UNASSIGNED: LEOPARD syndrome (LS) is a rare genetic disorder presenting various clinical manifestations from childhood, complicating its diagnosis. In this study, we aim to refine the imaging presentation of LS and emphasize the importance of multimodality imaging in enhancing diagnostic accuracy and preventing serious cardiovascular events.
UNASSIGNED: A 41-year-old woman was admitted to hospital with a suspected apical tumor detected by a transthoracic echocardiogram (TTE), which was later identified as apical myocardial hypertrophy through cardiac magnetic resonance imaging (CMR). She had abnormal electrocardiograms from the age of 2 years and freckles around the age of 4 years. In recent years, she has been experiencing exertional dyspnea. Supplemental coronary computer tomography angiography (CCTA) revealed diffuse coronary dilatation. Both multimodality imaging and clinical manifestations led to a suspicion of LS, which was confirmed by subsequent genetic testing. The patient declined further treatment. A 3-month follow-up CMR showed no significant change in the lesion.
UNASSIGNED: This report elucidates the diagnostic transition from an initial suspicion of an apical tumor by TTE to a definitive diagnosis of left ventricular apical hypertrophy by CMR in a 41-year-old woman with LS. It underscores the value of multimodality imaging (TTE, CCTA, CMR) in unraveling unusual cardiac manifestations in rare genetic disorders such as LS.

暂无摘要。

暂无摘要。

UNASSIGNED: This study aimed to develop a deep learning radiomic model using multimodal imaging to differentiate benign and malignant breast tumours.
UNASSIGNED: Multimodality imaging data, including ultrasonography (US), mammography (MG), and magnetic resonance imaging (MRI), from 322 patients (112 with benign breast tumours and 210 with malignant breast tumours) with histopathologically confirmed breast tumours were retrospectively collected between December 2018 and May 2023. Based on multimodal imaging, the experiment was divided into three parts: traditional radiomics, deep learning radiomics, and feature fusion. We tested the performance of seven classifiers, namely, SVM, KNN, random forest, extra trees, XGBoost, LightGBM, and LR, on different feature models. Through feature fusion using ensemble and stacking strategies, we obtained the optimal classification model for benign and malignant breast tumours.
UNASSIGNED: In terms of traditional radiomics, the ensemble fusion strategy achieved the highest accuracy, AUC, and specificity, with values of 0.892, 0.942 [0.886-0.996], and 0.956 [0.873-1.000], respectively. The early fusion strategy with US, MG, and MRI achieved the highest sensitivity of 0.952 [0.887-1.000]. In terms of deep learning radiomics, the stacking fusion strategy achieved the highest accuracy, AUC, and sensitivity, with values of 0.937, 0.947 [0.887-1.000], and 1.000 [0.999-1.000], respectively. The early fusion strategies of US+MRI and US+MG achieved the highest specificity of 0.954 [0.867-1.000]. In terms of feature fusion, the ensemble and stacking approaches of the late fusion strategy achieved the highest accuracy of 0.968. In addition, stacking achieved the highest AUC and specificity, which were 0.997 [0.990-1.000] and 1.000 [0.999-1.000], respectively. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity of 1.000 [0.999-1.000] under the early fusion strategy.
UNASSIGNED: This study demonstrated the potential of integrating deep learning and radiomic features with multimodal images. As a single modality, MRI based on radiomic features achieved greater accuracy than US or MG. The US and MG models achieved higher accuracy with transfer learning than the single-mode or radiomic models. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity under the early fusion strategy, showed higher diagnostic performance, and provided more valuable information for differentiation between benign and malignant breast tumours.

Differentiating between benign and malignant sacral tumors is crucial for determining appropriate treatment options. This study aims to develop two benchmark fusion models and a deep learning radiomic nomogram (DLRN) capable of distinguishing between benign and malignant sacral tumors using multiple imaging modalities. We reviewed axial T2-weighted imaging (T2WI) and non-contrast computed tomography (NCCT) of 134 patients pathologically confirmed as sacral tumors. The two benchmark fusion models were developed using fusion deep learning (DL) features and fusion classical machine learning (CML) features from multiple imaging modalities, employing logistic regression, K-nearest neighbor classification, and extremely randomized trees. The two benchmark models exhibiting the most robust predictive performance were merged with clinical data to formulate the DLRN. Performance assessment involved computing the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV). The DL benchmark fusion model demonstrated superior performance compared to the CML fusion model. The DLRN, identified as the optimal model, exhibited the highest predictive performance, achieving an accuracy of 0.889 and an AUC of 0.961 in the test sets. Calibration curves were utilized to evaluate the predictive capability of the models, and decision curve analysis (DCA) was conducted to assess the clinical net benefit of the DLR model. The DLRN could serve as a practical predictive tool, capable of distinguishing between benign and malignant sacral tumors, offering valuable information for risk counseling, and aiding in clinical treatment decisions.

