Immune response and mesenchymal transition of papillary thyroid carcinoma reflected in ultrasonography features assessed by radiologists and deep learning.

INTRODUCTION: Ultrasonography (US) features of papillary thyroid cancers (PTCs) are used to select nodules for biopsy due to their association with tumor behavior. However, the molecular biological mechanisms that lead to the characteristic US features of PTCs are largely unknown. OBJECTIVES: This study aimed to investigate the molecular biological mechanisms behind US features assessed by radiologists and three convolutional neural networks (CNN) through transcriptome analysis. METHODS: Transcriptome data from 273 PTC tissue samples were generated and differentially expressed genes (DEGs) were identified according to US feature. Pathway enrichment analyses were also conducted by gene set enrichment analysis (GSEA) and ClusterProfiler according to assessments made by radiologists and three CNNs - CNN1 (ResNet50), CNN2 (ResNet101) and CNN3 (VGG16). Signature gene scores for PTCs were calculated by single-sample GSEA (ssGSEA). RESULTS: Individual suspicious US features consistently suggested an upregulation of genes related to immune response and epithelial-mesenchymal transition (EMT). Likewise, PTCs assessed as positive by radiologists and three CNNs showed the coordinate enrichment of similar gene sets with abundant immune and stromal components. However, PTCs assessed as positive by radiologists had the highest number of DEGs, and those assessed as positive by CNN3 had more diverse DEGs and gene sets compared to CNN1 or CNN2. The percentage of PTCs assessed as positive or negative concordantly by radiologists and three CNNs was 85.6% (231/273) and 7.1% (3/273), respectively. CONCLUSION: US features assessed by radiologists and CNNs revealed molecular biologic features and tumor microenvironment in PTCs.

Learnability of Thyroid Nodule Assessment on Ultrasonography: Using a Big Data Set.

OBJECTIVE: The aims of the work described here were to evaluate the learnability of thyroid nodule assessment on ultrasonography (US) using a big data set of US images and to evaluate the diagnostic utilities of artificial intelligence computer-aided diagnosis (AI-CAD) used by readers with varying experience to differentiate benign and malignant thyroid nodules. METHODS: Six college freshmen independently studied the "learning set" composed of images of 13,560 thyroid nodules, and their diagnostic performance was evaluated after their daily learning sessions using the "test set" composed of images of 282 thyroid nodules. The diagnostic performance of two residents and an experienced radiologist was evaluated using the same "test set." After an initial diagnosis, all readers once again evaluated the "test set" with the assistance of AI-CAD. RESULTS: Diagnostic performance of almost all students increased after the learning program. Although the mean areas under the receiver operating characteristic curves (AUROCs) of residents and the experienced radiologist were significantly higher than those of students, the AUROCs of five of the six students did not differ significantly compared with that of the one resident. With the assistance of AI-CAD, sensitivity significantly increased in three students, specificity in one student, accuracy in four students and AUROC in four students. Diagnostic performance of the two residents and the experienced radiologist was better with the assistance of AI-CAD. CONCLUSION: A self-learning method using a big data set of US images has potential as an ancillary tool alongside traditional training methods. With the assistance of AI-CAD, the diagnostic performance of readers with varying experience in thyroid imaging could be further improved.

Diagnosis of thyroid micronodules on ultrasound using a deep convolutional neural network.

To assess the performance of deep convolutional neural network (CNN) to discriminate malignant and benign thyroid nodules < 10 mm in size and compare the diagnostic performance of CNN with those of radiologists. Computer-aided diagnosis was implemented with CNN and trained using ultrasound (US) images of 13,560 nodules ≥ 10 mm in size. Between March 2016 and February 2018, US images of nodules < 10 mm were retrospectively collected at the same institution. All nodules were confirmed as malignant or benign from aspirate cytology or surgical histology. Diagnostic performances of CNN and radiologists were assessed and compared for area under curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Subgroup analyses were performed based on nodule size with a cut-off value of 5 mm. Categorization performances of CNN and radiologists were also compared. A total of 370 nodules from 362 consecutive patients were assessed. CNN showed higher negative predictive value (35.3% vs. 22.6%, P = 0.048) and AUC (0.66 vs. 0.57, P = 0.04) than radiologists. CNN also showed better categorization performance than radiologists. In the subgroup of nodules ≤ 5 mm, CNN showed higher AUC (0.63 vs. 0.51, P = 0.08) and specificity (68.2% vs. 9.1%, P < 0.001) than radiologists. Convolutional neural network trained with thyroid nodules ≥ 10 mm in size showed overall better diagnostic performance than radiologists in the diagnosis and categorization of thyroid nodules < 10 mm, especially in nodules ≤ 5 mm.

