Tumor Grading via Decorrelated Sparse Survival Regression

European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 413–418

Abstract

In medical pathology, tumor grading is concerned with estimating the risk posed by a tumor, based on its pathological features.One way to infer risk scores is survival regression, i.e. using machine learning to infer a score that predicts the remaining survival time of a patient.Unfortunately, if applied naively, such a score is a mix of the intrinsic risk posed by the tumor and other risk factors, like the progression of the tumor or patient gender and age.We provide the first survival regression model that disentangles tumor grading from undesired correlations, while retaining a high degree of model interpretability, thanks to convex optimization, nonnegativity constraints, sparsity, and linearity.We evaluate the proposed approach both on simulated and real-world data from N = 114 patients at the University Clinic Aachen.

Authors 3

1. Benjamin Paaßen
2. Nadine Therese Gaisa Aachen Institute of Pathology

   RWTH Aachen University · Universitätsklinikum Aachen · Universität Ulm · University Hospital Ulm

   Affiliation as printed

   - Institute of Pathology University Hospital RWTH Aachen University , Aachen , Germany

   - Institute of Pathology University Hospital Ulm , University of Ulm , Ulm , Germany

3. Mark-Sebastian Bösherz Aachen Institute of Pathology

   RWTH Aachen University · Universitätsklinikum Aachen

   Affiliation as printed

   - Institute of Pathology University Hospital RWTH Aachen University , Aachen , Germany