AIMC Topic: Radiomics
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CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study.
Translational stroke research
Jun 13, 2023
This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional risk factors and radiomics features. This multicenter retrospective study enrolled 586 patients wit...
Editorial for "Preoperative Discrimination of CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytoma: A Deep Learning-Based Radiomics Model Using MRI".
Journal of magnetic resonance imaging : JMRI
Jun 12, 2023
Prenatal Diagnosis of Placenta Accreta Spectrum Disorders: Deep Learning Radiomics of Pelvic MRI.
Journal of magnetic resonance imaging : JMRI
May 24, 2023
BACKGROUND: Diagnostic performance of placenta accreta spectrum (PAS) by prenatal MRI is unsatisfactory. Deep learning radiomics (DLR) has the potential to quantify the MRI features of PAS.
Investigation of radiomics and deep convolutional neural networks approaches for glioma grading.
Biomedical physics & engineering express
Mar 23, 2023
To determine glioma grading by applying radiomic analysis or deep convolutional neural networks (DCNN) and to benchmark both approaches on broader validation sets.Seven public datasets were considered: (1) low-grade glioma or high-grade glioma (369 p...
Radiomic-based machine learning model for the accurate prediction of prostate cancer risk stratification.
The British journal of radiology
Jan 4, 2023
OBJECTIVES: To precisely predict prostate cancer (PCa) risk stratification, we constructed a machine learning (ML) model based on magnetic resonance imaging (MRI) radiomic features.
Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics.
Journal of neurosurgery
Dec 2, 2022
OBJECTIVE: The aim of this study was to build a convolutional neural network (CNN)-based prediction model of glioblastoma (GBM) molecular subtype diagnosis and prognosis with multimodal features.
Study on predicting breast cancer Ki-67 expression using a combination of radiomics and deep learning based on multiparametric MRI.
Magnetic resonance imaging
Sep 1, 2025
PURPOSE: To develop a multiparametric breast MRI radiomics and deep learning-based multimodal model for predicting preoperative Ki-67 expression status in breast cancer, with the potential to advance individualized treatment and precision medicine fo...
Deep Learning
ROC Curve
Image Processing, Computer-Assisted
Contrast Media
Sensitivity and Specificity
Breast Neoplasms
Radiomics
Middle Aged
Ki-67 Antigen
Diffusion Magnetic Resonance Imaging
Multiparametric Magnetic Resonance Imaging
Magnetic Resonance Imaging
Predictive Value of Tests
Breast
Image Interpretation, Computer-Assisted
Humans
Female
Reproducibility of Results
Aged
Adult
Comparison of clinical, radiomics, deep learning, and fusion models for predicting early recurrence in locally advanced rectal cancer based on multiparametric MRI: a multicenter study.
European journal of radiology
Aug 1, 2025
OBJECTIVE: Predicting early recurrence (ER) in locally advanced rectal cancer (LARC) is critical for clinical decision-making. This study aimed at comparing clinical, deep learning (DL), radiomics, and two fusion models for ER prediction based on mul...
Paving a Path to Clinical Impact with Radiomics: Enabling Reproducibility and Reach.
Cancer research
Jul 2, 2025
Radiomics, the extraction of quantitative data from images, holds promise for noninvasively characterizing tumor phenotypes. Tools like LIFEx have improved the accessibility, transparency, and reproducibility of radiomic feature extraction by offerin...
Exploring best-performing radiomic features with combined multilevel discrete wavelet decompositions for multiclass COVID-19 classification using chest X-ray images.
Computers in biology and medicine
Jul 1, 2025
Discrete wavelet transforms have been applied in many machine learning models for the analysis of COVID-19; however, little is known about the impact of combined multilevel wavelet decompositions for the disease identification. This study proposes a ...
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