Texture Analysis of T1-Weighted Contrast-Enhanced Magnetic Resonance Imaging Potentially Predicts Outcomes of Patients with Non-Wingless-Type/Non-Sonic Hedgehog Medulloblastoma

Differentiation and prediction of pneumoconiosis stage by computed tomography texture analysis based on U-Net neural network

BACKGROUND AND OBJECTIVE

The progressive worsening of pneumoconiosis will ensue a hazardous physical condition in patients. This study details the differential diagnosis of the pneumoconiosis stage, by employing computed tomography (CT) texture analysis, based on U-Net neural network.

METHODS

The pneumoconiosis location from 92 patients at various stages was extracted by U-Net neural network. Mazda software was employed to analyze the texture features. Three dimensionality reduction methods set the best texture parameters. We applied four methods of the B11 module to analyze the selected texture parameters and calculate the misclassified rate (MCR). Finally, the receiver operating characteristic curve (ROC) of the texture parameters was analyzed, and the texture parameters with diagnostic efficiency were evaluated by calculating the area under curve (AUC).

RESULTS

The original film was processed by Gaussian and Laplace filters for a better display of the segmented area of pneumoconiosis in all stages. The MCR value obtained by the NDA analysis method under the MI dimension reduction method was the lowest, at 10.87%. In the filtered texture feature parameters, the best AUC was 0.821.

CONCLUSIONS

CT texture analysis based on the U-Net neural network can be used to identify the staging of pneumoconiosis.

Texture Features of Computed Tomography Image under the Artificial Intelligence Algorithm and Its Predictive Value for Colorectal Liver Metastasis

The aim of this research was to investigate the predictive role of texture features in computed tomography (CT) images based on artificial intelligence (AI) algorithms for colorectal liver metastases (CRLM). A total of 150 patients with colorectal cancer who were admitted to the hospital were selected as the research objects and randomly divided into three groups with 50 cases in each group. The patients who were found to suffer from the CRLM in the initial examination were included in group A. Patients who were found with CRLM in the follow-up were assigned to group B (B1: metastasis within 0.5 years, 16 cases; B2: metastasis within 0.5–1.0 years, 17 cases; and B3: metastasis within 1.0–2.0 years, 17 cases). Patients without liver metastases during the initial examination and subsequent follow-up were designated as group C. Image textures were analyzed for patients in each group. The prediction accuracy, sensitivity, and specificity of CRLM in patients with six classifiers were calculated, based on which the receiver operator characteristic (ROC) curves were drawn. The results showed that the logistic regression (LR) classifier had the highest prediction accuracy, sensitivity, and specificity, showing the best prediction effect, followed by the linear discriminant (LD) classifier. The prediction accuracy, sensitivity, and specificity of the LR classifier were higher in group B1 and group B3, and the prediction effect was better than that in group B2. The texture features of CT images based on the AI algorithms showed a good prediction effect on CRLM and had a guiding significance for the early diagnosis and treatment of CRLM. In addition, the LR classifier showed the best prediction effect and high clinical value and can be popularized and applied.

Risk Factors for Survival in Patients With Medulloblastoma: A Systematic Review and Meta-Analysis

Background

Conventional parameters show limited and unreliable correlations with medulloblastoma prognosis.

Aim

To evaluate the factors influencing overall survival (OS), event-free survival (EFS), and progression-free survival (PFS) in patients with medulloblastoma.

Methods

PubMed, EMBASE, the Cochrane Library, and Web of Science were searched for studies published up to May 2021. The associations between various clinical and treatment factors and survival parameters were assessed.

Results

Twenty-nine studies (8455 patients) were included. Desmoplastic medulloblastoma (HR=0.41, 95%CI: 0.31-0.56), M0 disease (HR=2.07, 95%CI: 1.48-2.89), WNT, SSH, group 4 (all P<0.05 vs. group 3), GTR vs. STR (HR=1.37, 95%CI: 1.04-1.08), radiotherapy (HR=0.45, 95%CI: 0.20-0.80), craniospinal irradiation (HR=0.49, 95%CI: 0.38-0.64), and high 5hmC levels (HR=2.90, 95%CI: 1.85-4.55) were associated with a better OS. WNT, SSH, group 4 (all P<0.05 vs. group 3), residual tumor ≤1.5 cm2 (HR=2.08, 95%CI: 1.18-3.68), GTR vs. STR (HR=1.31, 95%CI: 1.03-1.68), craniospinal irradiation (HR=0.46, 95%CI: 0.37-0.57), high 5hmC levels (HR=3.10, 95%CI: 2.01-4.76), and <49 days between resection and radiotherapy (HR=2.54, 95%CI: 1.48-4.37) were associated with better PFS. Classic vs. desmoplastic medulloblastoma (HR=1.81, 95%CI: 1.04-3.16), SSH, WNT (both P<0.05 vs, non-SSH/non-WNT), GTR vs. STR (HR=2.01, 95%CI: 1.42-2.85), and radiotherapy (HR=0.31, 95%CI: 0.15-0.64) were associated with a better EFS.

Conclusion

Histology, molecular subgroup, GTR, and radiotherapy are significantly associated with survival parameters in patients with medulloblastoma. Nevertheless, high-quality prospective cohort studies are necessary to improve the conclusions.