The Impact of China's Family Floating Population on the Participation of Medical Insurance in the Inflow Areas.

Background: With the transformation of China's economy and society, the floating population has also shown a new development trend, from individual migration to co-migration with family members. In 2020, among the 376 million floating population, the population flowing to cities and towns was 330 million, accounting for nearly 88.1%. The family mobility of the floating population is not just a simple personal gathering or geographical migration, but a profound adjustment of the living environment, social interaction and the interests of family members. Migrants no longer simply play the role of " urban passers-by", but gradually move with spouses, children, parents, and even settle in the city, which will inevitably produce different public service and social security needs. Objective: To explore the impact of floating population's familyization on the participation of medical insurance in the inflow areas. Methods: This study adopted the form of non-systematic literature review. The key words were floating population and medical insurance. The related analysis of PubMed, Embase, CNKI, Wanfang, and VIP databases were reviewed and summarized. Results: Due to the flow between domestic immigrants and regions, their medical insurance is difficult to be guaranteed. The domestic floating population's demand for health services is increasing, but the coverage of medical services provided by medical insurance is not comprehensive enough. Conclusion: It is necessary to integrate the medical insurance system and improve the adaptability of medical insurance to family mobility; protect the welfare needs of migrant families and increase their willingness to participate in medical insurance at the destination; pay attention to the interaction and integration of floating population families, understand and guide them to participate in the status quo of medical insurance, and improve the status quo.

Magnetic resonance imaging and deoxyribonucleic acid methylation-based radiogenomic models for survival risk stratification of glioblastoma.

Glioblastoma multiforme (GBM) is one of the deadliest tumours. This study aimed to construct radiogenomic prognostic models of glioblastoma overall survival (OS) based on magnetic resonance imaging (MRI) Gd-T1WI images and deoxyribonucleic acid (DNA) methylation-seq and to understand the related biological pathways. The ResNet3D-18 model was used to extract radiomic features, and Lasso-Cox regression analysis was utilized to establish the prognostic models. A nomogram was constructed by combining the radiogenomic features and clinicopathological variables. The DeLong test was performed to compare the area under the curve (AUC) of the models. We screened differentially expressed genes (DEGs) with original ribonucleic acid (RNA)-seq in risk stratification and used Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) annotations for functional enrichment analysis. For the 1-year OS models, the AUCs of the radiogenomic set, methylation set and deep learning set in the training cohort were 0.864, 0.804 and 0.787, and those in the validation cohort were 0.835, 0.768 and 0.651, respectively. The AUCs of the 0.5-, 1- and 2-year nomograms in the training cohort were 0.943, 0.861 and 0.871, and those in the validation cohort were 0.864, 0.885 and 0.805, respectively. A total of 245 DEGs were screened; functional enrichment analysis showed that these DEGs were associated with cell immunity. The survival risk-stratifying radiogenomic models for glioblastoma OS had high predictability and were associated with biological pathways related to cell immunity.

Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes.

PURPOSE: To investigate the application of deep learning combined with traditional radiomics methods for classifying enlarged cervical lymph nodes. METHODS: The clinical and computed tomography (CT) imaging data of 276 patients with enlarged cervical lymph nodes (150 with lymph-node metastasis, 65 with lymphoma, and 61 with benign lymphadenopathy) who were treated at the hospital from January 2015 to January 2021 were retrospectively analysed. The patients were randomly divided into a training group and a test group at a ratio of 8:2. The radiomics features were extracted using one-by-one convolution and neural network activation, filtered with the least absolute shrinkage and selection operator (LASSO) model, and used to construct a discrimination model with PyTorch. Then, the performance of the model was compared with the radiologists' diagnostic performance. The neural network model was evaluated using the area under the receiver-operator characteristic curve (AUC), and the accuracy, sensitivity, and specificity were analysed. RESULTS: A total of 102 features, comprising five traditional radiomic features and 97 deep learning features, were selected with LASSO and used to construct a discrimination model, which achieved a total accuracy of 87.50%. The AUC value, specificity, and sensitivity were, respectively, 0.92, 92.30%, and 90.00% for metastatic lymph nodes, 0.87, 95.45%, and 83.33% for benign lymphadenopathy, and 0.88, 90.47%, and 85.71% for lymphoma. The accuracies of the radiologists' diagnoses were 62.68% and 62.68%. The diagnostic performance of the model was significantly different from that of the radiologists (p < 0.05). CONCLUSION: CT-based deep learning combined with the traditional radiomics methods has a high diagnostic value for the classification of cervical enlarged lymph nodes.

Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning.

BACKGROUND: Computational aid for diagnosis based on convolutional neural network (CNN) is promising to improve clinical diagnostic performance. Therefore, we applied pretrained CNN models in multiparametric magnetic resonance (MR) images to classify glioma mimicking encephalitis and encephalitis. METHODS: A data set containing 3064 MRI brain images from 164 patients with a final diagnosis of glioma (n = 56) and encephalitis (n = 108) patients and divided into training and testing sets. We applied three MRI modalities [fluid attenuated inversion recovery (FLAIR), contrast enhanced-T1 weighted imaging (CE-T1WI) and T2 weighted imaging (T2WI)] as the input data to build three pretrained deep CNN models (Alexnet, ResNet-50, and Inception-v3), and then compared their classification performance with radiologists' diagnostic performance. These models were evaluated by using the area under the receiver operator characteristic curve (AUC) of a five-fold cross-validation and the accuracy, sensitivity, specificity were analyzed. RESULTS: The three pretrained CNN models all had AUC values over 0.9 with excellent performance. The highest classification accuracy of 97.57% was achieved by the Inception-v3 model based on the T2WI data. In addition, Inception-v3 performed statistically significantly better than the Alexnet architecture (p<0.05). For Inception-v3 and ResNet-50 models, T2WI offered the highest accuracy, followed by CE-T1WI and FLAIR. The performance of Inception-v3 and ResNet-50 had a significant difference with radiologists (p<0.05), but there was no significant difference between the results of the Alexnet and those of a more experienced radiologist (p >0.05). CONCLUSIONS: The pretrained CNN models can automatically and accurately classify these two diseases and further help to improving clinical diagnostic performance.

Application of middle-down approach in quantitation and catabolite identification of protein by LC-high-resolution mass spectrometry.

Aim: To further enhance the detection sensitivity and increase resolving power of top-down intact protein bioanalysis, middle-down approach was explored. Materials & methods: An monoclonal antibody (mAb) was used as a model protein to evaluate quantitative bioanalytical assay performance and a disulfide linked dimer protein was investigated for its pharmacokinetics properties and catabolism in vivo by middle-down approach. Results & Conclusion: For quantitation of the mAb, different subunits generated by middle-down approach provided different level of signal improvement in biological samples with Lc and half Fc giving five-times better sensitivity than intact mAb. For the dimer protein, middle-down analysis by reduction enabled effective differentiation of the unchanged protein and its oxidized form, and clearly elucidated their respective proteolytic catabolites.

One-slice CT image based kernelized radiomics model for the prediction of low/mid-grade and high-grade HNSCC.

An accurate grade prediction can help to appropriate treatment strategy and effective diagnosis to Head and neck squamous cell carcinoma (HNSCC). Radiomics has been studied for the prediction of carcinoma characteristics in medical images. The success of previous researches in radiomics is attributed to the availability of annotated all-slice medical images. However, it is very challenging to annotate all slices, as annotating biomedical images is not only tedious, laborious, and time consuming, but also demanding of costly, specialty-oriented skills, which are not easily accessible. To address this problem, this paper presents a model to integrate radiomics and kernelized dimension reduction into a single framework, which maps handcrafted radiomics features to a kernelized space where they are linearly separable and then reduces the dimension of features through principal component analysis. Three methods including baseline radiomics models, proposed kernelized model and convolutional neural network (CNN) model were compared in experiments. Results suggested proposed kernelized model best fit in one-slice data. We reached AUC of 95.91 % on self-made one-slice dataset, 67.33 % in predicting localregional recurrence on H&N dataset and 64.33 % on H&N1 dataset. While all other models were <76 %, <65 %, and <62 %. Though CNN model reached an incredible performance when predicting distant metastasis on H&N (AUC 0.88), model faced serious problem of overfitting in small datasets. When changing all-slice data to one-slice on both H&N and H&N1, proposed model suffered less loss on AUC (<1.3 %) than any other models (>3 %). These proved our proposed model is efficient to deal with the one-slice problem and makes using one-slice data to reduce annotation cost practical. This is attributed to the several advantages derived from the proposed kernelized radiomics model, including (1) the prior radiomics features reduced the demanding of huge amount of data and avoided overfitting; (2) the kernelized method mined the potential information contributed to predict; (3) generating principal components in kernelized features reduced redundant features.

