Oral squamous cell carcinoma (OSCC) is a common head and neck malignancy. Approximately 50% to 60% of patients with OSCC are diagnosed at a locally advanced stage (clinical staging III-IVa). Even with comprehensive and sequential treatment primarily based on surgery, the 5-year overall survival rate remains below 50%, and patients often suffer from postoperative functional impairments such as difficulties with speaking and swallowing. Programmed death receptor-1 (PD-1) inhibitors are increasingly used in the neoadjuvant treatment of locally advanced OSCC and have shown encouraging efficacy. However, clinical practice still faces key challenges, including the definition of indications, optimization of combination regimens, and standards for efficacy evaluation. Based on the latest research advances worldwide and the clinical experience of the expert group, this expert consensus systematically evaluates the application of PD-1 inhibitors in the neoadjuvant treatment of locally advanced OSCC, covering combination strategies, treatment cycles and surgical timing, efficacy assessment, use of biomarkers, management of special populations and immune related adverse events, principles for immunotherapy rechallenge, and function preservation strategies. After multiple rounds of panel discussion and through anonymous voting using the Delphi method, the following consensus statements have been formulated: 1) Neoadjuvant therapy with PD-1 inhibitors can be used preoperatively in patients with locally advanced OSCC. The preferred regimen is a PD-1 inhibitor combined with platinum based chemotherapy, administered for 2-3 cycles. 2) During the efficacy evaluation of neoadjuvant therapy, radiographic assessment should follow the dual criteria of Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and immune RECIST (iRECIST). After surgery, systematic pathological evaluation of both the primary lesion and regional lymph nodes is required. For combination chemotherapy regimens, PD-L1 expression and combined positive score need not be used as mandatory inclusion or exclusion criteria. 3) For special populations such as the elderly (≥ 70 years), individuals with stable HIV viral load, and carriers of chronic HBV/HCV, PD-1 inhibitors may be used cautiously under the guidance of a multidisciplinary team (MDT), with close monitoring for adverse events. 4) For patients with a poor response to neoadjuvant therapy, continuation of the original treatment regimen is not recommended; the subsequent treatment plan should be adjusted promptly after MDT assessment. Organ transplant recipients and patients with active autoimmune diseases are not recommended to receive neoadjuvant PD-1 inhibitor therapy due to the high risk of immune related activation. Rechallenge is generally not advised for patients who have experienced high risk immune related adverse events such as immune mediated myocarditis, neurotoxicity, or pneumonitis. 5) For patients with a good pathological response, individualized de escalation surgery and function preservation strategies can be explored. This consensus aims to promote the standardized, safe, and precise application of neoadjuvant PD-1 inhibitor strategies in the management of locally advanced OSCC patients.

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.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Objective To quantitatively assess the association of short-term exposure to polycyclic aromatic hydrocarbons (PAHs) in ambient fine particulate matter (PM2.5) with residents mortality. Methods A time-stratified case-crossover study was conducted from 2020 to 2022 among 10606 non-accidental residents by using the Guangzhou Cause of Death Surveillance System in Conghua District, Guangzhou. Exposure levels of PAHs in PM_2.5 and meteorological data during the study period were obtained from the Center for Disease Control and Prevention in Conghua District and the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0), respectively. Conditional Poisson regression model was used to estimate the exposure-response association between PAHs and the mortality risk. Results Fluoranthene, chrysene, benzo[k]fluoranthene, benzo[a]pyrene, and indeno[1,2,3-cd]pyrene were significantly associated with an increased risk of mortality. For every one interquartile range increase in exposure levels, the non-accidental mortality risks increased by 8.33% (95% CI: 1.80%, 15.27%), 4.67% (95% CI: 1.86%, 7.57%), 6.07% (95% CI: 2.08%, 10.21%), 4.62% (95% CI: 1.85%, 7.47%), and 4.70% (95% CI: 0.53%, 9.03%), respectively. The estimated non accidental deaths attributable to exposure to fluoranthene, chrysene, benzo[k]fluorine, benzo[a]pyrene and indine[1,2,3-cd]pyrene were 5.91%, 6.08%, 6.51%, 6.46%, and 4.21%, respectively. Conclusions Short-term exposure to PAHs in PM_2.5, including fluoranthene, chrysene, benzo[k]fluoranthene, benzo[a]pyrene and indine[1,2,3-cd]pyrene, was significantly associated with an increased risk of mortality among residents.