Radiomics Analysis of Gray-Scale Ultrasonographic Images of Papillary Thyroid Carcinoma > 1 cm: Potential Biomarker for the Prediction of Lymph Node Metastasis.

Purpose: This study aimed to investigate radiomics analysis of ultrasonographic images to develop a potential biomarker for predicting lymph node metastasis in papillary thyroid carcinoma (PTC) patients. Materials and Methods: This study included 431 PTC patients from August 2013 to May 2014 and classified them into the training and validation sets. A total of 730 radiomics features, including texture matrices of gray-level co-occurrence matrix and gray-level run-length matrix and single-level discrete two-dimensional wavelet transform and other functions, were obtained. The least absolute shrinkage and selection operator method was used for selecting the most predictive features in the training data set. Results: Lymph node metastasis was associated with the radiomics score (p < 0.001). It was also associated with other clinical variables such as young age (p = 0.007) and large tumor size (p = 0.007). The area under the receiver operating characteristic curve was 0.687 (95% confidence interval: 0.616-0.759) for the training set and 0.650 (95% confidence interval: 0.575-0.726) for the validation set. Conclusion: This study showed the potential of ultrasonography-based radiomics to predict cervical lymph node metastasis in patients with PTC; thus, ultrasonography-based radiomics can act as a biomarker for PTC.

Fine Needle Aspiration Cytology vs. Core Needle Biopsy for Thyroid Nodules: A Prospective, Experimental Study Using Surgical Specimen.

Purpose: To evaluate and compare the diagnostic outcomes of ultrasonography (US)-guided fine needle aspiration (FNA) and core needle biopsy (CNB) performed on the same thyroid nodule using a surgical specimen for direct comparison. Materials and Methods: We included 89 thyroid nodules from 88 patients from February 2015 to January 2016. The inclusion criterion was thyroid nodules measuring ≥ 20 mm (mean size: 40.0 ± 15.3 mm). Immediately after surgical resection, FNA and subsequent CNB were performed on the surgical specimen under US guidance. FNA and CNB cytopathologic results on the specimen were compared with the surgical diagnosis. Results: Among the 89 nodules, 30 were malignant and 59 were benign. Significantly higher inconclusive rates were seen in FNA for malignant than benign nodules (80.0% vs. 39.0%, p < 0.001). For CNB, conclusive and inconclusive rates did not differ between benign and malignant nodules (p = 0.796). Higher inconclusive rates were seen for FNA among cancers regardless of US features, and in the subgroup of size ≥ 40 mm (62.5% vs. 22.9%, p = 0.028). Eleven cancers were diagnosed with CNB (36.7%, 11/30), while none was diagnosed using FNA. Conclusion: In this experimental study using surgical specimens, CNB showed a potential to provide improved diagnostic sensitivity for thyroid cancer, especially when a conclusive diagnosis is limited with FNA.

Use of long non-coding RNAs for the molecular diagnosis of papillary thyroid cancer.

Objective: Improved molecular testing for common somatic mutations and the identification of mRNA and microRNA expression classifiers are promising approaches for the diagnosis of thyroid nodules. However, there is a need to improve the diagnostic accuracy of such tests for identifying thyroid cancer. Recent findings have revealed a crucial role of long non-coding RNAs (lncRNAs) in gene modulation. Thus, we aimed to evaluate the diagnostic value of selected lncRNAs from The Atlas of Noncoding RNAs in Cancer (TANRIC) thyroid cancer dataset. Methods: LncRNAs in TANRIC thyroid cancer dataset that have significantly increased or decreased expression in papillary thyroid cancer (PTC) tissues were selected as candidates for PTC diagnosis. Surgical specimens from patients who underwent thyroidectomy were used to determine the separation capability of candidate lncRNAs between malignant and benign nodules. Fine needle aspiration samples were obtained and screened for candidate lncRNAs to verify their diagnostic value. Results: LRRC52-AS1, LINC02471, LINC02082, UNC5B-AS1, LINC02408, MPPED2-AS1, LNCNEF, LOC642484, ATP6V0E2-AS1, and LOC100129129 were selected as the candidate lncRNAs. LRRC52-AS1, LINC02082, UNC5B-AS1, MPPED2-AS1, LNCNEF, and LOC100129129 expression levels were significantly increased or decreased in malignant nodules compared to those in benign nodules and paired normal thyroid tissues. The combination of LRRC52-AS1, LINC02082, and UNC5B-AS1 showed favorable results for the diagnosis of PTC from fine needle aspirates, with 88.9% sensitivity and 100.0% specificity. Conclusions: LncRNA expression analysis is a promising approach for advancing the molecular diagnosis of PTC. Further studies are needed to identify lncRNAs of additional diagnostic value.

