Application of the process-based teaching based on SPARK case database in the practice teaching of radiology in the musculoskeletal system for undergraduate medical students.

BACKGROUND: Process-based teaching is a new education model. SPARK case database is a free medical imaging case database. This manuscript aimed to explore the application of the process-based teaching based on SPARK case database in the practice teaching of radiology in the musculoskeletal system. METHODS: 117 third year medical students were included. They were divided into Group A, B, C and D according to the curriculum arrangement. Group A and B attended the experimental class at the same time, A was the experimental group, B was the control group. Group C and D attended experimental classes at the same time, C was the experimental group, D was the control group. The experimental group used SPARK case database, while the control group used traditional teaching model for learning. The four groups of students were respectively tested after the theoretical class, before the experimental class, after the experimental class, and one week after the experimental class to compare the results. Finally, all students used SPARK case database to study, and were tested one month after the experimental class to compare their differences. RESULTS: The scores after the theoretical class of Group A and B were (100.0 ± 25.4), (101.0 ± 23.8)(t=-0.160, P > 0.05), Group C and D were (94.7 ± 23.7), (92.1 ± 18.6)(t = 0.467, P > 0.05). The scores of Group A and B before and after the experimental class and one week after the experimental class were respectively (84.1 ± 17.4), (72.1 ± 21.3)(t = 2.363, P < 0.05), (107.6 ± 14.3), (102.1 ± 18.0)(t = 1.292, P > 0.05), (89.7 ± 24.3), (66.6 ± 23.2)(t = 3.706, P < 0.05). The scores of Group C and D were (94.0 ± 17.3), (72.8 ± 25.5)(t = 3.755, P < 0.05), (107.3 ± 20.3), (93.1 ± 20.9)(t = 2.652, P < 0.05), (100.3 ± 19.7), (77.2 ± 24.0)(t = 4.039, P < 0.05). The scores of Group A and B for one month after the experimental class were (86.6 ± 28.8), (84.5 ± 24.0)(t = 0.297, P > 0.05), and Group C and D were (95.7 ± 20.3), (91.7 ± 23.0)(t = 0.699, P > 0.05). CONCLUSIONS: The process-based teaching based on SPARK case database could improve the radiology practice ability of the musculoskeletal system of students.

Ultrasound gray scale ratio for differential diagnosis of papillary thyroid microcarcinoma from benign micronodule in patients with Hashimoto's thyroiditis.

BACKGROUND: To investigate the diagnostic value of ultrasound gray scale ratio (UGSR) in differentiating papillary thyroid microcarcinomas (PTMCs) from benign micronodules (BMNs) in patients with Hashimoto's thyroiditis (HT). METHODS: The ultrasound images of 285 PTMCs (from 247 patients) and 173 BMNs (from 140 patients) in the HT group, as well as 461 PTMCs (from 417 patients) and 234 BMNs (from 197 patients) in the non-HT group were retrospectively analyzed. The diagnosis of all cases was confirmed by histopathological examinations. The gray scale values of the nodules and surrounding thyroid tissues were measured and subsequently the UGSRs were calculated. Receiver operating characteristic curve analysis was used to determine the area under the curve (AUC), optimal UGSR threshold, sensitivity and specificity in differentiating PTMCs and BMNs in the two groups. RESULTS: The UGSR of PTMC and BMN was 0.52 ± 0.12 and 0.85 ± 0.24 in the HT group (P < 0.001), and 0.57 ± 0.13 and 0.87 ± 0.20 in the non-HT group (P < 0.001), respectively. The difference in PTMC-UGSR was significant between the two groups (P < 0.001), whereas BMN-UGSR did not differ between the two groups (P = 0.416). The AUC, optimal UGSR threshold, sensitivity and specificity of UGSR for differentiating PTMC and BMN in the HT and non-HT group were 0.890 versus 0.901, 0.68 versus 0.72, 91.23% versus 90.67%, and 77.46% versus 82.05%, respectively. CONCLUSIONS: The USGR of the HT group was lower than that of the non-HT group. Moreover, UGSR exhibited important diagnostic value in differentiating PTMC from BMN in both HT and non-HT groups.

Ultrasound grayscale ratio: a reliable parameter for differentiating between papillary thyroid microcarcinoma and micronodular goiter.

