Predicting tall-cell subtype of papillary thyroid carcinomas independently with preoperative multimodal ultrasound.

OBJECTIVES: This study aimed to explore the differences between tall-cell subtype of papillary thyroid carcinoma (TCPTC) and classical papillary thyroid carcinoma (cPTC) using multimodal ultrasound, and identify independent risk factors for TCPTC to compensate the deficiency of preoperative cytological and molecular diagnosis on PTC subtypes. METHODS: 46 TCPTC patients and 92 cPTC patients were included. Each patient received grey-scale ultrasound, color Dopplor flow imaging (CDFI) and shear-wave elastography (SWE) preoperatively. Clinicopathologic information, grey-scale ultrasound features, CDFI features and SWE features of 98 lesion were compared using univariate analysis to find out predictors of TCPTC, based on which, a predictive model was built to differentiate TCPTC from cPTC and validated with 40 patients. RESULTS: Univariate and multivariate analysis identified that extrathyroidal extension (OR, 15.12; 95% CI, 2.26-115.44), aspect ratio (≥0.91) (OR, 29.34; 95% CI, 1.29-26.23), and maximum diameter ≥ 14.6 mm (OR, 20.79; 95% CI, 3.87-111.47) were the independent risk factors for TCPTC. Logistic regression equation: p = 1/1+ExpΣ[-5.099 + 3.004 × (if size ≥14.6 mm)+2.957 × (if aspect ratio≥0.91)+2.819 × (if extra-thyroidal extension)]. The prediction model had a good discrimination performance for TCPTC: the AUC, sensitivity and specificity were 0.928, 0.848 and 0.954 in cohort 1, and the corresponding values in cohort 2 were 0.943, 0.923 and 0.926. CONCLUSION: Ultrasound has potential for differential diagnosis of TCPTC from cPTC. A prediction model based on ultrasound characteristics (extrathyroidal extension, aspect ratio ≥0.91, and maximum diameter ≥14.6 mm) was useful to predict TCPTC. ADVANCES IN KNOWLEDGE: Multimodal ultrasound prediction of TCPTC were supplements to preoperative cytological diagnosis and molecular diagnosis on PTC subtypes.

A deep learning fusion network trained with clinical and high-frequency ultrasound images in the multi-classification of skin diseases in comparison with dermatologists: a prospective and multicenter study.

Background: Clinical appearance and high-frequency ultrasound (HFUS) are indispensable for diagnosing skin diseases by providing internal and external information. However, their complex combination brings challenges for primary care physicians and dermatologists. Thus, we developed a deep multimodal fusion network (DMFN) model combining analysis of clinical close-up and HFUS images for binary and multiclass classification in skin diseases. Methods: Between Jan 10, 2017, and Dec 31, 2020, the DMFN model was trained and validated using 1269 close-ups and 11,852 HFUS images from 1351 skin lesions. The monomodal convolutional neural network (CNN) model was trained and validated with the same close-up images for comparison. Subsequently, we did a prospective and multicenter study in China. Both CNN models were tested prospectively on 422 cases from 4 hospitals and compared with the results from human raters (general practitioners, general dermatologists, and dermatologists specialized in HFUS). The performance of binary classification (benign vs. malignant) and multiclass classification (the specific diagnoses of 17 types of skin diseases) measured by the area under the receiver operating characteristic curve (AUC) were evaluated. This study is registered with www.chictr.org.cn (ChiCTR2300074765). Findings: The performance of the DMFN model (AUC, 0.876) was superior to that of the monomodal CNN model (AUC, 0.697) in the binary classification (P = 0.0063), which was also better than that of the general practitioner (AUC, 0.651, P = 0.0025) and general dermatologists (AUC, 0.838; P = 0.0038). By integrating close-up and HFUS images, the DMFN model attained an almost identical performance in comparison to dermatologists (AUC, 0.876 vs. AUC, 0.891; P = 0.0080). For the multiclass classification, the DMFN model (AUC, 0.707) exhibited superior prediction performance compared with general dermatologists (AUC, 0.514; P = 0.0043) and dermatologists specialized in HFUS (AUC, 0.640; P = 0.0083), respectively. Compared to dermatologists specialized in HFUS, the DMFN model showed better or comparable performance in diagnosing 9 of the 17 skin diseases. Interpretation: The DMFN model combining analysis of clinical close-up and HFUS images exhibited satisfactory performance in the binary and multiclass classification compared with the dermatologists. It may be a valuable tool for general dermatologists and primary care providers. Funding: This work was supported in part by the National Natural Science Foundation of China and the Clinical research project of Shanghai Skin Disease Hospital.