暂无摘要。

Left ventricular apical hypoplasia is a rare malformation recently described congenital abnormality characterized by: (1) truncation of the left ventricle, with the septum projecting toward the right ventricle; (2) abnormal papillary muscle originating from the flattened left ventricular apex; (3) a narrow right ventricle encompassing the periapical area of the left ventricle; (4) fatty infiltration of the apex of the left ventricle. We reported a case of LVAH and reviewed the patient\'s clinical presentation. And its morphologic characteristics were revealed by multimodality imaging, including echocardiography and cardiac magnetic resonance imaging. Additionally, we reviewed 41 cases from 32 reports to summarize the pathogenesis and analyzed the imaging manifestations of LVAH in this study, aiming to provide new ideas for the diagnosis and clinical management of LVAH patients.

The crucial pathophysiological and prognostic roles of the right ventricle in various diseases have been well-established. Nonetheless, conventional cardiovascular imaging modalities are frequently associated with intrinsic limitations when evaluating right ventricular (RV) morphology and function. The integration of artificial intelligence (AI) in multimodality imaging presents a promising avenue to circumvent these obstacles, paving the way for future fully automated imaging paradigms. This review aimed to address the current challenges faced by clinicians and researchers in integrating RV imaging and AI technology, to provide a comprehensive overview of the current applications of AI in RV imaging, and to offer insights into future directions, opportunities, and potential challenges in this rapidly advancing field.

Hepatocellular carcinoma (HCC) is a malignant tumor, which seriously jeopardizes human health. The 5-year relative survival rate of HCC is only about 18%. Sorafenib, a small molecule multi-targeted tyrosine kinase inhibitor (MTKI), has been classified as the first-line treatment scheme for HCC and has significantly extended the median survival time for patients with advanced HCC. Nevertheless, the emergence of sorafenib resistance has substantially hampered its further clinical application. Herein, the nano-platform based on phototherapy and small molecular targeted therapy (SMTT) was devised to overcome the sorafenib resistance and reduce the adverse effects. Hollow mesoporous manganese dioxide (H-MnO2) was prepared by hard template method, and the prepared H-MnO2 was used to load sorafenib and Chlorin e6 (Ce6). Subsequently, the nanoparticle (NPs) were modified with dopamine to optimize biocompatibility. The final prepared NPs (MCS NPs) exhibit regular spherical shape with a hydrated particle size of approximately 97.02 nm. MCS NPs can not only possess tumor microenvironment (TME) stimuli-responsive drug release performance but also can enhance the efficacy of photodynamic therapy and reverse sorafenib resistance by alleviating tumor hypoxia. Under the action of phototherapy (Ce6) combined with molecular targeted therapy (sorafenib), MCS NPs manifest a satisfactory antitumor effect for sorafenib-sensitive or sorafenib-resistant HCC cells, and retain the antiangiogenic properties of sorafenib. In the nude mouse subcutaneous tumor model constructed with sorafenib-resistant cells, MCS NPs demonstrated superior tumor imaging ability and excellent biocompatibility. The tumor inhibition rate of the MCS NPs group without laser irradiation was 53.4 %, while the MCS NPs group with laser irradiation was as high as 100 %. The novel smart TME-responsive nano-platform shows great potential for overcoming sorafenib resistance and realizes multimodality imaging and therapy of HCC.

UNASSIGNED: Lipomatous atrial septal hypertrophy (LASH) with atrial septal defect (ASD) is a rare congenital anomaly. Although LASH is a histologically benign cardiac lesion characterized by excessive fat deposition in the interatrial septum that spares the fossa ovale, it has been associated with supraventricular arrhythmias or sick sinus syndrome. Application of multimodal imaging is crucial for accurate diagnosis, appropriate treatment of LASH with ASD, and follow-up.
UNASSIGNED: A 68-year-old female patient presented with recurrent chest tightness and palpitation. Multimodal imaging revealed the characterizations of LASH and ASD. Two-dimensional transesophageal echocardiography showed a \"dumbbell\"-shaped involvement of the cephalad and caudal regions with sparing of a single secundum ASD. The septum with a brightness feature is an uncommon condition characterized by the deposition of unencapsulated fat cells in the atrial septum. Real-time four-dimensional transesophageal echocardiography reflected the lipomatous hypertrophy of the atrial septum and an oval-shaped ASD. Cardiac computer tomography angiography later confirmed this finding. The patient achieved a good clinical response with an ASD percutaneous occlusion guided by intracardiac echocardiography (ICE).
UNASSIGNED: This case demonstrates a LASH combined with ASD. Multimodality imaging can provide an accurate diagnosis and may guide the procedure for precise occlusion.