Sanker, Vivek

The intersection between radiomics, the computational analysis of imaging data, and machine learning (ML) may lead to new developments in the diagnosis, prognosis, and management of diseases. For spinal tumors specifically, applications of these fields appear promising. In this educational narrative review, we provide a summary of the current advancements in radiomics and artificial intelligence (AI), as well as applications of both fields in the diagnosis and management of spinal tumors. We also provide a suggested workflow of radiomics and machine learning analysis of spinal tumors for researchers, including a list and description of commonly used radiomic features. Future directions in the field of radiomics and machine learning applications to spinal tumors may involve validating already proposed algorithms with larger datasets, ensuring that all computational applications to patient care maintain high ethical standards, and continuing work in developing novel and highly accurate computational techniques to enhance patient outcomes.

Abstract INTRODUCTION Alzheimer's disease (AD) is a progressive neurodegenerative disorder that needs better predictive tools. Using the National Alzheimer's Coordinating Center Uniform Data Set, this study developed machine learning (ML) models and a practical clinical tool for AD prediction. METHODS Data from 52,537 individuals (22,371 with AD) and more than 200 variables were processed with MissForest imputation and genetic algorithm‐based selection. Multiple ML models were trained, and interpretability was performed using SHAP and permutation importance. A LightGBM model was refined through iterative backward feature elimination (IBFE) followed by manual refinement. RESULTS LightGBM performed best (receiver operating characteristic‐area under the curve [ROC‐AUC] 0.91, accuracy 82.0%). Key predictors included arthritis, age, body mass index, and heart rate. A 19‐feature model retained accuracy (81.2%) and ROC‐AUC (0.90). DISCUSSION This lightweight tool predicts AD using mostly routine variables. Limitations include its cross‐sectional nature, and would need external validation. An interactive web app and GitHub resource are available. Highlights Developed a lightweight ML based tool using 19 routinely available features. The lightweight model achieved an ROC-AUC of 0.90 for Alzheimer's disease prediction on NACC multicenter data. Genetic algorithm, IBFE, and manual refinement enabled optimal feature selection. Tool hosted on an open‐access platform for clinical and research use. SHAP analysis provided model interpretability and feature‐level insights.