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Objective:To explore the value of different machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features in preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Methods:The data of 132 patients with HCC confirmed by pathology in the First Affiliated Hospital of Soochow University from January 2015 to May 2020 were retrospectively analyzed, including 72 cases of positive MVI and 60 cases of negative MVI. According to the proportion of 7∶3, the cases were randomly divided into training set and validation set. The radiomics features of hepatobiliary phase images for HCC were extracted by PyRadiomics software. The clinical and radiomics features of the training set were screened by the least absolute shrinkage and selection operator (LASSO) regression with 5 fold cross-validation, and then the optimal feature subset was obtained. Six machine learning algorithms, including decision tree, extreme gradient boosting, random forest, support vector machine (SVM), generalized linear model (GLM) and neural network, were used to build the prediction models, and the ROC curves were used to evaluate the prediction ability of the models. DeLong test was used to compare the differences of area under the curve (AUC) for 6 machine learning algorithms.Results:Totally 14 features selected by LASSO regression were obtained to form the optimal feature subset, including 2 clinical features (maximum tumor diameter and alpha-fetoprotein) and 12 radiomics features. The AUCs of decision tree, extreme gradient boosting, random forest, SVM, GLM and neural network based on the optimal feature subset were 0.969, 1.000, 1.000, 0.991, 0.966, 1.000 in the training set and 0.781, 0.890, 0.920, 0.806, 0.684, 0.703 in the validation set, respectively. There were significant differences in the AUCs between extreme gradient boosting and GLM or neural network ( Z=2.857, 3.220, P=0.004, 0.001). The differences in AUCs between random forest and SVM, GLM, or neural network were significant ( Z=2.371, 3.190, 3.967, P=0.018, 0.001,<0.001). The difference in AUCs between SVM and GLM was statistically significant ( Z=2.621 , P=0.009). There were no significant differences in the AUCs among the other machine learning models ( P>0.05). Conclusion:Machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features can be used to preoperatively predict MVI of HCC, particularly the extreme gradient boosting and random forest models have high prediction efficiency.
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Objective:To explore the value of gadolinium-ethoxybenzyl- diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI nomogram model for preoperative prediction of Ki-67 expression in hepatocellular carcinoma (HCC).Methods:Data of 85 patients of HCC confirmed by postoperative pathology, who underwent preoperative Gd-EOB-DTPA enhanced MRI between September 2016 and August 2019 in the First Affiliated Hospital of Soochow University were retrospectively evaluated. According to postoperative immunohistochemistry Ki-67 index, the 85 patients were divided into Ki-67 low expression group(Ki-67 index ≤10%, n=20) and Ki-67 high expression group (Ki-67 index >10%, n=65). Clinical data (hepatitis, cirrhosis, etc.), qualitative imaging parameters (tumor margin, capsule, etc.) were compared by χ 2 test and quantitative parameters [lesion-to-normal parenchyma ratio-arterial phase (LNR-AP), lesion-to-normal parenchyma ratio-portal phase (LNR-PP), lesion-to-normal parenchyma ratio-equilibrium phase (LNR-EP) and lesion-to-normal parenchyma ratio-hepatobiliary phase (LNR-HBP)] were compared by independent sample t test. The above statistically significant parameters were included in multivariate logistic regression to identify the independent predictors of Ki-67 high expression and then the nomogram model for predicting Ki-67 expression of HCC was established. Results:alpha-fetoprotein (AFP) tumor margin, arterial rim enhancement between the Ki-67 low expression group and the Ki-67 high expression group had significant differences (χ 2 were 8.196, 10.538 and 4.717, respectively, P<0.05). LNR-AP, LNR-PP, LNR-EP and LNR-HBP between the two groups had significant differences ( t were 2.929, 2.773, 2.890 and 3.437, respectively, P<0.05).The result of multivariate logistic regression revealed that AFP≥20 μg/L, non-smooth tumor margin and low LNR-HBP were the independent predictors of Ki-67 high expression (odds ratio were 4.090, 3.509 and 0.042, respectively, P<0.05).The Gd-EOB-DTPA enhanced MRI nomogram model for predicting Ki-67 expression of HCC was established successfully. The Area under the receiver operating characteristic curve of the nomogram was 0.837 and the corrected predictive curve fitted the ideal curve, which suggested the model had a good predictive efficiency. Conclusion:Gd-EOB-DTPA enhanced MRI nomogram model has great value in preoperative prediction of Ki-67 expression of HCC, which provided a personalized prediction method for Ki-67 expression in patient with HCC.
