This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by integrating advanced machine learning (ML) techniques with comprehensive imaging and clinical data. A retrospective analysis was performed on 300 patients who received BEV treatment from September 2013 to January 2024. The dataset incorporated 13 predictive features: 8 clinical variables and 5 radiological variables. The dataset was divided into a training set (70%) and a test set (30%) using stratified sampling. Data preprocessing was carried out through methods such as handling missing values with the MICE method, detecting and adjusting outliers, and feature scaling. Four algorithms, namely Random Forest (RF), Logistic Regression, Gradient Boosting Tree, and Naive Bayes, were selected to construct binary classification models. A tenfold cross-validation strategy was implemented during training, and techniques like regularization, hyperparameter optimization, and oversampling were used to mitigate overfitting. The RF model demonstrated superior performance, achieving an accuracy of 0.89, a precision of 0.94, F1-score of 0.92, with both AUC-ROC and AUC-PR values reaching 0.91. Feature importance analysis consistently identified edema volume as the most significant predictor, followed by edema index, patient age, and tumor volume. Traditional multivariate logistic regression corroborated these findings, confirming that edema volume and edema index were independent predictors (p < 0.01). Our results highlight the potential of ML-driven predictive models in optimizing BEV treatment selection, reducing unnecessary treatment risks, and improving clinical decision-making in neuro-oncology.

To develop and validate machine learning (ML) models for diagnosing salivary gland adenoid cystic carcinoma (ACC) in the salivary glands based on clinical and ultrasound features. A total of 365 patients with ACC or non-ACC of the salivary glands treated at two centers were enrolled in training cohort, internal and external validation cohorts. Synthetic minority oversampling technique was used to address the class imbalance. The least absolute shrinkage and selection operator (LASSO) regression identified optimal features, which were subsequently utilized to construct predictive models employing five ML algorithms. The performance of the models was evaluated across a comprehensive array of learning metrics, prominently the area under the receiver operating characteristic curve (AUC). Through LASSO regression analysis, six key features-sex, pain symptoms, number, cystic areas, rat tail sign, and polar vessel-were identified and subsequently utilized to develop five ML models. Among these models, the support vector machine (SVM) model demonstrated superior performance, achieving the highest AUCs of 0.899 and 0.913, accuracy of 90.54% and 91.53%, and F1 scores of 0.774 and 0.783 in both the internal and external validation cohorts, respectively. Decision curve analysis further revealed that the SVM model offered enhanced clinical utility compared to the other models. The ML model based on clinical and US features provide an accurate and noninvasive method for distinguishing ACC from non-ACC. This machine learning model, constructed based on clinical and ultrasound characteristics, serves as a valuable tool for the identification of salivary gland adenoid cystic carcinoma. Rat tail sign and polar vessel on US predict adenoid cystic carcinoma (ACC). Machine learning models based on clinical and US features can identify ACC. The support vector machine model performed robustly and accurately.

High-grade gliomas, particularly glioblastoma (MeSH:Glioblastoma), are among the most aggressive and lethal central nervous system tumors, necessitating advanced diagnostic and prognostic strategies. This systematic review and epistemic meta-analysis explore the integration of Artificial Intelligence (AI) and Radiomics Inter-field (AIRI) to enhance predictive modeling for tumor progression. A comprehensive literature search identified 19 high-quality studies, which were analyzed to evaluate radiomic features and machine learning models in predicting overall survival (OS) and progression-free survival (PFS). Key findings highlight the predictive strength of specific MRI-derived radiomic features such as log-filter and Gabor textures and the superior performance of Support Vector Machines (SVM) and Random Forest (RF) models, achieving high accuracy and AUC scores (e.g., 98% AUC and 98.7% accuracy for OS). This research demonstrates the current state of the AIRI field and shows that current articles report their results with different performance indicators and metrics, making outcomes heterogenous and hard to integrate knowledge. Additionally, it was explored that today some articles use biased methodologies. This study proposes a structured AIRI development roadmap and guidelines, to avoid bias and make results comparable, emphasizing standardized feature extraction and AI model training to improve reproducibility across clinical settings. By advancing precision medicine, AIRI integration has the potential to refine clinical decision-making and enhance patient outcomes.