Background and Aims:Adhesive intestinal obstruction(AIO)is a type of mechanical bowel obstruction caused by abdominal or intestinal adhesions,and its onset and progression are closely associated with impaired intestinal mucosal barrier function.Danshen injection,a commonly used traditional Chinese medicine preparation with properties of promoting blood circulation and removing blood stasis,has shown therapeutic potential in various gastrointestinal diseases by improving microcirculation and promoting vasodilation.However,its specific mechanism of action in AIO remains unclear.This study was conducted to investigate the effects and potential mechanisms of Danshen injection on intestinal mucosal barrier function in a rat model of AIO.Methods:Forty rats with experimentally induced AIO were equally randomized into four groups:the model group(receiving intraperitoneal saline)and three Danshen-treated groups administered low,medium,and high doses of Danshen injection(1,2,and 4 mL/kg,respectively),once daily for 7 consecutive d.An additional 10 healthy rats received saline injections in the same manner and served as the normal control group.After the final intervention,all rats were euthanized under anesthesia.Hematoxylin and eosin(HE)staining was performed to evaluate the histopathological morphology of small intestinal tissues.Levels of D-lactic acid and endotoxin in peripheral blood were measured using enzyme-linked immunosorbent assay(ELISA).The expression levels of mucin 2(MUC2),mucin 3(MUC3),and human defensin 5(HD5)—key components of the intestinal mucus layer and innate immune response—were analyzed using quantitative real-time PCR(qRT-PCR).Colorimetric assays were conducted to assess oxidative stress markers in intestinal tissue,including nitric oxide synthase(NOS),malondialdehyde(MDA),superoxide dismutase(SOD),and glutathione peroxidase(GSH-Px).Western blot was used to determine the protein expression levels of endogenous antioxidant pathway components:nuclear factor erythroid 2-related factor 2(Nrf2),heme oxygenase-1(HO-1),and NAD(P)H:quinone oxidoreductase 1(NQO1).Results:HE staining showed no significant histological changes in the intestinal tissues of the normal control group,with a mucosal injury score of 0.The model and treatment groups exhibited varying degrees of villous disorganization and tissue edema,with injury scores of 4.69±0.62,3.36±0.41,2.29±0.22,and 1.53±0.14 in the model,low-,medium-,and high-dose groups,respectively(all P＜0.05 vs.model group).Compared with the normal control group,the other groups showed significantly increased levels of D-lactic acid and endotoxin in the blood(all P＜0.05);elevated expression of MUC2 and MUC3,reduced HD5 expression(all P＜0.05);increased NOS and MDA levels,decreased SOD and GSH-Px levels(all P＜0.05);downregulated expression of Nrf2,HO-1,and NQO1 proteins in intestinal tissues(all P＜0.05).These changes were significantly attenuated in the Danshen-treated groups in a dose-dependent manner(all P＜0.05).Conclusion:Danshen injection can alleviate intestinal mucosal injury in AIO rats,possibly by activating the Nrf2/HO-1/NQO1 signaling pathway and reducing oxidative stress,thereby enhancing the intestinal mucosal barrier function.

