## What is/are Deep Radiomics?

Deep Radiomics - Hence, the set of feature vectors can be used in downstream tasks as imaging markers, which we call deep radiomics.^{[1]}We termed this approach, “Deep Radiomics.

^{[2]}Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics.

^{[3]}To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients.

^{[4]}Deep radiomics uses CNNs to directly extract features and obviate the need for predefined features.

^{[5]}We modeled the classification task of histological subtypes based on modified ResNet-34 deep learning network, radiomics strategies and deep radiomics combined algorithm.

^{[6]}• The C-index of prognosis model based on deep radiomics combined classifier was 0.

^{[7]}METHODS We investigated an innovative approach based on a 3-dimensional (3D) deep radiomics pipeline to classify visual features of chest-abdomen computed tomography (CT) scans with the aim of distinguishing disease control from progressive disease to immune checkpoint inhibitors (ICIs).

^{[8]}Based on the author’s plenary speech at SPIE Optics + Photonics, August 2, 2021, here we provide a background where x-ray imaging meets deep learning, describe representative results on low-dose CT, sparse-data CT, and deep radiomics, and discuss opportunities to combine datadriven and model-based methods for x-ray CT, other imaging modalities, and their combinations so that imaging service can be significantly improved for precision medicine.

^{[9]}By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models.

^{[10]}The training performance shows the effectiveness for a combination of texture and deep radiomics features in lung segmentation.

^{[11]}We call this approach “Deep Radiomics”.

^{[12]}The proposed multiple abnormal tumor tissue segmentation step effectively fuses feature-based and feature-guided deep radiomics information in structural MRI.

^{[13]}

## deep radiomics combined

We modeled the classification task of histological subtypes based on modified ResNet-34 deep learning network, radiomics strategies and deep radiomics combined algorithm.^{[1]}• The C-index of prognosis model based on deep radiomics combined classifier was 0.

^{[2]}