BACKGROUND: The present study aimed to quantify and differentiate the echo levels of papillary thyroid microcarcinomas (PTMCs) and micronodular goiters (MNGs) using the ultrasound grayscale ratio (UGSR) and to investigate the repeatability of UGSR. METHODS: The ultrasound (US) data of 241 patients with 265 PTMCs and 141 patients with 168 MNGs confirmed by surgery and pathology were retrospectively analyzed. All patients had received outpatient ultrasonic examination and preoperative ultrasonic positioning. The RADinfo radiograph reading system was used to measure the grayscales of PTMC, MNG, and thyroid tissues at the same gain level, and the UGSR values of the PTMC, MNG, and thyroid tissue were calculated. The patients were divided into outpatient examination, preoperative positioning, and mean value groups, and the receiver operating characteristic (ROC) curves were calculated to obtain the optimal UGSR threshold to distinguish PTMC from MNG. The interclass correlation coefficient (ICC) was used to assess the consistency of UGSR measured in three groups. RESULTS: The UGSR values of the PTMC and MNG were 0.56 ± 0.14 and 0.80 ± 0.19 (t = 5.84, P < 0.001) in the outpatient examination group, 0.55 ± 0.14 and 0.80 ± 0.19 (t = 18.74, P < 0.001) in the preoperative positioning group, and 0.56 ± 0.12 and 0.80 ± 0.18 (t = 16.49, P < 0.001) in the mean value group. The areas under the ROC curves in the three groups were 0.860, 0.856, and 0.875, respectively. When the UGSR values for the outpatient examination, preoperative positioning, and mean value groups were 0.649, 0.646, and 0.657, respectively, each group obtained its largest Youden index. A reliable UGSR value was obtained between the outpatient examination and preoperative positioning groups (ICC = 0.79, P = 0.68). CONCLUSION: UGSR is a simple and repeatable method to distinguish PTMC from MNG, and hence, can be widely applicable.

Application of SPARK teaching in acute abdomen radiography teaching for undergraduate medical students.

BACKGROUND: Acute abdomen is a series of acute and severe abdominal diseases commonly encountered in clinic. It is important to strengthen the image teaching of acute abdomen for undergraduates. AIM: This study aimed to explore the application effect of SPARK[sub-speciality (S), problem-based learning (P), assessment (A), report (R) and reading skill (K)] teaching mode in the experimental teaching of acute abdomen for undergraduate medical students. METHODS: We selected a total of 58 third year medical students for observation. The students were divided into experimental group and control group. Among them, 29 students in the experimental group studied in SPARK teaching mode, 29 students in the control group studied in traditional teaching mode. The two groups of students were tested after the theory class, before and after the experimental class, and one week after the experimental class, to compare the application effects of the two teaching modes. After the test one week after the experimental class, the two groups of students jointly adopted SPARK mode to learn, and were tested again one month after the experimental class to compare whether the two groups of students achieved the same results. The total score of all tests was 150. RESULTS: The average scores of the experimental group and the control group after theory class were (69.0 ± 26.4) and (72.1 ± 24.1) respectively, with no statistical difference (t = 0.468, P = 0.642). The average scores of the experimental group before, after and one week after the experimental class were higher than those of the control group. The experimental group was (84.5 ± 23.1), (109.7 ± 23.8), (105.5 ± 31.0) respectively, and the control group was (52.8 ± 15.1), (93.8 ± 17.0), (80.0 ± 22.8) respectively. The differences were statistically significant (t = -6.195, P = 0.00; t = -2.919, P = 0.05; t = -3.569, P = 0.01). The average scores of the experimental group and the control group after one month were (99.0 ± 31.0) and (95.5 ± 25.6) respectively, and there was no significant difference between the two groups (t = -0.462, P = 0.646). CONCLUSIONS: The SPARK teaching mode was helpful for undergraduate medical students to consolidate image foundation, improve image reading skills.

Prognostic analysis of 131I efficacy after papillary thyroid carcinoma surgery based on CT radiomics.

OBJECTIVE: To develop and validate a radiomics-clinical combined model combining preoperative CT and clinical data from patients with papillary thyroid carcinoma (PTC) to predict the efficacy of initial postoperative 131I treatment. METHODS: A total of 181 patients with PTC who received total thyroidectomy and initial 131I treatment were divided into training and testing sets (7:3 ratio). Univariate analysis and multivariate logistic regression were used to screen clinical factors affecting the therapeutic response to 131I treatment and construct a clinical model. Radiomics features extracted from preoperative CT images of PTCs were dimensionally reduced through recursive feature elimination and least absolute shrinkage and selection operator. Logistic regression was used to establish a radiomics model, and a radiomics-clinical combined model was developed by integrating the clinical model. The area under the curve (AUC), sensitivity, and specificity were used to evaluate the prediction performance of each model. RESULTS: Multivariate analysis revealed that pre-131I treatment sTg was an independent clinical risk factor affecting the efficacy of initial 131I treatment (P = 0.002), and the AUC, sensitivity, and specificity for predicting the efficacy of initial 131I treatment were 0.895, 0.899, and 0.816, respectively. After dimensionality reduction, 14 key CT radiomics features of PTCs were included. The established radiomics model predicted the efficacy of 131I treatment in the training and testing sets with AUCs of 0.825 and 0.809, sensitivities of 0.828 and 0.636, and specificities of 0.745 and 0.944, respectively. The combined model improved the AUC, sensitivity, and specificity in both sets. CONCLUSION: The preoperative CT-based radiomics model can effectively predict the efficacy of initial postoperative 131I treatment in patients with intermediate- or high-risk PTC, and the radiomics-clinical combined model exhibits better predictive performance.

Gadolinium deposition in the liver and brain in a rat model with liver fibrosis after intravenous administration of gadoxetate disodium.

