Through software to extract and interpret morphometric and functionally indicators, imaging has a huge untapped potential to assist cancer research. Probabilistic imaging software can be transformative in providing minimally invasive, objective, and replicable evaluation of cancer treatment response in the era of non-cytotoxic treatment agents, multi-modality image-guided ablative therapies, and rapidly evolving computational resources. Highthroughput analysis and fine-grained distinction of many molecular targets necessitate the use of postprocessing methods. The software tools employed in these analyses must be stable and reliable over a wide range of information collected from various people, time periods, and institutions. To ensure the software’s validity, analysis methodologies must be clearly specified, analysis results must be documented, and explicit recommendations for their interpretation must be provided. However, there is a dearth of infrastructure to promote common data interchange and method sharing, as well as cancer research data in forms that facilitate quantitative analysis. As a result, we propose to create an interoperable imaging bioinformatics base for the development of software tools for quantifiable imaging protein biomarkers. This platform will allow for the archiving, organising, retrieval, and dissemination of data generated by new analytical tools, as well as the performance review of quantitative analytical techniques. The needs of active QIN research projects in quantifiable imaging biomarker discovery for prostate adenocarcinoma, brain and neck cancer, and glioblastoma multiforme will define its usefulness.