Ultrasound-Driven Non-Metallic Fenton-Active Center Construction for Extensive Chemodynamic Therapy.

Chemodynamic therapy (CDT) is an emerging tumor microenvironment-responsive cancer therapeutic strategy based on Fenton/Fenton-like reactions. However, the effectiveness of CDT is subject to the slow kinetic rate and non-homogeneous distribution of H2 O2 . In this study, a conceptual non-metallic "Fenton-active" center construction strategy is proposed to enhance CDT efficiency using Bi0.44 Ba0.06 Na0.5 TiO2.97 (BNBT-6) nanocrystals. The separated charge carriers under a piezoelectric-induced electric field synchronize the oxidation of H2 O and reduction of H2 O2 , which consequently increases hydroxyl radical (·OH) yield even under low H2 O2 levels. Moreover, acceptor doping induces electron-rich oxygen vacancies to facilitate the dissociation of H2 O2 and H2 O and further promote ·OH generation. In vitro and in vivo experiments demonstrate that BNBT-6 induces extensive intracellular oxidative stress and enhances cell-killing efficiency by activating necroptosis in addition to the conventional apoptotic pathway. This study proposes a novel design approach for nanomaterials used in CDT and presents a new treatment strategy for apoptosis-resistant tumors.

Assessment of the Diagnostic Performance of Clinical Examinations and High-Frequency Ultrasound in Patients With Pigmented Skin Tumors.

OBJECTIVES: To investigate whether the integration of high-frequency ultrasound (HFUS) to routine clinical examinations could improve diagnostic performance and management decision for pigmented skin tumors. METHODS: Three general practitioners trained previously and a dermatologist independently assessed pigmented skin tumors and rendered management decision based on clinical examinations alone or clinical examinations integrating HFUS. RESULTS: After integrating HFUS, the diagnostic area under the curve (AUC) (0.658-0.693 versus 0.848, all P < .05) and specificity (46.6-58.6% versus 89.7%, all P < .05) for pigmented skin malignancies were improved for general practitioners, meanwhile unnecessary biopsy rate reduced (42.9-53.6% versus 10.7%, P < .001). To the dermatologist, the diagnostic AUC (0.822 versus 0.949, P < .001), sensitivity (81.7% versus 96.7%, P = .012) and specificity (0.828 versus 0.931, P = .031) improved significantly, meanwhile both missed biopsy rate (14.5% versus 4.8%, P = .031) and unnecessary biopsy rate (19.6% versus 7.1%, P = .016) decreased. Additionally, the diagnostic performance of the general practitioner with integrating HFUS could be comparable with the dermatologist based on clinical examinations alone (all P > .05). CONCLUSIONS: As a complementary tool of clinical examinations, HFUS could help physicians differentiate pigmented skin malignancies and manage decision.

A 5G-based telerobotic ultrasound system provides qualified abdominal ultrasound services for patients on a rural island: a prospective and comparative study of 401 patients.

PURPOSE: To explore the feasibility of a 5G-based telerobotic ultrasound (US) system for providing qualified abdominal US services on a rural island. METHODS: This prospective study involved two medical centers (the tele-radiologist site's hospital and the patient site's hospital) separated by 72 km. Patients underwent 5G-based telerobotic US by tele-radiologists and conventional US by on-site radiologists from September 2020 to March 2021. The clinical feasibility and diagnostic performance of the 5G-based telerobotic abdominal US examination were assessed based on safety, duration, image quality, diagnostic findings, and questionnaires. RESULTS: A total of 401 patients (217 women and 184 men; mean age, 54.96 ± 15.43 years) were enrolled. A total of 90.1% of patients indicated no discomfort with the telerobotic US examination. For the examination duration, telerobotic US took longer than conventional US (12.54 ± 3.20 min vs. 7.23 ± 2.10 min, p = 0.001). For image quality scores, the results of the two methods were similar (4.54 ± 0.63 vs. 4.57 ± 0.61, p = 0.112). No significant differences were found between the two methods in measurements for the aorta, portal vein, gallbladder, kidney (longitudinal diameter), prostate, and uterus; however, telerobotic US underestimated the transverse diameter of the kidney (p < 0.05). A total of 504 positive results, including 31 different diseases, were detected. Among them, 455 cases were identified by the two methods; 17 cases were identified by telerobotic US only; and 32 cases were identified by conventional US only. There was good consistency in the diagnosis of 29 types of disease between the two methods (κ = 0.773-1.000). Furthermore, more than 90% of patients accepted the telerobotic US examination and agreed to pay additional fees in future. CONCLUSION: The 5G-based telerobotic US system can expand access to abdominal US services for patients in rural areas, thereby reducing health care disparities.