Objectives: To investigate gadolinium deposition in the liver and brain in a rat model with liver fibrosis (LF) after intravenous administration of gadoxetate disodium (GD) and the histological effects of gadolinium deposition in the liver and brain. Methods: Adult male Sprague-Dawley rats were randomly assigned to one of the three groups: 1) LF group received intraperitoneal injection of carbon tetrachloride (CCl4) for 9 weeks alone; 2) LF&GD group received CCl4 and intravenous administration of GD (for 5 consecutive days); 3) GD group received olive oil and GD. Seven days after the final injection of GD, the deep cerebellar nuclei (DCN) and liver were excised to determine gadolinium concentrations via inductively coupled plasma mass spectrometry, and histologic staining was performed. Bonferroni's post-hoc test and Wilcoxon rank sum test were used to compare the differences between the three groups. Results: The concentrations of retained gadolinium in the liver in the LF&GD group (2.18 ± 0.44 µg/g) were significantly greater compared to the LF group (0.02 ± 0.01 µg/g, P < 0.001) and GD group (0.37 ± 0.11 µg/g, P < 0.001). Also, the concentrations of retained gadolinium in DCN were increased in the LF&GD group (0.13 ± 0.06 µg/g) compared to the LF group (0.01 ± 0.00 µg/g, P < 0.001) and GD group (0.06 ± 0.02 µg/g, P = 0.019). No histopathological alterations were detected in the liver and DCN between LF&GD group and LF group. Conclusions: LF aggravated gadolinium deposition in the liver and DCN after administration of GD. However, no significant acute histological alterations were observed due to gadolinium deposition.

Risk stratification of papillary thyroid cancers using multidimensional machine learning.

BACKGROUND: Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics. MATERIALS AND METHODS: The 558 patients collected from June 2013 to November 2020 were allocated to three groups: the discovery set [274 patients, 274 formalin-fixed paraffin-embedded (FFPE)], the retrospective test set (166 patients, 166 FFPE), and the prospective test set (118 patients, 118 fine-needle aspiration). Proteomic profiling was conducted by FFPE and fine-needle aspiration tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system. RESULTS: The authors developed a machine learning model of 17 variables based on the multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and was validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI: 82.9-84.4) and 83.5% (95% CI: 82.2-84.2) in the retrospective and prospective test sets, respectively. CONCLUSION: This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multicentre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.

Diagnostic value of CT enhancement degree in lymph node metastasis of papillary thyroid cancer: A comparison of enhancement, ratio, and difference.

Objectives: To evaluate the value of computed tomography (CT) enhancement degree in diagnosing lymph node (LN) metastasis in papillary thyroid carcinoma (PTC) by determining the ratio and difference between the Hounsfield units (HU) of CT enhancement and plain scan of the LNs, as well as between the HU of CT-enhanced LNs and the sternocleidomastoid muscle. Methods: The plain and enhanced CT findings of 114 metastasis-positive LNs in 89 cases and 143 metastasis-negative LNs in 114 cases of PTC were analyzed retrospectively. Plain HU of LNs (PNHU), enhanced HU of LNs (ENHU), and enhanced HU of the sternocleidomastoid muscle (EMHU) were measured. The ENHU, difference between ENHU and PNHU (EN-PNHU), ratio of ENHU to PNHU (EN/PNHU), difference between ENHU and EMHU (EN-EMHU), and ratio of ENHU to EMHU (EN/EMHU) in metastasis-positive and metastasis-negative LN groups were calculated, the corresponding diagnostic efficacy for differentiating metastasis-positive from metastasis-negative LNs in PTC were sought using the receiver-operating curve. The interobserver agreement between readers was assessed using the interobserver correlation coefficient (ICC). Results: The ENHU of 114 metastasis-positive LNs and 143 metastasis-negative LNs was 113.39 ± 24.13 and 77.65 ± 15.93, EN-PNHU was 65.84 ± 21.72 HU and 34.07 ± 13.63 HU, EN/PNHU was 2.36 (1.98, 2.75) and 1.76 (1.54, 2.02), EN-EMHU was 49.42 ± 24.59 HU and 13.27 ± 15.41 HU, and EN/EMHU was 1.79 ± 0.40 and 1.21 ± 0.24, respectively (all P < 0.001). The area under the curve, cutoff value, sensitivity, specificity, and accuracy of ENHU for identifying metastasis-positive and metastasis-negative LNs were 0.895, 97.3 HU, 0.746, 0.895, and 0.829, EN-PNHU was 0.894, 47.8 HU, 0.807, 0.874, and 0.844, EN/PNHU was 0.831, 1.9, 0.877, 0.650, and 0.751, EN-EMHU was 0.890, 26.4 HU, 0.807, 0.839, and 0.825, and EN/EMHU was 0.888, 1.5, 0.728, 0.902, and 0.825, respectively. The readers had an excellent interobserver agreement on these five parameters (ICC = 0.874-0.994). Conclusion: In the preoperative evaluation of LN metastasis in PTC, ENHU, EN-PNHU, EN-EMHU, and EN/EMHU had similarly high diagnostic efficacy, with ENHU, EN-PNHU, and EN/EMHU having higher specificity and EN-PNHU and EN-EMHU having higher sensitivity.

