The radiomics, an emerging and promising field powered by artificial intelligence (AI) technologies like “big data analysis” and “machine learning” have the potential to answer these questions. Advanced uses of such data are changing the landscape of therapy and show a new potential in the identification of prognosis and predictive parameters, which can help in the future to improve, avoid useless treatments, and better personalize cancer therapies.

For example, by taking into account the clinical diagnosis and follow-up information of patients, and based on the principle of Radiomics — development of statistical models characterizing tumors at the molecular level, extracted automatically from multimodal patient medical images — a real decision-making support system can be created, based on the predictive profile of disease control and the patient-specific toxicity risk.


By combing with additional information extracted from patient records (like genomics, specific biomarkers, etc.), the system can guide the physicians and help them to better choose or adapt treatment, or target the patient receiving a specific treatment.

Moreover, the “machine learning” technologies have the capacity to enhance existing products, either by automating various tasks or by improving, simplifying and accelerating complex algorithms. In the context of molecular radiotherapy, the latest generation of segmentation algorithms based on deep learning could help analyzing dosimetric effects for more organs with more precision, reproducibility, and with limited workloads.

The machine learning technologies all work with the assumption of a learning curve, incremental or not, based on the analysis of as large as possible data sets. The models extracted by these algorithms can then be applied to new patient cases. The consequence of this is that historical information contained in previous patient cases and patient databases is of significant value for such technologies and future systems. This has been quickly understood by all actors having “data”. The value is in the data indeed. It is, however, of utmost importance that large data sets are being shared and put in common, instead of being kept in small silos.

Conclusion
Software technologies are not curing cancer patients by themselves. But software technologies are bound to play an increasing and tremendous role, for the benefit of patients first, cancer centers, and the whole biotechnology industry. Impacts and benefits will be throughout the full cycle, from diagnosis to patient post-treatment follow-up. Without being exhaustive, software technologies will help: to better characterize patients responding to a given therapy, to improve safety and minimize side effects or impacts on healthy organs, to optimize and personalize therapies for a given patient, to control “in vivo” during treatments, and to quantify patient post-treatment response. Software technologies are the keys to move away from the one-size-fits-all standard protocols toward a truly personalized medicine of the future. Nuclear medicine and the expanding molecular radiotherapy domain will first inherit from the tremendous advances performed during the last 20 years in external beam radiation therapy, and then accelerate to include AI-powered theranostic features.

About the author: Sébastien Vauclin, Ph.D., is the product manager in molecular imaging and molecular radiotherapy software at DOSIsoft.

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