Supplementary file 1_Downstream evaluation of synthetic AI-generated T1-weighted contrast-enhanced MR images in glioma segmentation and grading.docx

Objectives<p>The purpose of this study was to evaluate synthetic T₁ weighted post-contrast MR images generated from non-contrast-enhanced MR images, using deep learning (DL) methods, for automated brain tumor segmentation and automated classification of glioma grade based on imaging characteristics.</p>Materials and methods<p>Three DL models were developed for synthesizing T₁-weighted post-contrast MRI (T₁ce) images using a publicly available dataset: (1) a conditional neural field with shift modulation (CoNeS) model, (2) a denoising diffusion probabilistic model (DDPM), and (3) a hybrid CoNeS + DDPM model.

Five experimental settings were evaluated, incorporating combinations of real or synthetic T₁ce and pre-contrast images. Synthetic T₁ce images were evaluated in terms of image quality metrics, accuracy of tumor segmentation, and classification of cases into high-grade vs. low-grade glioma. Two classifier families were evaluated: (a) radiomics-based machine learning classifiers using random forest on extracted radiomic features, and (b) deep learning classifiers employing a convolutional neural network.</p>Results<p>The combined CoNeS + DDPM model performed best in image quality and similarity metrics on the test set.