Multimodal Detection and Prognostic Modeling of Atypical Teratoid Rhabdoid Tumors Using Machine Learning and Time Series Analysis - RajamohanReddy, Nadenlla; G Muneeswari, G Muneeswari (Journal of Neonatal Surgery, 2025)
Atypical Teratoid Rhabdoid Tumors (ATRT) present formidable diagnostic and prognostic challenges owing to their intricate morphological and molecular het- erogeneity levels. Existing diagnostic frameworks exhibit pronounced limitations, primarily due to their reliance on singular modalities and conventional ana- lytical paradigms, leading to suboptimal precision and delayed intervention. This study endeavors to address the diagnostic and prognostic inefficiencies by employing a sophisticated multimodal approach, amalgamating diverse medi- cal imaging and genomic sequencing modalities with advanced machine learning models and time series forecasting. We leveraged a synergistic integration of multiple modalities—MRI processed using Graph Neural Networks (GNN), CT scans analyzed through Recurrent Neural Networks (RNN), PET scans inter- preted via Generative Adversarial Networks (GAN), Genomic Sequencing probed by 1D Convolutional Neural Networks (CNN), and Diffusion Tensor Imaging (DTI) scrutinized through 3D CNNs. Each modality underwent individualized processing to extract nuanced characteristics of ATRT. Subsequently, the Vector AutoRegressive Moving Average with Exogenous Inputs (VARMAX) model was applied to each modality, enabling the forecasting of potential future brain dis- eases. The meticulous integration of these advanced methodologies culminated in substantial breakthroughs in various performance Indicators in the context of present models. Our framework demonstrated an increase in precision by 4.9%, an elevation in accuracy by 8.3%, an enhancement in recall by 8.5%, an aug- mentation in the AUC by 5.5%, an amplification in specificity by 2.9%, and a 3.9% reduction in delay. Furthermore, the deployment of VARMAX on indi- vidual modalities resulted in an additional 2.5% precision, 3.5% accuracy, 2.9% recall, 4.9% AUC, 3.4% specificity, and 1.9% reduction in prediction delays. This pioneering research stands as a beacon of refined diagnostic and prognostic resolution in the domain of ATRT, elucidating the transformative potential of multimodal fusion and advanced machine learning in mediating enhanced clinical outcomes. The notable enhancements in diagnostic metrics underscore the feasi- bility and efficacy of this comprehensive approach in early and accurate ATRT detection, facilitating timely and precise therapeutic interventions. Additionally, the prognostic advancements enabled by VARMAX provide pivotal insights into the potential evolution of brain diseases, aiding in the proactive management of pathological progressions. The integration of multimodal analytical paradigms with sophisticated machine learning and forecasting models has engendered a novel framework with substantial implications for the detection and prognosis of ATRT. This research bridges the existing diagnostic gaps and furnishes a robust platform for the exploration of similar integrative approaches in delineating other complex pathologies, thereby propelling the realms of computational diagnostics and personalized medicine towards unprecedented horizons