Purpose To explore the predictive value of CT-enhanced for tumor mutation burden(TMB)in hepatocellular carcinoma(HCC).Materials and Methods A total of 22 patients with pathologically confirmed HCC after undergoing radical resection in the First Affiliated Hospital,Army Medical University(Third Military Medical University)from January 2020 to January 2023 were collected,all of whom were quantified for TMB.Clinical,laboratory tests,CT imaging characteristics and follow-up of patients were recorded.Variables with P<0.2 were screened by stepwise regression analysis for independent risk factors for TMB.The area under the curve of receiver operating characteristic was used to assess the diagnostic efficacy.Results High TMB level was a risk factor for disease-free survival after HCC surgery(HR=1.115,P<0.05).According to the optimal cut-off value,TMB was classified into a high-risk group(>9.25 mutation/Mb)and low-risk group(≤9.25 mutation/Mb).Univariate analysis of intratumor ischemia or necrosis was statistically different between the high-risk and low-risk groups(P=0.005),and only intratumor ischemia or necrosis was an independent risk factor for predicting high TMB level by stepwise regression analysis(P<0.05).The area under the curve for predicting disease-free survival was 0.833(95%CI 0.615-0.956,P<0.001),with a sensitivity of 100.0%and a specificity of 66.7%.Conclusion High TMB level is associated with poor prognosis after HCC resection.Intratumor ischemia or necrosis have certain clinical value in predicting high TMB level,and are expected to provide a reference basis for personalized diagnosis and treatment of HCC patients.

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 evaluate the efficacy of gadolinium ethoxybenzyl- diethy-lenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI features in the preoperative prediction of tertiary lymphoid structures (TLS) within hepatocellular carcinoma (HCC) lesions.Methods:This retrospective cross-sectional study included clinical and pathological data from 297 HCC patients treated at the Southwest Hospital, Army Medical University between June 2021 and November 2022. Based on postoperative pathology, patients were categorized into TLS-negative ( n=93) and TLS-positive ( n=204) groups. MRI features of HCC lesions using Gd-EOB-DTPA enhancement and relevant clinical data were analyzed. Intergroup comparisons of imaging features and laboratory findings were performed using independent sample t-test, Mann-Whitney U test, χ2 test, or Fisher exact test, as appropriate. The logistic regression analysis was conducted to identify independent predictors of TLS positivity. A predictive model was constructed and visualized using a nomogram. The model′s predictive performance and clinical utility were assessed using the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The area under the ROC curve (AUC) was compared using the DeLong test. Results:Significant differences were observed between the TLS-negative and TLS-positive groups in alpha-fetoprotein (AFP) levels, intratumoral hemorrhage, and peritumoral satellite nodules in the hepatobiliary phase ( P<0.05). Multivariate logistic regression identified intratumoral hemorrhage ( OR=0.123, 95% CI 0.070-0.216, P<0.001) and peritumoral satellite nodules in the hepatobiliary phase ( OR=0.236, 95% CI 0.093-0.596, P=0.002) as independent predictive factors for TLS-positivity. The imaging model based on these two features yielded an AUC of 0.764 (95% CI 0.709-0.809) for predicting TLS-positivity. When combined with AFP levels, the resulting clinical-imaging model achieved a superior AUC of 0.784 (95% CI 0.732-0.829), which was significantly higher than that of the imaging model alone ( Z=2.20, P=0.028). A nomogram was constructed based on the clinical-imaging model. The calibration curve demonstrated good predictive performance of the nomogram, and the DCA showed that the curve remained above the default line across a range of reasonable threshold probabilities, indicating that patients could derive clinical benefit. Conclusion:A nomogram model based on Gd-EOB-DTPA enhanced MRI features combined with AFP levels can effectively predict the presence of TLS in HCC.

Objective To investigate the influencing factors of auditory hypersensitivity in normal hearing population and build a risk nomogram model according to the results,so as to provide scientific basis for early identi-fication of high risk population and formulation of prevention strategy.Methods A total of 410 volunteers with nor-mal pure tone hearing were recruited from March to July 2024.The hyperacusis questionnaire(HQ)was used to as-sess the audiroty hypersensitivity of the subjects.The participants were divided into a training set(n=287)and a validation set(n=123)according to a ratio of 7∶3.Binary Logistic model was used to construct risk model and no-mogram.Receiver operating characteristic(ROC)curve,Hosmer-Lemeshow calibration curve,clinical decision curve(DCA)and clinical impact curve were used to verify the differentiation,accuracy and clinical applicability of the model,respectively.Results Among 410 participants,54(13.17％)had hyperacusis including 38(13.24％)in the training set amd 16(13.01)in the validation set.LASSO regression and Logistic regression analysis showed that tinnitus(OR=3.784,95％CI=1.627-8.804),HADS-A(OR=3.860,95％CI=1.503-9.913),HADS-D(OR=3.118,95％CI=1.249-7.785),migraine(OR=2.821,95％CI=1.147-6.937)and noise exposure histo-ry(OR=3.799,95％CI=1.715-8.416)were the influential factors for hyperacusis in participants with normal hearing.Conclusion The incidence of hyperacusis in normal hearing population is 13.17％.Tinnitus,HADS-A,HADS-D,migraine and noise exposure history are related to the occurrence of hyperacusis in normal hearing popula-tion.The risk prediction nomogram model based on the above factors has good differentiation and calibration degree.It can effectively predict the risk of hyperacusis in normal hearing people,and has certain clinical practicability.

