A Comprehensive Machine Learning Benchmark Study for Radiomics-Based Survival Analysis of CT Imaging Data in Patients With Hepatic Metastases of CRC

MCML Authors

Theresa Stüber

→ Group Michael Ingrisch
 Clinical Data Science in Radiology

Balthasar Schachtner

Dr.

→ Group Michael Ingrisch
 Clinical Data Science in Radiology

Tobias Weber

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

David Rügamer

Prof. Dr.

Principal Investigator

Statistics, Data Science and Machine Learning

Andreas Bender

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

Andreas Mittermeier

Dr.

→ Group Michael Ingrisch
 Clinical Data Science in Radiology

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Michael Ingrisch

Prof. Dr.

Principal Investigator

Clinical Data Science in Radiology

Abstract

Optimizing a machine learning (ML) pipeline for radiomics analysis involves numerous choices in data set composition, preprocessing, and model selection. Objective identification of the optimal setup is complicated by correlated features, interdependency structures, and a multitude of available ML algorithms. Therefore, we present a radiomics-based benchmarking framework to optimize a comprehensive ML pipeline for the prediction of overall survival. This study is conducted on an image set of patients with hepatic metastases of colorectal cancer, for which radiomics features of the whole liver and of metastases from computed tomography images were calculated. A mixed model approach was used to find the optimal pipeline configuration and to identify the added prognostic value of radiomics features.

article

Investigative Radiology

Authors

Links

Research Areas

 A1 | Statistical Foundations & Explainability

 C1 | Medicine