Connective tissue diseases (CTDs) are autoimmune disorders primarily characterized by the involvement of multiple organs and systems. These diseases often have a hidden onset and complex progression, and are difficult to diagnose. Anti-U1 ribonucleoprotein (U1RNP) antibody is an important component of the anti-extractable nuclear antigen antibody spectrum, which has important clinical significance for the diagnosis and differential diagnosis of multiple CTDs and is related to organ involvement. This article introduces the characteristics of anti-U1RNP antibody and provides a comprehensive review of the recent research progress in anti-U1RNP antibodies in CTDs, aiming to help clinical workers better understand anti-U1RNP antibody.

Objective:To investigate the effects of Xiaomudan Granules on PERK/eIF2α/ATF4 pathway and lipid metabolism in NAFLD mice.Methods:Totally 60 mice were divided into a normal group of 10 mice and a high-fat diet group of 50 mice. The high-fat diet group was given a high-fat diet to establish a NAFLD mouse model. The high-fat feed group was divided into model group, metformin group, and Xiaomudan Granules low-, medium-, and high-dosage groups according to the random number table method. Xiaomudan Granules low-, medium-, and high-dosage groups were orally administered with concentrated Xiaomudan Granules at dosages of 11.7, 23.4, and 46.8 g/kg, respectively. The metformin group was orally administered with metformin solution at a dose of 0.2 g/kg, while the normal group and model group were orally administered with distilled water of equal volume once a day for 8 consecutive weeks. The liver oil red O staining of mice was observed in each group. The protein expressions of p-PERK, p-eIF2α, ATF4, C/EBP homologous protein (CHOP), CCAAT enhancer binding protein alpha (C/EBPα), C/EBPβ, and steroid regulatory element binding protein 2 (SREBP2) were detected by Western blot. Fluorescence quantitative PCR was used to detect the mRNA expressions of ATF4, CHOP, C/EBPα, C/EBPβ, and SREBP2.Results:The liver tissue structure and steatosis of mice were improved in Xiaomudan Granules groups. Compared with the model group, the expressions of p-PERK/PERK, ATF4, C/EBPα, C/EBPβ, and SREBP2 proteins decreased in Xiaomudan Granules groups and the metformin group ( P<0.01 or P<0.05). The expressions of p-eIF2α/eIF2α and CHOP proteins decreased in the Xiaomudan Granules medium- and high-dosage groups and the metformin group ( P<0.01); compared with the model group, the levels of ATF4, CHOP, C/EBPα, and C/EBPβ mRNA in Xiaomudan Granules groups and the metformin group decreased ( P<0.01), while the levels of SREBP2 mRNA in the Xiaomudan Granules medium- and high-dosage groups and the metformin group decreased ( P<0.01). Conclusion:Xiaomudan Granules may improve liver lipid metabolism and reduce liver fat deposition in NAFLD mice by regulating the PERK/eIF2α/ATF4 pathway.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Connective tissue diseases (CTDs) are autoimmune disorders primarily characterized by the involvement of multiple organs and systems. These diseases often have a hidden onset and complex progression, and are difficult to diagnose. Anti-U1 ribonucleoprotein (U1RNP) antibody is an important component of the anti-extractable nuclear antigen antibody spectrum, which has important clinical significance for the diagnosis and differential diagnosis of multiple CTDs and is related to organ involvement. This article introduces the characteristics of anti-U1RNP antibody and provides a comprehensive review of the recent research progress in anti-U1RNP antibodies in CTDs, aiming to help clinical workers better understand anti-U1RNP antibody.

Objective:To explore the association between childhood growing environment and depressive symptoms in young old persons aged 60 to 74 years.Methods:The data of the fourth wave of China Health and Re-tirement Longitudinal Study in 2018 and the life course survey in 2014 were used to secondary analysis.A total of 7 642 young old persons aged 60 to 74 years were included,and the 10-item of the Center for Epidemiological Stud-ies Depression(CES-D-10)scale was used to evaluate the depressive symptoms.The generalized linear mixed effects model was used to explore the relationship between childhood growing environment and depressive symp-toms in the young old persons.Results:The detection rate of depressive symptoms occurrence in the young old per-sons was 37.2％.The risk factors of depressive symptoms included female(OR=1.89),rural(OR=1.35),hav-ing hunger experience(OR=1.22),poor relationship with male dependents(OR=1.72),female caregiver's expe-riences of being bedridden due to illness(OR=1.38),community insecurity(OR=1.59),more harmonious neigh-borhood relationship(OR=1.20)and less harmonious neighborhood relationship(OR=1.81).The protective fac-tors of depressive symptoms occurrence included moderate(OR=0.79)and high(OR=0.50)per capita house-hold income,and educated father(OR=0.84)(P＜0.05).Conclusion:Childhood growing environment is an influ-ential factor of depressive symptoms in the young old persons.The long-term health effects of childhood environ-ment should be paid attention to.

