RESUMO
BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.
RESUMO
The major difficulties of human papillomavirus (HPV) treatment are its persistence and recurrence. The HPV E7 oncoprotein-loaded dendritic cells have been evaluated as cellular vaccine in previous reports. Plasmacytoid dendritic cells (pDCs) play an essential role of connecting the innate immune response and adaptive immune response in the immune system. But they function in HPV E7 loading is unclear. To investigate whether loading of the HPV type 6b, 11, and 16 E7 proteins affects the activity of pDCs, human peripheral blood-separated pDCs and mouse bone marrow-derived pDCs were pulsed with the HPV E7 proteins. The expression levels of CD40, CD80, CD86, and MHC II were significantly upregulated in pDCs upon HPV 6b/11 E7 protein pulse. The secretion and gene expression of type I IFN and IL-6 were both upregulated by HPV 6b/11 E7 proteins, more significant than HPV 16 E7 protein. The expression of essential factors of TLR signaling pathway and JNK/p38 MAP kinase signaling pathway were all increased in HPV 6b/11 E7 proteins pulsed pDCs. Our results suggest that HPV E7 proteins could promote the differentiation and maturation of pDCs and activate the TLR and MAPK pathway to induce host innate immune response. It might be conducive to explore novel immunotherapy targeting HPV infection with HPV E7 loaded pDC.
