Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer

X Yin, H Liao, H Yun, N Lin, S Li, Y Xiang… - Seminars in cancer biology, 2022 - Elsevier
Lung cancer accounts for the main proportion of malignancy-related deaths and most
patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have …

Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques

A Atmakuru, S Chakraborty, O Faust, M Salvi… - Expert Systems with …, 2024 - Elsevier
This study presents a comprehensive systematic review focusing on the applications of deep
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …

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] Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology

J Shao, J Ma, Q Zhang, W Li, C Wang - Seminars in cancer biology, 2023 - Elsevier
Personalized treatment strategies for cancer frequently rely on the detection of genetic
alterations which are determined by molecular biology assays. Historically, these processes …

[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 …

Feature selection methods and predictive models in CT lung cancer radiomics

G Ge, J Zhang - Journal of applied clinical medical physics, 2023 - Wiley Online Library
Radiomics is a technique that extracts quantitative features from medical images using data‐
characterization algorithms. Radiomic features can be used to identify tissue characteristics …

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 …

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 …

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 …

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 …