AIMC Topic: Radiomics
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An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 12, 2025
BACKGROUND: Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine l...
Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients.
BMC cancer
Mar 12, 2025
BACKGROUND: Early and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases is critical for guiding targeted therapy. This study aimed to develop a deep...
Development and validation of radiomics and deep transfer learning models to assess cognitive impairment in patients with cerebral small vessel disease.
Neuroscience
Mar 9, 2025
Cognitive impairment in cerebral small vessel disease (CSVD) progresses subtly but carries significant clinical consequences, necessitating effective diagnostic tools. This study developed and validated predictive models for CSVD-related cognitive im...
A Preoperative CT-based Multiparameter Deep Learning and Radiomic Model with Extracellular Volume Parameter Images Can Predict the Tumor Budding Grade in Rectal Cancer Patients.
Academic radiology
Mar 6, 2025
RATIONALE AND OBJECTIVES: To investigate a computed tomography (CT)-based multiparameter deep learning-radiomic model (DLRM) for predicting the preoperative tumor budding (TB) grade in patients with rectal cancer.
Preoperative multiclass classification of thymic mass lesions based on radiomics and machine learning.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 6, 2025
BACKGROUND: Apart from rare cases such as lymphomas, germ cell tumors, neuroendocrine neoplasms, and thymic hyperplasia, thymic mass lesions (TMLs) are typically categorized into cysts, and thymomas. However, the classification results cannot be dete...
Development of Hybrid radiomic Machine learning models for preoperative prediction of meningioma grade on multiparametric MRI.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Mar 5, 2025
PURPOSE: To develop and compare machine learning models for distinguishing low and high grade meningiomas on multiparametric MRI.
Impact of [F]FDG PET/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role.
Seminars in nuclear medicine
Mar 5, 2025
Lung cancer remains one of the most prevalent cancers globally and the leading cause of cancer-related deaths, accounting for nearly one-fifth of all cancer fatalities. Fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography ([F]FDG...
Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning.
Scientific reports
Mar 3, 2025
Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current dia...
MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.
Scientific reports
Mar 3, 2025
We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene...
Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study.
Biomedical engineering online
Mar 2, 2025
BACKGROUND: To develop and validate a model that integrates clinical data, deep learning radiomics, and radiomic features to predict high-risk patients for cage subsidence (CS) after lumbar fusion.
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