Voxel-wise supervised analysis of tumors with multimodal engineered features to highlight interpretable biological patterns.

ABSTRACT:

Background

Translation of predictive and prognostic image-based learning models to clinical applications is challenging due in part to their lack of interpretability. Some deep-learning-based methods provide information about the regions driving the model output. Yet, due to the high-level abstraction of deep features, these methods do not completely solve the interpretation challenge. In addition, low sample size cohorts can lead to instabilities and suboptimal convergence for models involving a large number of parameters such as convolutional neural networks.

Purpose

Here, we propose a method for designing radiomic models that combines the interpretability of handcrafted radiomics with a sub-regional analysis.

Materials and methods

Our approach relies on voxel-wise

SUBMITTER: Escobar T 

PROVIDER: S-EPMC9325536 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature