False-positive or false-negative detection, and the resulting inappropriate treatments in cancer metastasis cases, have led to numerous fatal instances due to human errors. Traditional cancer diagnoses are often subjectively interpreted through naked-eye observation, which can vary among different medical practitioners. In this research, we propose a novel deep learning-based framework called Intelligent Tumor Analytics (ITA). ITA facilitates on-the-fly assessment of Whole Slide Imaging (WSI) at the histopathological level, primarily utilizing cellular appearance, spatial arrangement, and the relative proximities of various cell types (e.g., tumor cells, immune cells, and other objects of interest) observed within scanned WSI images of tumors. By automatically quantifying relevant indicators and estimating their scores, ITA establishes a standardized evaluation that aligns with widely recognized international tumor grading standards, including the TNM and Nottingham Grading Standards. The objective measurements and assessments offered by ITA provide informative and unbiased insights to users (i.e., pathologists) involved in determining prognosis and treatment plans. The quantified information regarding tumor risk and potential for further metastasis possibilities serves as crucial early knowledge during cancer development.