Comparing Handcrafted Radiomics Versus Latent Deep Learning Features of Admission Head CT for Hemorrhagic Stroke Outcome Prediction

Abstract

Handcrafted radiomics use predefined formulas to extract quantitative features from medical images, whereas deep neural networks learn de novo features through iterative training. We compared these approaches for predicting 3-month outcomes and hematoma expansion from admission non-contrast head CT in acute intracerebral hemorrhage (ICH). Training and cross-validation were performed using a multicenter trial cohort (n = 866), with external validation on a single-center dataset (n = 645). We trained multiscale U-shaped segmentation models for hematoma segmentation and extracted (i) radiomics from the segmented lesions and (ii) two latent deep feature sets-from the segmentation encoder and a generative autoencoder trained on dilated lesion patches. Features were reduced with unsupervised Non-Negative Matrix Factorization (NMF) to 128 per set and used-alone or in combination-for six machine-learning classifiers to predict 3-month clinical outcomes and (>3, >6, >9 mL) hematoma expansion thresholds. The addition of latent deep features to radiomics numerically increased model prediction performance for 3-month outcomes and hematoma expansion using Random Forest, XGBoost, Extra Trees, or Elastic Net classifiers; however, the improved accuracy only reached statistical significance in predicting >3 mL hematoma expansion. Clinically, these consistent but modest increases in prediction performance may improve risk stratification at the individual level. Nevertheless, the latent deep features show potential for extracting additional clinically relevant information from admission head CT for prognostication in hemorrhagic stroke.

Keywords: U-net segmentation; generative auto-encoders; intracerebral hemorrhage; latent deep features; radiomics; stroke.

Figure 1

Integrating a multiscale model into nnU-Net with multiple outputs for loss function. The final nn-UNET model included both the original and the ½ scale head CT images for segmentation of hematomas.

Figure 2

The overall pipeline for extraction of radiomic features and latent deep learning features. The radiomic features were extracted from the hematoma lesion segmented by nnU-Net. A set of latent deep learning features were extracted from the encoder of the U-shaped segmentation neural network at the bottleneck. Then, dilated masks of the hematoma were used as input for a Variational Autoencoder–Generative Adversarial Network (VAE-GAN), and an additional set of latent deep features were extracted from the encoder bottleneck as the model regenerates the CT images within the dilated hematoma mask.

Figure 3

The AUCs of each prediction model with different inputs in independent tests for functional outcome and >3-, >6-, and >9 mL hematoma expansion. Green indicates higher performance, yellow indicates moderate performance, and red indicates lower performance values. Darker green corresponds to the best-performing models within each prediction task.

Figure 4

The example calibration curves and decision curve of the best model for poor outcome prediction (Extra Trees model with radiomics and latent deep features from nnU-Net and VAE-GAN).

Figure 5

The group-level SHAP contribution across models in outcome prediction.