Deep learning with radiomics for disease diagnosis and treatment: challenges and potential

X Zhang, Y Zhang, G Zhang, X Qiu, W Tan, X Yin… - Frontiers in …, 2022 - frontiersin.org
The high-throughput extraction of quantitative imaging features from medical images for the
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly developing and …

[HTML][HTML] [18F] FDG-PET/CT radiomics and artificial intelligence in lung cancer: technical aspects and potential clinical applications

R Manafi-Farid, E Askari, I Shiri, C Pirich… - Seminars in nuclear …, 2022 - Elsevier
Lung cancer is the second most common cancer and the leading cause of cancer-related
death worldwide. Molecular imaging using [18 F] fluorodeoxyglucose Positron Emission …

[HTML][HTML] Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images

I Shiri, M Amini, M Nazari, G Hajianfar, AH Avval… - Computers in biology …, 2022 - Elsevier
Objective To investigate the impact of harmonization on the performance of CT, PET, and
fused PET/CT radiomic features toward the prediction of mutations status, for epidermal …

[HTML][HTML] Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection

E Avard, I Shiri, G Hajianfar, H Abdollahi… - Computers in Biology …, 2022 - Elsevier
Objective Robust differentiation between infarcted and normal tissue is important for clinical
diagnosis and precision medicine. The aim of this work is to investigate the radiomic …

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

I Shiri, Y Salimi, M Pakbin, G Hajianfar, AH Avval… - Computers in biology …, 2022 - Elsevier
Background We aimed to analyze the prognostic power of CT-based radiomics models
using data of 14,339 COVID-19 patients. Methods Whole lung segmentations were …

[HTML][HTML] Overall survival prognostic modelling of non-small cell lung cancer patients using positron emission tomography/computed tomography harmonised radiomics …

M Amini, G Hajianfar, AH Avval, M Nazari… - Clinical Oncology, 2022 - Elsevier
Aims Despite the promising results achieved by radiomics prognostic models for various
clinical applications, multiple challenges still need to be addressed. The two main limitations …

High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms

I Shiri, S Mostafaei, A Haddadi Avval, Y Salimi… - Scientific reports, 2022 - nature.com
We aimed to construct a prediction model based on computed tomography (CT) radiomics
features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A …

Clinical application of AI-based PET images in oncological patients

J Dai, H Wang, Y Xu, X Chen, R Tian - Seminars in Cancer Biology, 2023 - Elsevier
Based on the advantages of revealing the functional status and molecular expression of
tumor cells, positron emission tomography (PET) imaging has been performed in numerous …

Myocardial perfusion SPECT imaging radiomic features and machine learning algorithms for cardiac contractile pattern recognition

M Sabouri, G Hajianfar, Z Hosseini, M Amini… - Journal of Digital …, 2023 - Springer
A U-shaped contraction pattern was shown to be associated with a better Cardiac
resynchronization therapy (CRT) response. The main goal of this study is to automatically …

Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study

M Amini, M Pursamimi, G Hajianfar, Y Salimi… - Scientific reports, 2023 - nature.com
This study aimed to investigate the diagnostic performance of machine learning-based
radiomics analysis to diagnose coronary artery disease status and risk from rest/stress …