BACKGROUND: The increasing morbidity of liver cancer has led to a growing demand for transplantation. Split liver transplantation(SLT) is a promising way to ameliorate organ shortages. However, the safety and efficacy...BACKGROUND: The increasing morbidity of liver cancer has led to a growing demand for transplantation. Split liver transplantation(SLT) is a promising way to ameliorate organ shortages. However, the safety and efficacy of SLT are still controversial. The aim of this study was to assess the clinical outcome of SLT in liver cancer patients at our center. METHODS: A total of 74 patients who received liver transplantation at a tertiary hospital from March 2019 to July 2023 were retrospectively studied, of whom 37 recipients underwent SLT and 37 recipients underwent whole-graft liver transplantation(WGLT). Clinical data were analyzed and compared between patients who underwent SLT and WGLT.RESULTS: SLT and WGLT were successfully performed, with no intraoperative transplantrelated mortality. Postoperatively, no significant differences in total bilirubin(TB, P=0.266), alanine transaminase(ALT, P=0.403) and aspartate transaminase(AST, P=0.160) levels within 30 d were detected between the two groups. The transplant-related mortality rates were 8.1% in the SLT group and 5.4% in the WGLT group within 30 d of surgery(P=1.000), and 10.8% and 8.1%, respectively, at 90 d after surgery(P=1.000). There were no significant differences in overall survival(OS) and progress-free survival(PFS) between the SLT and WGLT groups(P=0.910, P=0.190). CONCLUSION: Our results show that SLT does not imply additional risks in treating liver cancer compared with WGLT.展开更多
Background:Gallbladder carcinoma(GBC)is highly malignant,and its early diagnosis remains difficult.This study aimed to develop a deep learning model based on contrast-enhanced computed tomography(CT)images to assist r...Background:Gallbladder carcinoma(GBC)is highly malignant,and its early diagnosis remains difficult.This study aimed to develop a deep learning model based on contrast-enhanced computed tomography(CT)images to assist radiologists in identifying GBC.Methods:We retrospectively enrolled 278 patients with gallbladder lesions(>10 mm)who underwent contrast-enhanced CT and cholecystectomy and divided them into the training(n=194)and validation(n=84)datasets.The deep learning model was developed based on ResNet50 network.Radiomics and clinical models were built based on support vector machine(SVM)method.We comprehensively compared the performance of deep learning,radiomics,clinical models,and three radiologists.Results:Three radiomics features including LoG_3.0 gray-level size zone matrix zone variance,HHL firstorder kurtosis,and LHL gray-level co-occurrence matrix dependence variance were significantly different between benign gallbladder lesions and GBC,and were selected for developing radiomics model.Multivariate regression analysis revealed that age≥65 years[odds ratios(OR)=4.4,95%confidence interval(CI):2.1-9.1,P<0.001],lesion size(OR=2.6,95%CI:1.6-4.1,P<0.001),and CA-19-9>37 U/mL(OR=4.0,95%CI:1.6-10.0,P=0.003)were significant clinical risk factors of GBC.The deep learning model achieved the area under the receiver operating characteristic curve(AUC)values of 0.864(95%CI:0.814-0.915)and 0.857(95%CI:0.773-0.942)in the training and validation datasets,which were comparable with radiomics,clinical models and three radiologists.The sensitivity of deep learning model was the highest both in the training[90%(95%CI:82%-96%)]and validation[85%(95%CI:68%-95%)]datasets.Conclusions:The deep learning model may be a useful tool for radiologists to distinguish between GBC and benign gallbladder lesions.展开更多
基金Key Project of Traditional Chinese Medicine Science and Technology Plan of Zhejiang Province (GZY-ZJ-KJ-24077)National Natural Science Foundation of China (No. U23A202181, 8207101520, 82272860)+2 种基金Central Guidance on Local Science and Technology Development Fund of Zhejiang Province (2023ZY1017)Fundamental Research Funds for the Central Universities (No. 226-2023-00038)Special Financial Support for Zhejiang Traditional Chinese Medicine Innovation Teams。
文摘BACKGROUND: The increasing morbidity of liver cancer has led to a growing demand for transplantation. Split liver transplantation(SLT) is a promising way to ameliorate organ shortages. However, the safety and efficacy of SLT are still controversial. The aim of this study was to assess the clinical outcome of SLT in liver cancer patients at our center. METHODS: A total of 74 patients who received liver transplantation at a tertiary hospital from March 2019 to July 2023 were retrospectively studied, of whom 37 recipients underwent SLT and 37 recipients underwent whole-graft liver transplantation(WGLT). Clinical data were analyzed and compared between patients who underwent SLT and WGLT.RESULTS: SLT and WGLT were successfully performed, with no intraoperative transplantrelated mortality. Postoperatively, no significant differences in total bilirubin(TB, P=0.266), alanine transaminase(ALT, P=0.403) and aspartate transaminase(AST, P=0.160) levels within 30 d were detected between the two groups. The transplant-related mortality rates were 8.1% in the SLT group and 5.4% in the WGLT group within 30 d of surgery(P=1.000), and 10.8% and 8.1%, respectively, at 90 d after surgery(P=1.000). There were no significant differences in overall survival(OS) and progress-free survival(PFS) between the SLT and WGLT groups(P=0.910, P=0.190). CONCLUSION: Our results show that SLT does not imply additional risks in treating liver cancer compared with WGLT.
基金the National Natural Science Foundation of China(81572975)Key Research and Devel-opment Project of Science and Technology Department of Zhejiang(2015C03053)+1 种基金Chen Xiao-Ping Foundation for the Development of Science and Technology of Hubei Province(CXPJJH11900009-07)Zhejiang Provincial Program for the Cultivation of High-level Innovative Health Talents.
文摘Background:Gallbladder carcinoma(GBC)is highly malignant,and its early diagnosis remains difficult.This study aimed to develop a deep learning model based on contrast-enhanced computed tomography(CT)images to assist radiologists in identifying GBC.Methods:We retrospectively enrolled 278 patients with gallbladder lesions(>10 mm)who underwent contrast-enhanced CT and cholecystectomy and divided them into the training(n=194)and validation(n=84)datasets.The deep learning model was developed based on ResNet50 network.Radiomics and clinical models were built based on support vector machine(SVM)method.We comprehensively compared the performance of deep learning,radiomics,clinical models,and three radiologists.Results:Three radiomics features including LoG_3.0 gray-level size zone matrix zone variance,HHL firstorder kurtosis,and LHL gray-level co-occurrence matrix dependence variance were significantly different between benign gallbladder lesions and GBC,and were selected for developing radiomics model.Multivariate regression analysis revealed that age≥65 years[odds ratios(OR)=4.4,95%confidence interval(CI):2.1-9.1,P<0.001],lesion size(OR=2.6,95%CI:1.6-4.1,P<0.001),and CA-19-9>37 U/mL(OR=4.0,95%CI:1.6-10.0,P=0.003)were significant clinical risk factors of GBC.The deep learning model achieved the area under the receiver operating characteristic curve(AUC)values of 0.864(95%CI:0.814-0.915)and 0.857(95%CI:0.773-0.942)in the training and validation datasets,which were comparable with radiomics,clinical models and three radiologists.The sensitivity of deep learning model was the highest both in the training[90%(95%CI:82%-96%)]and validation[85%(95%CI:68%-95%)]datasets.Conclusions:The deep learning model may be a useful tool for radiologists to distinguish between GBC and benign gallbladder lesions.
