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As a positive features in C-TIRADS (Chinese Thyroid Imaging Reporting and Data System) guidelines, solid features represent the latest positive indicator, which differs from those in other guidelines. This study was to explore the diagnostic value of the solid features for thyroid nodules of different sizes. Methods: Between January 2022 and October 2023, a total of 1561 patients with 1790 thyroid nodules confirmed by surgical pathology were prospectively included in this study. These nodules were divided into three groups based on their maximum diameter: Group A1 (≤ 10mm), Group A2 (> 10mm, < 20mm), and Group A3 (≥ 20mm). The component characteristics of thyroid nodules in each group were analyzed. Based on the surgical pathology results, Receiver Operating Characteristic (ROC) curves were constructed to evaluate the diagnostic efficiency of C-TIRADS solid features for thyroid nodules of different sizes. Results: As the size of thyroid nodules increased, the incidence of cystic changes in both benign and malignant nodules showed a linear increasing trend (Z-values of 46.251 and 156.586, respectively; P values < 0.001 for both). The occurrence rate of solid malignant nodules was higher than that of benign nodules in all groups, with Area Under Curve (AUC) values of 0.620, 0.723, and 0.767, respectively. Conclusion: With the increase in the size of thyroid nodules, the diagnostic value of solid features for thyroid cancer progressively increases. The specificity of thyroid nodule diagnosis also increases progressively. Which may have certain value in the evaluation process using C-TIRADS, particularly in the management of thyroid nodules in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learning (DL) is a promising technique in differential diagnosis. However, its feasibility for discriminating HCM and HHD has not been investigated. Purpose: To examine the feasibility of DL in differentiating HCM and HHD based on T1 images and compare its diagnostic performance with other methods. Study Type: Retrospective. Population: 128 HCM patients (men, 75; age, 50 years ± 16) and 59 HHD patients (men, 40; age, 45 years ± 17). Field Strength/Sequence: 3.0T; Balanced steady‐state free precession, phase‐sensitive inversion recovery (PSIR) and multislice native T1 mapping. Assessment: Compare HCM and HHD patients baseline data. Myocardial T1 values were extracted from native T1 images. Radiomics was implemented through feature extraction and Extra Trees Classifier. The DL network is ResNet32. Different input including myocardial ring (DL‐myo), myocardial ring bounding box (DL‐box) and the surrounding tissue without myocardial ring (DL‐nomyo) were tested. We evaluate diagnostic performance through AUC of ROC curve. Statistical Tests: Accuracy, sensitivity, specificity, ROC, and AUC were calculated. Independent t test, Mann–Whitney U‐test and Chi‐square test were adopted for HCM and HHD comparison. P < 0.05 was considered statistically significant. Results: DL‐myo, DL‐box, and DL‐nomyo models showed an AUC (95% confidential interval) of 0.830 (0.702–0.959), 0.766 (0.617–0.915), 0.795 (0.654–0.936) in the testing set. AUC of native T1 and radiomics were 0.545 (0.352–0.738) and 0.800 (0.655–0.944) in the testing set. Data Conclusion: The DL method based on T1 mapping seems capable of discriminating HCM and HHD. Considering diagnostic performance, the DL network outperformed the native T1 method. Compared with radiomics, DL won an advantage for its high specificity and automated working mode. Level of Evidence: 4 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

A combination of diagnostic and therapeutic ultrasound (US) techniques may be able to provide the basis of specific therapeutic protocols, particularly for the treatment of tumors. Nanotechnology may aid the progression towards the use of US for tumor diagnosis and targeted therapy. The current study investigated in vivo and in vitro US contrast imaging using nanocapsules (NCs), and also US and US‑targeted microbubble destruction (UTMD) therapy using drug‑loaded NCs for pancreatic cancer in vitro. In the current study, the NCs were made from the polymer nanomaterial poly(lactic‑co‑glycolic acid)‑monomethoxypoly(ethylene glycol) (PLGA‑mPEG), encapsulated with paclitaxel (PTX), to create PTX‑PLGA‑mPEG NCs. The PTX‑PLGA‑mPEG NCs were used as a US contrast agent (UCA), which produced satisfactory US contrast‑enhanced images in vitro and in vivo of the rabbit kidneys, with good contrast compared with lesions in the peripheral regions. However, clear contrast‑enhanced images were not obtained using PTX‑PLGA‑mPEG NCs as a UCA, when imaging the superficial pancreatic tumors of nude mice in vivo. Subsequently, fluorescence and flow cytometry were used to measure the NC uptake rate of pancreatic tumor cells under various US or UTMD conditions. An MTT assay was used to evaluate the efficiency of PTX and PTX‑PLGA‑mPEG NCs in killing tumor cells following 24 or 48 h of US or UTMD therapy, compared with controls. The specific US or UTMD conditions had been previously demonstrated to be optimal through repeated testing, to determine the conditions by which cells were not impaired and the efficiency of uptake of nanoparticles was highest. The current study demonstrated high cellular uptake rates of PLGA‑mPEG NCs and high tumor cell mortality with PTX‑PLGA‑mPEG NCs under US or UTMD optimal conditions. It was concluded that the use of NCs in US‑mediated imaging and antitumor therapy may provide a novel application for US.