Objective:To compare the patient-reported outcomes and short-term clinical outcomes between robotic-assisted and laparoscopic-assisted radical gastrectomy for locally advanced gastric cancer.Methods:This single-center prospective randomized controlled trial was conducted in the Department of Gastrointestinal Surgery, Affiliated Hospital of Qingdao University from October 2020 to August 2022. Patients with locally advanced gastric cancer who were to undergo radical gastrectomy were selected and randomly divided into two groups according to 1∶1, and received robotic surgery and laparoscopic surgery, respectively. Patient-reported outcomes and short-term clinical outcomes (including postoperative complications, surgical quality and postoperative short-term recovery) were compared between the two groups by independent sample t test, Mann-Whitney U test, repeated ANOVA, generalized estimating equation, χ2 test and Fisher′s exact test. Results:A total of 237 patients were enrolled for modified intention-to-treat analysis (120 patients in the robotic group, 117 patients in the laparoscopic group). There were 180 males and 59 females, aged (63.0±10.2) years (range: 30 to 85 years). The incidence of postoperative complications was similar between the robotic group and laparoscopic group (16.7% (20/120) vs. 15.4% (18/117), χ2=0.072, P=0.788). The robotic group had higher patient-reported outcomes scores in general health status, emotional, and social domains compared to the laparoscopic group, differences in time effect, intervention effect, and interaction effect were statistically significant (general health status: χ2 value were 275.68, 3.91, 6.38, P value were <0.01, 0.048, 0.041; emotional: χ2 value were 77.79, 6.04, 6.15, P value were <0.01, 0.014, 0.046; social: χ2 value were 148.00, 7.57, 5.98, P value were <0.01, 0.006, 0.048). However, the financial burden of the robotic group was higher, the differences in time effect, intervention effect and interaction effect were statistically significant ( χ2 value were 156.24, 4.08, 36.56, P value were <0.01, 0.043,<0.01). Conclusion:Compared to the laparoscopic group, the robotic group could more effectively relieve postoperative negative emotions and improve recovery of social function in patients.

Objective To investigate the effect of Zuogui Jiangtang Jieyu Formula(左归降糖解郁方,ZJJF)on hippocampal neuron apoptosis in diabetic rats with depression and to ascertain whether its mechanism involves the regulation of JNK signaling pathway. Methods(i)A total of 72 specific pathogen-free(SPF)grade male Sprague Dawley(SD)rats were randomly divided into six groups,with 12 rats in each group:control,model,metformin(Met,0.18 g/kg)+fluoxetine(Flu,1.8 mg/kg),and the high-,medium-,and low-ZJJF dosages(ZJJF-H,20.52 g/kg;ZJJF-M,10.26 g/kg;ZJJF-L,5.13 g/kg)groups.All groups except control group were injected once via the tail vein with streptozotocin(STZ,38 mg/kg)combined with 28 d of chronic unpredictable mild stress(CUMS)to establish diabetic rat models with de-pression.During the CUMS modeling period,treatments were administered via gavage,with control and model groups receiving an equivalent volume of distilled water for 28 d.The effi-cacy of ZJJF in reducing blood sugar and alleviating depression was evaluated by measuring fasting blood glucose,insulin,and glycated hemoglobin levels,along with behavioral assess-ments,including the open field test(OFT),forced swim test(FST),and sucrose preference test(SPT).Hippocampal tissue damage and neuronal apoptosis were evaluated using hema-toxylin-eosin(HE)staining and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling(TUNEL)staining.Apoptosis-related proteins Bax,Bcl-2,caspase-3,and the ex-pression levels of JNK/Elk-1/c-fos signaling pathway were detected using Western blot and real-time quantitative polymerase chain reaction(RT-qPCR).(ii)To further elucidate the role of JNK signaling pathway in hippocampal neuronal apoptosis and the pharmacological ef-fects of ZJJF,an additional 50 SPF grade male SD rats were randomly divided into five groups,with 10 rats in each group:control,model,SP600125(SP6,a JNK antagonist,10 mg/kg),ZJJF(20.52 g/kg),and ZJJF(20.52 g/kg)+Anisomycin(Aniso,a JNK agonist,15 mg/kg)groups.Ex-cept for control group,all groups were established as diabetic rat models with depression,and treatments were administered via gavage for ZJJF and intraperitoneal injection for SP6 and Aniso for 28 d during the CUMS modeling period.Behavioral changes in rats were evaluated through the OFT,FST,and SPT,and hippocampal neuron damage and apoptosis were ob-served using