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Updated: Mar 26, 2026

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Published on: April 23, 2021

A method for using real world data in breast cancer modeling.

Monika Pobiruchin¹, Sylvia Bochum², Uwe M Martens²

¹Heilbronn University, GECKO Institute for Medicine, Informatics and Economics, Max-Planck-Str. 39, 74081 Heilbronn, Germany.

Journal of Biomedical Informatics

|February 9, 2016

Summary

This summary is machine-generated.

This study introduces a novel method for creating realistic Markov models using real-world cancer registry data. The approach offers improved health economic evaluations and planning for healthcare decision-makers.

Keywords:

Cancer registry Disease model Markov model Real world data Secondary use

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Published on: May 17, 2019

Area of Science:

Oncology
Health Economics
Biostatistics

Background:

Healthcare institutions are generating vast amounts of real-world clinical data.
This data presents opportunities for developing robust disease models for health economic evaluations.
Cancer registries are a valuable, yet underutilized, source for clinical data.

Purpose of the Study:

To propose and validate a novel approach for developing Markov models using real-world cancer registry data.
To demonstrate the semi-automatic extraction of disease models from clinical data.
To enhance the accuracy of health economic evaluations through data-driven models.

Main Methods:

A semi-automatic process was developed to map database structures to disease states using expert knowledge.
Java software was utilized to automatically derive model structure and transition probabilities.
Longitudinal data from 892 breast cancer patients (HER2-positive and -negative, with/without Trastuzumab) over eight years was used.

Main Results:

Generated Markov models were comparable to a published reference model in structure and treatment effects.
The data-driven models provided a more detailed representation of transition probabilities, particularly for disease-free survival and recurrence.
The method successfully reconstructed a breast cancer reference model using real-world data.

Conclusions:

This work presents a semi-automatic approach to extract Markov models from clinical cancer registry data.
The developed method enables the creation of more realistic disease models.
Healthcare decision-makers can leverage these models for improved planning and resource allocation.