Tele-Mentored Handheld Ultrasound System for General Practitioners: A Prospective, Descriptive Study in Remote and Rural Communities.

BACKGROUND: Rural general practitioners (GPs) have insufficient diagnostic information to deal with complex clinical scenarios due to the inequality in medical imaging resources in rural and remote communities. The objective of this study is to explore the value of a tele-mentored handheld ultrasound (tele-HHUS) system, allowing GPs to provide ultrasound (US) services in rural and remote communities. METHODS: Overall, 708 patients underwent tele-HHUS examination between March and October 2021 and March and April 2022 across thirteen primary hospitals and two tertiary-care general hospitals. All US examinations were guided and supervised remotely in real time by US experts more than 300 km away using the tele-HHUS system. The following details were recorded: location of tele-HHUS scanning, primary complaints, clinical diagnosis, and US findings. The recommendations (referral or follow-up) based on clinical experience alone were compared with those based on clinical experience with tele-HHUS information. RESULTS: Tele-HHUS examinations were performed both in hospital settings (90.6%, 642/708) and out of hospital settings (9.4%, 66/708). Leaving aside routine physical examinations, flank pain (14.2%, 91/642) was the most common complaint in inpatients, while chest distress (12.1%, 8/66) and flank discomfort (12.1%, 8/66) were the most common complaints in out-of-hospital settings. Additionally, the referral rate increased from 5.9% to 8.3% (kappa = 0.202; p = 0.000). CONCLUSIONS: The tele-HHUS system can help rural GPs perform HHUS successfully in remote and rural communities. This novel mobile telemedicine model is valuable in resource-limited areas.

Diagnostic value of high-frequency ultrasound (HFUS) in evaluation of subcutaneous lesions.

BACKGROUND: It is unknown whether high-frequency ultrasound (HFUS) can evaluate invisible subcutaneous lesions. We aimed to investigate the diagnostic value of HFUS in invisible subcutaneous lesions. METHOD: Patients with invisible subcutaneous lesions were prospectively recruited from two centres. Before undergoing biopsy or surgery, each lesion was independently evaluated by two clinicians. One provides a clinical diagnosis by only clinical examination and the other provides an integrated diagnosis by combining clinical examination and HFUS information. Diagnoses were classified as correct, wrong, and indeterminate. A total of 391 lesions from 355 patients were enrolled, including 225 epidermoid cysts, 77 lipomas, 25 pilomatrixomas, 21 haemangiomas, 19 dermatofibromas, 11 dermatofibrosarcoma protuberans (DFSP), 7 neurofibromas, and 6 leiomyomas. Using pathological results as the gold standard, diagnostic performance was compared. RESULTS: The number of correct diagnoses increased from 185 (47.3%) by clinical examination alone to 316 (80.8%) after the addition of HFUS (P < 0.05). Meanwhile, the indeterminate diagnosis rate decreased from 143 (36.6%) to 10 (2.6%). Using HFUS, the accuracy improved significantly for epidermoid cysts (59.6% vs. 86.7%), lipomas (50.6% vs. 94.8%), pilomatrixomas (0% vs. 48.0%), haemangiomas (23.8% vs. 57.1%), and DFSPs (0% vs. 81.8%) (all p < 0.05). However, HFUS did not significantly improve the diagnostic accuracy of dermatofibromas (15.8% vs. 21.1%, p > 0.999), neurofibromas (42.9% vs. 71.4%, p = 0.625), or leiomyomas (16.7% vs. 100%, p = 0.063). CONCLUSION: Combining HFUS and clinical examination can generally improve the diagnostic accuracy and decrease the indeterminacy of invisible subcutaneous lesions, especially epidermoid cysts, lipomas, pilomatrixomas, haemangiomas, and DFSPs. However, for some rare lesions, HFUS cannot provide useful information.

