This study evaluates the reproducibility of machine learning models that integrate radiomics and deep features (features extracted from a 3D autoencoder neural network) to classify various brain hemorrhages effectively. Using a dataset of 720 patients, we extracted 215 radiomics features (RFs) and 15,680 deep features (DFs) from CT brain images. With rigorous screening based on Intraclass Correlation Coefficient thresholds (>0.75), we identified 135 RFs and 1054 DFs for analysis. Feature selection techniques such as Boruta, Recursive Feature Elimination (RFE), XGBoost, and ExtraTreesClassifier were utilized alongside 11 classifiers, including AdaBoost, CatBoost, Decision Trees, LightGBM, Logistic Regression, Naive Bayes, Neural Networks, Random Forest, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). Evaluation metrics included Area Under the Curve (AUC), Accuracy (ACC), Sensitivity (SEN), and F1-score. The model evaluation involved hyperparameter optimization, a 70:30 train–test split, and bootstrapping, further validated with the Wilcoxon signed-rank test and q-values. Notably, DFs showed higher accuracy. In the case of RFs, the Boruta + SVM combination emerged as the optimal model for AUC, ACC, and SEN, while XGBoost + Random Forest excelled in F1-score. Specifically, RFs achieved AUC, ACC, SEN, and F1-scores of 0.89, 0.85, 0.82, and 0.80, respectively. Among DFs, the ExtraTreesClassifier + Naive Bayes combination demonstrated remarkable performance, attaining an AUC of 0.96, ACC of 0.93, SEN of 0.92, and an F1-score of 0.92. Distinguished models in the RF category included SVM with Boruta, Logistic Regression with XGBoost, SVM with ExtraTreesClassifier, CatBoost with XGBoost, and Random Forest with XGBoost, each yielding significant q-values of 42. In the DFs realm, ExtraTreesClassifier + Naive Bayes, ExtraTreesClassifier + Random Forest, and Boruta + k-NN exhibited robustness, with 43, 43, and 41 significant q-values, respectively. This investigation underscores the potential of synergizing DFs with machine learning models to serve as valuable screening tools, thereby enhancing the interpretation of head CT scans for patients with brain hemorrhages.

Due to different treatment strategies, it is extremely important to differentiate between glioblastoma multiforme (GBM) and brain metastases (MET). It often proves difficult to distinguish between GBM and MET using MRI due to their similar appearance on the imaging modalities. Surgical methods are still necessary for definitive diagnosis, despite the importance of magnetic resonance imaging in detecting, characterizing, and monitoring brain tumors. We introduced an accurate, convenient, and user‐friendly method to differentiate between GBM and MET through routine MRI sequence and radiomics analyses. We collected 91 patients from one institution, including 50 with GBM and 41 with MET, which were proven pathologically. The tumors separately were segmented on all MRI images (T1‐weighted imaging (T1WI), contrast‐enhanced T1‐weighted imaging (T1C), T2‐weighted imaging (T2WI), and fluid‐attenuated inversion recovery (FLAIR)) to form the volume of interest (VOI). Eight ML models and feature reduction strategies were evaluated using routine MRI sequences (T1W, T2W, T1‐CE, and FLAIR) in two methods with (second model) and without wavelet transform (first model) radiomics. The optimal model was selected based on each model’s accuracy, AUC‐roc, and F1‐score values. In this study, we have achieved the result of 0.98, 0.99, and 0.98 percent for accuracy, AUC‐roc, and F1‐score, respectively, which have yielded a better result than the first model. In most investigated models, there were significant improvements in the multidimensional wavelets model compared to the non‐multidimensional wavelets model. Multidimensional discrete wavelet transform can analyze hidden features of the MRI from a different perspective and generate accurate features which are highly correlated with the model accuracy.

Background Before antibiotics were available, X-ray therapy was used to treat pneumonia, but its effectiveness is unknown. It was not fully understood how low-dose radiation could reduce inflammation in early attempts to treat pneumonia with X-rays. Exposure to low-dose radiation therapy (LDRT) may activate anti-inflammatory molecules such as TGF-b1 and IL-10. This systematic review and meta-analysis aimed to determine whether LDRT effectively treats COVID-19-associated pneumonia. Methods This study followed the PRISMA guidelines (Principles for Reporting Systematic Reviews and Meta-Analyses). A search was conducted on Google Scholar and PubMed for articles published before June 12, 2022. The Newcastle–Ottawa Scale (NOS) was used to evaluate the included research quality. Results In these studies, patients were given lung radiation at doses ranging from 0.5 to 1.5 Gy. Except for one study, all clinical, radiological, and survival outcomes improved. The study showed that whole-lung radiation at doses of 0.5–1.5 Gy was beneficial to patients with COVID-19-associated pneumonia who were oxygen-dependent. In these studies, the reported SR was 59% (95% CI, 44–75%). Conclusions LDRT has the potential to be a viable therapy option, although large-scale randomized trials are necessary, particularly in the elderly, who are more at risk of mortality from COVID-19.