Objective To investigate the clinical value of baseline 18F-fluorodeoxyglucose(18F-FDG)PET/CT metabolic parame-ters and certain clinical indicators in predicting the prognosis of patients with diffuse large B-cell lymphoma(DLBCL).Methods A retrospec-tive analysis was conducted on the baseline 18 F-FDG PET/CT data of 73 DLBCL patients who received R-CHOP treatment.Patients were divided into progression group(24 cases)and non-progression group(49 cases)based on disease progression within 2 years post-treatment.The lesion maximum standardized uptake value(SUVmax),tumour-to-liver blood pool SUVmax ratio(LLR)and tumour-to-mediastinal blood pool SUVmax ratio(L-BPR)were analyzed using receiver operating characteristic(ROC)curves.Kaplan-Meier(K-M)survival curves analysis were performed based on the optimal thresholds of SUVmax,LLR and L-BPR.x2 tests were used to analyze and compare the relationship between each parameter and disease progression.Indicators that were significant in the x2 tests were included in the multivariate Cox regression analysis.Results The area under the curve(AUC)for LLR,L-BPR,and SUVmax were 0.920,0.914,and 0.848,respectively,with optimal thresholds of 7.41,10.67,and 14.70.Based on these thresholds,K-M survival curves analysis showed that the 2-year progression-free survival(PFS)rates for DLBCL patients were 79.2％vs 30.8％(P＜0.001),74.3％vs 22.0％(P=0.009),and 79.5％vs 51.6％(P=0.002),respectively.Significant differences were observed between the progression and non-progression groups in terms of LLR,L-BPR,SUVmax,extranodal involvement,international prognostic index(IPI)score,lactate dehydrogenase(LDH)level,Eastern Cooperative Oncology Group(ECOG)score,and β2-microglobulin(β2-MG)levels(P＜0.05).Multivariate Cox regression analysis revealed that the IPI score and LLR were independent predictors affecting the 2-year PFS of DLBCL patients(P＜0.05).Conclusion Baseline 18F-FDG PET/CT metabolic parameter LLR and IPI score are inde-pendent factors for predicting the prognosis of DLBCL patients.

Objective To observe the effect of Kaixin Powder on neuroinflammation in APP/PS1 mice by regulating WDFY1/TLR4/NF-κB signaling pathway;To explore its mechanism of intervening in Alzheimer disease(AD).Methods APP/PS1 transgenic mice were randomly divided into model group,donepezil hydrochloride group(0.66 mg/kg),and Kaixin Powder low-,medium-and high-dosage groups(1.625,3.25,6.5 g/kg),C57BL/6J mice were set as blank control group,with 8 mice in each group,and corresponding drug intervention was given to medicaction group for 24 weeks.Morris water maze,Y maze and novel object recognition experiments were conducted to assess the cognitive function and learning and memory abilities of mice,immunohistochemical staining was used to detect the deposition of β-amyloid protein(Aβ)in hippocampus,the morphology and Nissl bodies of hippocampal CA1 neurons were observed using HE staining and Nissl staining,ELISA was used to detect the serum contents of interleukin(IL)-6,IL-17,IL-1β and tumor necrosis factor-α(TNF-α),Western blot was used to detect the protein expression of calcium-binding adapter molecule 1(Iba1),glial fibrillary acidic protein(GFAP),WDFY1,Toll like receptor 4(TLR4),Toll like receptor associated molecule(TRAM),TIR domain adapter protein(TRIF),NF-κB p65 and p-NF-κB p65 in hippocampal tissue,RT-qPCR was used to detect the mRNA expression of WDFY1,TLR4,TRAM,TRIF and NF-κB p65 in hippocampal tissue.Results Compared with the blank control group,the model group had significantly prolonged escape latency,reduced platform crossings,decreased autonomous reaction alternation rate and relative recognition index(P<0.05,P<0.01),with increased deposition of Aβ in hippocampal tissue(P<0.01),damaged morphological structure of neurons,reduced number of neurons and Nissl bodies,the serum contents of IL-6,IL-17,IL-1β and TNF-α significantly increased,the expression of Iba1,GFAP,WDFY1,TLR4,TRAM,TRIF,p-NF-κB p65 protein and WDFY1,TLR4,TRAM,TRIF mRNA in hippocampal tissue significantly increased(P<0.01).Compared with the model group,Kaixin Powder groups and donepezil hydrochloride group had significantly shortened escape latency and increased platform crossings,autonomous reaction alternation rate and relative recognition index(P<0.05,P<0.01),hippocampal Aβ deposition reduced in Kaixin Powder medium-,high-dosage groups and donepezil hydrochloride group,the morphological structure of neurons recovered,the number of neurons and Nissl bodies increased,the serum contents of IL-6,IL-17,IL-1β and TNF-α significantly decreased(P<0.05,P<0.01),and the protein expression of Iba1,GFAP,WDFY1,TLR4,TRAM,TRIF,p-NF-κB p65 and the mRNA expressions of WDFY1,TLR4,TRAM and TRIF in hippocampal tissue significantly decreased(P<0.05,P<0.01).Conclusion Kaixin Powder can improve cognitive function and learning and memory abilities in AD model mice,alleviate hippocampal neuron damage and Aβ deposition,inhibit the activation of microglia and astrocytes,and thereby reduce serum inflammatory cytokine release.Its mechanism may be related to regulating the WDFY1/TLR4/NF-κB signaling pathway to inhibit neuroinflammation.

