US-guided thermal ablation is a promising treatment alternative for suitable candidates with T1N0M0 papillary thyroid carcinoma, offering satisfactory therapeutic outcomes through adherence to standardized procedures and rigorous postoperative follow-up.

Gastric cancer (GC) is one of the most common and fatal malignancies globally. While microsatellite instability (MSI) index has earlier been correlated with survival outcome in gastric cancer patients, the present study aims to construct a risk-stratification model based on immune-related genes in GC patients with varying MSI status. The univariate and multivariate Cox regression analyses identified SEMA7A, NUDT6, SCGB3A1, NPR3, PTH1R, and SHC4 as signature genes, which were used to build the prognostic model for GC patients with microsatellite instability-low (MSI-L) and microsatellite stable (MSS). Whereas, for GC patients with microsatellite instability-high (MSI-H), prognostic model was established with three genes (SEMA6A, LTBP1, and BACH2), based on the univariate and multivariate Cox regression, and Kaplan-Meier survival analyses. The prognostic immune-related gene signature identified in this study may offer new targets for personalized treatment and immunotherapy for GC patients with MSI-H or MSI-L/MSS status. The Cancer Genome Atlas (TCGA) and ImmPort databases were used to extract expression data and to explore prognostic genes from the immune-related genes (IRGs), respectively. Univariate and multivariate Cox regression analysis were applied to identify IRGs correlated with patient prognosis. The regulatory network between prognostic IRGs and TFs were performed using R software.

Purpose: To construct machine learning models based on radiomics features combing conventional transrectal ultrasound (B-mode) and contrast-enhanced ultrasound (CEUS) to improve prostate cancer (PCa) detection in peripheral zone (PZ). Methods: A prospective study of 166 men (72 benign, 94 malignant lesions) with targeted biopsy-confirmed pathology who underwent B-mode and CEUS examinations was performed. Risk factors, including age, serum total prostate-specific antigen (tPSA), free PSA (fPSA), f/t PSA, prostate volume and prostate-specific antigen density (PSAD), were collected. Time-intensity curves were obtained using SonoLiver software for all lesions in regions of interest. Four parameters were collected as risk factors: the maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (MTT). Radiomics features were extracted from the target lesions from B-mode and CEUS imaging. Multivariable logistic regression analysis was used to construct the model. Results: A total of 3306 features were extracted from seven categories. Finally, 32 features were screened out from radiomics models. Five models were developed to predict PCa: the B-mode radiomics model (B model), CEUS radiomics model (CEUS model), B-CEUS combined radiomics model (B-CEUS model), risk factors model, and risk factors-radiomics combined model (combined model). Age, PSAD, tPSA, and RT were significant independent predictors in discriminating benign and malignant PZ lesions (P < 0.05). The risk factors model combing these four predictors showed better discrimination in the validation cohort (area under the curve [AUC], 0.84) than the radiomics images (AUC, 0.79 on B model; AUC, 0.78 on CEUS model; AUC, 0.83 on B-CEUS model), and the combined model (AUC: 0.89) achieved the greatest predictive efficacy. Conclusion: The prediction model including B-mode and CEUS radiomics signatures and risk factors represents a promising diagnostic tool for PCa detection in PZ, which may contribute to clinical decision-making.

We aimed to construct and validate a multimodality MRI combined with ultrasound based on radiomics for the evaluation of benign and malignant breast diseases. The preoperative enhanced MRI and ultrasound images of 131 patients with breast diseases confirmed by pathology in Aerospace Center Hospital from January 2021 to August 2023 were retrospectively analyzed, including 73 benign diseases and 58 malignant diseases. Ultrasound and 3.0 T multiparameter MRI scans were performed in all patients. Then, all the data were divided into training set and validation set in a 7:3 ratio. Regions of interest were drawn layer by layer based on ultrasound and MR enhanced sequences to extract radiomics features. The optimal radiomic features were selected by the best feature screening method. Logistic Regression classifier was used to establish models according to the best features, including ultrasound model, MRI model, ultrasound combined with MRI model. The model efficacy was evaluated by the area under the curve (AUC) of the receiver operating characteristic, sensitivity, specificity, and accuracy. The F-test based on ANOVA was used to screen out 20 best ultrasonic features, 11 best MR Features, and 14 best features from the combined model. Among them, texture features accounted for the largest proportion, accounting for 79%.The ultrasound combined with MR Image fusion model based on logistic regression classifier had the best diagnostic performance. The AUC of the training group and the validation group were 0.92 and 091, the sensitivity was 0.80 and 0.67, the specificity was 0.90 and 0.94, and the accuracy was 0.84 and 0.79, respectively. It was better than the simple ultrasound model (AUC of validation set was 0.82) or the simple MR model (AUC of validation set was 0.85). Compared with the traditional ultrasound or magnetic resonance diagnosis of breast diseases, the multimodal model of MRI combined with ultrasound based on radiomics can more accurately predict the benign and malignant breast diseases, thus providing a better basis for clinical diagnosis and treatment.

