## What is/are Learning Radiomics?

Learning Radiomics - This study aimed to perform DM prediction through deep learning radiomics.^{[1]}The novel space-resolved radiomics and deep learning radiomics approaches outperformed conventional radiomics techniques.

^{[2]}This study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage.

^{[3]}Using the internal group, models were trained with CT findings evaluated by radiologists, radiomics, and deep learning radiomics (DLR) to predict 5-year pNEN recurrence.

^{[4]}We aimed to develop an updated deep learning radiomics model of elastography (DLRE2.

^{[5]}The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging.

^{[6]}The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size.

^{[7]}Patients and Methods After dividing 332 patients from five hospitals into training (n = 236) and validation (n = 96) datasets, non-invasive models, including clinical/semantic factors (ModelCS), deep learning radiomics (ModelD), and both (ModelCSD), were constructed to stratify patients according to the risk of aggressive PD.

^{[8]}Background Our aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients.

^{[9]}Our aims were to develop a deep learning radiomics model based on computed tomography (CT) data for preoperative evaluation of hepatocellular carcinoma (HCC) differentiation (low vs high grade), and to preliminarily explore the biological basis of the radiomics model.

^{[10]}A 3D deep-learning radiomics model (3D-DLRM) was developed and validated based on pretreatment CT images to predict the response to CCRT.

^{[11]}This study aimed to develop and validate the performance of a deep learning radiomics (DLR) model for CK19 identification in HCC based on preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI).

^{[12]}In this talk, we will be exploring deep learning radiomics applications in cancer patients from both single time point and longitudinal imaging data.

^{[13]}In this regard, deep learning radiomics solutions have the promise of extracting the most useful features on their own in an end-to-end fashion without having access to the annotated boundaries.

^{[14]}BACKGROUND AND OBJECTIVE The capability of deep learning radiomics (DLR) to extract high-level medical imaging features has promoted the use of computer-aided diagnosis of breast mass detected on ultrasound.

^{[15]}Results Four machine-learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group.

^{[16]}A nomogram was constructed for predicting the prognosis of patients with different treatment regimens using multi-task deep learning radiomics and pre-treatment MR images, based on which an optimal treatment regimen was recommended.

^{[17]}The aim is to develop and validate a machine learning radiomics model to predict local tumor progression based on the pre-ablation CT scan of colorectal liver metastases patients.

^{[18]}We aimed to build deep learning radiomics (DLR) models of dual-energy computed tomography (DECT) to classify LNM status of PDAC and to stratify the overall survival before treatment.

^{[19]}CONCLUSION We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma.

^{[20]}For many of the deep learning radiomics applications, region of interest definition is based on a single point placement within the tumour volume, essentially replacing full tumour segmentations with approximate localisation and minimising the need for human input.

^{[21]}OBJECTIVES To noninvasively differentiate meningioma grades by deep learning radiomics (DLR) model based on routine post-contrast MRI.

^{[22]}Based on pre-operative axial T2-weighted images, machine learning radiomics was performed.

^{[23]}

## Deep Learning Radiomics

This study aimed to perform DM prediction through deep learning radiomics.^{[1]}The novel space-resolved radiomics and deep learning radiomics approaches outperformed conventional radiomics techniques.

^{[2]}This study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage.

^{[3]}Using the internal group, models were trained with CT findings evaluated by radiologists, radiomics, and deep learning radiomics (DLR) to predict 5-year pNEN recurrence.

^{[4]}We aimed to develop an updated deep learning radiomics model of elastography (DLRE2.

^{[5]}Patients and Methods After dividing 332 patients from five hospitals into training (n = 236) and validation (n = 96) datasets, non-invasive models, including clinical/semantic factors (ModelCS), deep learning radiomics (ModelD), and both (ModelCSD), were constructed to stratify patients according to the risk of aggressive PD.

^{[6]}Background Our aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients.

^{[7]}Our aims were to develop a deep learning radiomics model based on computed tomography (CT) data for preoperative evaluation of hepatocellular carcinoma (HCC) differentiation (low vs high grade), and to preliminarily explore the biological basis of the radiomics model.

^{[8]}This study aimed to develop and validate the performance of a deep learning radiomics (DLR) model for CK19 identification in HCC based on preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI).

^{[9]}In this talk, we will be exploring deep learning radiomics applications in cancer patients from both single time point and longitudinal imaging data.

^{[10]}In this regard, deep learning radiomics solutions have the promise of extracting the most useful features on their own in an end-to-end fashion without having access to the annotated boundaries.

^{[11]}BACKGROUND AND OBJECTIVE The capability of deep learning radiomics (DLR) to extract high-level medical imaging features has promoted the use of computer-aided diagnosis of breast mass detected on ultrasound.

^{[12]}A nomogram was constructed for predicting the prognosis of patients with different treatment regimens using multi-task deep learning radiomics and pre-treatment MR images, based on which an optimal treatment regimen was recommended.

^{[13]}We aimed to build deep learning radiomics (DLR) models of dual-energy computed tomography (DECT) to classify LNM status of PDAC and to stratify the overall survival before treatment.

^{[14]}For many of the deep learning radiomics applications, region of interest definition is based on a single point placement within the tumour volume, essentially replacing full tumour segmentations with approximate localisation and minimising the need for human input.

^{[15]}OBJECTIVES To noninvasively differentiate meningioma grades by deep learning radiomics (DLR) model based on routine post-contrast MRI.

^{[16]}

## Machine Learning Radiomics

The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size.^{[1]}The aim is to develop and validate a machine learning radiomics model to predict local tumor progression based on the pre-ablation CT scan of colorectal liver metastases patients.

^{[2]}CONCLUSION We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma.

^{[3]}Based on pre-operative axial T2-weighted images, machine learning radiomics was performed.

^{[4]}

## learning radiomics model

We aimed to develop an updated deep learning radiomics model of elastography (DLRE2.^{[1]}The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging.

^{[2]}The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size.

^{[3]}Our aims were to develop a deep learning radiomics model based on computed tomography (CT) data for preoperative evaluation of hepatocellular carcinoma (HCC) differentiation (low vs high grade), and to preliminarily explore the biological basis of the radiomics model.

^{[4]}A 3D deep-learning radiomics model (3D-DLRM) was developed and validated based on pretreatment CT images to predict the response to CCRT.

^{[5]}The aim is to develop and validate a machine learning radiomics model to predict local tumor progression based on the pre-ablation CT scan of colorectal liver metastases patients.

^{[6]}

## learning radiomics application

In this talk, we will be exploring deep learning radiomics applications in cancer patients from both single time point and longitudinal imaging data.^{[1]}For many of the deep learning radiomics applications, region of interest definition is based on a single point placement within the tumour volume, essentially replacing full tumour segmentations with approximate localisation and minimising the need for human input.

^{[2]}