ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Purpose: This study aimed to evaluate the safety and efficacy of gemcitabine and cisplatin (GP) regimen in combination with immune checkpoint inhibitor sintilimab as neoadjuvant therapy for muscle-invasive bladder cancer (MIBC) patients and the feasibility of the following selective bladder sparing surgery. Materials and Methods: Patients with histopathologically confirmed urothelial carcinoma without distant metastases (T2-4a, N ≤ 1, M0, American Joint Committee of Cancer 8th) and with adequate organ function will be enrolled. The therapeutic regimen was sintilimab 200 mg once on day 8, gemcitabine 1,000 mg/m2 and cisplatin 35 mg/m2 once on days 1 and 8, every 21 days for four cycles. The primary endpoint was pathologic complete response (pCR, pT0N0) rate. The secondary end points were ypT < 2 rate, R0 resection rate, event-free survival, and safety. Results: From May 4, 2020, to May 20, 2023, 55 patients were enrolled. Forty-six patients were evaluated for efficacy. Among the 42 patients who underwent surgery, 16 patients (38.0%) achieved pCR. Thirty-three patients (78.6%) achieved pT < 2. With a median follow-up of 15.7 months, the 1-year event-free survival was 91.3%. Notwithstanding the poor pathological baseline characteristic of a high T3-T4a proportion (39.1%), a promising bladder preservation (including 22 patients transurethral resection of bladder tumor, 5 patients partial cystectomy, and 4 surveillances) rate was achieved (67.4%). The most common grade ≥ 3 treatment-related adverse events was neutropenia (n=15, 27.3%), which was related to chemotherapy. There were no grade 3 immune-related adverse events. Conclusion Neoadjuvant GP plus sintilimab is a promising regimen for MIBC patients, with relatively high pT < 2 rate and triggering the emerging roles for the multi-disciplinary team decision-making for bladder sparing surgery.

ObjectiveTo explore the clinical and phenomics characteristics of patients with phlegm-stasis binding syndrome and its accompanied patterns in stable angina pectoris （SAP） of coronary heart disease （CHD）. MethodsA multicenter cross-sectional study design was adopted. A total of 300 patients with SAP of CHD were enrolled and classified into 120 cases of phlegm-stasis binding syndrome， 125 cases of qi deficiency-accompanied syndrome， 38 cases of qi stagnation-accompanied syndrome， and 17 cases of toxin accumulation-accompanied syndrome according to traditional Chinese medicine （TCM） patterns. Data of patients with different TCM patterns were collected， including general condition， TCM symptoms， blood lipids， coagulation function， immune indicators， and serum metabolomics. Metabolic pathway enrichment analysis was used to compare differences in phenomics characteristics among groups. Metabolites with variable importance in projection （VIP） ≥1 and fold change （FC） ≥2 were considered as representative differential metabolites. ResultsPatients in each TCM pattern type were most commonly in the 60-75 years age group， with a relatively high proportion of males. Regarding TCM symptoms， patients with phlegm-stasis binding syndrome most commonly presented with wiry-choppy or wiry-slippery pulse， chest pain， and chest tightness； in patients with qi deficiency-accompanied syndrome， fatigue， chest pain， and weak pulse were most common； in patients with qi stagnation-accompanied sydnrome， wiry-choppy or wiry-slippery pulse， chest pain， and symptoms that increase or decrease with emotional changes， belching， or flatulence were most common； in patients with toxin accumulation-accompanied syndrome， bitter taste in the mouth， chest tightness， irritability， restlessness or manic delirium， and dry and hard stools or foul-smelling diarrhea were most common. Comparisons among the different TCM patterns showed statistically significant differences in coagulation parameters （P＜0.05）， whereas no statistically significant difference was found in blood lipid levels and immune indicators （P＞0.05）.Metabolomics analysis suggested that disorders of glycerophosphate metabolism and valine， leucine， and isoleucine metabolism are characteristic features of phlegm-stasis binding syndrome and its accompanied patterns. The representative differential metabolites between phlegm-stasis binding syndrome and qi deficiency-accompanied syndrome were 7,8-dihydrobiopterin （FC = 3.58） and oxypurinol （FC = 124.50）. Those between phlegm-stasis binding syndrome and qi stagnation-accompanied syndrome were cytidine 5′-diphosphocholine （FC = 2.62） and D-mannosamine （FC = 2.99）. Those between phlegm-stasis binding syndrome and toxin accumulation-accompanied syndrome were anserine （FC = 7.83） and canrenone （FC = 8.94）. ConclusionThere are certain differences in the clinical characteristics and phenomics characteristics among patients with SAP due to CHD exhibiting phlegm-stasis binding syndrome and its accompanied patterns. The phenomics characteristics mainly involve biological alterations such as lipid metabolism disorders， amino acid metabolism abnormalities， and multiple immune indicators activation， which may provide a reference for precise differentiation and treatment of SAP of CHD in TCM clinical practice.