Traditional Chinese Medicine Shi-Bi-Man regulates lactic acid metabolism and drives hair follicle stem cell activation to promote hair regeneration.

BACKGROUND: As a supplement for promoting hair health, Shi-Bi-Man (SBM) is a prescription comprising various traditional Chinese medicines. Though SBM has been reported to promote hair regeneration, its molecular mechanism remains unclear. Cynomolgus monkeys (Macaca fascicularis) are non-human primates with a gene expression profile similar to that of humans. The purpose of this research is to evaluate the effect of SBM on promoting hair regeneration in cynomolgus monkeys and to reveal the underlying mechanism. METHODS: The effect of SBM on hair regeneration was observed by skin administration on 6 cynomolgus monkeys with artificial back shaving. The molecular mechanism of SBM was studied using single-cell RNA sequencing (scRNA-seq) in combination with quantitative polymerase chain reaction (qPCR) detection for gene transcription level, and immunofluorescence staining verification for protein level. RESULTS: SBM significantly induced hair regeneration in cynomolgus monkeys, increased hair follicle number and facilitated hair follicle development. ScRNA-seq revealed an increase in the number of hair follicle stem cells (HFSCs) with a higher activation state, as evidenced by the higher expression of activation marker LDHA related to metabolism and the proliferation marker MKI67. Immunofluorescence analysis at the protein level and qPCR at the mRNA level confirmed the sequencing data. Cellchat analysis revealed an enrichment of ligand-receptor pairs involved in intercellular communication in Laminin-related pathways. CONCLUSION: SBM significantly promotes hair regeneration in cynomolgus monkeys. Mechanically, SBM can up-regulate LDHA-mediated lactic acid metabolism and drive HFSC activation, which in turn promotes the proliferation and differentiation of HFSCs.

B-mode ultrasound based CAD for liver cancers via multi-view privileged information learning.

B-mode ultrasound-based computer-aided diagnosis model can help sonologists improve the diagnostic performance for liver cancers, but it generally suffers from the bottleneck due to the limited structure and internal echogenicity information in B-mode ultrasound images. Contrast-enhanced ultrasound images provide additional diagnostic information on dynamic blood perfusion of liver lesions for B-mode ultrasound images with improved diagnostic accuracy. Since transfer learning has indicated its effectiveness in promoting the performance of target computer-aided diagnosis model by transferring knowledge from related imaging modalities, a multi-view privileged information learning framework is proposed to improve the diagnostic accuracy of the single-modal B-mode ultrasound-based diagnosis for liver cancers. This framework can make full use of the shared label information between the paired B-mode ultrasound images and contrast-enhanced ultrasound images to guide knowledge transfer It consists of a novel supervised dual-view deep Boltzmann machine and a new deep multi-view SVM algorithm. The former is developed to implement knowledge transfer from the multi-phase contrast-enhanced ultrasound images to the B-mode ultrasound-based diagnosis model via a feature-level learning using privileged information paradigm, which is totally different from the existing learning using privileged information paradigm that performs knowledge transfer in the classifier. The latter further fuses and enhances feature representation learned from three pre-trained supervised dual-view deep Boltzmann machine networks for the classification task. An experiment is conducted on a bimodal ultrasound liver cancer dataset. The experimental results show that the proposed framework outperforms all the compared algorithms with the best classification accuracy of 88.91 ± 1.52%, sensitivity of 88.31 ± 2.02%, and specificity of 89.50 ± 3.12%. It suggests the effectiveness of our proposed MPIL framework for the BUS-based CAD of liver cancers.

Multi-View Feature Transformation Based SVM+ for Computer-Aided Diagnosis of Liver Cancers With Ultrasound Images.

It is feasible to improve the performance of B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) for liver cancers by transferring knowledge from contrast-enhanced ultrasound (CEUS) images. In this work, we propose a novel feature transformation based support vector machine plus (SVM+) algorithm for this transfer learning task by introducing feature transformation into the SVM+ framework (named FSVM+). Specifically, the transformation matrix in FSVM+ is learned to minimize the radius of the enclosing ball of all samples, while the SVM+ is used to maximize the margin between two classes. Moreover, to capture more transferable information from multiple CEUS phase images, a multi-view FSVM+ (MFSVM+) is further developed, which transfers knowledge from three CEUS images from three phases, i.e., arterial phase, portal venous phase, and delayed phase, to the BUS-based CAD model. MFSVM+ innovatively assigns appropriate weights for each CEUS image by calculating the maximum mean discrepancy between a pair of BUS and CEUS images, which can capture the relationship between source and target domains. The experimental results on a bi-modal ultrasound liver cancer dataset demonstrate that MFSVM+ achieves the best classification accuracy of 88.24±1.28%, sensitivity of 88.32±2.88%, specificity of 88.17±2.91%, suggesting its effectiveness in promoting the diagnostic accuracy of BUS-based CAD.

