Impact of sarcopenia on postoperative outcomes after hepatectomy in older patients with intrahepatic cholangiocarcinoma: A multicentre cohort study.

BACKGROUND AND AIMS: Sarcopenia is associated with poor prognosis, but its role in older patients with intrahepatic cholangiocarcinoma (ICC) is unclear. We aimed to evaluate the impact of sarcopenia on the prognosis of older patients with ICC undergoing hepatectomy. METHODS: A total of 363 patients with ICC following hepatectomy from 2015 to 2021 were retrospectively reviewed at five institutions. Sarcopenia was evaluated using skeletal muscle index by computed tomography images. Patients were divided into four subgroups according to sarcopenia and age. Postoperative outcomes including complication, overall survival (OS) and recurrence-free survival (RFS) were evaluated. Risk factors were identified through univariate and multivariate Cox regression analyses. RESULTS: 302 patients were included in the analysis. The median age was 63 years and there were 128 patients (42.4%) aged over 65 years. 192 patients (63.6%) were diagnosed with sarcopenia, while 180 patients (59.6%) experienced myosteatosis. Older patients experienced a higher incidence of sarcopenia and myosteatosis, and worse postoperative outcomes than younger patients. In the subgroup of patients with sarcopenia, older patients experienced a significant shorter OS than younger patients, which was not observed in patients without sarcopenia. According to the multivariate Cox regression analysis, lymphatic metastasis (p < .001), blood transfusion (p = .004), low serum albumin (p = .051), sarcopenia (p = .024), and myosteatosis (p = .004) were identified as independent risk factors of OS in older patients, meanwhile tumour size (p = .013) and lymphatic metastasis (p < .001) were independent risk factors of RFS. CONCLUSIONS: Sarcopenia and myosteatosis have a significant adverse impact on postoperative outcomes in older patients with ICC undergoing hepatectomy.

Gut microbiome as a biomarker for predicting early recurrence of HBV-related hepatocellular carcinoma.

To investigate the potential of the gut microbiome as a biomarker for predicting the early recurrence of HBV-related hepatocellular carcinoma (HCC), we enrolled 124 patients diagnosed with HBV-associated HCC and 82 HBV-related hepatitis, and 86 healthy volunteers in our study, collecting 292 stool samples for 16S rRNA sequencing and 35 tumor tissue samples for targeted metabolomics. We performed an integrated bioinformatics analysis of gut microbiome and tissue metabolome data to explore the gut microbial-liver metabolite axis associated with the early recurrence of HCC. We constructed a predictive model based on the gut microbiota and validated its efficacy in the temporal validation cohort. Dialister, Veillonella, the Eubacterium coprostanoligenes group, and Lactobacillus genera, as well as the Streptococcus pneumoniae and Bifidobacterium faecale species, were associated with an early recurrence of HCC. We also found that 23 metabolites, including acetic acid, glutamate, and arachidonic acid, were associated with the early recurrence of HCC. A comprehensive analysis of the gut microbiome and tissue metabolome revealed that the entry of gut microbe-derived acetic acid into the liver to supply energy for tumor growth and proliferation may be a potential mechanism for the recurrence of HCC mediated by gut microbe. We constructed a nomogram to predict early recurrence by combining differential microbial species and clinical indicators, achieving an AUC of 78.0%. Our study suggested that gut microbes may serve as effective biomarkers for predicting early recurrence of HCC, and the gut microbial-tumor metabolite axis may explain the potential mechanism by which gut microbes promote the early recurrence of HCC.

Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study.