A modified method for CT radiomics region-of-interest segmentation in adrenal lipid-poor adenomas: a two-institution comparative study.

Objective: This study aimed to investigate the application of modified region-of-interest (ROI) segmentation method in unenhanced computed tomography in the radiomics model of adrenal lipid-poor adenoma, and to evaluate the diagnostic performance using an external medical institution data set and select the best ROI segmentation method. Methods: The imaging data of 135 lipid-poor adenomas and 102 non-adenomas in medical institution A and 30 lipid-poor adenomas and 43 non-adenomas in medical institution B were retrospectively analyzed, and all cases were pathologically or clinically confirmed. The data of Institution A builds the model, and the data of Institution B verifies the diagnostic performance of the model. Semi-automated ROI segmentation of tumors was performed using uAI software, using maximum area single-slice method (MAX) and full-volume method (ALL), as well as modified single-slice method (MAX_E) and full-volume method (ALL_E) to segment tumors, respectively. The inter-rater correlation coefficients (ICC) was performed to assess the stability of the radiomics features of the four ROI segmentation methods. The area under the curve (AUC) and at least 95% specificity pAUC (Partial AUC) were used as measures of the diagnostic performance of the model. Results: A total of 104 unfiltered radiomics features were extracted using each of the four segmentation methods. In the ROC analysis of the radiomics model, the AUC value of the model constructed by MAX was 0.925, 0.919, and 0.898 on the training set, the internal validation set, and the external validation set, respectively, and the AUC value of MAX_E was 0.937, 0.931, and 0.906, respectively. The AUC value of ALL was 0.929, 0.929, and 0.918, and the AUC value of ALL_E was 0.942, 0.926, and 0.927, respectively. In all samples, the pAUCs of MAX, MAX_E, ALL, and ALL_E were 0.021, 0.025, 0.018, and 0.028, respectively. Conclusion: The diagnostic performance of the radiomics model constructed based on the full-volume method was better than that of the model based on the single-slice method. The model constructed using the ALL_E method had a stronger generalization ability and the highest AUC and pAUC value.

Quantitative analysis of dual-phase enhanced CT in cervical lymph node metastasis of papillary thyroid carcinoma: a comparative study along with pathological manifestations.

PURPOSE: This study aimed to investigate the diagnostic value of dual-phase enhanced computed tomography (CT) in the cervical lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) by analyzing the dual-phase enhanced Hounsfield units (HUs) of lymph node and sternocleidomastoid muscle, and the ratio and difference. METHODS: The CT arterial-phase and venous-phase imaging data of 143 metastasis-positive lymph nodes (MPLNs) in 88 cases and 172 metastasis-negative lymph nodes (MNLNs) in 128 cases with PTC were retrospectively analyzed. All lymph nodes were confirmed by surgical pathology. The arterial-phase HU of lymph nodes (ANHU), venous-phase HU of lymph nodes (VNHU), arterial-phase HU of the sternocleidomastoid muscle (AMHU) and venous-phase HU of the sternocleidomastoid muscle (VMHU) were measured, and their difference and ratio (ANHU-AMHU, ANHU/AMHU, VNHU-VMHU, VNHU/VMHU) were calculated. The cutoff values and corresponding diagnostic efficacy for diagnosing LNM in PTC were sought by performing the receiver operating characteristic curves. The maximum pathological diameter (MPD) measured on pathological sections of lymph nodes was compared with the maximum transverse diameter (MTD) and maximum sagittal diameter (MSD) and their average values on CT images. RESULTS: The ANHU, and VNHU of MPLNs and MNLNs were 111.89 ± 33.26 and 66.12 (56.81-76.86) (P < 0.001), and 99.07 ± 23.27 and 75.47 ± 13.95 (P < 0.001), respectively. The area under the curve, sensitivity, and specificity of the arterial-phase three parameters (ANHU, ANHU-AMHU, ANHU/AMHU) for diagnosing LNM were (0.877-0.880), (0.755-0.769), and (0.901-0.913), respectively, and the venous-phase three parameters (VNHU, VNHU-VMHU, VNHU/VMHU) were (0.801-0.817), (0.650-0.678), and (0.826-0.901), respectively. Compared with MPD, MTD (Z = -2.686, P = 0.007) and MSD (Z = -3.539, P < 0.001) were significantly different, while (MTD + MSD)/2 was not statistically different (Z = -0.038b, P = 0.969). CONCLUSION: In the differential diagnosis of cervical LNM of PTC by dual-phase enhanced CT angiography, the arterial phase had higher diagnostic efficacy.

Adult Primary Pineal Alveolar Rhabdomyosarcoma with FOXO1 Gene Rearrangement and OLIG2 Expression: A Rare Case Report and Literature Review.

