Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia.

The study aims to develop a magnetic resonance imaging (MRI)-based radiomics model for the diagnosis of classic trigeminal neuralgia (cTN). This study involved 350 patients with cTN and 100 control participants. MRI data were collected retrospectively for all the enrolled subjects. The symptomatic side trigeminal nerve regions of patients and both sides of the trigeminal nerve regions of control participants were manually labeled on MRI images. Radiomics features of the areas labeled were extracted. Principle component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) regression were utilized as the preliminary feature reduction methods to decrease the high dimensionality of radiomics features. Machine learning methods were established, including LASSO logistic regression, support vector machine (SVM), and Adaboost methods, evaluating each model's diagnostic abilities using 10-fold cross-validation. All the models showed excellent diagnostic ability in predicting trigeminal neuralgia. A prospective study was conducted, 20 cTN patients and 20 control subjects were enrolled to validate the clinical utility of all models. Results showed that the radiomics models based on MRI can predict trigeminal neuralgia with high accuracy, which could be used as a diagnostic tool for this disorder.

Super-resolution of brain tumor MRI images based on deep learning.

INTRODUCTION: To explore and evaluate the performance of MRI-based brain tumor super-resolution generative adversarial network (MRBT-SR-GAN) for improving the MRI image resolution in brain tumors. METHODS: A total of 237 patients from December 2018 and April 2020 with T2-fluid attenuated inversion recovery (FLAIR) MR images (one image per patient) were included in the present research to form the super-resolution MR dataset. The MRBT-SR-GAN was modified from the enhanced super-resolution generative adversarial networks (ESRGAN) architecture, which could effectively recover high-resolution MRI images while retaining the quality of the images. The T2-FLAIR images from the brain tumor segmentation (BRATS) dataset were used to evaluate the performance of MRBT-SR-GAN contributed to the BRATS task. RESULTS: The super-resolution T2-FLAIR images yielded a 0.062 dice ratio improvement from 0.724 to 0.786 compared with the original low-resolution T2-FLAIR images, indicating the robustness of MRBT-SR-GAN in providing more substantial supervision for intensity consistency and texture recovery of the MRI images. The MRBT-SR-GAN was also modified and generalized to perform slice interpolation and other tasks. CONCLUSIONS: MRBT-SR-GAN exhibited great potential in the early detection and accurate evaluation of the recurrence and prognosis of brain tumors, which could be employed in brain tumor surgery planning and navigation. In addition, this technique renders precise radiotherapy possible. The design paradigm of the MRBT-SR-GAN neural network may be applied for medical image super-resolution in other diseases with different modalities as well.

Development and Validation of a Plasma FAM19A5 and MRI-Based Radiomics Model for Prediction of Parkinson's Disease and Parkinson's Disease With Depression.

Background: Prediction and early diagnosis of Parkinson's disease (PD) and Parkinson's disease with depression (PDD) are essential for the clinical management of PD. Objectives: The present study aimed to develop a plasma Family with sequence similarity 19, member A5 (FAM19A5) and MRI-based radiomics nomogram to predict PD and PDD. Methods: The study involved 176 PD patients and 181 healthy controls (HC). Sandwich enzyme-linked immunosorbent assay (ELISA) was used to measure FAM19A5 concentration in the plasma samples collected from all participants. For enrolled subjects, MRI data were collected from 164 individuals (82 in the PD group and 82 in the HC group). The bilateral amygdala, head of the caudate nucleus, putamen, and substantia nigra, and red nucleus were manually labeled on the MR images. Radiomics features of the labeled regions were extracted. Further, machine learning methods were applied to shrink the feature size and build a predictive radiomics signature. The resulting radiomics signature was combined with plasma FAM19A5 concentration and other risk factors to establish logistic regression models for the prediction of PD and PDD. Results: The plasma FAM19A5 levels (2.456 ± 0.517) were recorded to be significantly higher in the PD group as compared to the HC group (2.23 ± 0.457) (P < 0.001). Importantly, the plasma FAM19A5 levels were also significantly higher in the PDD subgroup (2.577 ± 0.408) as compared to the non-depressive subgroup (2.406 ± 0.549) (P = 0.045 < 0.05). The model based on the combination of plasma FAM19A5 and radiomics signature showed excellent predictive validity for PD and PDD, with AUCs of 0.913 (95% CI: 0.861-0.955) and 0.937 (95% CI: 0.845-0.970), respectively. Conclusion: Altogether, the present study reported the development of nomograms incorporating radiomics signature, plasma FAM19A5, and clinical risk factors, which might serve as potential tools for early prediction of PD and PDD in clinical settings.