PURPOSE: Postoperative early recurrence (ER) leads to a poor prognosis for intrahepatic cholangiocarcinoma (ICC). We aimed to develop machine learning (ML) radiomics models to predict ER in ICC after curative resection. METHODS: Patients with ICC undergoing curative surgery from three institutions were retrospectively recruited and assigned to training and external validation cohorts. Preoperative arterial and venous phase contrast-enhanced computed tomography (CECT) images were acquired and segmented. Radiomics features were extracted and ranked through their importance. Univariate and multivariate logistic regression analysis was used to identify clinical characteristics. Various ML algorithms were used to construct radiomics-based models, and the predictive performance was evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. RESULTS: 127 patients were included for analysis: 90 patients in the training set and 37 patients in the validation set. Ninety-two patients (72.4%) experienced recurrence, including 71 patients exhibiting ER. Male sex, microvascular invasion, TNM stage, and serum CA19-9 were identified as independent risk factors for ER, with the corresponding clinical model having a poor predictive performance (AUC of 0.685). Fifty-seven differential radiomics features were identified, and the 10 most important features were utilized for modelling. Seven ML radiomics models were developed with a mean AUC of 0.87 ± 0.02, higher than the clinical model. Furthermore, the clinical-radiomics models showed similar predictive performance to the radiomics models (AUC of 0.87 ± 0.03). CONCLUSION: ML radiomics models based on CECT are valuable in predicting ER in ICC.

Association of gut microbiome and primary liver cancer: A two-sample Mendelian randomization and case-control study.

BACKGROUND AND AIMS: Observational epidemiology studies suggested a relationship between the gut microbiome and primary liver cancer. However, the causal relationship remains unclear because of confounding factors and reverse causality. We aimed to explore the causal role of the gut microbiome in the development of primary liver cancer, including hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). METHODS: Mendelian randomization (MR) study was conducted using summary statistics from genome-wide association studies (GWAS) of the gut microbiome and liver cancer, and sequencing data from a case-control study validated the findings. A 5-cohort GWAS study in Germany (N = 8956) served as exposure, whilst the UK biobank GWAS study (N = 456 348) served as an outcome. The case-control study was conducted at the First Affiliated Hospital of Wenzhou Medical University from December 2018 to October 2020 and included 184 HCC patients, 63 ICC patients and 40 healthy controls. RESULTS: A total of 57 features were available for MR analysis, and protective causal associations were identified for Family_Ruminococcaceae (OR = 0.46 [95% CI, 0.26-0.82]; p = .009) and Genus_Porphyromonadaceae (OR = 0.59 [95% CI, 0.42-0.83]; p = .003) with HCC, and for Family_Porphyromonadaceae (OR = 0.36 [95% CI, 0.14-0.94]; p = .036) and Genus_Bacteroidetes (OR = 0.55 [95% CI, 0.34-0.90]; p = .017) with ICC respectively. The case-control study results showed that the healthy controls had a higher relative abundance of Family_Ruminococcaceae (p = .00033), Family_Porphyromonadaceae (p = .0055) and Genus_Bacteroidetes (p = .021) than the liver cancer patients. CONCLUSIONS: This study demonstrates that Ruminococcaceae, Porphyromonadaceae and Bacteroidetes are related to a reduced risk of liver cancer (HCC or ICC), suggesting potential significance for the prevention and control of liver cancer.

Amphipol-facilitated elucidation of the functional tetrameric complex of full-length cytochrome P450 CYP2B4 and NADPH-cytochrome P450 oxidoreductase.

Interactions of membrane-bound mammalian cytochromes P450 (CYPs) with NADPH-cytochrome P450 oxidoreductase (POR), which are required for metabolism of xenobiotics, are facilitated by membrane lipids. A variety of membrane mimetics, such as phospholipid liposomes and nanodiscs, have been used to simulate the membrane to form catalytically active CYP:POR complexes. However, the exact mechanism(s) of these interactions are unclear because of the absence of structural information of full-length mammalian CYP:POR complexes in membranes. Herein, we report the use of amphipols (APols) to form a fully functional, soluble, homogeneous preparation of full-length CYP:POR complexes amenable to biochemical and structural study. Incorporation of CYP2B4 and POR into APols resulted in a CYP2B4:POR complex with a stoichiometry of 1:1, which was fully functional in demethylating benzphetamine at a turnover rate of 37.7 ± 2.2 min-1, with a coupling efficiency of 40%. Interestingly, the stable complex had a molecular weight (Mw) of 338 ± 22 kDa determined by multiangle light scattering, suggestive of a tetrameric complex of 2CYP2B4:2POR embedded in one APol nanoparticle. Moreover, negative stain electron microscopy (EM) validated the homogeneity of the complex and allowed us to generate a three-dimensional EM map and model consistent with the tetramer observed in solution. This first report of the full-length mammalian CYP:POR complex by transmission EM not only reveals the architecture that facilitates electron transfer but also highlights a potential use of APols in biochemical and structural studies of functional CYP complexes with redox partners.

