Breast cancer therapy planning - a novel support concept for a sequential decision making problem

Area of Science:

• Oncology
• Medical Informatics
• Decision Science

Background:

• Breast cancer is a leading cause of mortality in women, necessitating effective and individualized treatment strategies.
• Current breast cancer therapy planning relies on guidelines, publications, and experience, often involving time-consuming information searches.
• Clinical decision-making in breast cancer treatment faces challenges in efficiently evaluating numerous therapy options for each patient.

Purpose of the Study:

• To address weaknesses in routine breast cancer therapy planning.
• To develop a novel decision support concept for optimizing breast cancer treatment selection.
• To formulate breast cancer therapy planning as a multi-criteria sequential decision-making problem.

Main Methods:

• Development of a data model for patient cases, including detailed therapy descriptions.
• Introduction of a mathematical framework to quantify the therapeutic relevance of medical information.
• Application of these components to create a decision support system for breast cancer treatment planning.

Main Results:

• The proposed approach models breast cancer therapy planning as a structured decision problem.
• The mathematical notion of therapeutic relevance allows for systematic evaluation of treatment options.
• The decision support concept aims to streamline and enhance the accuracy of clinical choices.

Conclusions:

• This research offers a novel, mathematically grounded approach to breast cancer therapy planning.
• The developed decision support concept has the potential to improve the efficiency and effectiveness of clinical routines.
• Further implementation and validation are expected to refine breast cancer treatment strategies.