This paper reports the clinical data and the diagnosis and treatment of a patient with exogenous insulin autoimmune syndrome(EIAS)induced by long-term use of exogenous insulin(Eucrin 50).For diabetes patients with hyperinsulin-induced hypoglycemia,detection of IAA is helpful for diagnosis EIAS.Due to different test methods affect IAA results,if negative,EIAS cannot be completely excluded.The polyethylene glycol precipitation method can assist in early diagnosis.

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 influencing factors of auditory hypersensitivity in normal hearing population and build a risk nomogram model according to the results,so as to provide scientific basis for early identi-fication of high risk population and formulation of prevention strategy.Methods A total of 410 volunteers with nor-mal pure tone hearing were recruited from March to July 2024.The hyperacusis questionnaire(HQ)was used to as-sess the audiroty hypersensitivity of the subjects.The participants were divided into a training set(n=287)and a validation set(n=123)according to a ratio of 7∶3.Binary Logistic model was used to construct risk model and no-mogram.Receiver operating characteristic(ROC)curve,Hosmer-Lemeshow calibration curve,clinical decision curve(DCA)and clinical impact curve were used to verify the differentiation,accuracy and clinical applicability of the model,respectively.Results Among 410 participants,54(13.17％)had hyperacusis including 38(13.24％)in the training set amd 16(13.01)in the validation set.LASSO regression and Logistic regression analysis showed that tinnitus(OR=3.784,95％CI=1.627-8.804),HADS-A(OR=3.860,95％CI=1.503-9.913),HADS-D(OR=3.118,95％CI=1.249-7.785),migraine(OR=2.821,95％CI=1.147-6.937)and noise exposure histo-ry(OR=3.799,95％CI=1.715-8.416)were the influential factors for hyperacusis in participants with normal hearing.Conclusion The incidence of hyperacusis in normal hearing population is 13.17％.Tinnitus,HADS-A,HADS-D,migraine and noise exposure history are related to the occurrence of hyperacusis in normal hearing popula-tion.The risk prediction nomogram model based on the above factors has good differentiation and calibration degree.It can effectively predict the risk of hyperacusis in normal hearing people,and has certain clinical practicability.

Objective To investigate the clinical value of baseline 18F-fluorodeoxyglucose(18F-FDG)PET/CT metabolic parame-ters and certain clinical indicators in predicting the prognosis of patients with diffuse large B-cell lymphoma(DLBCL).Methods A retrospec-tive analysis was conducted on the baseline 18 F-FDG PET/CT data of 73 DLBCL patients who received R-CHOP treatment.Patients were divided into progression group(24 cases)and non-progression group(49 cases)based on disease progression within 2 years post-treatment.The lesion maximum standardized uptake value(SUVmax),tumour-to-liver blood pool SUVmax ratio(LLR)and tumour-to-mediastinal blood pool SUVmax ratio(L-BPR)were analyzed using receiver operating characteristic(ROC)curves.Kaplan-Meier(K-M)survival curves analysis were performed based on the optimal thresholds of SUVmax,LLR and L-BPR.x2 tests were used to analyze and compare the relationship between each parameter and disease progression.Indicators that were significant in the x2 tests were included in the multivariate Cox regression analysis.Results The area under the curve(AUC)for LLR,L-BPR,and SUVmax were 0.920,0.914,and 0.848,respectively,with optimal thresholds of 7.41,10.67,and 14.70.Based on these thresholds,K-M survival curves analysis showed that the 2-year progression-free survival(PFS)rates for DLBCL patients were 79.2％vs 30.8％(P＜0.001),74.3％vs 22.0％(P=0.009),and 79.5％vs 51.6％(P=0.002),respectively.Significant differences were observed between the progression and non-progression groups in terms of LLR,L-BPR,SUVmax,extranodal involvement,international prognostic index(IPI)score,lactate dehydrogenase(LDH)level,Eastern Cooperative Oncology Group(ECOG)score,and β2-microglobulin(β2-MG)levels(P＜0.05).Multivariate Cox regression analysis revealed that the IPI score and LLR were independent predictors affecting the 2-year PFS of DLBCL patients(P＜0.05).Conclusion Baseline 18F-FDG PET/CT metabolic parameter LLR and IPI score are inde-pendent factors for predicting the prognosis of DLBCL patients.