Noncoding RNA-mediated molecular bases of chemotherapy resistance in hepatocellular carcinoma.

Despite the significant progress in decreasing the occurrence and mortality of hepatocellular carcinoma (HCC), it remains a public health issue worldwide on the basis of its late presentation and tumor recurrence. To date, apart from surgical interventions, such as surgical resection, liver transplantation and locoregional ablation, current standard antitumor protocols include conventional cytotoxic chemotherapy. However, due to the high chemoresistance nature, most current therapeutic agents show dismal outcomes for this refractory malignancy, leading to disease relapse. Nevertheless, the molecular mechanisms involved in chemotherapy resistance remain systematically ambiguous. Herein, HCC is hierarchically characterized by the formation of primitive cancer stem cells (CSCs), progression of epithelial-mesenchymal transition (EMT), unbalanced autophagy, delivery of extracellular vesicles (EVs), escape of immune surveillance, disruption of ferroptosis, alteration of the tumor microenvironment and multidrug resistance-related signaling pathways that mediate the multiplicity and complexity of chemoresistance. Of note, anecdotal evidence has corroborated that noncoding RNAs (ncRNAs) extensively participate in the critical physiological processes mentioned above. Therefore, understanding the detailed regulatory bases that underlie ncRNA-mediated chemoresistance is expected to yield novel insights into HCC treatment. In the present review, a comprehensive summary of the latest progress in the investigation of chemotherapy resistance concerning ncRNAs will be elucidated to promote tailored individual treatment for HCC patients.

Exploration and validation of a novel ferroptosis-related gene signature predicting the prognosis of intrahepatic cholangiocarcinoma.

Ferroptosis plays an important role in intrahepatic cholangiocarcinoma (ICC). We aim to develop a new ferroptosis-related gene signature predicting the prognosis of ICC. We download RNA expression profiles and clinical data of ICC from TCGA and GEO databases. Ferroptosis-related differentially expressed genes (DEGs) are screened by the Wilcoxon signed-rank test. GO and KEGG enrichment analyses are performed to understand the function of DEGs and co-expressed genes. Univariate Cox and LASSO regression are used to develop a ferroptosis-related gene signature. Receiver operating characteristic (ROC) curves and Kaplan-Meier (KM) analysis were used to evaluate the prognostic value. RNA sequencing is performed in 30 patients with ICC in our medical center to validate the prognostic value of the gene signature. We identify 44 ferroptosis-related DEGs, among which four (ACSL4, IREB2, NFE2L2, and TP53) are associated with overall survival (OS). Functional enrichment analysis shows that ferroptosis-associated DEGs have an important impact on ICC carcinogenesis. A new ferroptosis-related gene signature based on DEGs is built, and the prognostic ability is confirmed by KM and ROC curves (AUC=0.777, 0.75, 0.799 for 12, 24, and 36 months, respectively). Patients with high risk scores have worse OS ( P=0.0081). In the validation cohort, the expression of DEGs is in accordance with that in the exploration cohort. The four-gene signature is also demonstrated to have a favorable prognostic value (AUC=0.69). A new predictive model based on four ferroptosis-related genes (ACSL4, IREB2, NFE2L2, and TP53) is established and shows favorable prognostic value.