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<p><b>OBJECTIVE</b>To research the central molecular mechanism of gastric motility in functional dyspepsia (FD) rats treated with electroacupuncture (EA) at and points of stomach.</p><p><b>METHODS</b>A total of 30 SD rats were randomized into a blank group, a model group, a Zhongwan+Weishu group, a Weishu group and a Zhongwan group, 6 rats in each group. FD rats were established by moderate clipping tail infuriation and irregular feeding except in the blank group. EA was used at "Zhongwan"(CV 12),"Weishu"(BL 21), and"Zhongwan"(CV 12) +"Weishu"(BL 21) in the corresponding groups for 7 days, once a day, and 20 min a time. No intervention was used in the blank and model groups. Grabbing and fixation were applied in the model group. Gastric antrum motion range and frequency were recorded by gastrointestinal pressure transducer. The expression of subunit NR1 of N-methyl-D-aspartate recepter (NMDAR) in dorsal motor nucleus of the vagus (DMV) was determined by Western blotting. The content of serum nitric oxide (NO) was measured by ELISA.</p><p><b>RESULTS</b>Compared with the blank group, the gastric antrum motion range and NR1 in the DMV decreased and the serum NO content increased in the model group (all <0.05). Compared with the model group, the gastric antrum motion range and NR1 in the DMV increased and the serum NO content decreased in the three EA groups (all <0.05). Compared with the Zhongwan and Weishu groups, the gastric antrum motion range and NR1 in the DMV increased in the Zhongwan + Weishu group (all <0.05). Compared with Zhongwan + Weishu and Zhongwan groups, the expression of NO in the Weishu group decreased (both <0.05). The gastric antrum motion frequency among the 5 groups had no statistical significance (all >0.05).</p><p><b>CONCLUSION</b>EA at the and points can regulate the gastric motility in FD rats which may be by modulating the activity of NMDAR in the central DMV region, thus regulating the serum NO content.</p>
Subject(s)
Animals , Rats , Acupuncture Points , Dyspepsia , Therapeutics , Electroacupuncture , Gastrointestinal Motility , N-Methylaspartate , Metabolism , Nitric Oxide , Blood , Random Allocation , Rats, Sprague-Dawley , Stomach , Vagus Nerve , MetabolismABSTRACT
Retinoid X receptor α (RXRα) and its N-terminally truncated version tRXRα play important roles in tumorigenesis, while some RXRα ligands possess potent anti-cancer activities by targeting and modulating the tumorigenic effects of RXRα and tRXRα. Here we describe NSC-640358 (N-6), a thiazolyl-pyrazole derived compound, acts as a selective RXRα ligand to promote TNFα-mediated apoptosis of cancer cell. N-6 binds to RXRα and inhibits the transactivation of RXRα homodimer and RXRα/TR3 heterodimer. Using mutational analysis and computational study, we determine that Arg316 in RXRα, essential for 9-cis-retinoic acid binding and activating RXRα transactivation, is not required for antagonist effects of N-6, whereas Trp305 and Phe313 are crucial for N-6 binding to RXRα by forming extra π-π stacking interactions with N-6, indicating a distinct RXRα binding mode of N-6. N-6 inhibits TR3-stimulated transactivation of Gal4-DBD-RXRα-LBD by binding to the ligand binding pocket of RXRα-LBD, suggesting a strategy to regulate TR3 activity indirectly by using small molecules to target its interacting partner RXRα. For its physiological activities, we show that N-6 strongly inhibits tumor necrosis factor α (TNFα)-induced AKT activation and stimulates TNFα-mediated apoptosis in cancer cells in an RXRα/tRXRα dependent manner. The inhibition of TNFα-induced tRXRα/p85α complex formation by N-6 implies that N-6 targets tRXRα to inhibit TNFα-induced AKT activation and to induce cancer cell apoptosis. Together, our data illustrate a new RXRα ligand with a unique RXRα binding mode and the abilities to regulate TR3 activity indirectly and to induce TNFα-mediated cancer cell apoptosis by targeting RXRα/tRXRα.