The Value of Ultrasound for Differentiating Trichilemmal Cysts From Epidermoid Cysts.

OBJECTIVES: This study was aimed to evaluate the diagnostic performance of ultrasound (US) in differentiating trichilemmal cysts (TCs) from epidermoid cysts (ECs). METHODS: Based on clinical and ultrasound features, a prediction model was established and validated. 164 cysts in the pilot cohort and another 69 in the validation cohort diagnosed with TCs or ECs histopathologically were evaluated. The same radiologist performed all ultrasound examinations. RESULTS: For clinic features, TCs tended to occur in females compared with ECs (66.7 vs 28.5%; P < .001). In addition, TCs were prone to occur in the hairy area compared with ECs (77.8 vs 13.1%; P < .001). For ultrasound features, the internal hyperechogenicity and cystic change were more likely to appear in TCs in comparison with ECs (92.6 vs 25.5%; P < .001; 70.4 vs 23.4%; P < .001, respectively). Upon the features mentioned above, a prediction model was established with the areas under the receiver operating characteristic curves of 0.936 and 0.864 in the pilot and validation cohorts, respectively. CONCLUSIONS: US is promising for differentiating TCs from ECs and is valuable for their clinical management.

The Value of Ultra-High-Frequency Ultrasound for the Differentiation between Superficial Basal Cell Carcinoma and Bowen's Disease.

BACKGROUND: The similar visual appearance of superficial basal cell carcinoma (sBCC) and Bowen's disease (BD) may cause confusion for diagnosis. OBJECTIVE: The aim of the study was to investigate the value of ultra-high-frequency ultrasound (uHFUS) in differentiating sBCC from BD. MATERIALS AND METHODS: This prospective study included a pilot cohort of 110 patients (73 BDs and 37 sBCCs) from November 2016 to October 2020 and a validation cohort of 42 patients (30 BDs and 12 sBCCs) from July 2021 to December 2021. Clinical and uHFUS features of pathologically confirmed sBCC and BD were assessed. A predictive model was developed based on the uHFUS features of the pilot cohort. Subsequently, the model was validated and compared with clinical diagnosis in the validation cohort. RESULTS: uHFUS features with significant differences between sBCC and BD included lesion surface, skin layer involvement, hyperkeratosis, and hyperechoic spots (all p < 0.05). A prediction model based on the above features was established to identify sBCC and BD in the pilot and validation cohorts with areas under the curve (AUC) of 0.908 and 0.923, sensitivity of 82.3% and 83.3%, specificity of 91.9% and 91.7%, and accuracy of 85.5% and 85.7%, respectively, which were significantly higher than those obtained by clinical diagnosis based on photographic pictures of lesions, with the AUC of 0.692, sensitivity of 63.3%, specificity of 75.3%, and accuracy of 66.7% (all p < 0.05). CONCLUSION: uHFUS provides detailed internal features of sBCC and BD, which facilitates the differentiation between sBCC and BD, and its diagnostic performance is superior to clinical diagnosis.

The role of ultrasound in screening subclinical psoriatic arthritis in patients with moderate to severe psoriasis.