Inhibitor of Differentiation 1 (Id1) in Cancer and Cancer Therapy.

The inhibitor of DNA binding (Id) proteins are regulators of cell cycle and cell differentiation. Of all Id family proteins, Id1 is mostly linked to tumorigenesis, cellular senescence as well as cell proliferation and survival. Id1 is a stem cell-like gene more than a classical oncogene. Id1 is overexpressed in numerous types of cancers and exerts its promotion effect to these tumors through different pathways. Briefly, Id1 was found significantly correlated with EMT-related proteins, K-Ras signaling, EGFR signaling, BMP signaling, PI3K/Akt signaling, WNT and SHH signaling, c-Myc signaling, STAT3 signaling, RK1/2 MAPK/Egr1 pathway and TGF-ß pathway, etc. Id1 has potent effect on facilitating tumorous angiogenesis and metastasis. Moreover, high expression of Id1 plays a facilitating role in the development of drug resistance, including chemoresistance, radiation resistance and resistance to drugs targeting angiogenesis. However, controversial results were also obtained. Overall, Id1 represent a promising target of anti-tumor therapeutics based on its potent promotion effect to cancer. Numerous drugs were found exerting their anti-tumor function through Id1-related signaling pathways, such as fucoidan, berberine, tetramethylpyrazine, crizotinib, cannabidiol and vinblastine.

Clinical outcome of endoscopic covered metal stenting for resolution of benign biliary stricture: Systematic review and meta-analysis.

BACKGROUND AND AIM: Management of benign biliary stricture is challenging. Endoscopic therapy has evolved as the first-line treatment for various benign biliary strictures. However, covered self-expandable metal stents (CSEMS) have not been approved by the United States Food and Drug Administration for the treatment of benign biliary stricture. With this goal, we conducted the present systemic review and meta-analysis to evaluate the efficacy and safety of endoscopic stenting with CSEMS in the treatment of benign biliary stricture. METHODS: Systematic review and meta-analysis by searching PubMed, MEDLINE and Embase databases. RESULTS: In total, 37 studies (1677 patients) fulfilled the inclusion criteria. Pooled stricture resolutions were achieved in 83% of cases. Median stent dwelling time was 4.4 months, with median endoscopic retrograde cholangiopancreatography sessions of 2.0. Stricture recurrence at 4-year follow up was 11% (95% CI, 8-14%). Pooled complication rate was 23% (95% CI, 20-26%). CONCLUSIONS: Placement of CSEMS is effective in the treatment of benign biliary stricture with relatively short stenting duration and low long-term stricture recurrence rate. However, more prospectively randomized studies are required to confirm the results.

The Mediation/Moderation Effects of Gut Microbiota on Sleep Quality and Primary Liver Cancer: A Mendelian Randomization and Case-Control Study.

Background: Primary liver cancer (PLC) is a fatal malignancy, sleep quality and gut microbiota were shown to be associated with PLC. However, the mechanism of how sleep quality affects PLC is unclear. This study aims to investigate the mediation/moderation effects of gut microbiota on sleep quality and the occurrence of PLC. Methods: The causality of sleep quality and the occurrence of PLC was detected through the Mendelian randomization (MR) analysis based on the data including 305,359 individuals (Finland Database) and 456,348 participants (UK Biobank). The primary method used for MR analysis was inverse-variance weighted analysis. Gut microbiota' mediation/moderation effects were uncovered in the case-control study including 254 patients with PLC and 193 people with benign liver diseases through the mediation/moderation effect analyses. People's sleep quality was evaluated through the Pittsburgh sleep quality index (PSQI). Results: Poor sleep quality could lead to PLC through the MR analysis (P = 0.026). The case-control study uncovered that Actinobacteria had mediation effects on the relationship between PSQI score, self-sleep quality, and the occurrence of PLC (P = 0.048, P = 0.046). Actinobacteria and Bifidobacterium could inhibit the development of PLC caused by short night sleep duration (P = 0.021, P = 0.022). Erysipelotrichales could weaken the influence of daytime dysfunction on PLC (P = 0.033). Roseburia modulated the contribution of nocturnal insomnia and poor sleep quality to PLC (P = 0.009, P = 0.017). Conclusion: Poor sleep quality was associated with PLC. Gut microbiota' mediation/moderation effects on poor sleep quality and the occurrence of PLC prompted an insightful idea for the prevention of PLC.

Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.

Impact of sarcopenia on the short-term and long-term outcomes of intrahepatic cholangiocarcinoma undergoing hepatectomy: A multi-center study.

BACKGROUND: Sarcopenia is associated with adverse prognosis of intrahepatic cholangiocarcinoma (iCCA) after surgery. METHODS: 321 patients with iCCA undergoing surgery were retrospectively recruited and assigned to training and validation cohort. Skeletal muscle index (SMI) was assessed to define sarcopenia. Logistic regression and cox regression analysis were used to identify risk factors. A novel sarcopenia-based nomogram was constructed and validated by ROC curves, calibration curves, and DCA curves. RESULTS: 260 patients were included for analysis. The median age was 63.0 years and 161 patients (61.9%) were diagnosed with sarcopenia. Patients with sarcopenia exhibited a higher rate of postoperative complications, a worse OS and RFS than patients without sarcopenia. Sarcopenia, low albumin and intraoperative blood transfusion were independent risk factors of postoperative complications, while sarcopenia and low albumin were risk factors of high CCI≥26.2. Sarcopenia, high PS score, low-undifferentiated differentiation, perineural invasion, TNM stage III-IV were risk factors of OS, and a novel nomogram based on these five factors was built to predict the 12-, 24-, and 36-months OS, with the mean AUC > 0.6. CONCLUSION: Sarcopenia is negatively associated with both postoperative complications and survival prognosis of iCCA undergoing hepatectomy.

Association of glucose-lowering drug target and risk of gastrointestinal cancer: a mendelian randomization study.

BACKGROUND & AIMS: Glucose-lowering drug is associated with various cancers, but the causality with gastrointestinal cancer risk is rarely reported. We aimed to explore the causality between them in this Mendelian randomization (MR) study. METHODS: Two-sample MR, summary-data-based (SMR), mediation MR, and colocalization analyses was employed. Ten glucose-lowering drug targets (PPARG, DPP4, GLP1R, INSR, SLC5A2, ABCC8, KCNJ11, ETFDH, GPD2, PRKAB1) and seven types of gastrointestinal cancer (anal carcinoma, cardia cancer, gastric cancer, hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), pancreatic cancer, rectum cancer) were included. Patients with gastrointestinal cancers from six different large GWAS databases, including the UK Biobank and Finnish cohorts were incorporated, for discovery and external validation. Meta-analysis was employed to integrate the results from both discovery and validation cohorts, thereby ensuring the reliability of findings. RESULTS: ABCC8/KCNJ11 were associated with pancreatic cancer risk in both two-sample MR (odds ratio (OR): 15.058, per standard deviation unit (SD) change of glucose-lowering durg target perturbation equivalent to 1 SD unit of HbA1c lowering; 95% confidence interval (95% CI): 3.824-59.295; P-value = 0.0001) and SMR (OR: 1.142; 95% CI: 1.013-1.287; P-value = 0.030) analyses. The mediation effect of body mass index (OR: 0.938; 95% CI: 0.884-0.995; proportion of mediation effect: 3.001%; P-value = 0.033) on ABCC8/KCNJ11 and pancreatic cancer was uncovered. Strong connections of DPP4 with anal carcinoma (OR: 0.123; 95% CI: 0.020-0.745; P-value = 0.023) and ICC (OR: 7.733; 95% CI: 1.743-34.310; P-value = 0.007) were detected. PPARG was associated with anal carcinoma (OR: 12.909; 95% CI: 3.217-51.795; P-value = 0.0003), HCC (OR: 36.507; 95% CI: 8.929-149.259; P-value < 0.0001), and pancreatic cancer (OR: 0.110; 95% CI: 0.071-0.172; P-value < 0.0001). SLC5A2 was connected with pancreatic cancer (OR: 8.096; 95% CI: 3.476-18.857; P-value < 0.0001). Weak evidence indicated the connections of GLP1R, GPD2, and PRKAB1 with anal carcinoma, cardia cancer, ICC, and rectum cancer. In addition, the corresponding results were consistently validated in both the validation cohorts and the integrated outcomes. CONCLUSIONS: Some glucose-lowering drugs were associated with gastrointestinal cancer risk, which might provide new ideas for gastrointestinal cancer treatment.