Alveolar rhabdomyosarcoma is a common malignant soft tissue tumor in child and adolescents. Intracranial alveolar rhabdomyosarcoma in adults is rare, especially in the pineal region. We present a case of primary alveolar rhabdomyosarcoma of the pineal gland in a 36-year-old Chinese male with a chief complaint of dizziness, headache and a loss of balance when walking. Imaging identified a space-occupying mass in the pineal region with obstructive hydrocephalus. An endoscopic-assisted pineal mass resection was performed. Pathology revealed a solid, sheet-like growth of medium-sized, round or oval cells with map-like necrosis and some rhabdomyoblasts. The tumor cells were diffusely positive for desmin, myogenin, MyoD1, ALK, and OLIG2. Fluorescence in situ hybridization (FISH) detected FOXO1 gene rearrangement. This rare case is presented to expand the knowledge of pineal gland tumors and alert us to pay attention to the differential diagnosis of OLIG2-positive round-cell tumors of the central nervous system.

Differential diagnostic value of plain CT scan in adrenal adenoma and non-adenoma: A two-center control study of mean attenuation value, minimum attenuation value, and CT histogram.

Objectives: To investigate the value of mean attenuation value (AVmean), minimum attenuation value (AVmin), and CT histogram (CTH) for the differential diagnosis of adrenal adenoma and non-adenoma in two medical centers. Methods: The plain CT data of 403 cases of adrenal adenoma and 141 cases of non-adenoma in center A were retrospectively analyzed, and compared with data of 86 cases of adenoma and 71 cases of non-adenoma in center B. All cases were confirmed by pathology or clinical follow-up. The diagnostic efficacy of AVmean ≤ 10 Hounsfield units (HU), AVmin ≤ 0 HU, and CTH negative pixels ≥ 10% for adrenal adenoma, and AVmin and CTH for adenoma with AVmean > 10Hu were compared between the two medical centers. Results: In medical centers A and B, the AUC of AVmean for the differential diagnosis of adenoma and non-adenoma was 0.956 and 0.956, respectively, and the corresponding sensitivity, specificity, and accuracy were, 0.591 and 0.663, 1.000 and 1.000, 0.697, and 0.815, respectively, when the threshold was ≤ 10 HU. The AUC of AVmin was 0.941 and 0.958, respectively, and the corresponding sensitivity, specificity, and accuracy were 0.869 and 0.826, 0.986, and 0.972, 0.899, and 0.892, respectively, when the threshold was ≤ 0 HU. The AUC of CTH negative pixels was 0.948 and 0.952, respectively, and the corresponding sensitivity, specificity, and accuracy were 0.759 and 0.674, 1.000 and 1.000, 0.822, and 0.822, respectively, when the threshold was ≥ 10%. Among adenoma with AVmean >10 HU, the best threshold of AVmin in center A and center B were -0.250HU and 2.375HU, and the corresponding AUC, sensitivity and specificity were 0.858 and 0.846, 0.691 and 0.586, 0.986 and 0.958; the best threshold of CTH in center A and center B were 0.895% and 0.775%, and the corresponding AUC, sensitivity and specificity were 0.873 and 0.822, 0.818 and 0.724, 0.837 and 0.915. Conclusion: AVmean, AVmin, and CTH are all important parameters for differentiating adrenal adenoma from non-adenoma. Even for adenomas with AVmean > 10 HU, AVmin and CTH still had high diagnostic efficiency. The three parameters are complementary, assisting clinicians to develop personalized treatments.

The value of ultrasound grayscale ratio in the diagnosis of papillary thyroid microcarcinomas and benign micronodules in patients with Hashimoto's thyroiditis: A two-center controlled study.

Objective: The value of ultrasound grayscale ratio (UGSR) in the diagnosis of papillary thyroid microcarcinomas (PTMCs) and benign micronodules (BMNs) has been recognized by some authors, but studies have not examined these aspects in patients with Hashimoto's thyroiditis (HT). This retrospective study investigated the value of UGSR in the diagnosis of PTMCs and BMNs in patients with HT using data from two medical centers. Methods: Ultrasound images of 428 PTMCs in 368 patients with HT and 225 BMNs in 181 patients with HT in center A were retrospectively analyzed and compared to the ultrasound images of 412 PTMCs in 324 patients with HT and 315 BMNs in 229 patients with HT in medical center B. All of the cases were surgically confirmed. The UGSR was calculated as the ratio of the grayscale value of lesions to the surrounding normal thyroid tissues. The optimal UGSR thresholds for the PTMCs and BMNs in patients with HT from the two medical centers were determined using a receiver operating characteristic (ROC) curve. Furthermore, other statistics, including the area under the curve (AUC), the optimal UGSR threshold, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of the two medical centers, were pair analyzed in this study. Results: The UGSR of PTMCs and BMNs in patients with HT from medical center A were 0.513 (0.442, 0.592) and 0.857 (0.677, 0.977) (Z = -15.564, p = 0), and those from medical center B were 0.514 (0.431, 0.625) and 0.917 (0.705, 1.131) (Z = -15.564, p = 0). For both medical centers A and B, the AUC, optimal UGSR threshold, sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the UGSR in differentiating between PTMCs and BMNs in patients with HT were 0.870 and 0.889, 0.68 and 0.70, 0.921 and 0.898, 0.747 and 0.759, 0.874 and 0.829, 0.832 and 0.848, and 0.861 and 0.836, respectively. There were no significant differences in the UGSR for the PTMCs between patients from the two medical centers (Z = -0.815, p = 0.415), while there was a significant difference in the UGSR of the BMNs between patients from the two medical centers (Z = -3.637, p = 0). Conclusion: In the context of HT, UGSR still has high sensitivity, accuracy, and stability in differentiating between PTMCs and BMNs, making it a complementary differentiator of thyroid imaging reporting and data systems. However, due to its low specificity, a comprehensive analysis of other ultrasound signs is required.

