Radiomics-based prediction models have shown promise in predicting Radiation Pneumonitis (RP), a common adverse outcome of chest irradiation. Τhis study looks into more than just RP: it also investigates a bigger shift in the way radiomics-based models work. By integrating multi-modal radiomic data, which includes a wide range of variables collected from medical images including cutting-edge PET/CT imaging, we have developed predictive models that capture the intricate nature of illness progression. Radiomic features were extracted using PyRadiomics, encompassing intensity, texture, and shape measures. The high-dimensional dataset formed the basis for our predictive models, primarily Gradient Boosting Machines (GBM)—XGBoost, LightGBM, and CatBoost. Performance evaluation metrics, including Multi-Modal AUC-ROC, Sensitivity, Specificity, and F1-Score, underscore the superiority of the Deep Neural Network (DNN) model. The DNN achieved a remarkable Multi-Modal AUC-ROC of 0.90, indicating superior discriminatory power. Sensitivity and specificity values of 0.85 and 0.91, respectively, highlight its effectiveness in detecting positive occurrences while accurately identifying negatives. External validation datasets, comprising retrospective patient data and a heterogeneous patient population, validate the robustness and generalizability of our models. The focus of our study is the application of sophisticated model interpretability methods, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), to improve the clarity and understanding of predictions. These methods allow clinicians to visualize the effects of features and provide localized explanations for every prediction, enhancing the comprehensibility of the model. This strengthens trust and collaboration between computational technologies and medical competence. The integration of data-driven analytics and medical domain expertise represents a significant shift in the profession, advancing us from analyzing pixel-level information to gaining valuable prognostic insights.

Radiomics has shown great promise in predicting various diseases. Researchers have previously attempted to include radiomics in their automated detection, diagnosis, and segmentation algorithms, taking these steps based on the promising outcomes of radiomics-based studies. As a result of the increased attention given to this topic, numerous institutions have developed their own radiomics software. These packages, on the other hand, have been utilized interchangeably without regard for their fundamental differences. The primary purpose of this study was to explore benefits of predictive model performance for radiation pneumonitis (RP), which is the most frequent side effect of chest radiotherapy, and through this work, we developed a radiomics model based on deep learning that intends to increase RP prediction performance by combining more data points and digging deeper into these data. In order to evaluate the most popular machine learning models, radiographic characteristics were used, and we recorded the most important of them. The high dimensionality of radiomic datasets is a major issue. The method proposed for use in data problems is the synthetic minority oversampling technique, which we used in order to create a balanced dataset by leveraging suitable hardware and open-source software. The present study assessed the efficacy of various machine learning models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and deep neural network (DNN), in predicting radiation pneumonitis by utilizing specific radiomics features. The findings of the study indicate that the four models displayed satisfactory efficacy in forecasting radiation pneumonitis. The DNN model demonstrated the highest area under the receiver operating curve (AUC-ROC) value, which was 0.87, suggesting its superior predictive capacity among the models considered. The AUC-ROC values for the random forest, SVM, and logistic regression models were 0.85, 0.83, and 0.81, respectively.

Background: Lung cancer still maintains the leading position among causes of death in the world; the process of early detection surely contributes to changes in the survival of patients. Standard diagnostic methods are grossly insensitive, especially in the early stages. In this paper, radiomic features are discussed that can assure improved diagnostic accuracy through automated lung cancer detection by considering the important feature categories, such as texture, shape, and intensity, originating from the CT DICOM images. Methods: We developed and compared the performance of two machine learning models—DenseNet-201 CNN and XGBoost—trained on radiomic features with the ability to identify malignant tumors from benign ones. Feature importance was analyzed using SHAP and techniques of permutation importance that enhance both the global and case-specific interpretability of the models. Results: A few features that reflect tumor heterogeneity and morphology include GLCM Entropy, shape compactness, and surface-area-to-volume ratio. These performed excellently in diagnosis, with DenseNet-201 producing an accuracy of 92.4% and XGBoost at 89.7%. The analysis of feature interpretability ascertains its potential in early detection and boosting diagnostic confidence. Conclusions: The current work identifies the most important radiomic features and quantifies their diagnostic significance through a properly conducted feature selection process reflecting stability analysis. This provides the blueprint for feature-driven model interpretability in clinical applications. Radiomics features have great value in the automated diagnosis of lung cancer, especially when combined with machine learning models. This might improve early detection and open personalized diagnostic strategies for precision oncology.

The combination of a sulfonylurea with a biguanide improves the pancreatic β-cell insulin secretion and the insulin utilization in peripheral tissues in NIDDM. This open, crossover, randomised and prospective study was designed to compare the effects of the fixed combination glibenclamide-metformin (GL-METF) - 2.5 and 400 mg respectively, with the fixed combination glibenclamide-phenformin (GL-PHEN) - 2.5 and 25 mg respectively, on NIDDM diabetes control. Thirty NIDDM patients, in ideal metabolic control, who were being treated with GL-PHEN were divided in two groups. One group received GL-PHEN for 12 weeks followed by 12 weeks treatment with GL-METF and the reverse treatment was given to the second group. A statistically significant decrease of post-prandial blood glucose (p = 0.034) and glycosylated haemo-globin (p < 0.02) values was observed under GL-METF treatment compared to those with GL-PHEN. The values of lactic acid were within normal limits during both treatments. The insulin secretion after breakfast was similar with both drug compounds. The BMI of the patients remained the same during a follow-up study of 24 weeks. Lipid metabolism did not change significantly during the trial and the safety parameters (renal and liver function, full blood count) remained unchanged. In conclusion, the administration of GL-METF leads to better diabetes control in NIDDM patients compared to that of GL-PHEN.

TRadiomics is a new term in radiology that refers to the extraction of a large number of quantitative information from medical pictures. Artificial intelligence (AI) is a wide term for a collection of advanced computing algorithms that, in essence, analyze patterns in data to make predictions on previously unknown data sets. Because of its superior ability to handle large amounts of data as compared to traditional statistical methods, it can be combined with AI.

Objectives: Radiation-induced pneumonitis (RP) is a critical complication of radiotherapy in lung cancer patients, and its early detection remains a challenge due to the limited availability of annotated CT imaging data and the subtle nature of disease evolution. The objective of this study is to enhance the detection and localization of RP in CT by integrating advanced data augmentation, self-supervised learning, and synthetic data generation techniques. Methods: A conditional Generative Adversarial Network (cGAN) was used to create synthetic RP images conditioned on lung segmentation masks to create anatomically plausible data for augmenting the training sets. The pipeline was created to possess double-stage self-supervised training with hierarchical pretext tasks to achieve robust features. Results: The performance of the proposed framework, for a 5-fold cross-validation, has an average accuracy of 94.04%, precision of 92.06%, with a recall of 95.1%, an F1-score of 93.56%, and area under the curve of 95.94%. Conclusions: The model was demonstrated to possess superior performance and stability in RP detection and localization, which suggests potential clinical translation. Advances in knowledge: The paper offers a novel fusion of cGAN-generated synthetic data, spatial attention, and contrastive learning to address RP detection in limited data. Interpretability is achieved by introducing Bayesian uncertainty estimation to provide translational value in clinical practice.