eening results of DS. It can also be used to detect CNVs of microdeletion/microduplication syndromes.
To explore the clinical value of using radiomics models based on different MRI sequences in the assessment of hepatic metastasis of rectal cancer.
140 patients with pathologically confirm edrectal cancer were included in the study. They underwent baseline magnetic resonance imaging (MRI) between April 2015 and May 2018 before receiving any treatment. According to the results of liver biopsy, surgical pathology, and imaging, patients were put into two groups, the patients with hepatic metastasis and those without. T2 weighted images (T2WI), diffusion weighted images (DWI) and apparent diffusion coefficient (ADC) images were used to draw the region of interest (ROI) of primary lesions on consecutive slices on ITK-SNAP. 3-D ROIs were generated and loaded into Artificial Intelligent Kit for extraction of radiomics features and 396 features were extracted for each sequence. The feature data were preprocessed on Python and the samples were oversampled, using Support Vector Machine-Synthetic Minority Over-Samplid high
comparable to the
of the T2WI model and ADC model. The performance of T2WI and ADC alone models were second to that of the combined model, while the DWI alone model showed relatively poor performance.
Radiomics models based on MRI could be effectively used in assessing liver metastasis in rectal cancer, which may help determine clinical staging and treatment.
Radiomics models based on MRI could be effectively used in assessing liver metastasis in rectal cancer, which may help determine clinical staging and treatment.
To assess the clinical effectiveness of boundary recognition of upper abdomen organs on CT images based on neural network model and the combination of different slices.
A total of 2 000 patients who underwent upper abdomen enhanced CT scans from March 2018 to March 2019 were included in the study. The quality of the CT images met the requirements for clinical diagnosis. Sunitinib Eight boundary layers (the upper and lower edge of liver, the upper and lower edge of spleen, the lower edge of left kidney, the lower edge of right kidney, the lower edge of the stomach and the lower edge of the gallbladder) of the main organs in the upper abdomen were labeled. The model training (training set, verification set and test set) based on different neural network methods and combinations of different slices were then performed to assess the accuracy of boundary recognition. Furthermore, clinical data from 50 cases were used as test group for assessing the accuracy and clinical effectiveness of this model.
The fusion model crinuous slices, the fusion model integrating different weight proportions demonstrates the highest accuracy for identifying the boundary of upper abdominal organs on CT images, achieving high examination effectiveness in clinical practice.
By increasing boundary change logics in the continuous slices, the fusion model integrating different weight proportions demonstrates the highest accuracy for identifying the boundary of upper abdominal organs on CT images, achieving high examination effectiveness in clinical practice.
A predictive model of Alzheimer's disease (AD) was established based on brain surface meshes and geometric deep learning, and its performance was evaluated.
Seventy-six clinically diagnosed AD patients and 83 healthy older adults were enrolled and randomly assigned to the training set and the test set according to a 4-to-1 ratio. Brain surface mesh was constructed from 3-D T1-weighted high-resolution structural MR volumes of each participant. After applying a series of simplification to the surface meshes, the training set was fed into the geometric deep neural network for training. The performance of the prediction model was evaluated with the test set, and the evaluation metrics included accuracy, sensitivity and specificity.
The prediction model trained on the right brain surface meshes with 6 000 faces achieved the best performance, with accuracy reaching 93.8%, sensitivity, 91.7%, and specificity, 94.1%. The evolution of the brain surface meshes during convolution and pooling revealed that AD patients had diffuse brain tissue loss compared with healthy older adults.
Morphological brain analysis based on mesh data and geometric deep learning has great potential in the differential diagnosis of AD.
Morphological brain analysis based on mesh data and geometric deep learning has great potential in the differential diagnosis of AD.
To compare the noise reduction performance of conventional filtering and artificial intelligence-based filtering and interpolation (AIFI) and to explore for optimal parameters of applying AIFI in the noise reduction of abdominal magnetic resonance imaging (MRI).
Sixty patients who underwent upper abdominal MRI examination in our hospital were retrospectively included. The raw data of T1-weighted image (T1WI), T2-weighted image (T2WI), and dualecho sequences were reconstructed with two image denoising techniques, conventional filtering and AIFI of different levels of intensity. The difference in objective image quality indicators, peak signal-to-noise ratio (pSNR) and image sharpness, of the different denoising techniques was compared. Two radiologists evaluated the image noise, contrast, sharpness, and overall image quality. Their scores were compared and the interobserver agreement was calculated.
Compared with the original images, improvement of varying degrees were shown in the pSNR and the sharpnessng AIFI technique for noise reduction in abdominal MRI were conventional filtering+AIFI level=3 in the T1WI sequence and AIFI level=4 in the T2WI and dualecho sequences.
AIFI is superior to filtering in imaging denoising at medium and high levels. It is a promising noise reduction technique. The optimal parameters of using AIFI for abdominal MRI are Filtering+AIFI level=3 in the T1WI sequence and AIFI level=4 in T2WI and dualecho sequences.
AIFI is superior to filtering in imaging denoising at medium and high levels. It is a promising noise reduction technique. The optimal parameters of using AIFI for abdominal MRI are Filtering+AIFI level=3 in the T1WI sequence and AIFI level=4 in T2WI and dualecho sequences.Sunitinib
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