Glioma Single-Cell Biomechanical Analysis by Cyclic Conical Constricted Microfluidics.

Determining the grade of glioma is a critical step in choosing patients' treatment plans in clinical practices. The pathological diagnosis of patient's glioma samples requires extensive staining and imaging procedures, which are expensive and time-consuming. Current advanced uniform-width-constriction-channel-based microfluidics have proven to be effective in distinguishing cancer cells from normal tissues, such as breast cancer, ovarian cancer, prostate cancer, etc. However, the uniform-width-constriction channels can result in low yields on glioma cells with irregular morphologies and high heterogeneity. In this research, we presented an innovative cyclic conical constricted (CCC) microfluidic device to better differentiate glioma cells from normal glial cells. Compared with the widely used uniform-width-constriction microchannels, the new CCC configuration forces single cells to deform gradually and obtains the biophysical attributes from each deformation. The human-derived glioma cell lines U-87 and U-251, as well as the human-derived normal glial astrocyte cell line HA-1800 were selected as the proof of concept. The results showed that CCC channels can effectively obtain the biomechanical characteristics of different 12-25 µm glial cell lines. The patient glioma samples with WHO grades II, III, and IV were tested by CCC channels and compared between Elastic Net (ENet) and Lasso analysis. The results demonstrated that CCC channels and the ENet can successfully select critical biomechanical parameters to differentiate the grades of single-glioma cells. This CCC device can be potentially further applied to the extensive family of brain tumors at the single-cell level.

18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging of pediatric neuroblastoma: a multi-omics parameters method to predict MYCN copy number category.

Background: The MYCN copy number category is closely related to the prognosis of neuroblastoma (NB). Therefore, this study aimed to assess the predictive ability of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features for MYCN copy number in NB. Methods: A retrospective analysis was performed on 104 pediatric patients with NB that had been confirmed by pathology. To develop the Bio-omics model (B-model), which incorporated clinical and biological aspects, PET/CT radiographic features, PET quantitative parameters, and significant features with multivariable stepwise logistic regression were preserved. Important radiomics features were identified through least absolute shrinkage and selection operator (LASSO) and univariable analysis. On the basis of radiomics features obtained from PET and CT scans, the radiomics model (R-model) was developed. The significant bio-omics and radiomics features were combined to establish a Multi-omics model (M-model). The above 3 models were established to differentiate MYCN wild from MYCN gain and MYCN amplification (MNA). The calibration curve and receiver operating characteristic (ROC) curve analyses were performed to verify the prediction performance. Post hoc analysis was conducted to compare whether the constructed M-model can distinguish MYCN gain from MNA. Results: The M-model showed excellent predictive performance in differentiating MYCN wild from MYCN gain and MNA, which was better than that of the B-model and R-model [area under the curve (AUC) 0.83, 95% confidence interval (CI): 0.74-0.92 vs. 0.81, 95% CI: 0.72-0.90 and 0.79, 95% CI: 0.69-0.89]. The calibration curve showed that the M-model had the highest reliability. Post hoc analysis revealed the great potential of the M-model in differentiating MYCN gain from MNA (AUC 0.95, 95% CI: 0.89-1). Conclusions: The M-model model based on bio-omics and radiomics features is an effective tool to distinguish MYCN copy number category in pediatric patients with NB.

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.