CT-Based Radiomics Signature for the Preoperative Discrimination Between Head and Neck Squamous Cell Carcinoma Grades.

Background: Radiomics has been widely used to non-invasively mine quantitative information from medical images and could potentially predict tumor phenotypes. Pathologic grade is considered a predictive prognostic factor for head and neck squamous cell carcinoma (HNSCC) patients. A preoperative histological assessment can be important in the clinical management of patients. We applied radiomics analysis to devise non-invasive biomarkers and accurately differentiate between well-differentiated (WD) and moderately differentiated (MD) and poorly differentiated (PD) HNSCC. Methods: This study involved 206 consecutive HNSCC patients (training cohort: n = 137; testing cohort: n = 69). In total, we extracted 670 radiomics features from contrast-enhanced computed tomography (CT) images. Radiomics signatures were constructed with a kernel principal component analysis (KPCA), random forest classifier and a variance-threshold (VT) selection. The associations between the radiomics signatures and HNSCC histological grades were investigated. A clinical model and combined model were also constructed. Areas under the receiver operating characteristic curves (AUCs) were applied to evaluate the performances of the three models. Results: In total, 670 features were selected by the KPCA and random forest methods from the CT images. The radiomics signatures had a good performance in discriminating between the two cohorts of HNSCC grades, with an AUC of 0.96 and an accuracy of 0.92. The specificity, accuracy, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) of the abovementioned method with a VT selection for determining HNSCC grades were 0.83, 0.92, 0.96, 0.94, and 0.91, respectively; without VT, the corresponding results were 0.70, 0.83, 0.88, 0.80, and 0.84. The differences in accuracy, sensitivity and NPV were significant between these approaches (p < 0.05). The AUCs with VT and without VT were 0.96 and 0.89, respectively (p < 0.05). Compared to the combined model and the radiomics signatures, The clinical model had a worse performance, and the differences were significant (p < 0.05). The combined model had the best performance, but the difference between the combined model and the radiomics signature weren't significant (p > 0.05). Conclusions: The CT-based radiomics signature could discriminate between WD and MD and PD HNSCC and might serve as a biomarker for preoperative grading.

Multiphase contrast-enhanced computed tomography imaging features of salivary duct carcinoma: differentiation from other salivary gland malignancies.

OBJECTIVE: The aim of this study was to investigate the imaging features of salivary duct carcinoma (SDC) with multiphase contrast-enhanced computed tomography (CECT) and to compare them with those of mucoepidermoid carcinoma (MEC), adenoid cystic carcinoma (ACC), and acinic cell carcinoma. STUDY DESIGN: A total of 63 patients with histologically diagnosed salivary gland malignancies underwent preoperative multiphase CECT. Clinical information, location, size, mass pattern, enhancement pattern, borders, invasion of adjacent tissues, and lymph node metastasis were evaluated. Computed tomography (CT) number attenuation patterns were calculated. RESULTS: SDCs were significantly more common in males and in the parotid gland (P ≤ .018). They were more likely to invade into adjacent tissues and metastasize to lymph nodes (P ≤ .032). Six SDCs (66.7%) had comedonecrosis, as detected on histopathologic examination, and 3 lesions presented cribriform necrosis on CECT. CT numbers during delayed-phase scanning were significantly higher in SDC than in ACC (P = .031). Significant differences were discovered between MEC and ACC for CT numbers during arterial-phase scanning (P = .047) and in the ratio of CT numbers (P = .018). CONCLUSIONS: SDC exhibits some specific CT features, and multiphase CECT imaging is useful in the differential diagnosis of salivary gland malignancies.

Simultaneous quantification of total antibody and antibody-conjugated drug for XMT-1522 in human plasma using immunocapture-liquid chromatography/mass spectrometry.

