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.

This study aimed to develop an interpretable machine learning model integrating delayed-phase contrast-enhanced CT radiomics with clinical features for noninvasive prediction of pathological grading in appendiceal pseudomyxoma peritonei (PMP), using Shapley Additive Explanations (SHAP) for model interpretation. This retrospective study analyzed 158 pathologically confirmed PMP cases (85 low-grade, 73 high-grade) from January 4, 2015 to April 30, 2024. Comprehensive clinical data including demographic characteristics, serum tumor markers (CEA, CA19-9, CA125, D-dimer, CA-724, CA-242), and CT-peritoneal cancer index (CT-PCI) were collected. Radiomics features were extracted from preoperative contrast-enhanced CT scans using standardized protocols. After rigorous feature selection and five-fold cross-validation, we developed three predictive models: clinical-only, radiomics-only, and a combined clinical-radiomics model using logistic regression. Model performance was evaluated through ROC analysis (AUC), Delong test, decision curve analysis (DCA), and Brier score, with SHAP values providing interpretability. The combined model demonstrated superior performance, achieving AUCs of 0.91 (95%CI:0.86–0.95) and 0.88 (95%CI:0.82–0.93) in training and testing sets respectively, significantly outperforming standalone models (P < 0.05). DCA confirmed greater clinical utility across most threshold probabilities, with favorable Brier scores (training:0.124; testing:0.142) indicating excellent calibration. SHAP analysis identified the top predictive features: wavelet-LHH_glcm_InverseVariance (radiomics), original_shape_Elongation (radiomics), and CA-199 (clinical). Our SHAP-interpretable combined model provides an accurate, noninvasive tool for PMP grading, facilitating personalized treatment decisions. The integration of radiomics and clinical data demonstrates superior predictive performance compared to conventional approaches, with potential to improve patient outcomes.

Objective: The aim of this study is to investigate the value of ultrasound combined with computed tomography (CT) in identifying early low-grade appendiceal mucinous neoplasm and appendicitis.Methods: Patients with early low-grade appendiceal mucinous neoplasm and appendicitis from September 2017 to September 2021, including 40 patients with low-grade appendiceal mucinous neoplasm and 40 patients with appendicitis, were collected in this study. Clinical data as well as ultrasound and CT findings of all patients were retrospectively analyzed. Univariate and multivariate logistic regression analyses were applied to establish the ultrasound model, the CT model, and the combined model.Results: The nomogram showed that specific characteristics of CT were dilated appendiceal diameter and clear surrounding fat space in the low-grade appendiceal mucinous neoplasm and that specific characteristics of ultrasound were thin or clear layer appendix wall and flocculent echo in the appendix cavity. These four features were used to construct a nomogram for predicting early low-grade appendiceal mucinous neoplasm, and the area under the curve value was 0.839.Conclusion: Ultrasound combined with CT for diagnosis of early low-grade appendiceal mucinous neoplasm has a significant value; when found significantly dilated appendix in the lower right abdomen, with thin wall, wall calcification, clear surrounding fat space, and progressive enhancement, especially non-specific symptoms similar to appendicitis, the physician should timely consider the possibility of low-grade appendiceal mucinous neoplasm.

Neurosyphilis is a heterogeneous manifestation of Treponema pallidum infection with diverse clinical and radiological presentations. Concurrent involvement

Purpose To compare noncontrast multiparametric MRI (mpMRI) and contrast-enhanced CT (CECT) for agreement with intraoperative peritoneal cancer index (PCI) and detection of key imaging features in pseudomyxoma peritonei (PMP). Materials and Methods This retrospective study included 40 patients with pathologically confirmed PMP who underwent cytoreductive surgery and preoperative mpMRI and CECT in 2024. Intraoperative PCI across 13 abdominopelvic regions served as the reference standard. Total PCI agreement was assessed using intraclass correlation coefficients (ICCs) and Bland–Altman 95% limits of agreement (LoA), with analyses stratified by PCI (≤ 20 vs > 20). Regional agreement and sensitivities for key imaging features were also compared. Results Forty patients (median age, 60 years; 25 women) were included. mpMRI showed greater agreement with intraoperative total PCI than CECT (ICC, 0.98 vs 0.90; difference, 0.08 [95% CI: 0.05, 0.15]), with narrower LoA (7.1 vs 19.1 points). Agreement remained greater with mpMRI for PCI ≤ 20 (ICC, 0.91 vs 0.58) and PCI > 20 (ICC, 0.95 vs 0.74). mpMRI showed higher regional agreement in 11 of 13 regions after multiple-comparison correction. mpMRI had higher sensitivity for mucinous ascites (100% vs 61%; P = 0.002) and intralesional septation (95% vs 37%; P = 0.002), whereas CECT had higher sensitivity for intralesional calcification (100% vs 0%; P = 0.021). Conclusion Noncontrast mpMRI showed greater agreement with intraoperative PCI than CECT and higher sensitivity for mucinous ascites and intralesional septation, whereas CECT better detected intralesional calcification.

To develop and externally validate DATUnet—a hybrid deep learning framework integrating DenseBlock encoding, atrous spatial pyramid pooling (ASPP), a