title: Cloud-based characterization of tumour lesions in cancer patients : a business plan for a University spin-off
Creators: name: Beyer, Thomas
Publisher: name: TU Wien
Year: 2017
Subjects: subject: Imaging
subject: healthcare
subject: machine learning
subject: predictive analytics
subject: academic spin-off

description: This project aims at deriving a business plan for an academic spin-off (ASO). The business idea is a cloud-based computing platform for characterizing tumour load in oncology patients based on the provision of non-invasive biomarker information. Subsequent tumour characterization through predictive analytics shall be used for the stratification of individual treatment options, specific to the patient, thus, helping to bring down overall healthcare costs. First, the concept of state-of-the-art cancer patient management will be introduced. Today, cancer patients are diagnosed through non-invasive anatomical and/or molecular imaging as well as a series of clinical tests that frequently include invasive biopsies. Medical experts face a diagnostic dilemma that is two-fold. On the one hand, not all tests may be performed for a particular patient to support the most accurate diagnosis. On the other hand, many more data from other but similar patient cohorts are available, yet remain inaccessible and understood by the very medical expert, and, therefore, cannot be used easily as part of the knowledge build-up of that expert. As a consequence, diagnostic interpretations may be challenged in complex cases, and suboptimal or inefficient therapies may be ordered that do not help the patient. With the onset of high-power IT structures, the concept of big data has made its way to healthcare. Numerous data (imaging, immuno-histochemical markers, clinical records, etc.) are available and await conjoint analysis for the generation of new knowledge that can be absorbed by the medical expert. Today, several companies offer on-demand services through cloud-based data sharing and analysis tools. A recent survey conducted by the author provides insight into the readiness of healthcare stakeholders to adopt a cloud-based decision support algorithm specifically for tumour characterization. The results indicate a generally positive perception of such a service if it was approved by the authorities. The second part of this thesis highlights the business idea and value proposition, which is that of a dedicated computer-supported clinical decision system. As the idea for a cloud-based tumour characterization span out of ongoing academic research, the third part of this thesis will probe the theoretical framework for turning such an innovative idea into a business considering the specific boundary conditions of an ASO. These conditions entail the need to complement academic know-how with business expertise, the need to address various interests over intellectual property rights, and the start-up and early growth strategy, to name a few. The fourth section of this work is dedicated to the actual business plan. Here, important learning points from the earlier theoretical work shall be highlighted to support the choice of arguments and quantitative estimates for the planned business. Finally, this work will conclude with a summary of the main teaching points from the development of a strategy and a business plan for a ASO and a very specific cloud-based service offering in the context of oncology healthcare.