High Bile Titer and High Bile to Serum Ratio of CYFRA 21 - 1 Reliably Discriminate Malignant Biliary Obstruction Caused by Cholangiocarcinoma

INTRODUCTION

Previously we demonstrated that elevated serum CYFRA 21 - 1 is a reliable diagnostic and prognostic biomarker for biliary tract cancers. This study aims to explore the diagnostic performance of bile CYFRA 21 - 1 (bCYFRA 21 - 1) in discriminating malignant biliary obstruction (MBO) caused by cholangiocarcinoma (CCA).

METHODS

77 CCA patients ((17 intrahepatic CCA (iCCA), 49 perihilar CCA (pCCA) and 11 distal CCA (dCCA)) and 43 benign patients with biliary obstruction were enrolled. Serum and bile levels of CYFRA 21 - 1, carcinoembryonic antigen (CEA) and carbohydrate antigen 19 - 9 (CA19-9) were quantified. Diagnostic performances of these biomarkers were estimated by receiver operator characteristic curves. Subgroups analysis of these tumor markers among CCA subtypes was performed.

RESULTS

High bCYFRA 21 - 1 (cut-off value of 59.25 ng/mL with sensitivity of 0.889 and specificity of 0.750) and high bile to serum ratio of CYFRA 21 - 1 (b/sCYFRA 21 - 1, cut-off value of 31.55 with sensitivity of 0.741 and specificity of 0.778) achieved better diagnostic performance than any other biomarker in discriminating MBO. Subgroup analysis revealed that bCYFRA 21 - 1 was significantly elevated in all CCA subtypes; moreover b/sCYFRA 21 - 1 was upregulated in pCCA and dCCA (the mean b/sCYFRA 21 - 1 of pCCA was highest among CCA subtypes: 57.90, IQR 29.82-112.27).

CONCLUSIONS

Both high biliary CYFRA 21 - 1 and high bile to serum ratio of CYFRA 21 - 1 were reliable diagnostic biomarkers for MBO caused by CCA.

Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature

OBJECTIVES

To develop and validate an 18F-FDG PET/CT-based clinical-radiological-radiomics nomogram and evaluate its value in the diagnosis of MYCN amplification (MNA) in paediatric neuroblastoma (NB) patients.

METHODS

A total of 104 patients with NB were retrospectively included. We constructed a nomogram to predict MNA based on radiomics signatures, clinical and radiological features. The multivariable logistic regression and the least absolute shrinkage and selection operator (LASSO) were used for feature selection. Radiomics models are constructed using decision trees (DT), logistic regression (LR) and support vector machine (SVM) classifiers. A clinical-radiological (C-R) model was developed using clinical and radiological features. A clinical-radiological-radiomics (C-R-R) model was developed using the C-R model of the best radiomics model. The prediction performance was verified by receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) in the training and validation cohorts.

RESULTS

The present study showed that four radiomics signatures were significantly correlated with MNA. The SVM classifier was the best model of radiomics signature. The C-R-R model has the best discriminant ability to predict MNA, with AUCs of 0.860 (95% CI, 0.757-0.963) and 0.824 (95% CI, 0.657-0.992) in the training and validation cohorts, respectively. The calibration curve indicated that the C-R-R model has the goodness of fit and DCA confirms its clinical utility.

CONCLUSION

Our research provides a non-invasive C-R-R model, which combines the radiomics signatures and clinical and radiological features based on 18F-FDGPET/CT images, shows excellent diagnostic performance in predicting MNA, and can provide useful biological information with stratified therapy.

CRITICAL RELEVANCE STATEMENT

Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma.

KEY POINTS

• Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma. • SF, LDH, necrosis and TLG are the independent risk factors of MYCN amplification. • Clinical-radiological-radiomics model improved the predictive performance of MYCN amplification.

Contrastive self-supervised learning for diabetic retinopathy early detection

Diabetic Retinopathy (DR) is the major cause of blindness, which seriously threatens the world's vision health. Limited medical resources make early diagnosis and a large-scale screening of DR difficult. Most of the current automatic diagnostic methods are mostly based on deep learning and large-scale labeled data. However, the insufficiency of manual annotations for medical images still is a great challenge of training deep neural networks. Self-supervised learning methods are proposed to learn general features from dataset without manual annotations. Inspired by this, we proposed a deep learning based DR classification model (SimCLR-DR). In this paper, we first use contrastive self-learning algorithm to pre-train the encoder based on convolution network with unlabeled retinal images, then retrain the encoder with classifier on a small annotated training data to detect referable DR. The experimental results on Kaggle dataset show that this proposed method can overcome the training data insufficiency problem and performs better than transfer learning. SimCLR-DR is a good beginning for other deep learning based medical image detection approaches facing the challenge of insufficient annotated data. Figure presents an overview of the proposed framework, which contains three main steps: (i) Data preprocessing; (ii) Pretext task of SimCLR-DR based on contrastive learning; (iii) Downstream Task of SimCLRDR based on CNN.