Objective Objective To analyze the potential spatial factors affecting the genetic differentiation of Oncomelania hupensis robertsoni in Yunnan Province. Methods A total of 13 administrative villages were selected from schistosomiasis-endemic areas of Yunnan Province as O. hupensis snail sampling sites. At least 200 snails were collected in each site, and the spatial variable data of each site were recorded, including longitude, latitude and altitude. Thirty active and Schistosoma japonicum uninfected O. hupensis snails were selected from each sampling site by means of the crawling method and the cercarial shedding method. Genomic DNA was extracted from O. hupensis snails. Following PCR amplification, purification of PCR amplification products and sequencing, the gene sequences of O. hupensis snail samples were spliced and edited using the DNAstar software and the NCBI database to yield the complete mitochondrial sequences of O. hupensis snails at each sampling site, and the mitochondrial genetic distance matrix of O. hupensis robertsoni was calculated at each sampling site. The geographical coordinates of each sampling site were marked using the software ArcGIS 10.2, and the straight-line geographical distance between each sampling site was calculated. The altitude difference, longitude difference and latitude difference between each sampling site were calculated using the Excel software, and the correlation between the mitochondrial genetic distance matrix of O. hupensis robertsoni and each spatial variable matrix was examined by using the Mantel test at 13 sampling sites in Yunnan Province. Results Among the 13 O. hupensis snail sampling sites in Yunnan Province, the largest mitochondrial genetic distance of O. hupensis robertsoni snail populations was seen between Anding Village, Nanjian Yi Autonomous County and Caizhuang Village, Midu County (26.244 2), and the largest geographical distance was seen between Dongyuan Village, Gucheng District and Cangling Village, Chuxiong County (272.64 km). The highest altitude difference was seen between Anding Village, Nanjian Yi Autonomous County and Dongyuan Village, Gucheng District (1 086.10 m), and the largest longitude difference was found between Qiandian Village, Eryuan County and Cangling Village, Chuxiong County (1.86°), while the largest latitude difference was measured between Leqiu Village, Nanjian Yi Autonomous County and Dongyuan Village, Gucheng District (1.81°). In addition, the mitochondrial genetic distance of O. hupensis robertsoni snail populations was positively correlated with altitude at 13 snail sampling sites in Yunnan Province (r = 0.542 8, P < 0.001), and showed no significant correlations with geographical distance (r = 0.093 4, P > 0.05), longitude (r = −0.199 5, P > 0.05) or latitude (r = 0.205 7, P > 0.05). Conclusion Altitude may be a potential spatial factor affecting the genetic differentiation of O. hupensis robertsoni in Yunnan Province.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

Objective: To evaluate the safety and efficacy of preoperative autologous blood donation (PABD) under anesthesia monitoring in elective surgical procedures, and to provide scientific data for promoting its clinical application. Methods: 1) A total of 1 164 patients scheduled for elective surgery and met the criteria for stored autologous blood transfusion in our hospital from March 2022 to September 2023 were enrolled. Prior to surgery, stored autotransfusion was performed under anesthesia monitoring. During the operation, blood pressure (BP), heart rate (HR), blood oxygen saturation (SpO ) and other basic life indicators before and after blood collection were recorded and analyzed. Adverse reactions during blood collection were documented, and potential influencing factors were analyzed. 2) The autologous transfusion group (experimental group, patients receiving intraoperative autologous blood reinfusion) was compared with the allogeneic transfusion group (control group, patients without PABD during the same period) using propensity score matching. The length of hospital stay, transfusion-related costs, perioperative hemoglobin (Hb), hematocrit (Hct), platelet count (Plt) and coagulation function were compared between the two groups after matching. Results: 1) Three patients (0.26%) had adverse reactions during blood collection. Autologous blood transfusion was performed in 443 patients (38.1%) during or after operation, with no adverse reaction during blood transfusion. 2) The systolic blood pressure (SBP) and diastolic blood pressure (DBP) of patients after blood collection were lower than before blood collection, and the SpO was higher than before blood collection, with statistically significant differences (P<0.05); There was no significant difference in heart rate before and after blood collection (P>0.05); Our analysis found that age, gender, blood collection volume, department, or mild-to-moderate circulatory system complications didn’t significantly affect BP, HR and SpO fluctuations (P>0.05). 3) The experimental group had shorter hospital stays and lower transfusion costs than the control group (P<0.05). 4) No significant differences were observed in Hb, Hct, Plt levels or coagulation function (PT, APTT) between the two groups after operation (P>0.05). The hospitalization duration and transfusion related expenses in the experimental group were lower than those in the control group (P<0.05). Conclusion: PABD under anesthesia monitoring is safe and feasible in elective surgeries across diverse patient groups and surgical fields. It reduces the costs and conserves blood resources, which is worthy of further promotion.