Quantitative Analysis and Pathological Basis of Signal Intensity on T2-Weighted MR Images in Benign and Malignant Parotid Tumors.

OBJECTIVE: To investigate the value of the signal intensity on T2-weighted magnetic resonance (MR) imaging using quantitative analysis in the differentiation of parotid tumors. MATERIALS AND METHODS: MR data of 80 pleomorphic adenomas (PAs), 68 Warthin tumors (WTs), and 34 malignant tumors (MTs) confirmed by surgery and histology were retrospectively analyzed. The signal intensities of tumor, normal parotid gland, spinal cord, and buccal subcutaneous fat were measured, and the signal intensity ratios (SIRs) between the tumor and the three references were calculated. Receiver operating characteristic curve was used to determine the optimal threshold and diagnostic efficiency of SIR for differentiating PAs, WTs, and MTs. RESULTS: The area under the curve (AUC) of tumor to parotid gland SIR (SIRP), tumor to spinal cord SIR (SIRC), and tumor to buccal subcutaneous fat SIR (SIRF) for differentiating PAs and WTs was 0.922, 0.918, and 0.934, respectively. The sensitivity and specificity at an optimal SIR threshold were 86.3% and 91.2%, 80.0% and 97.1%, and 85.0% and 94.1%, respectively. The AUC of SIRP, SIRC, and SIRF for distinguishing PAs from MTs was 0.793, 0.802, and 0.774, respectively. The sensitivity and specificity at an optimal SIR threshold was 86.3% and 61.8%, 80.0% and 73.5%, and 82.5% and 73.5%, respectively. The AUC of SIRP, SIRC, and SIRF for distinguishing WTs from MTs was 0.716, 0.709, and 0.759, respectively. The sensitivity and specificity at an optimal SIR threshold were 61.8% and 82.4%, 55.9% and 82.4%, and 64.7% and 86.8%, respectively. CONCLUSION: SIRP, SIRC, and SIRF on T2-weighted MR images had high diagnostic efficiency for differentiating between PAs and WTs, while SIRP and SIRC for differentiating between PAs and MTs, and SIRF for differentiating between WTs and MTs had relatively high diagnostic efficiency.

A Control Study on the Value of the Ultrasound Grayscale Ratio for the Differential Diagnosis of Thyroid Micropapillary Carcinoma and Micronodular Goiter in Two Medical Centers.

OBJECTIVE: To investigate the value of ultrasound gray-scale ratio (UGSR) for the differential diagnosis of papillary thyroid microcarcinoma (PTMC) and micronodular goiter (MNG) in two medical centers. METHODS: Ultrasound images of 881 PTMCs from 785 patients and 744 MNGs from 687 patients in center A were retrospectively analyzed and compared with 243 PTMCs from 203 patients and 251 MNGs from 198 patients in center B. All cases were confirmed by surgery and histology. The grayscale values of thyroid lesions and surrounding normal tissues were measured, and the UGSR was calculated. The optimal UGSR threshold for identifying PTMCs and MNGs in two medical centers was determined by receiver operating characteristic (ROC) curve, and the area under the curve (AUC), optimal UGSR threshold, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were compared between the two medical centers. RESULTS: The UGSR values of PTMCs and MNGs in medical center A were 0.5537 (0.4699, 0.6515) and 0.8708 (0.7616, 1.0123) (Z = -27.691, P = 0), respectively, whereas those in medical center B were 0.5517 (0.4698, 0.6377) and 0.8539 (0.7366, 0.9929) (Z = -16.057, P = 0), respectively. The UGSR of PTMCs and MNGs did not differ significantly between the two medical centers (Z = -0.609, P = 0.543 and Z = -1.394, P = 0.163, respectively). The AUC, optimal UGSR threshold, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the two medical centers were 0.898 vs. 0.918, 0.7214 vs. 0.6911, 0.881 vs. 0.868, 0.817 vs. 0.833, 0.851 vs. 0.834, 0.853 vs. 0.867, and 0.852 vs. 0.850, respectively. CONCLUSIONS: UGSR can quantify the echo intensity of PTMCs and MNGs and is therefore valuable for the differential diagnosis of the two diseases. The diagnostic efficacy was consistent between the two medical centers. This method should be widely promoted and applied.

