The integration of digital pathology with whole slide image (WSI) represents a transformative advance in the field of medical diagnostics. This dissertation investigates the application of WSI across three pivotal areas: three-dimensional (3D) reconstruction of large tissues at cellular resolution for quantitative analysis, sampling error quantification using liver tissue cases, predictive response analysis, and survival prediction, specifically targeting histopathological evaluations in breast cancer treatment. Our initial study focuses on 3D virtual needle biopsies, utilizing 3D reconstructions of histopathological images to quantify and reduce sampling errors, thus enhancing diagnostic reliability. Subsequently, we introduce NACNet, a novel histology context-aware transformer based graph convolution network to predict responses to neoadjuvant chemotherapy in Triple-Negative Breast Cancer (TNBC) using WSI data. By extracting and integrating graph based spatial Tissue microenvironment (TME) features from WSIs with image features, NACNet surpasses traditional diagnostic methods in predictive accuracy. Our final project extends this approach by developing a multimodal deep learning model that combines WSI features with genomic and clinical data to forecast long-term survival outcomes in breast cancer patients. This model serves not only as a prognostic tool but also aids in the customization of treatment protocols, thereby advancing the practice of precision oncology. Through these studies, this dissertation demonstrates the critical role of WSI in reducing sampling discrepancies, improving accuracy in treatment response prediction, and enhancing survival analysis in clinical settings. The collective findings underscore the significant potential for WSI-assisted diagnostics to inform more precise and personalized patient care strategies in oncology.