OBJECTIVE: To identify patients in the subclinical psoriatic arthritis (Sub-PsA) phase by ultrasound (US) and provide a solution to screen them. METHODS: A total of 490 participants with moderate-to-severe psoriasis were evaluated. Among them, 384 participants without arthritis symptoms were enrolled into the silent psoriasis group and 106 participants with arthritis symptoms, called prodromal/active PsA phase, were enrolled into the clinical PsA group. Another 80 non-psoriasis participants were enrolled into the control group. Each participant received clinical assessments and US examinations of 60 joints, 38 tendons, and 40 entheses. We compared the incidences of synovio-enthesitis, synovitis, tenosynovitis, erosion, and dactylitis detected on US among the three groups. Subsequently, on the basis of significant US findings, we distinguished Sub-PsA from psoriasis alone (PsO) in the silent psoriasis group and analyzed the clinical characteristics, mainly including basic clinical characteristics, body surface area (BSA), and Psoriasis Area and Severity Index (PASI) score. RESULTS: Only synovio-enthesitis significantly differed between the control group and the silent psoriasis group (1.3% vs. 16.1%, p < 0.001). The knee was the most commonly involved site of synovio-enthesitis (79.0%). Taking synovio-enthesitis as the standard, 16.1% of silent psoriasis participants and 12.7% of all psoriasis participants were in the Sub-PsA phase. Furthermore, there were no differences in BSA and PASI among the three phases of PsO, Sub-PsA, and prodromal/active PsA. CONCLUSIONS: Since the psoriasis patients in Sub-PsA phase was as high as 12.7% in all patients with moderate-to-severe psoriasis, US-detected synovio-enthesitis was recommended routinely for screening them regardless of arthritis symptoms, especially in the lower limbs. KEY POINTS: • Synovio-enthesitis on ultrasound was significantly associated with subclinical psoriatic arthritis, especially in the lower limbs. • Routine ultrasound evaluation could help screen psoriasis patients in the subclinical psoriatic arthritis phase, which was as high as 12.7% in all psoriasis patients.

5G-Based Telerobotic Ultrasound System Improves Access to Breast Examination in Rural and Remote Areas: A Prospective and Two-Scenario Study.

OBJECTIVE: Ultrasound (US) plays an important role in the diagnosis and management of breast diseases; however, effective breast US screening is lacking in rural and remote areas. To alleviate this issue, we prospectively evaluated the clinical availability of 5G-based telerobotic US technology for breast examinations in rural and remote areas. METHODS: Between September 2020 and March 2021, 63 patients underwent conventional and telerobotic US examinations in a rural island (Scenario A), while 20 patients underwent telerobotic US examination in a mobile car located in a remote county (Scenario B) in May 2021. The safety, duration, US image quality, consistency, and acceptability of the 5G-based telerobotic US were assessed. RESULTS: In Scenario A, the average duration of the telerobotic US procedure was longer than that of conventional US (10.3 ± 3.3 min vs. 7.6 ± 3.0 min, p = 0.017), but their average imaging scores were similar (4.86 vs. 4.90, p = 0.159). Two cases of gynecomastia, one of lactation mastitis, and one of postoperative breast effusion were diagnosed and 32 nodules were detected using the two US methods. There was good interobserver agreement between the US features and BI-RADS categories of the identical nodules (ICC = 0.795-1.000). In Scenario B, breast nodules were detected in 65% of the patients using telerobotic US. Its average duration was 10.1 ± 2.3 min, and the average imaging score was 4.85. Overall, 90.4% of the patients were willing to choose telerobotic US in the future, and tele-sonologists were satisfied with 85.5% of the examinations. CONCLUSION: The 5G-based telerobotic US system is feasible for providing effective breast examinations in rural and remote areas.

Value of conventional ultrasound and shear wave elastography in the assessment of muscle mass and function in elderly people with type 2 diabetes.

OBJECTIVES: We assessed muscle mass and function using ultrasound (US) and shear wave elastography (SWE) for sarcopenia in elderly patients with type 2 diabetes. METHODS: There were 84 patients with type 2 diabetes enrolled in this study; of these, 30 had sarcopenia and 54 did not. We measured appendicular skeletal muscle mass index (ASMI), handgrip strength, calf circumference, 6-m walking speed, and 5-time chair stand test. All patients were in the supine position with their knees in straight and bent poses in turn. The US-derived thickness (Tstraight, Tbent), cross-sectional area (CSAstraight, CSAbent), and SWE (SWEstraight, SWEbent) of the rectus femoris muscle (RFM) were measured and the differences (ΔT, ΔCSA, ΔSWE) were calculated. We assessed the correlations of clinical indicators with US and SWE features. We then compared the clinical indicators and US and SWE features between patients with and without sarcopenia to determine independent predictors. Diagnostic models were established based on these independent predictors. RESULTS: The ASMI was correlated with Tbent (r = 0.57, p < 0.001) and CSAbent (r = 0.50, p < 0.001). Handgrip strength was correlated with Tbent (r = 0.53, p < 0.001) and CSAbent (r = 0.51, p < 0.001). Between patients with and without sarcopenia, the indicators of age, ΔCSA, and ΔSWE were statically different (all p ≤ 0.001). Based on these results, a diagnostic model for sarcopenia was established with 83.3% sensitivity, 83.3% specificity, and 83.3% accuracy. CONCLUSIONS: In elderly people with type 2 diabetes, sarcopenia patients had smaller muscle CSA and less stiffness than non-sarcopenia patients. US and SWE might be useful to screen them. KEY POINTS: • Sarcopenia is common in elderly people with type 2 diabetes. • Ultrasound and shear wave elastography might be useful methods for quantitatively assessing muscle mass and strength. • Ultrasound and shear wave elastography might be useful methods for screening sarcopenia in elderly patients with type 2 diabetes.