The Diagnostic Role of Computed Tomography for ACR TI-RADS 4-5 Thyroid Nodules With Coarse Calcifications.

Objectives: Coarse calcifications are prone to cause echo attenuation during ultrasonography (US) and hence affect the classification of benign and malignant nodules. This study aimed to investigate the diagnostic role of computed tomography (CT) for differentiating the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) 4-5 nodules with coarse calcifications. Methods: CT data of 216 ACR TI-RADS 4-5 nodules with coarse calcifications confirmed by surgery and pathology in 207 patients were analyzed retrospectively. Halo sign, artifacts, and CT values (i.e., Hounsfield unit) of the nodules were determined by two radiologists. Univariate analysis and binary logistic regression were used to determine the relationship of halo sign, artifact, and CT value with benign nodules. A predictive model for benign nodules with coarse calcifications was then constructed. The receiver operating characteristic (ROC) curve was used to analyze the predictive value of halo sign, artifact, CT value, and logistic regression model. Results: Of the 216 ACR TI-RADS 4-5 nodules with coarse calcifications, 170 were benign and 46 were malignant. There were 92 benign and 7 malignant nodules with halo sign (χ2 = 22.067, P < 0.001), and 79 benign and 10 malignant nodules with artifacts (χ2 = 9.140, P < 0.001). The CT values of benign and malignant nodules were 791 (543-1,025) Hu and 486 (406-670) Hu, respectively (Z = -5.394, P < 0.001). Binary logistic regression demonstrated that the halo sign, artifact, and CT value were independent predictors for benign nodules with coarse calcifications. The area under the ROC curve (AUC) of halo sign, artifact, CT value and regression model for predicting benign nodules with coarse calcifications were 0.776, 0.711, 0.784, and 0.850, respectively, and the optimal threshold of CT value was 627.5 Hu. Conclusion: Halo sign, artifact, and CT value > 627.5 Hu were helpful for identifying ACR TI-RADS 4-5 thyroid benign nodules with coarse calcifications. The diagnostic performance of the logistic regression model was higher than that of any single indicator. Accurate identification of these indicators could identify benign nodules and reduce unnecessary surgical trauma.

Hounsfield Unit Values in ACR TI-RADS 4-5 Thyroid Nodules with Coarse Calcifications: An Important Imaging Feature Helpful for Diagnosis.

PURPOSE: The purpose of this study is to investigate the diagnostic role of Hounsfield unit (HU) values on noncontrast computed tomography (CT) for differentiating benignity from malignancy in the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) 4-5 nodules with coarse calcifications. PATIENTS AND METHODS: CT images of 216 ACR TI-RADS 4-5 nodules with coarse calcifications from 207 patients who underwent surgery in our hospital between 2017 and 2019 were retrospectively reviewed. The average HU values (AHUVs) and maximum HU values (MHUVs) of the nodules were measured on noncontrast CT. The distribution of AHUVs and MHUVs in benign and malignant nodules with coarse calcifications was analyzed using the Mann-Whitney test. Receiver operating characteristic (ROC) curves were used to identify the best cut-off values. Diagnostic performances were assessed according to the area under the ROC curve (AUC), sensitivity and specificity. RESULTS: Of the 216 ACR TI-RADS 4-5 nodules with coarse calcifications, 170 were benign and 46 were malignant. The AHUVs of benign and malignant nodules were 791 HU [interquartile range (IQR), 543-1025 HU] and 486 HU (IQR, 406-670 HU), respectively (P < 0.001). The MHUVs of benign and malignant nodules were 1084 HU (IQR, 717-1477 HU) and 677 HU (IQR, 441-986 HU), respectively (P < 0.001). The AUCs for AHUVs and MHUVs for predicting benign nodules with coarse calcifications were 0.759 and 0.732, and the cut-off values were 627.5 HU and 806.0 HU, with sensitivities of 67.6% and 68.8% and specificities of 73.9% and 67.4%, respectively. The sensitivity and specificity of the combination were 68.8% and 76.1%. CONCLUSION: AHUVs and MHUVs were helpful in differentiating benignity from malignancy in ACR TI-RADS 4-5 nodules with coarse calcifications. This may provide an important basis for reducing misdiagnosis and unnecessary aspiration or surgical trauma.

The clinical significance of ultrasound grayscale ratio in differentiating markedly hypoechoic and anechoic minimal thyroid nodules.