XMT-1522, an antibody-drug conjugate (ADC) currently in Phase I clinical development, represents the first Dolaflexin®-based, cleavable ADC with a high drug-antibody ratio (DAR). In this work, a novel immunocapture LC-MS/MS method was successfully developed for the simultaneous quantification of both total antibody and cleavable antibody-conjugated drug auristatin F-hydroxypropylamide (AF-HPA) in human plasma. This method utilized microwave-assisted enzymatic digestion for the total antibody and chemical release of the drug from ADC on a 96-well based immunocapture sample preparation platform. The total antibody and the conjugated drug AF-HPA were separated and subsequently quantified concurrently by LC-MS/MS. The linear range of the standard curve for total antibody was from 50 to 5000 ng/mL and for AF-HPA was from 3.3 to 330 ng/mL. The linearities showed R2 ≥ 0.993 for total antibody and R2 ≥ 0.996 for AF-HPA, respectively. The intra- and inter-day precision and accuracy were well within 15%. The validated method, with the characteristics of high efficiency, great selectivity, free of carryover, short LC-MS/MS time (˜3.5 min) and low sample volume (20 µl), was successfully applied for analyzing Phase 1 cancer patient samples.

Mechanism of Reduction in IgG and IgE Binding of ß-Lactoglobulin Induced by Ultrasound Pretreatment Combined with Dry-State Glycation: A Study Using Conventional Spectrometry and High-Resolution Mass Spectrometry.

Bovine ß-lactoglobulin (ß-Lg) is one of major allergens in cow's milk. Previous study showed that ultrasound treatment induced the conformational changes of ß-Lg and promoted the glycation in aqueous solutions, which is, however, less efficient compared with dry-state. In this work, the effect of ultrasound pretreatment combined with dry-state glycation on the IgG and IgE binding of ß-Lg was studied. Dry-state glycation with mannose after ultrasound pretreatment at 0-600 W significantly reduced the IgG and IgE binding of ß-Lg, with the lowest values observed at 400 W. The decrease in the IgG and IgE binding of ß-Lg was attributed to the increase in glycation extent and the changes of secondary and tertiary structure, which reflected in the increase of UV absorbance, α-helix and ß-sheet contents, as well as the decrease of intrinsic fluorescence intensity, surface hydrophobicity, ß-turn, and random coil contents. Moreover, ultrasound pretreatment promoted the reduction of IgG and IgE binding abilities by improving glycation, reflecting in the increase of the glycation sites and the degree of substitution per peptide (DSP) value determined by Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). Ultrasound pretreatment at 400 W showed the most significantly enhanced glycation extent. Besides, the results suggested FTICR-MS could provide insights into the glycation at molecular level, which was conducive to the understanding of the mechanism of the reduction in the IgG and IgE binding of ß-Lg. Therefore, ultrasound pretreatment combined with dry-state glycation may be a promising method for ß-Lg hyposensitization.

Overcoming the Hydrolytic Lability of a Reaction Intermediate in Production of Protein/Drug Conjugates: Conjugation of an Acyclic Nucleoside Phosphonate to a Model Carrier Protein.

Acquired immunodeficiency syndrome (AIDS) remains one of the most serious public health challenges and a significant cause of mortality for certain populations. Despite the large number of antiretrovirals developed in the past two decades, the inability of small molecule therapeutics to target HIV reservoirs directly creates a significant obstacle to their effective utilization. Indeed, achieving the desired therapeutic outcome in the absence of an effective means of targeted delivery must rely on dosage escalation, which frequently causes severe toxicity. This problem may be solved by conjugation of antiretroviral agents to endogenous proteins that are specifically recognized by HIV reservoirs (such as macrophages) for internalization and catabolism. However, conjugation of a large class of antiretroviral agents (acyclic nucleoside phosphonates, such as adefovir, tenofovir, and cidofovir) to a protein is challenging due to rapid degradation of the activated form of the drug (e.g., adefovir phosphonoimidazolide) in an aqueous environment. A novel synthetic strategy introduced in this work overcomes the instability of the activated form of adefovir by emulating the first step of its metabolic pathway (phosphorylation), making it highly reactive toward primary amine groups of proteins and, at the same time, less prone to hydrolysis by water. Efficient conjugation of the phosphorylated form of adefovir to a protein following activation with EDC (1-ethyl-3-(3-(dimethylamino)propyl)carbodiimide hydrochloride) and imidazole was demonstrated using a model protein. Mass spectrometry (MS) was used to identify conditions that favor formation of conjugates with minimal side products, and online ion exchange chromatography/MS analysis of the products revealed the presence of multiple positional isomers within the 1:1 protein/drug conjugates. Both liquid chromatography/MS data and the analysis of ions produced upon top-down fragmentation of the 1:1 conjugates suggest that conjugation of phosphorylated adefovir to the protein occurs not only at primary amines but also at hydroxyl groups. The new conjugation protocol can be used to attach adefovir and other acyclic nucleoside phosphonates to proteins recognized by the cell surface receptors specific to macrophages (such as the haptoglobin/hemoglobin complex), enabling targeted drug delivery directly to HIV reservoirs.

