Role of Three-Dimensional Convolution Neural Networks (3D- CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis

Abstract

Three-dimensional convolutional neural networks (3D CNNs) have transformed oncology imaging by offering superior performance in tumor detection, classification, segmentation, and prognosis prediction. Unlike traditional two-dimensional CNNs, 3D CNNs can effectively analyze volumetric medical imaging data, improving spatial feature extraction and diagnostic accuracy across imaging modalities such as CT, MRI, PET, and ultrasound. This systematic review and meta-analysis evaluates the diagnostic capabilities and clinical utility of 3D CNNs across 22 studies, including 11 eligible for quantitative synthesis. The pooled sensitivity, specificity, and AUC-ROC were 0.72, 0.73, and 0.77 respectively, with a diagnostic odds ratio (DOR) of 10.38, indicating favorable discriminative performance. Subgroup analyses revealed superior accuracy in lung cancer and CT-based models, with DenseNet and ResNet architectures outperforming traditional CNNs. Technical innovations including multi-modal fusion, spatial context integration, and explainable AI techniques enhance model robustness and clinical trust. However, high heterogeneity (I² > 95%) across studies, attributable to variations in imaging protocols, dataset quality, and model design, underscores the need for standardized methodologies. Limitations such as computational demands, annotation variability, and generalization challenges persist. Future directions emphasize the integration of explainable AI, PACS-compatible user interfaces, and federated learning frameworks to bridge institutional gaps. This review highlights the significant potential of 3D CNNs in advancing precision oncology, while also identifying the infrastructural and methodological refinements needed to enable widespread clinical adoption.