Objective:To analyze the effects of different psychological interventions on cancer patients' fear of cancer recurrence(FCR).Methods:Randomized controlled trials examining the effects of various psychological interventions on FCR among cancer patients were searched for in both Chinese and English literature databases.A network meta-analysis was conducted to explore the intervention effects,utilizing standardized mean difference(SMD),95％confidence interval(CI),and surface under the cumulative ranking(SUCRA)of FCR as the effect indicators.Results:A total of 34 studies involving 3 772 participants were included,of which five types of psycho-logical interventions were evaluated,namely acceptance and commitment therapy(ACT),gratitude-expansion thera-py(GET),cognitive behavioral therapy(CBT),mindfulness therapy(MT),and multi-component psychological in-tervention(Mul).According to the time of effect evaluation,three effect evaluation timing were confirmed,with immediate post-intervention group,1-2 months post-intervention group,and 3-6 months post-intervention group identified.Compared with conventional care,in the immediate post-intervention group,ACT(SMD=-1.80,95％CI:-2.47--1.13),GET(SMD=-1.33,95％CI:-2.07--0.59),MT(SMD=-0.59,95％CI:-1.03--0.14)and CBT(SMD=-0.54,95％CI:-0.93--0.15)could effectively reduce FCR,and the SUCRA val-ue of ACT was upmost to 0.96.In the 1-2 months post-intervention group,GET(SMD=-2.32,95％CI:-2.99--1.65)and ACT(SMD=-1.46,95％CI:-2.23--0.70)could effectively reduce FCR,and the SUCRA value of GET was upmost to 0.99.In the 3-6 months post-intervention group,Mul(SMD=-1.82,95％CI:-3.03--0.61)and MT(SMD=-1.43,95％CI:-2.33--0.54)could effectively reduce FCR,and the SU-CRA value of Mul was upmost to 0.90.Conclusion:Different psychological interventions vary in their effectiveness on FCR across effect evaluation time points,highlighting the need for tailored approaches on mitigating FCR in clin-ical practice.

OBJECTIVE To evaluate the effects of phthalic acid esters(PAEs)on the expression of heme oxygenase-1(HO-1)in HepG2 cells,and to construct an HO-1-based three-dimensional quantita-tive structure-activity relationship(3D-QSAR)model.METHODS ① HepG2 cells were treated with seven types of PAEs:di-(2-ethylhexyl)phthalate(DEHP),di-n-octyl phthalate(DnOP),dimethyl phthalate(DMP),diethyl phthalate(DEP),dihexyl phthalate(DHXP),dimethylglycol phthalate(DMEP),and dibutyl phthalate(DBP),at final concentrations of 0(DMSO,solvent control),0.062 5,0.125,0.25,0.5 and 1 mmol·L-1(n=6)for 48 h at 37℃.The expression level of HO-1 was measured by Western blotting.② A 3D-QSAR model was constructed using comparative molecular similarity indices analysis(CoMSIA)based on the measured HO-1 levels.The applicability domain(AD)of the model was evaluated using the leverage method.Model fitting quality and predictive ability were evaluated via the KNIME Enalos+node to verify model stability.Additionally,molecular docking was performed to validate the binding interactions between PAEs and HO-1.RESULTS ① Compared with the solvent control group,48 h of exposure to 0.062 5 mmol·L-1 PAEs(DMP,DMEP,DEHP,DnOP and DEP)significantly increased HO-1 protein expressions,while 1 mmol·L-1 PAEs(DMP,DBP,DnOP,DEP and DHXP)significantly suppressed HO-1 expressions.② The 3D-QSAR model showed a non-cross-validated coefficient(R2)of 0.996 and a cross-validated coefficient(Q2)of 0.548.All the seven PAEs in the 3D-QSAR model were within the applicability domain(AD)and passed external validation.Molecular docking results indi-cated that DBP,DnOP,DEHP and DHXP exhibited stronger binding affinities to HO-1.CONCLUSION Forty-eight hours of exposure of HepG2 cells to 1 mmol·L-1 PAEs can significantly suppress HO-1 expres-sions.The 3D-QSAR model established in this study provides a potential tool for predicting the HO-1-related toxic effects of novel PAEs.