ObjectiveTo explore the clinical and phenomics characteristics of patients with phlegm-stasis binding syndrome and its accompanied patterns in stable angina pectoris （SAP） of coronary heart disease （CHD）. MethodsA multicenter cross-sectional study design was adopted. A total of 300 patients with SAP of CHD were enrolled and classified into 120 cases of phlegm-stasis binding syndrome， 125 cases of qi deficiency-accompanied syndrome， 38 cases of qi stagnation-accompanied syndrome， and 17 cases of toxin accumulation-accompanied syndrome according to traditional Chinese medicine （TCM） patterns. Data of patients with different TCM patterns were collected， including general condition， TCM symptoms， blood lipids， coagulation function， immune indicators， and serum metabolomics. Metabolic pathway enrichment analysis was used to compare differences in phenomics characteristics among groups. Metabolites with variable importance in projection （VIP） ≥1 and fold change （FC） ≥2 were considered as representative differential metabolites. ResultsPatients in each TCM pattern type were most commonly in the 60-75 years age group， with a relatively high proportion of males. Regarding TCM symptoms， patients with phlegm-stasis binding syndrome most commonly presented with wiry-choppy or wiry-slippery pulse， chest pain， and chest tightness； in patients with qi deficiency-accompanied syndrome， fatigue， chest pain， and weak pulse were most common； in patients with qi stagnation-accompanied sydnrome， wiry-choppy or wiry-slippery pulse， chest pain， and symptoms that increase or decrease with emotional changes， belching， or flatulence were most common； in patients with toxin accumulation-accompanied syndrome， bitter taste in the mouth， chest tightness， irritability， restlessness or manic delirium， and dry and hard stools or foul-smelling diarrhea were most common. Comparisons among the different TCM patterns showed statistically significant differences in coagulation parameters （P＜0.05）， whereas no statistically significant difference was found in blood lipid levels and immune indicators （P＞0.05）.Metabolomics analysis suggested that disorders of glycerophosphate metabolism and valine， leucine， and isoleucine metabolism are characteristic features of phlegm-stasis binding syndrome and its accompanied patterns. The representative differential metabolites between phlegm-stasis binding syndrome and qi deficiency-accompanied syndrome were 7,8-dihydrobiopterin （FC = 3.58） and oxypurinol （FC = 124.50）. Those between phlegm-stasis binding syndrome and qi stagnation-accompanied syndrome were cytidine 5′-diphosphocholine （FC = 2.62） and D-mannosamine （FC = 2.99）. Those between phlegm-stasis binding syndrome and toxin accumulation-accompanied syndrome were anserine （FC = 7.83） and canrenone （FC = 8.94）. ConclusionThere are certain differences in the clinical characteristics and phenomics characteristics among patients with SAP due to CHD exhibiting phlegm-stasis binding syndrome and its accompanied patterns. The phenomics characteristics mainly involve biological alterations such as lipid metabolism disorders， amino acid metabolism abnormalities， and multiple immune indicators activation， which may provide a reference for precise differentiation and treatment of SAP of CHD in TCM clinical practice.

To address the scarcity of annotated data for pediatric abdominal CT imaging and the insufficient generalization capability of existing models, we constructed a self-supervised pretraining architecture tailored for pediatric CT domain adaptation based on the visual foundation model DINOv3, and validated its performance in the task of pediatric abdominal multi-organ segmentation.

Poloxamer， as a non-ionic surfactant， exhibits a unique triblock [polyethylene oxide-poly （propylene oxide）-polyethylene oxide] structure， which endows it with broad application potential in various fields， including solid dispersion technology， nanotechnology， gel technology， biologics， gene engineering and 3D printing. As a carrier， it enhances the solubility and bioavailability of poorly soluble drugs. In the field of nanotechnology， it serves as a stabilizer etc.， enriching preparation methods. In gel technology， its self-assembly behavior and thermosensitive properties facilitate controlled drug release. In biologics， it improves targeting efficiency and reduces side effects. In gene engineering， it enhances delivery efficiency and expression levels. In 3D printing， it provides novel strategies for precise drug release control and the production of high-quality biological products. As a versatile material， poloxamer holds promising prospects in the pharmaceutical field.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.