AIMC Topic: Radiomics
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Morphometric and radiomics analysis toward the prediction of epilepsy associated with supratentorial low-grade glioma in children.
Cancer imaging : the official publication of the International Cancer Imaging Society
May 19, 2025
OBJECTIVES: Understanding the impact of epilepsy on pediatric brain tumors is crucial to diagnostic precision and optimal treatment selection. This study investigated MRI radiomics features, tumor location, voxel-based morphometry (VBM) for gray matt...
Preoperative DBT-based radiomics for predicting axillary lymph node metastasis in breast cancer: a multi-center study.
BMC medical imaging
May 19, 2025
BACKGROUND: In the prognosis of breast cancer, the status of axillary lymph nodes (ALN) is critically important. While traditional axillary lymph node dissection (ALND) provides comprehensive information, it is associated with high risks. Sentinel ly...
Radiomic analysis based on machine learning of multi-sequences MR to assess early treatment response in locally advanced nasopharyngeal carcinoma.
Science progress
May 8, 2025
ObjectiveThe prediction of early response in locally advanced nasopharyngeal carcinoma (LA-NPC) after concurrent chemoradiotherapy (CCRT) is important for determining the need for timely consolidation therapy. We developed a radiomic analysis of mult...
Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy.
BMC cancer
May 6, 2025
BACKGROUND: The early prediction of lymph node positivity (LN+) after neoadjuvant chemotherapy (NAC) is crucial for optimizing individualized treatment strategies. This study aimed to integrate radiomic features and clinical biomarkers through machin...
The Application Status of Radiomics-Based Machine Learning in Intrahepatic Cholangiocarcinoma: Systematic Review and Meta-Analysis.
Journal of medical Internet research
May 5, 2025
BACKGROUND: Over the past few years, radiomics for the detection of intrahepatic cholangiocarcinoma (ICC) has been extensively studied. However, systematic evidence is lacking in the use of radiomics in this domain, which hinders its further developm...
Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.
BMC medical imaging
May 3, 2025
OBJECTIVE: Pituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke's cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar region lesions with similar imaging characteristics, making differential diagnosis challenging. Thi...
Humans
Machine Learning
Retrospective Studies
Female
Aged
Adenoma
Craniopharyngioma
Middle Aged
Young Adult
Diagnosis, Differential
Central Nervous System Cysts
Adolescent
Adult
Meningioma
Support Vector Machine
Radiomics
Meningeal Neoplasms
Magnetic Resonance Imaging
Male
Sella Turcica
Pituitary Neoplasms
A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study.
International journal of surgery (London, England)
May 1, 2025
BACKGROUND: The majority of patients with hepatocellular carcinoma (HCC) miss the opportunity of radical resection, making immune check-point inhibitors (ICIs)-based conversion therapy a primary option. However, challenges persist in predicting respo...
Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.
Journal of thoracic imaging
May 1, 2025
PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor ( EGFR ) and TP53 mutations and to assess the models' capacities t...
Tumor grade-titude: XGBoost radiomics paves the way for RCC classification.
European journal of radiology
Apr 29, 2025
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 R...
Female
Reproducibility of Results
Aged
Aged, 80 and over
Adult
Humans
Machine Learning
Tomography, X-Ray Computed
Neoplasm Grading
Radiographic Image Interpretation, Computer-Assisted
Kidney Neoplasms
Carcinoma, Renal Cell
Sensitivity and Specificity
Boosting Machine Learning Algorithms
Radiomics
Male
Middle Aged
Contrast Media
Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features.
Cancer imaging : the official publication of the International Cancer Imaging Society
Apr 29, 2025
BACKGROUND: The appropriate ligation level of the inferior mesenteric artery (IMA) in left-sided colorectal cancer (CRC) surgery is debated, with metastasis in No. 253 lymph node (No. 253 LN) being a key determining factor. This study aimed to develo...
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