intermediate malignancy risk in the group of partially cystic or iso- and hyperechoic nodules. The malignancy risk of thyroid nodules was stratified into five TIRADS categories by integrating the type and predictive values of US predictors based on solidity and echogenicity. Conclusion: The malignancy risk of thyroid nodules can be stratified by TIRADS according to US patterns by combining solidity, echogenicity, and suspicious US features. The proposed risk-stratification system based on solidity and echogenicity will be useful for risk stratification and management decision of thyroid nodules.