Purpose To evaluate the efficacy of ultra-high field 5.0T MRI combined with histogram analysis for diagnosing prostate cancer and benign prostatic hyperplasia.Materials and Methods This retrospective analysis included data from 63 patients with prostatic diseases at the Chongqing University Three Gorges Hospital from January to May 2024,comprising 31 cases of prostate cancer and 32 cases of benign prostatic hyperplasia.MRI sequences included T2WI,T1WI,diffusion-weighted imaging,intravoxel incoherent motion and T2 mapping.Histogram data of apparent diffusion coefficient,true diffusion coefficient,pseudo-diffusion coefficient,perfusion fraction and T2 relaxation time were calculated,and diagnostic efficacy was assessed using the area under the receiver operating characteristic curve.Results In prostate cancer,the 10th percentile,the 90th percentile,mean,median,and minimum values of histogram parameters from apparent diffusion coefficient,true diffusion coefficient,pseudo-diffusion coefficient,perfusion fraction and T2 mapping were significantly lower than those of benign prostatic hyperplasia(Z=-6.036--3.368,all P<0.05).Notably,the combined model of apparent diffusion coefficient,intravoxel incoherent motion and T2 mapping parameters achieved an the area under the curve of 0.987,with sensitivity and specificity of 96.77%and 96.87%,respectively.Conclusion This study confirms that 5.0T MRI histogram analysis technique demonstrates significant diagnostic efficacy in differentiating prostate cancer from benign prostatic hyperplasia.

Objective To compare and analyze the impact of intraocular higher-order aberrations and total ocular higher-order aberrations on visual quality after the implantation of different types of multifocal intraocular lenses(MIOL),in order to provide personalized MIOL selection recommendations for patients.Methods A total of 107 patients(107 eyes)with age-related cataract were selected and divided into the SBL-3,MF15,ZMB00,and Zeiss809 groups based on the type of MIOL implanted.Uncorrected distance visual acuity(UCDVA),best-corrected distance visual acuity(BCDVA),best-corrected near visual acuity(BCNVA),optical parameters and wavefront aberrations were compared 3 months postop-eratively.Results Preoperatively,there were no statistically significant differences in any optical parameters or visual acuity among the four groups(all P＞0.05).At three months postoperatively,there were no significant differences in UCD-VA,BCDVA,BCNVA,corneal aberration,or intraocular spherical aberration among the four groups(all P＞0.05).How-ever,significant differences were observed in the predicted visual acuity at 100％,20％,and 9％contrast(100％VA,20％VA,9％VA),streller ratio(SR),modulation transfer function cutoff frequency(MTF cutoff),intraocular coma,intraocu-lar trefoil,total higher-order aberrations,and the average modulation transfer function height values(MTF AH)of the en-tire eye across various spatial frequencies showed significant differences(all P＜0.05).Pairwise comparisons showed that the ZMB00 and Zeiss809 groups had significantly better 100％VA,20％VA,9％VA,SR,MTF cutoff,and MTF AH,and sig-nificantly lower intraocular coma,intraocular trefoil,and total higher-order aberrations than the SBL-3 group(all P＜0.05).The MF15 group only had significantly lower intraocular trefoil than the SBL-3 group(P＜0.05).Correlation analy-sis results at 3 months postoperatively showed that in the SBL-3 and MF15 groups,UCDVA(logMAR)was positively corre-lated with the objective scattering index and negatively correlated with 100％VA,20％VA,9％VA,SR,and MTF cutoff(all P＜0.05).MTF AH was negatively correlated with intraocular coma and total higher-order aberrations,and positively cor-related with intraocular spherical aberration(all P＜0.05).In the ZMB00 and Zeiss809 groups,MTF AH was negatively cor-related with intraocular coma,intraocular trefoil,and total higher-order aberrations(all P＜0.05),but showed no correla-tion with intraocular spherical aberration(all P＞0.05).Conclusion There are no differences in UCDVA,BCDVA,or BCNVA among patients implanted with the four types of MIOLs,but differences existed in objective visual quality.The ob-jective visual quality of ZMB00 and Zeiss809 is significantly better than that of SBL-3,making them the preferred choices for patients with high-quality visual demands;SBL-3 is suitable for basic demand scenarios;MF15,which is only superior to SBL-3 in terms of intraocular trefoil,can be a compromise option for those sensitive to trefoil aberration.

Early correction of childhood malocclusion is timely managing morphological, structural, and functional abnormalities at different dentomaxillofacial developmental stages. The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion. This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence, aiming to provide general guidance on appropriate imaging examination selection, comprehensive and accurate imaging assessment for early orthodontic treatment patients.
Humans ; Malocclusion/diagnostic imaging* ; Child ; Consensus

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*