High Diagnostic Accuracy of Epigenetic Imprinting Biomarkers in Thyroid Nodules.

PURPOSE: To explore the novel diagnostic value of epigenetic imprinting biomarkers in thyroid nodules. PATIENTS AND METHODS: A total of 550 patients with fine-needle aspiration (FNA)-evaluated and histopathologically confirmed thyroid nodules were consecutively recruited from eight medical centers. Quantitative chromogenic imprinted gene in situ hybridization (QCIGISH) was used to assess the allelic expression of imprinted genes SNRPN and HM13, on the basis of which a diagnostic grading model for thyroid nodules was developed. The model was retrospectively trained on 124 postsurgical thyroid samples, optimized on 32 presurgical FNA samples, and prospectively validated on 394 presurgical FNA samples. Blinded central review-based cytopathologic and histopathologic diagnoses were used as the reference standard. RESULTS: For thyroid malignancy, the QCIGISH test achieved an overall diagnostic sensitivity of 100% (277/277), a specificity of 91.5% (107/117; 95% CI, 86.4 to 96.5), a positive predictive value (PPV) of 96.5% (95% CI, 94.4 to 98.6), and a negative predictive value (NPV) of 100% in the prospective validation, with a diagnostic accuracy of 97.5% (384/394; 95% CI, 95.9 to 99.0). QCIGISH demonstrated a PPV of 97.8% (95% CI, 94.7 to 100) and NPV of 100%, with a diagnostic accuracy of 98.2% (111/113; 95% CI, 95.8 to 100), for indeterminate Bethesda III-V thyroid nodules. QCIGISH demonstrated a PPV of 96.6% (95% CI, 91.9 to 100) and a NPV of 100%, with a diagnostic accuracy of 97.5% (79/81; 95% CI, 94.2 to 100), for Bethesda III-IV. For Bethesda VI, QCIGISH showed a 100% (184/184) accuracy. CONCLUSION: This imprinting biomarker-based test can effectively distinguish malignant from benign thyroid nodules. The high PPV and NPV make the test both an excellent rule-in and rule-out diagnostic tool. With such a diagnostic performance and its technical simplicity, this novel thyroid molecular test is clinically widely applicable.

Ultrasound-based computer-aided diagnosis for cytologically indeterminate thyroid nodules with different radiologists.

PURPOSE: To evaluate a computer-aided diagnosis (CAD) technique in predicting malignancy for cytologically indeterminate thyroid nodules (TNs) as compared with different experienced radiologists. METHOD: 436 patients with 436 cytologically indeterminate TNs on fine-needle aspiration cytology (FNAC) were included and all were confirmed by surgical pathology. They were retrospectively analyzed with respect to ultrasound (US) characteristics using a commercially available CAD system (AmCAD-UT; AmCad BioMed, Taiwan, China) and reviewed by one junior and one senior radiologists.The CAD system and different experienced radiologists stratified the risk of malignancy using ACR TI-RADS category. The diagnostic performance by different experienced radiologists independently and after consulting the CAD (different experienced radiologists + CAD) and by the CAD alone were compared. RESULTS: The different experienced radiologists showed significantly higher specificities than the CAD system alone. The combination of radiologist and CAD system showed improved diagnostic performance with an AUC (Area under the curve) of 0.740 in the senior radiologist and 0.677 in the junior radiologist, as compared with CAD (AUC: 0.585) alone (all P < 0.05). The combination of senior radiologist and CAD system had the highest diagnostic performance (AUC: 0.740) and specificity (68.9%) compared to the others (all P < 0.05). CONCLUSION: The CAD system may play the potential role as a decision-making assistant alongside radiologists for differential diagnosis of TNs with indeterminate cytology.