In this study, we propose a novel approach to enhancing transfer learning by optimizing data selection through deep learning techniques and corresponding innovative learning strategies. This method is particularly beneficial when the available dataset has reached its limit and cannot be further expanded. Our approach focuses on maximizing the use of existing data to improve learning outcomes which offers an effective solution for data-limited applications in medical imaging classification. The proposed method consists of two stages. In the first stage, an original network performs the initial classification. When the original network exhibits low confidence in its predictions, ambiguous classifications are passed to a secondary decision-making step involving a newly trained network, referred to as the True network. The True network shares the same architecture as the original network but is trained on a subset of the original dataset that is selected based on consensus among multiple independent networks. It is then used to verify the classification results of the original network, identifying and correcting any misclassified images. To evaluate the effectiveness of our approach, we conducted experiments using thyroid nodule ultrasound images with the ResNet101 and Vision Transformer architectures along with eleven other pre-trained neural networks. The proposed method led to performance improvements across all five key metrics, accuracy, sensitivity, specificity, F1-score, and AUC, compared to using only the original or True networks in ResNet101. Additionally, the True network showed strong performance when applied to the Vision Transformer and similar enhancements were observed across multiple convolutional neural network architectures. Furthermore, to assess the robustness and adaptability of our method across different medical imaging modalities, we applied it to dermoscopic images and observed similar performance enhancements. These results provide evidence of the effectiveness of our approach in improving transfer learning-based medical image classification without requiring additional training data.

The use of Artificial Intelligence (AI) methods for the analysis of CT (computed tomography) images has greatly contributed to the development of an effective computer-assisted diagnosis (CAD) system for lung cancer (LC). However, complex structures, multiple radiographic interrelations, and the dynamic locations of abnormalities within lung CT images make extracting relevant information to process and implement LC CAD systems difficult. These prominent problems are addressed in this paper by presenting a hybrid method of LC malignancy classification, which may help researchers and experts properly engineer the model's performance by observing how the model makes decisions. The proposed methodology is named IncCat-LCC: Explainer (Inception Net Cat Boost LC Classification: Explainer), which consists of feature extraction (FE) using the handcrafted radiomic Feature (HcRdF) extraction technique, InceptionNet CNN Feature (INCF) extraction, Vision Transformer Feature (ViTF) extraction, and XGBOOST (XGB)-based feature selection, and the GPU based CATBOOST (CB) classification technique. The proposed framework achieves better and highest performance scores for lung nodule multiclass malignancy classification when evaluated using metrics such as accuracy, precision, recall, f-1 score, specificity, and area under the roc curve as 96.74 %, 93.68 %, 96.74 %, 95.19 %, 98.47 % and 99.76 % consecutively for classifying highly normal class. Observing the explainable artificial intelligence (XAI) explanations will help readers understand the model performance and the statistical outcomes of the evaluation parameter. The work presented in this article may improve the existing LC CAD system and help assess the important parameters using XAI to recognize the factors contributing to enhanced performance and reliability.

Early detection and accurate diagnosis are essential to improving patient outcomes. The use of convolutional neural networks (CNNs) for tumor detection has shown promise, but existing models often suffer from overparameterization, which limits their performance gains. In this study, fuzzy sigmoid convolution (FSC) is introduced along with two additional modules: top-of-the-funnel and middle-of-the-funnel. The proposed methodology significantly reduces the number of trainable parameters without compromising classification accuracy. A novel convolutional operator is central to this approach, effectively dilating the receptive field while preserving input data integrity. This enables efficient feature map reduction and enhances the model's tumor detection capability. In the FSC-based model, fuzzy sigmoid activation functions are incorporated within convolutional layers to improve feature extraction and classification. The inclusion of fuzzy logic into the architecture improves its adaptability and robustness. Extensive experiments on three benchmark datasets demonstrate the superior performance and efficiency of the proposed model. The FSC-based architecture achieved classification accuracies of 99.17%, 99.75%, and 99.89% on three different datasets. The model employs 100 times fewer parameters than large-scale transfer learning architectures, highlighting its computational efficiency and suitability for detecting brain tumors early. This research offers lightweight, high-performance deep-learning models for medical imaging applications.

Oculomics is the science of analyzing ocular data to identify, diagnose, and manage systemic disease. This article focuses on prescreening, its use with retinal images analyzed by artificial intelligence (AI), to identify ocular or systemic disease or potential disease in asymptomatic individuals. The implementation of prescreening in a coordinated care system, defined as Healthcare From the Eye prescreening, has the potential to improve access, affordability, equity, quality, and safety of health care on a global level. Stakeholders include physicians, payers, policymakers, regulators and representatives from industry, government, and data privacy sectors. The combination of AI analysis of ocular data with automated technologies that capture images during routine eye examinations enables prescreening of large populations for chronic disease. Retinal images can be acquired during either a routine eye examination or in settings outside of eye care with readily accessible, safe, quick, and noninvasive retinal imaging devices. The outcome of such an examination can then be digitally communicated across relevant stakeholders in a coordinated fashion to direct a patient to screening and monitoring services. Such an approach offers the opportunity to transform health care delivery and improve early disease detection, improve access to care, enhance equity especially in rural and underserved communities, and reduce costs. With effective implementation and collaboration among key stakeholders, this approach has the potential to contribute to an equitable and effective health care system.