Application of Artificial Intelligence Computer-Assisted Diagnosis Originally Developed for Thyroid Nodules to Breast Lesions on Ultrasound.

As thyroid and breast cancer have several US findings in common, we applied an artificial intelligence computer-assisted diagnosis (AI-CAD) software originally developed for thyroid nodules to breast lesions on ultrasound (US) and evaluated its diagnostic performance. From January 2017 to December 2017, 1042 breast lesions (mean size 20.2 ± 11.8 mm) of 1001 patients (mean age 45.9 ± 12.9 years) who underwent US-guided core-needle biopsy were included. An AI-CAD software that was previously trained and validated with thyroid nodules using the convolutional neural network was applied to breast nodules. There were 665 benign breast lesions (63.0%) and 391 breast cancers (37.0%). The area under the receiver operating characteristic curve (AUROC) of AI-CAD to differentiate breast lesions was 0.678 (95% confidence interval: 0.649, 0.707). After fine-tuning AI-CAD with 1084 separate breast lesions, the diagnostic performance of AI-CAD markedly improved (AUC 0.841). This was significantly higher than that of radiologists when the cutoff category was BI-RADS 4a (AUC 0.621, P < 0.001), but lower when the cutoff category was BI-RADS 4b (AUC 0.908, P < 0.001). When applied to breast lesions, the diagnostic performance of an AI-CAD software that had been developed for differentiating malignant and benign thyroid nodules was not bad. However, an organ-specific approach guarantees better diagnostic performance despite the similar US features of thyroid and breast malignancies.

Sarcopenia increases the risk of major organ or vessel invasion in patients with papillary thyroid cancer.

While sarcopenia is associated with poor overall survival and cancer-specific survival in solid cancer patients, the impact of sarcopenia on clinicopathologic features that can influence conventional papillary thyroid cancer (PTC) prognosis remains unclear. To investigate the impact of sarcopenia on aggressive clinicopathologic features in PTC patients, prospectively collected data on 305 patients who underwent surgery for PTC with preoperative staging ultrasonography and bioelectrical impedance analysis were retrospectively analyzed. Nine sarcopenia patients with preoperative sarcopenia showed more patients aged 55 or older (p = 0.022), higher male proportion (p < 0.001), lower body-mass index (p = 0.015), higher incidence of major organ or vessel invasion (p = 0.001), higher T stage (p = 0.002), higher TNM stage (p = 0.007), and more tumor recurrence (p = 0.023) compared to the non-sarcopenia patients. Unadjusted and adjusted logistic regression analyses showed that sarcopenia (odds ratio (OR) 9.936, 95% confidence interval (CI) 2.052-48.111, p = 0.004), tumor size (OR 1.048, 95% CI 1.005-1.093, p = 0.027), and tumor multiplicity (OR 3.323, 95% CI 1.048-10.534, p = 0.041) significantly increased the risk of T4 cancer. Sarcopenia patients showed significantly lower disease-free survival probability compared to non-sarcopenia patients. Therefore, preoperative sarcopenia in PTC patients should raise clinical suspicion for a more locally advanced disease and direct appropriate management and careful follow-up.

Using ultrasonographic features to predict the outcomes of patients with small papillary thyroid carcinomas: a retrospective study implementing the 2015 ATA patterns and ACR TI-RADS categories.

