AIMC Topic: Radiomics
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Association between deep learning radiomics based on placental MRI and preeclampsia with fetal growth restriction: A multicenter study.
European journal of radiology
Feb 5, 2025
PURPOSE: Preeclampsia (PE) is associated with placental insufficiency and could lead to adverse pregnancy outcomes. The study aimed to develop a placental T2-weighted image-based automatic quantitative model for the identification of PE pregnancies a...
Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study.
BMC medical imaging
Feb 5, 2025
BACKGROUND: Long-term severe cholangitis can lead to dense adhesions and increased fragility of the bile duct, complicating surgical procedures and elevating operative risk in children with pancreaticobiliary maljunction (PBM). Consequently, preopera...
Habitat-Based Radiomics for Revealing Tumor Heterogeneity and Predicting Residual Cancer Burden Classification in Breast Cancer.
Clinical breast cancer
Feb 4, 2025
PURPOSE: To investigate the feasibility of characterizing tumor heterogeneity in breast cancer ultrasound images using habitat analysis technology and establish a radiomics machine learning model for predicting response to neoadjuvant chemotherapy (N...
Radiomics Analysis of Different Machine Learning Models based on Multiparametric MRI to Identify Benign and Malignant Testicular Lesions.
Academic radiology
Feb 3, 2025
RATIONALE AND OBJECTIVES: To develop and validate a machine learning-based prediction model for the use of multiparametric magnetic resonance imaging(MRI) to predict benign and malignant lesions in the testis.
Machine Learning-Based CT Radiomics Model to Predict the Risk of Hip Fragility Fracture.
Academic radiology
Feb 3, 2025
RATIONALE AND OBJECTIVES: This research aimed to develop a combined model based on proximal femur attenuation values and radiomics features at routine CT to predict hip fragility fracture using machine learning methods.
Radiomics in glioma: emerging trends and challenges.
Annals of clinical and translational neurology
Feb 3, 2025
Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning alg...
Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.
Journal of cancer research and clinical oncology
Feb 3, 2025
OBJECTIVE: The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to...
Multiscale deep learning radiomics for predicting recurrence-free survival in pancreatic cancer: A multicenter study.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jan 31, 2025
PURPOSE: This multicenter study aimed to develop and validate a multiscale deep learning radiomics nomogram for predicting recurrence-free survival (RFS) in patients with pancreatic ductal adenocarcinoma (PDAC).
A comparative study of statistical, radiomics, and deep learning feature extraction techniques for medical image classification in optical and radiological modalities.
Computers in biology and medicine
Jan 31, 2025
Feature extraction in ML plays a crucial role in transforming raw data into a more meaningful and interpretable representation. In this study, we thoroughly examined a range of feature extraction techniques and assessed their impact on the binary cla...
A machine learning-based 18F-FDG PET/CT multi-modality fusion radiomics model to predict Mediastinal-Hilar lymph node metastasis in NSCLC: a multi-centre study.
Clinical radiology
Jan 29, 2025
AIM: To develop and validate a machine learning (ML) model based on positron emission tomography/computed tomography (PET/CT) multi-modality fusion radiomics to improve the prediction efficiency of mediastinal-hilar lymph node metastasis (LNM).
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