PURPOSE: This study explored ultrasound grayscale ratios (USGRs) for differentiating markedly hypoechoic and anechoic minimal thyroid nodules. MATERIALS AND METHODS: Longitudinal scan images of 193 markedly hypoechoic papillary thyroid microcarcinoma (PTMC) lesions from 184 patients were retrospectively reviewed using RADinfo and compared with 123 anechoic micronodular goiters (MNGs) from 110 patients. Final diagnosis was validated by pathological examination; MNGs predominantly manifested with cyst formation. Grayscale values of PTMC, MNG, and normal surrounding tissues were obtained from grayscale histograms; USGRs (grayscale ratios of pathologic tissue to surrounding normal tissue) of PTMC and MNG were calculated. Optimal USGRs for differentiating PTMC and MNG were determined with receiver operating characteristic (ROC) curves. RESULTS: Among 193 PTMC and 123 MNG lesions, USGRs were 0.24-0.51 (mean ± standard deviation [SD]: 0.41 ± 0.07) and 0.01-0.38 (mean ± SD: 0.12 ± 0.08), respectively. The area under the ROC curve for distinguishing markedly hypoechoic PTMC and anechoic MNG was 0.992. As USGRs decreased, sensitivity decreased and specificity increased for MNG diagnosis. At a USGR of 0.26, the Youden index was high (0.933), corresponding to 94.3% sensitivity and 99% specificity for predicting anechoic MNG. At a USGR of 0.23, sensitivity and specificity for diagnosing anechoic MNG were 92.7% and 100%, respectively. In contrast, as USGR increased, sensitivity decreased and specificity increased for predicting PTMC. At a USGR of 0.38, sensitivity and specificity for diagnosing markedly hypoechoic PTMC were 68.4% and 100%, respectively. CONCLUSIONS: USGRs could objectively quantize grayscale values of markedly hypoechoic and anechoic lesions, enabling accurate and quantitative determination of nodular properties.

Differential diagnosis value of the ultrasound gray scale ratio for papillary thyroid microcarcinomas and micronodular goiters.

BACKGROUND: To investigate the differential diagnosis value of the ultrasound gray scale ratio (UGSR) for papillary thyroid microcarcinomas (PTMCs) and micronodular goiters (MNGs). METHODS: A retrospective analysis was performed using ultrasound images from 521 PTMC patients (561 PTMC lesions) and 405 MNG patients (515 MNG lesions). All cases were surgically and histologically confirmed. Gray scale values of the thyroid lesions and the surrounding normal thyroid tissue were measured. The thyroid lesion to normal thyroid tissue (UGSR) was calculated. Statistical analysis was performed with Mann-Whitney test. Receiver operating characteristic curve determined the optimal UGSR threshold for differentiating PTMCs and MNGs. RESULTS: In 561 PTMCs, the mean UGSR was 0.54 (SD: 0.16; range: 0.24-1.26). In 515 MNGs, the mean UGSR was 0.87 (SD: 0.22; range: 0.34-2.06), with significant difference between values of PTMCs and MNGs (P<0.001). The UGSR area under the curve to differentiate PTMCs and MNGs was 0.895. When the UGSR decreased, the UGSR to PTMC sensitivity decreased and the specificity increased. When the UGSR was chosen to be 0.99, 0.72, 0.63 or 0.34, the sensitivity was 98.4% and 87.0%, 73.8% and 5.9% respectively, and specificity was 25.1% and 80.4%, 90.1% and 100.0% respectively. When the UGSR was 0.72, the Youden index maximum was 0.674. CONCLUSIONS: The UGSR allows potential differentiation PTMCs and MNGs.

Diagnostic value of computed tomography (CT) histogram analysis in thyroid benign solitary coarse calcification nodules.

This study was to investigate the diagnostic value of the computed tomography (CT) histogram in thyroid benign solitary coarse calcification nodules (BSCNs). A total of 89 thyroid solitary coarse calcification nodules (coarse calcification ≥5 mm, no definite soft tissue around calcification) confirmed either by surgery or histopathological examination in 86 cases enrolled from January 2009 to December 2015 were evaluated. These included 56 BSCNs and 33 malignant solitary coarse calcification nodules (MSCNs). Overall, 27 cut-off values were calculated by N (4≤N≤30) times of 50 Hounsfield units (HU) in the range of 200 to 1500 HU, and each cut-off value and the differences in the corresponding area percentages in the CT histogram were recorded for BSCN and MSCN. The optimal cut-off value and the corresponding area percentage were established by receiver operating characteristic (ROC) curve analysis. In the 19 groups with an ROC area under curve (AUC) of more than 0.7, at a cut-off value of 800 HU and at an area percentage of no more than 93.8%, the ROC AUC reached the maximum of 0.79, and the accuracy, sensitivity, and specificity were 75.3%, 80.4%, and 66.7%, respectively. At a cut-off value of 1050 HU and at an area percentage of no more than 93.6%, the accuracy, sensitivity, and specificity were 71.9%, 60.7%, and 90.9%, respectively. At a cut-off of 1150 HU and area of no more than 98.4%, the accuracy, sensitivity, and specificity were 70.8%, 57.1%, and 93.9%, respectively. At a cut-off of 600 HU and area of no more than 12.1%, the accuracy, sensitivity, and specificity were 61.8%, 39.3%, and 100.0%, respectively. Compared with the cut-off value of 800 HU and an area percentage of no more than 93.8%, the sensitivity of cut-off values and minimum areas of 1050 HU and 93.6%, of 1150 HU and 98.4%, and of 600 HU and 12.1%, was gradually decreasing; however, their specificity was gradually increasing. This can provide an important basis for reducing the misdiagnosis and unnecessary surgical trauma.