OBJECTIVE To evaluate the effects of phthalic acid esters(PAEs)on the expression of heme oxygenase-1(HO-1)in HepG2 cells,and to construct an HO-1-based three-dimensional quantita-tive structure-activity relationship(3D-QSAR)model.METHODS ① HepG2 cells were treated with seven types of PAEs:di-(2-ethylhexyl)phthalate(DEHP),di-n-octyl phthalate(DnOP),dimethyl phthalate(DMP),diethyl phthalate(DEP),dihexyl phthalate(DHXP),dimethylglycol phthalate(DMEP),and dibutyl phthalate(DBP),at final concentrations of 0(DMSO,solvent control),0.062 5,0.125,0.25,0.5 and 1 mmol·L-1(n=6)for 48 h at 37℃.The expression level of HO-1 was measured by Western blotting.② A 3D-QSAR model was constructed using comparative molecular similarity indices analysis(CoMSIA)based on the measured HO-1 levels.The applicability domain(AD)of the model was evaluated using the leverage method.Model fitting quality and predictive ability were evaluated via the KNIME Enalos+node to verify model stability.Additionally,molecular docking was performed to validate the binding interactions between PAEs and HO-1.RESULTS ① Compared with the solvent control group,48 h of exposure to 0.062 5 mmol·L-1 PAEs(DMP,DMEP,DEHP,DnOP and DEP)significantly increased HO-1 protein expressions,while 1 mmol·L-1 PAEs(DMP,DBP,DnOP,DEP and DHXP)significantly suppressed HO-1 expressions.② The 3D-QSAR model showed a non-cross-validated coefficient(R2)of 0.996 and a cross-validated coefficient(Q2)of 0.548.All the seven PAEs in the 3D-QSAR model were within the applicability domain(AD)and passed external validation.Molecular docking results indi-cated that DBP,DnOP,DEHP and DHXP exhibited stronger binding affinities to HO-1.CONCLUSION Forty-eight hours of exposure of HepG2 cells to 1 mmol·L-1 PAEs can significantly suppress HO-1 expres-sions.The 3D-QSAR model established in this study provides a potential tool for predicting the HO-1-related toxic effects of novel PAEs.

Objective:To analyze the effects of different psychological interventions on cancer patients' fear of cancer recurrence(FCR).Methods:Randomized controlled trials examining the effects of various psychological interventions on FCR among cancer patients were searched for in both Chinese and English literature databases.A network meta-analysis was conducted to explore the intervention effects,utilizing standardized mean difference(SMD),95％confidence interval(CI),and surface under the cumulative ranking(SUCRA)of FCR as the effect indicators.Results:A total of 34 studies involving 3 772 participants were included,of which five types of psycho-logical interventions were evaluated,namely acceptance and commitment therapy(ACT),gratitude-expansion thera-py(GET),cognitive behavioral therapy(CBT),mindfulness therapy(MT),and multi-component psychological in-tervention(Mul).According to the time of effect evaluation,three effect evaluation timing were confirmed,with immediate post-intervention group,1-2 months post-intervention group,and 3-6 months post-intervention group identified.Compared with conventional care,in the immediate post-intervention group,ACT(SMD=-1.80,95％CI:-2.47--1.13),GET(SMD=-1.33,95％CI:-2.07--0.59),MT(SMD=-0.59,95％CI:-1.03--0.14)and CBT(SMD=-0.54,95％CI:-0.93--0.15)could effectively reduce FCR,and the SUCRA val-ue of ACT was upmost to 0.96.In the 1-2 months post-intervention group,GET(SMD=-2.32,95％CI:-2.99--1.65)and ACT(SMD=-1.46,95％CI:-2.23--0.70)could effectively reduce FCR,and the SUCRA value of GET was upmost to 0.99.In the 3-6 months post-intervention group,Mul(SMD=-1.82,95％CI:-3.03--0.61)and MT(SMD=-1.43,95％CI:-2.33--0.54)could effectively reduce FCR,and the SU-CRA value of Mul was upmost to 0.90.Conclusion:Different psychological interventions vary in their effectiveness on FCR across effect evaluation time points,highlighting the need for tailored approaches on mitigating FCR in clin-ical practice.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.