Survival Benefit of Surgical Treatment for Elderly Patients with Intrahepatic Cholangiocarcinoma: A Retrospective Cohort Study in the SEER Database by Propensity Score Matching Analysis.

Despite a rising trend in intrahepatic cholangiocarcinoma (ICC) incidence in the elderly population worldwide, the benefit of surgery for those patients is still controversial. Data from 811 elderly patients diagnosed with non-metastatic ICC were obtained from the US surveillance, epidemiology, and end results (SEER) program database. Propensity score matched (PSM) was conducted for the better balance of baseline. The associations between tumor characteristics and surgery with overall survival (OS) and cancer specific survival (CSS) were estimated using hazard ratios (HR) and 95% confidence intervals (CI). The results showed that ICC patients above 60 years old taking surgery had better OS (hazard ratio [HR], 0.258; 95% CI, 0.205-0.324) and CSS (hazard ratio [HR], 0.239; 95% CI, 0.188-0.303) than patients without surgery. Similar trends in patients above 65 years old, above 70 years old, above 75 years old, and above 80 years old were observed, separately. This benefit was also showed in lymph node-negative (N0) and lymph node-positive (N1) subgroups and N0 patients are more likely to take an advantage from surgery than N1 patients. The different outcomes between surgery and non-surgery suggest that surgical treatment may be recommended for elderly ICC if the tumor is resectable to ensure optimal treatment.

Association between gut microbiota and gastrointestinal cancer: a two-sample bi-directional Mendelian randomization study.

Background: The gut microbiome is closely related to gastrointestinal (GI) cancer, but the causality of gut microbiome with GI cancer has yet to be fully established. We conducted this two-sample Mendelian randomization (MR) study to reveal the potential causal effect of gut microbiota on GI cancer. Materials and methods: Summary-level genetic data of gut microbiome were derived from the MiBioGen consortium and the Dutch Microbiome Project. Summary statistics of six GI cancers were drawn from United Kingdom Biobank. Inverse-variance-weighted (IVW), MR-robust adjusted profile score (MR-RAPS), and weighted-median (WM) methods were used to evaluate the potential causal link between gut microbiota and GI cancer. In addition, we performed sensitivity analyses and reverse MR analyses. Results: We identified potential causal associations between 21 bacterial taxa and GI cancers (values of p < 0.05 in all three MR methods). Among them, phylum Verrucomicrobia (OR: 0.17, 95% CI: 0.05-0.59, p = 0.005) retained a strong negative association with intrahepatic cholangiocarcinoma after the Bonferroni correction, whereas order Bacillales (OR: 1.67, 95% CI: 1.23-2.26, p = 0.001) retained a strong positive association with pancreatic cancer. Reverse MR analyses indicated that GI cancer was associated with 17 microbial taxa in all three MR methods, among them, a strong inverse association between colorectal cancer and family Clostridiaceae1 (OR: 0.91, 95% CI: 0.86-0.96, p = 0.001) was identified by Bonferroni correction. Conclusion: Our study implicates the potential causal effects of specific microbial taxa on GI cancer, potentially providing new insights into the prevention and treatment of GI cancer through specific gut bacteria.

Synergistic Effect of Lenvatinib and Chemotherapy in Hepatocellular Carcinoma Using Preclinical Models.

