Identifying optimal candidates for post-TIPS patients with HCC undergoing TACE: a multicenter observational study.

OBJECTIVE: To develop a prognostic model for post-transjugular intrahepatic portosystemic shunt (TIPS) patients with hepatocellular carcinoma (HCC) beyond the Milan criteria treated by transarterial chemoembolization (TACE). DESIGN: Between January 2013 and January 2020, 512 patients with HCC beyond the Milan criteria who underwent TACE after TIPS were retrospectively recruited from 15 tertiary centers. Patients were randomly sorted into a training set (n = 382) and a validation set (n = 130). Medical data and overall survival were assessed. A prediction model was developed using multivariate Cox regression analyses. Predictive performance and discrimination were evaluated and compared with other prognostic models. RESULTS: Vascular invasion, log10(AFP), 1/creatinine, extrahepatic spread, and log10(ALT) were the most significant prognostic factors of survival. These five parameters were included in a new VACEA score. This score was able to stratify patients in the training set into four distinct risk grades whose median overall survival were 25.2, 15.1, 8.9, and 6.2 months, respectively. The 6-month, 1-year, 2-year, and 3-year AUROC values and C-index of the VACEA model were 0.819, 0.806, 0.779, 0.825, and 0.735, respectively, and higher than those of other seven currently available models in both the training and validation sets, as well as in different subgroups. CONCLUSION: The VACEA score could stratify post-TIPS patients with HCC beyond the Milan criteria treated by TACE and help to identify candidates who benefit from this treatment. KEY POINTS: • Vascular invasion, AFP, creatinine, extrahepatic spread, and ALT were independent significant prognostic factors of survival for HCC patients who underwent TACE after TIPS. • Our new model, named VACEA score, can accurately predict prognosis at the individual level and stratify patients into four distinct risk grades. • The VACEA model showed better prognostic discrimination and calibration than other current TACE-/TIPS-specific models Graphical abstract.

Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics.

OBJECTIVE: To investigate externally validated magnetic resonance (MR)-based and computed tomography (CT)-based machine learning (ML) models for grading clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS: Patients with pathologically proven ccRCC in 2009-2018 were retrospectively included for model development and internal validation; patients from another independent institution and The Cancer Imaging Archive dataset were included for external validation. Features were extracted from T1-weighted, T2-weighted, corticomedullary-phase (CMP), and nephrographic-phase (NP) MR as well as precontrast-phase (PCP), CMP, and NP CT. CatBoost was used for ML-model investigation. The reproducibility of texture features was assessed using intraclass correlation coefficient (ICC). Accuracy (ACC) was used for ML-model performance evaluation. RESULTS: Twenty external and 440 internal cases were included. Among 368 and 276 texture features from MR and CT, 322 and 250 features with good to excellent reproducibility (ICC ≥ 0.75) were included for ML-model development. The best MR- and CT-based ML models satisfactorily distinguished high- from low-grade ccRCCs in internal (MR-ACC = 73% and CT-ACC = 79%) and external (MR-ACC = 74% and CT-ACC = 69%) validation. Compared to single-sequence or single-phase images, the classifiers based on all-sequence MR (71% to 73% in internal and 64% to 74% in external validation) and all-phase CT (77% to 79% in internal and 61% to 69% in external validation) images had significant increases in ACC. CONCLUSIONS: MR- and CT-based ML models are valuable noninvasive techniques for discriminating high- from low-grade ccRCCs, and multiparameter MR- and multiphase CT-based classifiers are potentially superior to those based on single-sequence or single-phase imaging. KEY POINTS: • Both the MR- and CT-based machine learning models are reliable predictors for differentiating high- from low-grade ccRCCs. • ML models based on multiparameter MR sequences and multiphase CT images potentially outperform those based on single-sequence or single-phase images in ccRCC grading.

Combination of hepatocyte fraction and diffusion-weighted imaging as a predictor in quantitative hepatic fibrosis evaluation.

OBJECTIVE: To investigate the performance of the combined hepatocyte fraction (HepF) and apparent diffusion coefficient (ADC) values to stage hepatic fibrosis (HF) in patients with hepatitis B/C. MATERIALS AND METHODS: A total of 281 patients with hepatitis B/C prospectively underwent gadoxetate disodium-based T1 mapping and diffusion-weighted imaging. HepF was determined from pre and postcontrast T1 mapping with pharmacokinetics. The independent predictors of the HF stage (S0-4) were identified from HepF, ADC, conventional T1-based parameters, and age using a logistic regression analysis. The performances of independent and combined predictors in diagnosing various HF stages were compared by analyzing receiver operating characteristic curves. The intraclass correlation coefficient (ICC) was used to assess the interobserver reproducibility of each predictor. RESULTS: In total, 167 patients with various stages of HF were included. All measurements had excellent interobserver agreement (ICC ≥ 0.75). The hepatic relative enhancement, HepF ,and ADC values were significantly different among various HF stages (p < 0.05). The HepF and ADC were independent predictors of > S0, > S1, > S2 , and > S3 disease (p < 0.05). T1Liver, T1Spleen, and T1Liver/Spleen were independent predictors of S > 2 disease (p < 0.05). The performance of HepF combined with the ADC (area under the curve (AUC) = 0.84-0.95) was higher than HepF (AUC = 0.79-0.92) or ADC (AUC = 0.82-0.89) alone in diagnosing > S0, > S1, > S2 , and > S3 disease. CONCLUSION: The combined predictor of HepF and ADC shows acceptable performance for staging HF.

Facile preparation of fluffy N-doped carbon modified with Ag nanoparticles as a highly active and reusable catalyst for catalytic reduction of nitroarenes.

Novel fluffy smoke like mesoporous N-doped carbon (mNC) was prepared through a facile one-pot carbonization method using the low-cost melamine and polyacrylonitrile as the precursor materials. The obtained mNC material exhibits a high surface area, rich nitrogen content and can be used as an ideal catalyst support to fabricate noble metal modified nanocatalysts. Here, small Ag nanoparticles were supported on the mNC material with high dispersion to give the Ag/mNC nanocatalyst. The obtained Ag/mNC nanocatalyst was used in the catalytic reduction of nitroarenes and showed excellent catalytic activity, probably due to the small Ag NPs which highly dispersed on the fluffy mNC material that can enhance the accessibility and mass transfer, and subsequently enhance the catalytic activity. It is worth mentioning that, in the catalytic reduction of nitroarenes with halogenated groups, almost no dehalogenation phenomenon can be observed, which implies the superior catalytic chemoselectivity of the Ag/mNC nanocatalyst.

Three new derivatives of anti-HIV-1 polyphenols isolated from Salvia yunnanensis.

During the study of anti-HIV-1 active components of the aqueous extracts of the roots of Salvia yunnanensis, three new derivatives of polyphenols, namely: methyl salvianolate A (2), ethyl salvianolate A (3) and cis-lithospermic acid (5) were isolated along with two known polyphenols, salvianolic acid A (1) and lithospermic acid (4) their structures were elucidated on the basis of NMR and MS spectral analyses. The anti-HIV-1 activities of the 5 polyphenols were tested for the inhibition of P24 antigen in HIV-1 infected MT-4 cell cultures and HIV-1 replicative enzymes in vitro.