The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.

in Insights into imaging by Yixin Wang, Zongtao Hu, Hongzhi Wang

TLDR

  • Radiomics has significant applications in brain tumor research, but its interpretability is a major challenge hindering its translation into clinical practice.
  • The study aims to enhance the interpretability of handcrafted-feature radiomics and deep learning-based radiomics by integrating biological domain knowledge with interpretability methods.
  • Enhancing interpretability may improve decision-making and increase confidence in radiomic models, ultimately promoting its translation into clinical practice.

Abstract

Radiomics has widespread applications in the field of brain tumor research. However, radiomic analyses often function as a 'black box' due to their use of complex algorithms, which hinders the translation of brain tumor radiomics into clinical applications. In this review, we will elaborate extensively on the application of radiomics in brain tumors. Additionally, we will address the interpretability of handcrafted-feature radiomics and deep learning-based radiomics by integrating biological domain knowledge of brain tumors with interpretability methods. Furthermore, we will discuss the current challenges and prospects concerning the interpretability of brain tumor radiomics. Enhancing the interpretability of radiomics may make it more understandable for physicians, ultimately facilitating its translation into clinical practice. CRITICAL RELEVANCE STATEMENT: The interpretability of brain tumor radiomics empowers neuro-oncologists to make well-informed decisions from radiomic models. KEY POINTS: Radiomics makes a significant impact on the management of brain tumors in several key clinical areas. Transparent models, habitat analysis, and feature attribute explanations can enhance the interpretability of traditional handcrafted-feature radiomics in brain tumors. Various interpretability methods have been applied to explain deep learning-based models; however, there is a lack of biological mechanisms underlying these models.

Overview

  • The study aims to explore the application of radiomics in brain tumors, focusing on the interpretability of handcrafted-feature radiomics and deep learning-based radiomics.
  • The review integrates biological domain knowledge of brain tumors with interpretability methods to enhance the understanding of radiomics in clinical practice.
  • The primary objective is to enhance the interpretability of radiomics, making it more understandable for physicians and facilitating its translation into clinical practice.

Comparative Analysis & Findings

  • Traditional handcrafted-feature radiomics can be enhanced using transparent models, habitat analysis, and feature attribute explanations to improve interpretability.
  • Deep learning-based models have been applied with various interpretability methods; however, there is limited understanding of the biological mechanisms underlying these models.
  • The lack of interpretability in radiomics hinders its translation into clinical applications, highlighting the need for enhanced interpretability methods.

Implications and Future Directions

  • Enhancing the interpretability of radiomics may lead to better decision-making by neuro-oncologists and increased confidence in radiomic models.
  • Future research should focus on developing biological mechanisms for deep learning-based models, enabling better understanding and translation into clinical practice.
  • The integration of biological domain knowledge with interpretability methods may facilitate the development of more transparent and explainable radiomic models.
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