Objective:To investigate the clinical value of abdominal adipose volume in predicting early tumor recurrence after resection of hepatocellular carcinoma (HCC).Methods:The retrospective case-control study was conducted. The clinicopathological data of 132 HCC patients with tumor diameter ≤5 cm who were admitted to The First Affiliated Hospital of Army Medical University from December 2017 to October 2019 were collected. There were 110 males and 22 females, aged (51±4)years. All patients underwent resection of HCC. Preoperative computer tomography scanning was performed and the visceral and subcutaneous fats of patients were quantified using the Mimics Research 21.0 software. Based on time to postoperative tumor recurrence patients were divided to two categories: early recurrence and non-early recurrence. Observation indicators: (1) consistency analy-sis; (2) analysis of factors influencing early tumor recurrence after resection of HCC and construction of prediction model. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the t test. Measurement data with skewed distribu-tion were represented as M( Q1,Q3) or M(range), and comparison between groups was conducted using the Mann-Whitney U test. Count data were expressed as absolute numbers, and comparison between groups was conducted using the chi-square test or Fisher exact probability. Consistency analysis was conducted using the intragroup correlation coefficient (ICC) test. Multivariate analysis was performed using the binary Logistic regression model forward method. Independent risk factors influencing early tumor recurrence after resection of HCC were screened. The area under curve (AUC) of receiver operating characteristic (ROC) curve was applied to select the optimal cut-off value to classify high and low risks of recurrence. The Kaplan-Meier method was used to draw survival curve and calculate survival time. The Log-Rank test was used for survival analysis. Results:(1) Consistency analysis. The consistency ICC of abdominal fat parameters of visceral fat volume (VFV), subcutaneous fat volume, visceral fat area, and subcutaneous fat area measured by 2 radiologists were 0.84, 1.00, 0.86, and 0.94, respectively. (2) Analysis of factors influencing early tumor recurr-ence after resection of HCC and construction of prediction model. All 132 patients were followed up after surgery for 662(range, 292-1 111)days. During the follow-up, there were 52 patients with non-early recurrence and 80 patients with early recurrence. Results of multivariate analysis showed that VFV was an independent factor influencing early tumor recurrence after resection of HCC ( odds ratio=4.07, 95% confidence interval as 2.27-7.27, P<0.05). The AUC of ROC curve based on VFV was 0.78 (95% confidence interval as 0.70-0.85), and the sensitivity and specificity were 72.2 % and 77.4 %, respectively. The optimal cut-off value of VFV was 1.255 dm 3, and all 132 patients were divided into the high-risk early postoperative recurrence group of 69 cases with VFV >1.255 dm 3, and the low-risk early postoperative recurrence group of 63 cases with VFV ≤1.255 dm 3. The disease-free survival time of the high-risk early postoperative recurrence group and the low-risk early post-operative recurrence group were 414(193,702)days and 1 047(620,1 219)days, showing a significant difference between them ( χ2=31.17, P<0.05). Conclusions:VFV is an independent factor influen-cing early tumor recurrence of HCC after resection. As a quantitative indicator of abdominal fat, it can predict the prognosis of HCC 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.

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.