• 3D visualization
• Age-related morphological changes
• Brain atrophy
• Brainlab software
• Preoperative planning
• Sylvian fissure
• Volumetric analysis

• Humans
• Aged
• Adult
• Middle Aged
• Male
• Female
• Aged, 80 and over
• Young Adult
• Adolescent
• Atrophy
• Aging/pathology
• Child
• Magnetic Resonance Imaging
• Imaging, Three-Dimensional
• Cerebral Cortex/diagnostic imaging
• Cerebral Cortex/pathology
• Cohort Studies

• ACPC
• Automated segmentation
• DTI
• Deep brain
• WAIR

• Humans
• Magnetic Resonance Imaging/methods
• Brain/diagnostic imaging
• Parkinson Disease/diagnostic imaging
• Male
• Female
• Middle Aged
• Aged
• White Matter/diagnostic imaging
• Diffusion Tensor Imaging
• Image Processing, Computer-Assisted/methods
• Image Interpretation, Computer-Assisted/methods

• brain
• neuroscience

• Humans
• Diffusion Tensor Imaging
• Brain/diagnostic imaging
• Brain/anatomy & histology
• Male
• Adult
• Atlases as Topic
• Magnetic Resonance Imaging
• Brain Mapping/methods
• Young Adult

• Ptolemée program, 21 0099 01 Fondation de l'Avenir pour la Recherche Médicale Appliquée (Fondation de l'Avenir)
• Ptolemée program, 21 0099 01 Fondation de l'Avenir pour la Recherche Médicale Appliquée (Fondation de l'Avenir)
• Fondation de l'Avenir pour la Recherche Médicale Appliquée (Fondation de l'Avenir)

• convolutional neural networks
• deep brain stimulation
• human–computer interaction
• separable machine learning
• subthalamic nucleus localization
• subthalamic nucleus segmentation

• extent of resection
• glioma
• image guided surgery
• maximal safe resection
• virtual iMRI

Accurate pretreatment prediction for disease progression of nasopharyngeal carcinoma is key to intensify therapeutic strategies to high-risk individuals. Our aim was to evaluate the value of baseline MRI-based radiomics machine-learning models in predicting the disease progression in nasopharyngeal carcinoma patients who achieved complete response after treatment.

In this retrospective study, 171 patients with pathologically confirmed nasopharyngeal carcinoma were included. Using hold-out cross validation scheme (7:3), relevant radiomic features were selected with the least absolute shrinkage and selection operator method based on baseline T2-weighted fat suppression and contrast-enhanced T1-weighted images in the training cohort. After Pearson’s correlation analysis of selected radiomic features, multivariate logistic regression analysis was applied to radiomic features and clinical characteristics selection. Logistic regression analysis and support vector machine classifier were utilized to build the predictive model respectively. The predictive accuracy of the model was evaluated by ROC analysis along with sensitivity, specificity and AUC calculated in the validation cohort.

A prediction model using logistic regression analysis comprising 4 radiomics features (HGLZE_T2H, HGLZE_T1, LDLGLE_T1, and GLNU_T1) and 5 clinical features (histology, T stage, N stage, smoking history, and age) showed the best performance with an AUC of 0.75 in the training cohort (95% CI: 0.66–0.83) and 0.77 in the validation cohort (95% CI: 0.64–0.90). The nine independent impact factors were entered into the nomogram. The calibration curves for probability of 3-year disease progression showed good agreement. The features of this prediction model showed satisfactory clinical utility with decision curve analysis.

A radiomics model derived from pretreatment MR showed good performance for predicting disease progression in nasopharyngeal carcinoma and may help to improve clinical decision making.

The online version contains supplementary material available at 10.1186/s40644-022-00448-4.

• Accuracy
• Automatic segmentation
• Deep learning
• Neural network
• Temporal bone CT

An increasing number of neurosurgeons use display of the dentato-rubro-thalamic tract (DRT) based on diffusion weighted imaging (dMRI) as basis for their routine planning of stimulation or lesioning approaches in stereotactic tremor surgery. An evaluation of the anatomical validity of the display of the DRT with respect to modern stereotactic planning systems and across different tracking environments has not been performed.

Distinct dMRI and anatomical magnetic resonance imaging (MRI) data of high and low quality from 9 subjects were used. Six subjects had repeated MRI scans and therefore entered the analysis twice. Standardized DICOM structure templates for volume of interest definition were applied in native space for all investigations. For tracking BrainLab Elements (BrainLab, Munich, Germany), two tensor deterministic tracking (FT2), MRtrix IFOD2 (https://www.mrtrix.org), and a global tracking (GT) approach were used to compare the display of the uncrossed (DRTu) and crossed (DRTx) fiber structure after transformation into MNI space. The resulting streamlines were investigated for congruence, reproducibility, anatomical validity, and penetration of anatomical way point structures.

In general, the DRTu can be depicted with good quality (as judged by waypoints). FT2 (surgical) and GT (neuroscientific) show high congruence. While GT shows partly reproducible results for DRTx, the crossed pathway cannot be reliably reconstructed with the other (iFOD2 and FT2) algorithms.

Since a direct anatomical comparison is difficult in the individual subjects, we chose a comparison with two research tracking environments as the best possible “ground truth.” FT2 is useful especially because of its manual editing possibilities of cutting erroneous fibers on the single subject level. An uncertainty of 2 mm as mean displacement of DRTu is expectable and should be respected when using this approach for surgical planning. Tractographic renditions of the DRTx on the single subject level seem to be still illusive.

• Brain
• DBS
• DRT
• Deep brain stimulation
• Diffusion tensor imaging
• Stereotaxy
• Tractography

• Deep Brain Stimulation
• Diffusion Tensor Imaging
• Essential Tremor/therapy
• Humans
• Reproducibility of Results
• Thalamus/diagnostic imaging
• Thalamus/surgery

• Albert-Ludwigs-Universität Freiburg im Breisgau (1016)

In recent years, artificial intelligence has been extensively applied in the diagnosis of gastric cancer based on medical imaging. In particular, using deep learning as one of the mainstream approaches in image processing has made remarkable progress. In this paper, we also provide a comprehensive literature survey using four electronic databases, PubMed, EMBASE, Web of Science, and Cochrane. The literature search is performed until November 2020. This article provides a summary of the existing algorithm of image recognition, reviews the available datasets used in gastric cancer diagnosis and the current trends in applications of deep learning theory in image recognition of gastric cancer. covers the theory of deep learning on endoscopic image recognition. We further evaluate the advantages and disadvantages of the current algorithms and summarize the characteristics of the existing image datasets, then combined with the latest progress in deep learning theory, and propose suggestions on the applications of optimization algorithms. Based on the existing research and application, the label, quantity, size, resolutions, and other aspects of the image dataset are also discussed. The future developments of this field are analyzed from two perspectives including algorithm optimization and data support, aiming to improve the diagnosis accuracy and reduce the risk of misdiagnosis.

• Endoscope
• Artificial intelligence
• Algorithm optimization
• Data support