E-Science technologies in a workflow for personalized medicine using cancer screening as a case study

Abstract

E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions.

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

“…With the adoption into routine clinical use in 2016, over 55,000 Swedish men were recruited to study the model’s efficacy [ 182 , 183 ]. It was then validated on nearly 10,000 men in Sweden, Norway, and Finland.…”

“…A pertinent example that did not benefit from the implementation of AI was the earlier referenced STHLM3 study, which benefitted from incorporating multiple clinical, biochemical, and radiological biomarkers [ 183 ]. Similarly, the radiogenomic models were seemingly improved with additional clinical data sources, but utilized conventional radiomic features in their modeling [ 47 , 157 , 165 , 182 ].…”

Using tears as a non-invasive source for early detection of breast cancer

“…As research continues to unfold, additional risk factors such as birth control use, hormone replacement therapy, breast tissue density, and obesity continue to increase the number of women who are classified as high-risk [ 6 ]. Massive efforts are currently focused on developing a personalized risk-based screening approach that considers individual biological characteristics, circumstances, and lifestyles [ 7 , 8 ]. Results from these studies could allow justification of focusing the most intensive screening on the portion of the population at the highest known risk of cancer formation.…”