Microvascular invasion (MVI) impairs long-term prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict MVI and patients' prognosis based on radiomic features of contrast-enhanced CT (CECT). Patients and Methods: HCC patients who underwent curative resection were enrolled. The radiomic features were extracted from the region of tumor, and the optimal MVI-related radiomic features were selected and applied to construct radiomic signature (Radscore). The prediction models were created according to the logistic regression and evaluated. Biomarkers were analyzed via q-PCR from randomly selected HCC patients. Correlations between biomarkers and radiomic signature were analyzed. Results: A total of 421 HCC patients were enrolled. A total of 1962 radiomic features were extracted from the region of tumor, and the 11 optimal MVI-related radiomic features showed a favor predictive ability with area under the curves (AUCs) of 0.796 and 0.810 in training and validation cohorts, respectively. Aspartate aminotransferase (AST), tumor number, alpha-fetoprotein (AFP) level, and radiomics signature were independent risk factors of MVI. The four factors were integrated into the novel nomogram, named as CRM, with AUCs of 0.767 in training cohort and 0.793 in validation cohort for predicting MVI, best among radiomics signature alone and clinical model. The nomogram was well-calibrated with favorable clinical value demonstrated by decision curve analysis and can divide patients into high-or low-risk subgroups of recurrence and mortality. In addition, gene BCAT1, DTGCU2, DOCK3 were analyzed via q-PCR and serum AFP were identified as having significant association with radiomics signature. Conclusion:The novel nomogram demonstrated good performance in preoperatively predicting the probability of MVI, which might guide clinical decision.
BACKGROUND Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with a rising incidence worldwide. The prognosis of HCC patients after radical resection remains poor. Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer, which can assist with cancer diagnosis, therapeutic decision-making and prognosis improvement. AIM To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival (OS) of HCC patients after radical hepatectomy. METHODS A total of 150 HCC patients were randomly divided into a training cohort ( n = 107) and a validation cohort ( n = 43). Radiomics features were extracted from the entire tumour lesion. The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature. Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram, incorporating clinicopathological characteristics and the radiomics signature. The accuracy of the nomogram was assessed with the concordance index, receiver operating characteristic (ROC) curve and calibration curve. The clinical utility was evaluated by decision curve analysis (DCA). Kaplan–Meier methodology was used to compare the survival between the low- and high-risk subgroups. RESULTS In total, seven radiomics features were selected to construct the radiomics signature. According to the results of univariate and multivariate Cox regression analyses, alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR) and radiomics signature were included to build the nomogram. The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774, respectively. ROC curve analysis for predicting 1-, 3-, and 5-year OS confirmed satisfactory accuracy [training cohort, area under the curve (AUC) = 0.850, 0.791 and 0.823, respectively; validation cohort, AUC = 0.905, 0.884 and 0.911, respectively]. The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival. DCA curves suggested that the nomogram had more benefit than traditional staging system models. Kaplan–Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival (all P < 0.0001). CONCLUSION The nomogram containing the radiomics signature, NLR and AFP is a reliable tool for predicting the OS of HCC patients.
As an oral mucosal drug delivery system, oral films have been of wide concern in recent years because of their advantages such as rapid absorption, being easy to swallow and avoiding the first-pass effect common for mucoadhesive oral films. However, the currently utilized manufacturing approaches including solvent casting have many limitations, such as solvent residue and difficulties in drying, and are not suitable for personalized customization. To solve these problems, the present study utilizes liquid crystal display (LCD), a photopolymerization-based 3D printing technique, to fabricate mucoadhesive films for oral mucosal drug delivery. The designed printing formulation includes PEGDA as the printing resin, TPO as the photoinitiator, tartrazine as the photoabsorber, PEG 300 as the additive and HPMC as the bioadhesive material. The influence of printing formulation and printing parameters on the printing formability of the oral films were elucidated in depth, and the results suggested that PEG 300 in the formulation not only provided the necessary flexibility of the printed oral films, but also improved drug release rate due to its role as pore former in the produced films. The presence of HPMC could greatly improve the adhesiveness of the 3D-printed oral films, but excessive HPMC increased the viscosity of the printing resin solution, which could strongly hinder the photo-crosslinking reaction and reduce printability. Based on the optimized printing formulation and printing parameters, the bilayer oral films containing a backing layer and an adhesive layer were successfully printed with stable dimensions, adequate mechanical properties, strong adhesion ability, desirable drug release and efficient in vivo therapeutic efficacy. All these results indicated that an LCD-based 3D printing technique is a promising alternative to precisely fabricate oral films for personalized medicine.
Background Since it’s a challenging task to precisely predict the prognosis of patients with hepatocellular carcinoma (HCC). We developed a nomogram based on a novel indicator GMWG [(Geometric Mean of gamma-glutamyltranspeptidase (GGT) and white blood cell (WBC)] and explored its potential in the prognosis for HCC patients. Methods The patients enrolled in this study were randomly assigned to training and validation cohorts. And we performed the Least Absolute Shrinkage and Selection Operator proportional hazards model (LASSO Cox) model with clinical characteristics, serum indexes, and novel GMWG. Multivariate analysis was performed to build a nomogram. The performance of the nomogram was evaluated by C-index, the area under the receiver operating characteristic curve (AUC), and the calibration curve. Kaplan-Meier curves showed discrimination of the nomogram. Clinical utility was assessed by decision curve analysis (DCA). The discrimination ability of the nomogram was determined by the net reclassification index (NRI). Results The geometric mean of GGT and white WBC count (GMWG), neutrophil to lymphocyte ratio (NLR), and tumor size were significantly associated with the overall survival (OS). The variables above were used to develop the nomogram. The indexes of nomogram were 0.70 and 071 in the training or validation cohort, respectively. AUC of 1-, 3- and 5-year OS showed satisfactory accuracy as well. The calibration curve showed agreement between the ideal and predicted values. Kaplan-Meier curves based on the overall survival (OS) and disease-free survival (DFS) showed significant differences between nomogram predictive low and high groups. DCA showed clinical utilities while NRI showed discrimination ability in both training or validation cohort. Conclusions GMWG might be a potential prognostic indicator for patients with HCC. The nomogram containing GMWG also showed satisfaction prediction capacity.
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