HE staining,Nissl staining,TUNEL staining,and transmission electron mi-croscopy(TEM).Changes in apoptosis-related proteins and JNK signaling pathway in the hippocampal tissues of rats were also analyzed. Results(i)ZJJF significantly reduced the high blood glucose,insulin,and glycated he-moglobin levels in model rats(P<0.01).It increased autonomous activity and decreased de-spair-like behaviors(P<0.01),improved the pathological damage of hippocampal neurons,increased the number of neuronal nuclei(P<0.01),and reduced the number of mechanocytes,vacuolar cells,and apoptotic neurons(P<0.05,P<0.01,and P<0.01,respec-tively).ZJJF down-regulated the expression levels of pro-apoptotic proteins Bax and caspase-3(P<0.01),up-regulated the anti-apoptotic protein Bcl-2(P<0.01),and significantly inhibit-ed the overexpression of phosphorylated JNK(p-JNK),Elk-1,and c-fos(P<0.01).(ii)SP6 in-creased autonomous activity and reduced despair time in model rats(P<0.05),although it had no significant effects on sucrose preference(P>0.05).It increased the number of Nissl bodies in hippocampal neurons(P<0.01),reduced the protein expression levels of Bax(P<0.01)and caspase-3(P<0.05),and decreased the number of apoptotic neurons(P<0.05).SP6 also increased the expression level of Bcl-2(P<0.01),and inhibited the high expression levels of p-JNK,Elk-1,and c-fos(P<0.01,P<0.01,and P<0.05,respectively),suggesting that hip-pocampal neuronal apoptosis in diabetic rats with depression is associated with abnormal ac-tivation of JNK signaling pathway.Compared with ZJJF group,ZJJF+Aniso group showed a decrease in sucrose preference(P<0.05)and an increase in despair time(P<0.01)with more notable hippocampal neuronal damage.This group also exhibited a decrease in expression level(P<0.01)Bcl-2 and an increase in expression levels of Bax,caspase-3,p-JNK,Elk-1,and c-fos(P<0.01,P<0.05,P<0.05,P<0.01,and P<0.05,respectively),indicating that the antidepressant effects of ZJJF,its improvement of neuronal apoptosis,and regulation of JNK signaling molecules could all be reversed by a specific JNK agonist. Conclusion ZJJF exerts a significant hypoglycemic effect and ameliorates the apoptosis of hippocampal neurons by inhibiting the activation of JNK signaling pathway,which is a promising formula for the treatment of diabetic depression in clinical settings.

Objective:To compare the patient-reported outcomes and short-term clinical outcomes between robotic-assisted and laparoscopic-assisted radical gastrectomy for locally advanced gastric cancer.Methods:This single-center prospective randomized controlled trial was conducted in the Department of Gastrointestinal Surgery, Affiliated Hospital of Qingdao University from October 2020 to August 2022. Patients with locally advanced gastric cancer who were to undergo radical gastrectomy were selected and randomly divided into two groups according to 1∶1, and received robotic surgery and laparoscopic surgery, respectively. Patient-reported outcomes and short-term clinical outcomes (including postoperative complications, surgical quality and postoperative short-term recovery) were compared between the two groups by independent sample t test, Mann-Whitney U test, repeated ANOVA, generalized estimating equation, χ2 test and Fisher′s exact test. Results:A total of 237 patients were enrolled for modified intention-to-treat analysis (120 patients in the robotic group, 117 patients in the laparoscopic group). There were 180 males and 59 females, aged (63.0±10.2) years (range: 30 to 85 years). The incidence of postoperative complications was similar between the robotic group and laparoscopic group (16.7% (20/120) vs. 15.4% (18/117), χ2=0.072, P=0.788). The robotic group had higher patient-reported outcomes scores in general health status, emotional, and social domains compared to the laparoscopic group, differences in time effect, intervention effect, and interaction effect were statistically significant (general health status: χ2 value were 275.68, 3.91, 6.38, P value were <0.01, 0.048, 0.041; emotional: χ2 value were 77.79, 6.04, 6.15, P value were <0.01, 0.014, 0.046; social: χ2 value were 148.00, 7.57, 5.98, P value were <0.01, 0.006, 0.048). However, the financial burden of the robotic group was higher, the differences in time effect, intervention effect and interaction effect were statistically significant ( χ2 value were 156.24, 4.08, 36.56, P value were <0.01, 0.043,<0.01). Conclusion:Compared to the laparoscopic group, the robotic group could more effectively relieve postoperative negative emotions and improve recovery of social function in patients.