Pseudomyxoma peritonei (PMP) is a rare mucinous neoplasm with a relatively low incidence of 1 to 2 per million individuals. It is typically characterized by a type of gelatinous ascites named “jelly belly”. Most cases of PMP occur in association with ruptured primary mucinous tumors of the appendix (90%). Periodically, PMP can originate from mucinous carcinomas at other sites, including the colorectum, gallbladder, and pancreas. However, unusual origin can occur, as noted in this case report. A 52-year-old woman had an unusual derivation of PMP from intestinal duplication. The patient complained of abdominal distension and increasing abdominal girth. Abdominal contrast-enhanced computed tomography showed a mass in the greater omentum located on the left side of the abdomen, likely to be a cystic mass of peritoneal origin. A PMP diagnosis was presumed based on the specific signs of the mass with flocculent and stripe-like echoes in ultrasound images. Ultrasound-guided percutaneous aspiration suggested a high likelihood of PMP. Once the PMP diagnosis was recognized, identification of the origin of the primary tumor was indicated. Thus, an exploratory laparoscopy was performed. In the absence of a primary tumor of appendix origin, the diagnosis of a low-grade mucinous neoplasm of intestinal duplication origin was finally confirmed by histopathology. PMP is secondary to mucinous carcinomas of the appendix mostly. This case resulted from an unusual derivation from intestinal duplication.

Objective: To evaluate left atrial volume and function in patients with paroxysmal atrial fibrillation (AF) combined with left atrial appendage thrombosis and patients with paroxysmal AF without left atrial appendage thrombosis by 3-dimensional speckle tracking imaging (3D-STI), and to explore the application value of this set of parameters in the evaluation of left atrial function in patients with paroxysmal AF. Materials and Methods: A total of 40 patients with paroxysmal AF admitted from December 2018 to December 2020 were selected as the observation group. All patients with paroxysmal AF in the observation group underwent transesophageal echocardiography. According to the presence of left atrial appendage thrombosis, the patients were divided into the AF without thrombosis group (24 cases) and the AF with thrombosis group (16 cases). Thirty normal people were selected as control group who were chosen as having no heart-related disease. The left atrial volume parameters (Left atrial maximum volume LAVmax, Left atrial minimum volume LAVmin, Left atrial volume before atrial contraction LAVpre-A, Left atrial stroke volume LAEV), left atrial ejection fraction (LAEF) and left atrial strain parameters (Left atrial reservoir longitudinal strain LASr, Left atrial conduit longitudinal strain LAScd, Left atrial contraction longitudinal strain LASct, Left atrial reservoir circumferential strain LASr-c, Left atrial conduit circumferential strain LAScd-c, Left atrial contraction circumferential strain LASct-c) of the 3 groups were measured by 3D-STI. Results: With the progression of paroxysmal AF, the left atrial volume increased, and the reservoir, conduit and contractile function were damaged. The left atrial volume continued to increase, and the reservoir, conduit and contractile function further decreased significantly in patients with AF combined with left atrial appendage thrombosis. LAEF was positively correlated with LASr and LASr_c. Conclusion: Real-time 3-dimensional spot tracking imaging (3D-STI) can evaluate the changes in left atrial volume and function in patients with paroxysmal AF, and has a certain reference value for clinical judgment of disease progression and prognosis.