Challenges Faced by Clinicians in the Personalized Treatment Planning: A Literature Review and the First Results of the Russian National Cancer Program.
P V ShegaiP A ShatalovAnastasia A ZabolotnevaN A FalaleevaSergey A IvanovAndrey D KaprinPublished in: Critical care research and practice (2021)
Advances in cancer molecular profiling have enabled the development of more effective approaches to the diagnosis and personalized treatment of tumors. However, treatment planning has become more labor intensive, requiring hours or even days of clinician effort to optimize an individual patient case in a trial-and-error manner. Lessons learned from the world cancer programs provide insights into ways to develop approaches for the treatment strategy definition which can be introduced into clinical practice. This article highlights the variety of breakthroughs in patients' cancer treatment and some challenges that this field faces now in Russia. In this report, we consider the key characteristics for planning an optimal clinical treatment regimen and which should be included in the algorithm of clinical decision support systems. We discuss the perspectives of implementing artificial intelligence-based systems in cancer treatment planning in Russia.
Keyphrases
- artificial intelligence
- papillary thyroid
- machine learning
- clinical practice
- clinical decision support
- deep learning
- squamous cell
- case report
- clinical trial
- newly diagnosed
- end stage renal disease
- chronic kidney disease
- randomized controlled trial
- prognostic factors
- study protocol
- childhood cancer
- electronic health record
- open label