Objective:To investigate the clinical value of preoperative gadolinium ethoxy-benzyldiethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) in predicting microvascular invasion (MVI) and intratumoral tertiary lymphoid structures (TLSs) in hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinicopathological data of 304 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and 10 HCC patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University from June 2021 to June 2023 were collected. There were 272 males and 42 females, aged (56±11)years. Using a random number table method, patients were divided into a training set including 220 cases and a validation set including 94 cases in a 7:3 ratio. Among the 314 patients, 106 cases had MVI and TLSs-positive HCC (MT-HCC), and 208 cases had non-MT-HCC. All patients underwent preoperative Gd-EOB-DTPA-enhanced MRI and radical resection. Observation indicators: (1) clinicopathological characteristics of MT-HCC and non-MT-HCC patients; (2) imaging characteristics of MT-HCC and non-MT-HCC patients; (3) imaging features associated with MT-HCC diagnosis; (4) nomogram predictive model construction and evaluation for MT-HCC. Comparison of measurement data with normal distribution between groups was analyzed using the t test. Comparison of measurement data with skewed distribution between groups was analyzed using the nonpara-meter rank sum test. Univariate analysis was conducted using the corresponding statistical methods based on data type. Multivariate analysis was conducted using the logistic regression model. A nomo-gram predictive model was constructed based on results of multivariate analysis, and receiver operating characteristic (ROC) curves were plotted to evaluate the model's performance with the area under curve (AUC). Calibration curve and decision curve analyses were used to assess the calibration and clinical validity of nomogram predictive model. Results:(1) Clinicopathological characteristics of MT-HCC and non-MT-HCC patients. In the training set, there were significant differences between MT-HCC and non-MT-HCC patients in terms of age, white blood cell count, and alpha fetoprotein level ( t=2.488, Z=-2.515, χ2=4.014, P<0.05). (2) Imaging characteristics of MT-HCC and non-MT-HCC patients. In the training set, there were significant differences in tumor morphology, intratumoral hemorrhage, peritumoral abnormal enhancement in arterial phase, capsule presence, intratumoral necrosis or ischemia >20%, intratumoral necrosis or ischemia >50%, peritumoral hypointensity in the hepatobiliary phase, intravascular tumor thrombus, arterial phase rim-like hyperenhancement, and mosaic architecture between MT-HCC and non-MT-HCC patients ( χ2=8.811, 5.586, 13.962, 31.616, 10.154, 4.835, 5.111, 14.425, 7.112, 5.526, P<0.05). (3) Imaging features associated with MT-HCC diagnosis. Results of multivariate analysis identified the absence of intratumoral hemorrhage, incom-plete capsule, and mosaic architecture as independent risk factors for diagnosing MT-HCC ( hazard ratio=3.846, 7.827, 2.345, P<0.05). (4) Nomogram predictive model construction and evaluation for MT-HCC. A nomogram predictive model for MT-HCC was constructed based on the independent risk factors (absence of intratumoral hemorrhage, incomplete capsule, and mosaic architecture) iden-tified in the multivariate analysis. The ROC curve analysis showed that AUC of nomogram predictive model was 0.778 (95% confidence interval as 0.714-0.843), with sensitivity and specificity of 0.857 and 0.573 in the training set. In the validation set, the area under the curve, sensitivity, and specifi-city were 0.825 (95% confidence interval as 0.745-0.926), 0.655, and 0.877, respectively. The calibra-tion curves for both the training set and the validation set closely aligned with the standard curve, indicating high calibration accuracy. The decision curve analysis demonstrated net clinical benefits at thresholds of 0.130-0.690 in the training set and 0.060-0.750 in the validation set. Conclusions:The absence of intratumoral hemorrhage, incomplete capsule, and mosaic architecture are independent risk factors for diagnosing MT-HCC. A nomogram model based on imaging features can predict MT-HCC in HCC patients.