Evaluation of Gallium as a Tracer of Exogenous Hemoglobin-Haptoglobin Complexes for Targeted Drug Delivery Applications.

Haptoglobin (Hp) is a plasma glycoprotein that generates significant interest in the drug delivery community because of its potential for delivery of antiretroviral medicines with high selectivity to macrophages and monocytes, the latent reservoirs of human immunodeficiency virus. As is the case with other therapies that exploit transport networks for targeted drug delivery, the success of the design and optimization of Hp-based therapies will critically depend on the ability to accurately localize and quantitate Hp-drug conjugates on the varying and unpredictable background of endogenous proteins having identical structure. In this work, we introduce a new strategy for detecting and quantitating exogenous Hp and Hp-based drugs with high sensitivity in complex biological samples using gallium as a tracer of this protein and inductively coupled plasma mass spectrometry (ICP MS) as a method of detection. Metal label is introduced by reconstituting hemoglobin (Hb) with gallium(III)-protoporphyrin IX followed by its complexation with Hp. Formation of the Hp/Hb assembly and its stability are evaluated with native electrospray ionization mass spectrometry. Both stable isotopes of Ga give rise to an abundant signal in ICP MS of a human plasma sample spiked with the metal-labeled Hp/Hb complex. The metal label signal exceeds the spectral interferences' contributions by more than an order of magnitude even with the concentration of the exogenous protein below 10 nM, the level that is more than adequate for the planned pharmacokinetic studies of Hp-based therapeutics. Graphical Abstract ᅟ.

Elementary Flux Mode Analysis Revealed Cyclization Pathway as a Powerful Way for NADPH Regeneration of Central Carbon Metabolism.

NADPH regeneration capacity is attracting growing research attention due to its important role in resisting oxidative stress. Besides, NADPH availability has been regarded as a limiting factor in production of industrially valuable compounds. The central carbon metabolism carries the carbon skeleton flux supporting the operation of NADPH-regenerating enzyme and offers flexibility in coping with NADPH demand for varied intracellular environment. To acquire an insightful understanding of its NADPH regeneration capacity, the elementary mode method was employed to compute all elementary flux modes (EFMs) of a network representative of central carbon metabolism. Based on the metabolic flux distributions of these modes, a cluster analysis of EFMs with high NADPH regeneration rate was conducted using the self-organizing map clustering algorithm. The clustering results were used to study the relationship between the flux of total NADPH regeneration and the flux in each NADPH producing enzyme. The results identified several reaction combinations supporting high NADPH regeneration, which are proven to be feasible in cells via thermodynamic analysis and coincident with a great deal of previous experimental report. Meanwhile, the reaction combinations showed some common characteristics: there were one or two decarboxylation oxidation reactions in the combinations that produced NADPH and the combination constitution included certain gluconeogenesis pathways. These findings suggested cyclization pathways as a powerful way for NADPH regeneration capacity of bacterial central carbon metabolism.

RP215 single chain fragment variable and single domain recombinant antibodies induce cell cycle arrest at G0/G1 phase in breast cancer.

BACKGROUND: Targeted therapy is an attractive approach to avoid the side effects of cancer treatment. Based on antibody-targeted superantigens, single chain variable fragment (scFv) and single domain (sdAb) antibodies, characterized by a low molecular weight, low immunogenicity and a high tumor penetration compared to monoclonal antibodies (mAb), have been increasingly used in gene-targeted therapy for cancer. In the present study, we aimed to develop the novel recombinant scFv-RP215 and sdAb-RP215 antibodies based on the variable regions of the RP215 monoclonal antibody (RP215-mAb) against CA215, a pan cancer marker expressed in various human tumor tissues, and to examine their biological activity in breast cancer cell lines. METHODS: The VH and VL genes were amplified from hybridoma cells secreting RP215-mAb by RT-PCR and joined with a linker using splicing by overlap extension PCR (SOE-PCR) to obtain the RP215-scFv gene, whereas the VH gene was used to generate the RP215-sdAb. Gene fragments of antibodies were subcloned into the pET32a(+) vector and expressed in Escherichia coli BL21. Western blot, indirect immunofluorescence (IF), ELISA and competitive ELISA were used to detect the immunoreactivity of scFv-RP215, sdAb-RP215, and RP215-mAb. The CCK-8 assay and cell cycle analysis were used to assess antibodies function. RESULTS: The novel recombinant scFv-RP215 and sdAb-RP215 antibodies were successfully developed based on the variable regions of the monoclonal antibody RP215 (RP215-mAb) against CA215. We verified that scFv-RP215 and sdAb-RP215 recognize CA215 on the surface of breast cancer cells (MB231, MCF7, MB468, SK-BR-3 and BT549) and characterized their activity and specificity. Our findings also indicate that scFv-RP215 and sdAb-RP215 induce cell cycle arrest at the G0/G1 phase in breast cancer cells. CONCLUSION: Our results showed that scFv-RP215 and sdAb-RP215 have excellent immunoreactivity and localize accurately to breast cancer cells in membrane-bound form, suggesting their potential as tumor targeting antibodies for breast cancer therapy.