BACKGROUND: The aim of this study was to evaluate whether risk stratification systems using ultrasonographic (US) features show associations with the outcomes of patients with small papillary thyroid carcinomas (PTCs). METHODS: This retrospective study received institutional review board approval. From March 2007 to February 2010, 775 patients who underwent surgery for small PTCs (10-20 mm) were included. Based on preoperative US features, PTCs were categorized according to the 2015 American Thyroid Association (ATA) guideline and the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS). The associations of clinicopathological and US features with postoperative patient outcomes were evaluated. RESULTS: In total, 61 patients had high-volume central lymph node metastasis (CLNM, 7.9%) and 100 patients had lateral lymph node metastasis (LLNM, 12.9%). In univariable analyses, a high number of suspicious US features and higher ACR TI-RADS point totals were significantly associated with both high-volume CLNM (P=0.001, each) and LLNM (P<0.001, each). In multivariable analyses of preoperative features, a higher number of suspicious US features and higher ACR TI-RADS point totals were independently associated with high-volume CLNM (odds ratio [OR], 1.516 and 1.201; P=0.002 and P=0.001, respectively) and LLNM (OR, 1.763 and 1.293; all P<0.001). Individual US features, ATA categories, and ACR TI-RADS point totals were not significantly associated with recurrence or distant metastasis. CONCLUSION: The number of suspicious US features and the ACR TI-RADS point total are potential risk factors for cervical lymph node metastasis in patients with small PTCs.

A beneficial role of computer-aided diagnosis system for less experienced physicians in the diagnosis of thyroid nodule on ultrasound.

Ultrasonography (US) is the primary diagnostic tool for thyroid nodules, while the accuracy is operator-dependent. It is widely used not only by radiologists but also by physicians with different levels of experience. The aim of this study was to investigate whether US with computer-aided diagnosis (CAD) has assisting roles to physicians in the diagnosis of thyroid nodules. 451 thyroid nodules evaluated by fine-needle aspiration cytology following surgery were included. 300 (66.5%) of them were diagnosed as malignancy. Physicians with US experience less than 1 year (inexperienced, n = 10), or more than 5 years (experienced, n = 3) reviewed the US images of thyroid nodules with or without CAD assistance. The diagnostic performance of CAD was comparable to that of the experienced group, and better than those of the inexperienced group. The AUC of the CAD for conventional PTC was higher than that for FTC and follicular variant PTC (0.925 vs. 0.499), independent of tumor size. CAD assistance significantly improved diagnostic performance in the inexperienced group, but not in the experienced groups. In conclusion, the CAD system showed good performance in the diagnosis of conventional PTC. CAD assistance improved the diagnostic performance of less experienced physicians in US, especially in diagnosis of conventional PTC.

Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network.

To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules ≥ 1 cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680-0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657-0.768 and 0.652 for AUS, 0.469-0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k = 0.543), substantial (k = 0.652), and moderate (k = 0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.

The Use of a Light-Emitting Diode Device for Neck Rejuvenation and Its Safety on Thyroid Glands.

Home-use light-emitting diodes (LEDs) are attracting growing attention regarding their anti-aging effects. Although most previous studies on the use of LED devices as a form of low-level laser therapy reported no significant adverse events, questions regarding the safety of using a light source on secretory tissues have been raised. This study aimed to assess the safety and efficacy of a home-use LED device for neck skin rejuvenation, particularly regarding its effect on thyroid gland morphology and function. Thyroid function tests and ultrasonographic analyses showed no significant changes after 16 weeks of LED use. Evaluation using the Lemperle wrinkle scale and global improvement scales by both investigators and subjects showed significant improvement after 16 weeks of daily application, as well as 8 weeks after discontinuation. Biophysical parameters, such as hydration, elasticity, and density, also showed significant improvements. Hence, the long-term use of the LED device was safe and effective for neck rejuvenation, and showed no significant side effects on the adjacent thyroid and parathyroid glands.

Comparison of diagnostic performance of the ACR and Kwak TIRADS applying the ACR TIRADS' size thresholds for FNA.