Purpose: The current study aimed to evaluate the synergistic efficacy of lenvatinib and FOLFOX (infusional fluorouracil (FU), folinic acid, and oxaliplatin) in hepatocellular carcinoma (HCC) using patient-derived xenograft (PDX) and PDX-derived organotypic spheroid (XDOTS) models in vivo and in vitro. Methods: PDX and matched XDOTS models originating from three patients with HCC were established. All models were divided into four groups and treated with drugs alone or in combination. Tumor growth in the PDX models was measured and recorded, and angiogenesis and phosphorylation of the vascular endothelial growth factor receptor (VEGFR2), rearranged during transfection (RET), and extracellular signal-regulated kinase (ERK) were detected using immunohistochemistry and Western blot assays. The proliferative ability of XDOTS was evaluated through active staining and immunofluorescence staining, and the effect of the combined medication was evaluated using the Celltiter-Glo luminescent cell viability assay. Results: Three PDX models with genetic characteristics similar to those of the original tumors were successfully established. Combining lenvatinib with FOLFOX led to a higher tumor growth inhibition rate than individual therapies (P < 0.01). Immunohistochemical analysis demonstrated that the combined treatment significantly inhibited the proliferation and angiogenesis of PDX tissues (P < 0.05), and Western blot analysis showed that the combined treatment significantly inhibited the phosphorylation of VEGFR2, RET, and ERK compared with single-agent treatment. Additionally, all three matched XDOTS models were successfully cultured with satisfactory activity and proliferation, and the combined therapies led to better suppression of XDOTS growth compared with individual therapy (P < 0.05). Conclusion: Lenvatinib combined with FOLFOX had a synergistic antitumor effect in HCC PDX and XDOTS models by inhibiting the phosphorylation of VEGFR, RET, and ERK.

Development of sarcopenia-based nomograms predicting postoperative complications of benign liver diseases undergoing hepatectomy: A multicenter cohort study.

Background: Sarcopenia has a remarkable negative impact on patients with liver diseases. We aimed to evaluate the impact of preoperative sarcopenia on the short-term outcomes after hepatectomy in patients with benign liver diseases. Methods: A total of 558 patients with benign liver diseases undergoing hepatectomy were prospectively reviewed. Both the muscle mass and strength were measured to define sarcopenia. Postoperative outcomes including complications, major complications and comprehensive complication index (CCI) were compared among four subgroups classified by muscle mass and strength. Predictors of complications, major complications and high CCI were identified by univariate and multivariate logistic regression analysis. Nomograms based on predictors were constructed and calibration cures were performed to verify the performance. Results: 120 patients were involved for analysis after exclusion. 33 patients were men (27.5%) and the median age was 54.0 years. The median grip strength was 26.5 kg and the median skeletal muscle index (SMI) was 44.4 cm2/m2. Forty-six patients (38.3%) had complications, 19 patients (15.8%) had major complications and 27 patients (22.5%) had a CCI ≥ 26.2. Age (p = 0.005), SMI (p = 0.005), grip strength (p = 0.018), surgical approach (p = 0.036), and operation time (p = 0.049) were predictors of overall complications. Child-Pugh score (p = 0.037), grip strength (p = 0.004) and surgical approach (p = 0.006) were predictors of major complications. SMI (p = 0.047), grip strength (p < 0.001) and surgical approach (p = 0.014) were predictors of high CCI. Among the four subgroups, patients with reduced muscle mass and strength showed the worst short-term outcomes. The nomograms for complications and major complications were validated by calibration curves and showed satisfactory performance. Conclusion: Sarcopenia has an adverse impact on the short-term outcomes after hepatectomy in patients with benign liver diseases and valuable sarcopenia-based nomograms were constructed to predict postoperative complications and major complications.

Machine Learning to Predict the Response to Lenvatinib Combined with Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma.