Radiological imaging of florid intravascular papillary endothelial hyperplasia in the mandibule: case report and literature review.

Intravascular papillary endothelial hyperplasia was a rare benign vascular proliferative process as a result of papillary proliferation of the endothelial cells within the vessels. To our knowledge, we reported the second case occurring in the madibule, and the first reported in 1984 in the literature. We discussed manifestations of multislice computed tomography and panoramic radiography about the lesion and relevant literature was reviewed.

Morphology transformation via pH-triggered self-assembly of peptides.

Three flexible peptides (P1: (C(17)H(35)CO-NH-GRGDG)(2)KG; P2: (Fmoc-GRGDG)(2)KG; P3: (CH(3)CO-NH-GRGDG)(2)KG) self-assembled to form a variety of morphologically distinct assemblies at different pHs. P1 formed nanofibers at pH 3, then self-assembled into nanospheres with pH up to 6 and further changed to lamellar structures when the pH value was further increased to 10. P2 aggregated into an entwined network structure at pH 3, and then self-assembled into well-defined nanospheres, lamellar structures, and vesicles via adjusting the pH value. However, P3 did not self-assemble into well-ordered nanostructures, presumably due to the absence of a large hydrophobic group. The varying self-assembly behaviors of the peptides at different pHs are attributed to molecular conformational changes. These self-assembled supramolecular materials might contribute to the development of new peptide-based biomaterials.

[The clinical application of hydrogen magnetic resonance spectroscopy combining with diffusion weight imaging in brain gliomas grading].

The present study was aimed to investigate the function of diffusion weight imaging (DWI) combining with magnetic resonance spectroscopy (MRS) in the grading of brain gliomas. 12 cases low grade and 17 cases high grade of brain gliomas patients were examined with DWI and MRS, with all tumors confirmed by pathology in advance. The apparent diffusion coefficient (ADC) values, their corresponding metabolite ratios of Cho/Cr, Cho/NAA and tumor cellularities of tumor solid enhanced parts were measured. The ratios of Cho/Cr and Cho/NAA and their corresponding ADC values had significant differences between their high and low grade gliomas values, respectively. The ADC values demonstrated a negative correlation with Cho/Cr, Cho/NAA, and a significant negative correlated with Cho/Cr. And the ADC values demonstrated strong negative correlations with tumor cellularities. DWI combining with MRS could provide more valuable information in evaluating gliomas grading.

[CT diagnosis of palatal malignant tumors].

To investigate CT features of palatal malignant tumors and to evaluate, CT diagnostic value. CT manifestations of 32 cases of palatal malignant tumors were analyzed retrospectively. All cases were confirmed by pathology. There were 27 males and 5 females between 23 and 80 years of age. Axial contrast enhancement CT scan was performed in all cases, among them, nonenhancement CT scan was also performed in 8 cases and coronal CT scan was performed in 2 cases. The main signs of palatal malignant tumors were as follows: (1) Masses in palate in 26 cases, thickened palatal soft tissue in 6 cases and destruction of palate bone in 8 cases. The lesions were located in soft palate in 22 cases, in hard palate in 4 cases, and in the junction region between soft and hard palate in 3 cases; (2) Other adjacent structures and organs were involved in 27 cases; (3) Lymph nodes of neck metastasis were demonstrated in 17 cases. CT is an excellent technique, which can show the gross pathologic features and the invasion pathway of palatine malignant tumors, and it can provide some important information for the relevant clinical treatment and prognosis.