AIMC Topic: Radiomics
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Fast prediction of personalized abdominal organ doses from CT examinations by radiomics feature-based machine learning models.
Scientific reports
Aug 20, 2024
The X-rays emitted during CT scans can increase solid cancer risks by damaging DNA, with the risk tied to patient-specific organ doses. This study aims to establish a new method to predict patient specific abdominal organ doses from CT examinations u...
Machine learning model for non-alcoholic steatohepatitis diagnosis based on ultrasound radiomics.
BMC medical imaging
Aug 20, 2024
BACKGROUND: Non-Alcoholic Steatohepatitis (NASH) is a crucial stage in the progression of Non-Alcoholic Fatty Liver Disease(NAFLD). The purpose of this study is to explore the clinical value of ultrasound features and radiological analysis in predict...
Optimizing evaluation of endometrial receptivity in recurrent pregnancy loss: a preliminary investigation integrating radiomics from multimodal ultrasound via machine learning.
Frontiers in endocrinology
Aug 20, 2024
BACKGROUND: Recurrent pregnancy loss (RPL) frequently links to a prolonged endometrial receptivity (ER) window, leading to the implantation of non-viable embryos. Existing ER assessment methods face challenges in reliability and invasiveness. Radiomi...
Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences.
Scientific reports
Aug 19, 2024
The aim of this study was to develop a medical imaging and comprehensive stacked learning-based method for predicting high- and low-risk thymoma. A total of 126 patients with thymomas and 5 patients with thymic carcinoma treated at our institution, i...
Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study.
Journal of imaging informatics in medicine
Aug 15, 2024
This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonance radiomics and clinical risk factors, for forecasting perineural invasion in rectal cancer. We retr...
Clinical applications of radiomics and deep learning in breast and lung cancer: A narrative literature review on current evidence and future perspectives.
Critical reviews in oncology/hematology
Aug 14, 2024
Radiomics, analysing quantitative features from medical imaging, has rapidly become an emerging field in translational oncology. Radiomics has been investigated in several neoplastic malignancies as it might allow for a non-invasive tumour characteri...
A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics.
Journal of translational medicine
Aug 14, 2024
BACKGROUND: Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection. Combining computational pathology and deep learning radiomics can enhance the detection of postope...
Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer.
Scientific reports
Aug 14, 2024
To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) burden in breast cancer. Invasive breast cancer patients with preoperative MRI were retrospectively enr...
Deep learning radiomics based on ultrasound images for the assisted diagnosis of chronic kidney disease.
Nephrology (Carlton, Vic.)
Aug 12, 2024
AIM: This study aimed to explore the value of ultrasound (US) images in chronic kidney disease (CKD) screening by constructing a CKD screening model based on grey-scale US images.
Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: A radiomics and deep learning analysis on longitudinal DCE-MRI data.
Breast (Edinburgh, Scotland)
Aug 9, 2024
PURPOSE: In breast cancer (BC) patients with clinical axillary lymph node metastasis (cN+) undergoing neoadjuvant therapy (NAT), precise axillary lymph node (ALN) assessment dictates therapeutic strategy. There is a critical demand for a precise meth...
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