BACKGROUND: Lenvatinib and transarterial chemoembolization (TACE) are first-line treatments for unresectable hepatocellular carcinoma (HCC), but the objective response rate (ORR) is not satisfactory. We aimed to predict the response to lenvatinib combined with TACE before treatment for unresectable HCC using machine learning (ML) algorithms based on clinical data. METHODS: Patients with unresectable HCC receiving the combination therapy of lenvatinib combined with TACE from two medical centers were retrospectively collected from January 2020 to December 2021. The response to the combination therapy was evaluated over the following 4-12 weeks. Five types of ML algorithms were applied to develop the predictive models, including classification and regression tree (CART), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The performance of the models was assessed by the receiver operating characteristic (ROC) curve and area under the receiver operating characteristic curve (AUC). The Shapley Additive exPlanation (SHAP) method was applied to explain the model. RESULTS: A total of 125 unresectable HCC patients were included in the analysis after the inclusion and exclusion criteria, among which 42 (33.6%) patients showed progression disease (PD), 49 (39.2%) showed stable disease (SD), and 34 (27.2%) achieved partial response (PR). The nonresponse group (PD + SD) included 91 patients, while the response group (PR) included 34 patients. The top 40 most important features from all 64 clinical features were selected using the recursive feature elimination (RFE) algorithm to develop the predictive models. The predictive power was satisfactory, with AUCs of 0.74 to 0.91. The SVM model and RF model showed the highest accuracy (86.5%), and the RF model showed the largest AUC (0.91, 95% confidence interval (CI): 0.61-0.95). The SHAP summary plot and decision plot illustrated the impact of the top 40 features on the efficacy of the combination therapy, and the SHAP force plot successfully predicted the efficacy at the individualized level. CONCLUSIONS: A new predictive model based on clinical data was developed using ML algorithms, which showed favorable performance in predicting the response to lenvatinib combined with TACE for unresectable HCC. Combining ML with SHAP could provide an explicit explanation of the efficacy prediction.

Predicting very early recurrence in intrahepatic cholangiocarcinoma after curative hepatectomy using machine learning radiomics based on CECT: A multi-institutional study.

BACKGROUND: Even after curative resection, the prognosis for patients with intrahepatic cholangiocarcinoma (iCCA) remains disappointing due to the extremely high incidence of postoperative recurrence. METHODS: A total of 280 iCCA patients following curative hepatectomy from three independent institutions were recruited to establish the retrospective multicenter cohort study. The very early recurrence (VER) of iCCA was defined as the appearance of recurrence within 6 months. The 3D tumor region of interest (ROI) derived from contrast-enhanced CT (CECT) was used for radiomics analysis. The independent clinical predictors for VER were histological stage, AJCC stage, and CA199 levels. We implemented K-means clustering algorithm to investigate novel radiomics-based subtypes of iCCA. Six types of machine learning (ML) algorithms were performed for VER prediction, including logistic, random forest (RF), neural network, bayes, support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). Additionally, six clinical ML (CML) models and six radiomics-clinical ML (RCML) models were developed to predict VER. Predictive performance was internally validated by 10-fold cross-validation in the training cohort, and further evaluated in the external validation cohort. RESULTS: Approximately 30 % of patients with iCCA experienced VER with extremely discouraging outcome (Hazard ratio (HR) = 5.77, 95 % Confidence Interval (CI) = 3.73-8.93, P < 0.001). Two distinct iCCA subtypes based on radiomics features were identified, and subtype 2 harbored a higher proportion of VER (47.62 % Vs 25.53 %) and significant shorter survival time than subtype 1. The average AUC values of the CML and RCML models were 0.744 ± 0.018, and 0.900 ± 0.014 in the training cohort, and 0.769 ± 0.065 and 0.929 ± 0.027 in the external validation cohort, respectively. CONCLUSION: Two radiomics-based iCCA subtypes were identified, and six RCML models were developed to predict VER of iCCA, which can be used as valid tools to guide individualized management in clinical practice.