Accurate estimation of treatment response can help clinicians identify patients who would potentially benefit from systemic therapy. This study aimed to develop and externally validate a model for predicting treatment response to systemic therapy in advanced gallbladder cancer (GBC). We recruited 399 eligible GBC patients across four institutions. Multivariable logistic regression analysis was performed to identify independent clinical factors related to therapeutic efficacy. This deep learning (DL) radiomics signature was developed for predicting treatment response using multiphase enhanced CT images. Then, the DL radiomic-clinical (DLRSC) model was built by combining the DL signature and significant clinical factors, and its predictive performance was evaluated using area under the curve (AUC). Gradient-weighted class activation mapping analysis was performed to help clinicians better understand the predictive results. Furthermore, patients were stratified into low- and high-score groups by the DLRSC model. The progression-free survival (PFS) and overall survival (OS) between the two different groups were compared. Multivariable analysis revealed that tumor size was a significant predictor of efficacy. The DLRSC model showed great predictive performance, with AUCs of 0.86 (95% CI, 0.82-0.89) and 0.84 (95% CI, 0.80-0.87) in the internal and external test datasets, respectively. This model showed great discrimination, calibration, and clinical utility. Moreover, Kaplan-Meier survival analysis revealed that low-score group patients who were insensitive to systemic therapy predicted by the DLRSC model had worse PFS and OS. The DLRSC model allows for predicting treatment response in advanced GBC patients receiving systemic therapy. The survival benefit provided by the DLRSC model was also assessed. Question No effective tools exist for identifying patients who would potentially benefit from systemic therapy in clinical practice. Findings Our combined model allows for predicting treatment response to systemic therapy in advanced gallbladder cancer. Clinical relevance With the help of this model, clinicians could inform patients of the risk of potential ineffective treatment. Such a strategy can reduce unnecessary adverse events and effectively help reallocate societal healthcare resources.

The aim of this study was to develop and validate a machine learning (ML) based prediction model for sentinel lymph node metastasis in breast cancer to identify patients with a high risk of sentinel lymph node metastasis. In this machine learning study, we retrospectively collected 225 female breast cancer patients who underwent sentinel lymph node biopsy (SLNB). Feature screening was performed using the logistic regression analysis. Subsequently, five ML algorithms, namely LOGIT, LASSO, XGBOOST, RANDOM FOREST model and GBM model were employed to train and develop an ML model. In addition, model interpretation was performed by the Shapley Additive Explanations (SHAP) analysis to clarify the importance of each feature of the model and its decision basis. Combined univariate and multivariate logistic regression analysis, identified Multifocal, LVI, Maximum Diameter, Shape US, Maximum Cortical Thickness as significant predictors. We than successfully leveraged machine learning algorithms, particularly the RANDOM FOREST model, to develop a predictive model for sentinel lymph node metastasis in breast cancer. Finally, the SHAP method identified Maximum Diameter and Maximum Cortical Thickness as the primary decision factors influencing the ML model's predictions. With the integration of pathological and imaging characteristics, ML algorithm can accurately predict sentinel lymph node metastasis in breast cancer patients. The RANDOM FOREST model showed ideal performance. With the incorporation of these models in the clinic, can helpful for clinicians to identify patients at risk of sentinel lymph node metastasis of breast cancer and make more reasonable treatment decisions.

Accurate preoperative assessment of axillary lymph node metastasis (ALNM) in breast cancer is crucial for guiding treatment decisions. This study aimed to develop a deep-learning radiomics model for assessing ALNM and to evaluate its impact on radiologists' diagnostic accuracy. This multicenter study included 866 breast cancer patients from 6 hospitals. The data were categorized into training, internal test, external test, and prospective test sets. Deep learning and handcrafted radiomics features were extracted from ultrasound images of primary tumors and lymph nodes. The tumor score and LN score were calculated following feature selection, and a clinical-radiomics model was constructed based on these scores along with clinical-ultrasonic risk factors. The model's performance was validated across the 3 test sets. Additionally, the diagnostic performance of radiologists, with and without model assistance, was evaluated. The clinical-radiomics model demonstrated robust discrimination with AUCs of 0.94, 0.92, 0.91, and 0.95 in the training, internal test, external test, and prospective test sets, respectively. It surpassed the clinical model and single score in all sets (P < .05). Decision curve analysis and clinical impact curves validated the clinical utility of the clinical-radiomics model. Moreover, the model significantly improved radiologists' diagnostic accuracy, with AUCs increasing from 0.71 to 0.82 for the junior radiologist and from 0.75 to 0.85 for the senior radiologist. The clinical-radiomics model effectively predicts ALNM in breast cancer patients using noninvasive ultrasound features. Additionally, it enhances radiologists' diagnostic accuracy, potentially optimizing resource allocation in breast cancer management.