OBJECTIVE: To investigate the diagnostic performances and unnecessary fine-needle aspiration (FNA) rates of two point-scale based TIRADS and compare them with a modified version using the ACR TIRADS' size thresholds. METHODS: Our Institutional Review Board approved this retrospective study and waived the requirement for informed consent. A total of 2106 thyroid nodules 10 mm or larger in size in 2084 patients with definitive cytopathologic findings were included. Ultrasonography categories were assigned according to each guideline. We applied the ACR TIRADS' size thresholds for FNA to the Kwak TIRADS and defined it as the modified Kwak TIRADS (mKwak TIRADS). Diagnostic performances and unnecessary FNA rates were evaluated for both the original and modified guidelines. RESULTS: Of the original guidelines, the ACR TIRADS had higher specificity, accuracy, and area under the receiver operating characteristic curve (AUC) (63.1%, 68.9%, and 0.748, respectively). When the size threshold of the ACR TIRADS was applied to the Kwak TIRADS, the resultant mKwak TIRADS had higher specificity, accuracy, and AUC (64.7%, 70.3%, and 0.765, respectively) than the ACR TIRADS. The mKwak TIRADS also had a lower unnecessary FNA rate than the ACR TIRADS (54.8% and 56.4%, respectively). The false-negative rate of the Kwak TIRADS was the lowest (1.9%) among all TIRADS. CONCLUSION: The modified Kwak TIRADS incorporating the size thresholds of the ACR TIRADS showed higher diagnostic performance and a lower unnecessary FNA rate than the original point-scale based TIRADS. KEY POINTS: • Of the original guidelines, the ACR TIRADS had the highest specificity, accuracy, and area under the receiver operating characteristic curve (AUC) (63.1%, 68.9%, and 0.748, respectively). • When the size threshold of the ACR TIRADS was applied to the Kwak TIRADS, the resultant modified version of Kwak TIRADS had higher specificity, accuracy, and AUC (64.7%, 70.3%, and 0.765, respectively) than the ACR TIRADS. • The false-negative rate of the Kwak TIRADS was the lowest (1.9%) among all TIRADS.

Implications of US radiomics signature for predicting malignancy in thyroid nodules with indeterminate cytology.

OBJECTIVES: The purpose of this study was to evaluate the role of the radiomics score using US images to predict malignancy in AUS/FLUS and FN/SFN nodules. METHODS: One hundred fifty-five indeterminate thyroid nodules in 154 patients who received initial US-guided FNA for diagnostic purposes were included in this retrospective study. A representative US image of each tumor was acquired, and square ROIs covering the whole nodule were drawn using the Paint program of Windows 7. Texture features were extracted by in-house texture analysis algorithms implemented in MATLAB 2019b. The LASSO logistic regression model was used to choose the most useful predictive features, and ten-fold cross-validation was performed. Two prediction models were constructed using multivariable logistic regression analysis: one based on clinical variables, and the other based on clinical variables with the radiomics score. Predictability of the two models was assessed with the AUC of the ROC curves. RESULTS: Clinical characteristics did not significantly differ between malignant and benign nodules, except for mean nodule size. Among 730 candidate texture features generated from a single US image, 15 features were selected. Radiomics signatures were constructed with a radiomics score, using selected features. In multivariable logistic regression analysis, higher radiomics score was associated with malignancy (OR = 10.923; p < 0.001). The AUC of the malignancy prediction model composed of clinical variables with the radiomics score was significantly higher than the model composed of clinical variables alone (0.839 vs 0.583). CONCLUSIONS: Quantitative US radiomics features can help predict malignancy in thyroid nodules with indeterminate cytology.

Combining radiomics with ultrasound-based risk stratification systems for thyroid nodules: an approach for improving performance.

