This study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic ...(CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups.
Little is known about the influences of maternal and infant correlates on maternal feeding beliefs and practices in the first 2 years of life, despite its important role in early obesogenic eating ...behaviours and weight gain.
Cross-sectional study using demographic data of mothers and infants obtained at 26-28 weeks of gestation, and postnatally from birth to 15 months, respectively. The Infant Feeding Questionnaire was administered at 15 months postpartum. The associations between maternal and infant characteristics with seven maternal feeding beliefs and practices subscales were evaluated using multivariate linear regression analysis.
Data obtained from the Singapore GUSTO (Growing Up in Singapore Towards healthy Outcomes) mother-offspring birth cohort.
Mothers and infants (n 1237).
Among other maternal correlates such as age, education, BMI, income and milk feeding practices, ethnicity was a consistent factor associated with six subscales, including concern about infant overeating/undereating and weight status, concern and awareness about infants' hunger and satiety cues, social interaction during feeding and feeding an infant on schedule. Similarly, among infant correlates such as gender and birth order, infant body size gain (reflected by BMI Z-score change from 0 to 15 months) was significantly associated with all subscales except feeding an infant on schedule. Overall, maternal correlates had greater influence on all subscales compared with infant correlates except for the maternal concern about infant undereating or becoming underweight subscale.
The present study highlights that maternal feeding beliefs and practices can be influenced by both maternal correlates and infant correlates at 15 months of age.
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