OBJECTIVES: To develop a radiomics score using ultrasound images to predict thyroid malignancy and to investigate its potential as a complementary tool to improve the performance of risk stratification systems. METHODS: We retrospectively included consecutive patients who underwent fine-needle aspiration (FNA) for thyroid nodules that were cytopathologically diagnosed as benign or malignant. Nodules were randomly assigned to a training and test set (8:2 ratio). A radiomics score was developed from the training set, and cutoff values based on the maximum Youden index (Rad_maxY) and for 5%, 10%, and 20% predicted malignancy risk (Rad_5%, Rad_10%, Rad_20%, respectively) were applied to the test set. The performances of the American College of Radiology (ACR) and the American Thyroid Association (ATA) guidelines were compared with the combined performances of the guidelines and radiomics score with interpretations from expert and nonexpert readers. RESULTS: A total of 1624 thyroid nodules from 1609 patients (mean age, 50.1 years [range, 18-90 years]) were included. The radiomics score yielded an AUC of 0.85 (95% CI: 0.83, 0.87) in the training set and 0.75 (95% CI: 0.69, 0.81) in the test set (Rad_maxY). When the radiomics score was combined with the ACR or ATA guidelines (Rad_5%), all readers showed increased specificity, accuracy, and PPV and decreased unnecessary FNA rates (all p < .05), with no difference in sensitivity (p > .05). CONCLUSION: Radiomics help predict thyroid malignancy and improve specificity, accuracy, PPV, and unnecessary FNA rate while maintaining the sensitivity of the ACR and ATA guidelines for both expert and nonexpert readers. KEY POINTS: • The radiomics score yielded an AUC of 0.85 and 0.75 in the training and test set, respectively. • For all readers, combining a 5% predicted malignancy risk cutoff for the radiomics score with the ACR and ATA guidelines significantly increased specificity, accuracy, and PPV and decreased unnecessary FNA rates, with no decrease in sensitivity. • Radiomics can help predict malignancy in thyroid nodules in combination with risk stratification systems, by improving specificity, accuracy, and PPV and unnecessary FNA rates while maintaining sensitivity for both expert and nonexpert readers.

Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer.

PURPOSE: To investigate whether a computer-aided diagnosis (CAD) program developed using the deep learning convolutional neural network (CNN) on neck US images can predict the BRAFV600E mutation in thyroid cancer. METHODS: 469 thyroid cancers in 469 patients were included in this retrospective study. A CAD program recently developed using the deep CNN provided risks of malignancy (0-100%) as well as binary results (cancer or not). Using the CAD program, we calculated the risk of malignancy based on a US image of each thyroid nodule (CAD value). Univariate and multivariate logistic regression analyses were performed including patient demographics, the American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TIRADS) categories and risks of malignancy calculated through CAD to identify independent predictive factors for the BRAFV600E mutation in thyroid cancer. The predictive power of the CAD value and final multivariable model for the BRAFV600E mutation in thyroid cancer were measured using the area under the receiver operating characteristic (ROC) curves. RESULTS: In this study, 380 (81%) patients were positive and 89 (19%) patients were negative for the BRAFV600E mutation. On multivariate analysis, older age (OR = 1.025, p = 0.018), smaller size (OR = 0.963, p = 0.006), and higher CAD value (OR = 1.016, p = 0.004) were significantly associated with the BRAFV600E mutation. The CAD value yielded an AUC of 0.646 (95% CI: 0.576, 0.716) for predicting the BRAFV600E mutation, while the multivariable model yielded an AUC of 0.706 (95% CI: 0.576, 0.716). The multivariable model showed significantly better performance than the CAD value alone (p = 0.004). CONCLUSION: Deep learning-based CAD for thyroid US can help us predict the BRAFV600E mutation in thyroid cancer. More multi-center studies with more cases are needed to further validate our study results.

Ultrasonography-Based Radiomics of Screening-Detected Ductal Carcinoma In Situ According to Visibility on Mammography.

ABSTRACT: Ductal carcinoma in situ (DCIS) has different prognostic factors according to the detection modality. The purpose of this study was to compare parameters from a radiomic analysis of ultrasonography (US) images for DCIS detected on screening mammography (MMG) and US and detected on screening US only. A total of 154 surgically confirmed DCIS visible on US were included. Regions of interest were drawn onto US images of DCIS, and texture analysis was performed. Lesions were classified into those detected by both US and MMG (the US-MMG group) and those detected by US only (the US group). Analysis parameters were compared between the US-MMG group and the US group. Ninety-six lesions were included in the US-MMG group and 58 lesions in the US group. Energy, entropy, maximum, mean absolute deviation, range, SD, and variance were significantly higher in the US-MMG group than the US group. Kurtosis, skewness, and uniformity were significantly lower in the US-MMG group than the US group. Among the 22 gray-level cooccurrence matrix parameters, 18, 21, 22, 20, and 21 parameters were significantly different between the 2 groups in 0, 45, 90, and 135 degrees and the average value. Among the 11 gray-level run-length matrix parameters, 6, 6, 7, 7, and 6 parameters were significantly different in 0, 45, 90, and 135 degrees and the average value. Inverse variance and gray-level nonuniformity were the most different features between the 2 groups. Screening-detected DCIS showed different radiomic features according to the detection modality.