官方服务：
资源简介：
The parameter group, the abbreviation and the full name of the corresponding textural indices are also ennumerated in the table. (XLSX)
应用场景：
创建时间：
2024-10-24
相关数据集
DataSheet_1_The Role of Preoperative Computed Tomography Radiomics in Distinguishing Benign and Malignant Tumors of the Parotid Gland.docx
ObjectiveThis study aimed to develop and validate an integrated prediction model based on clinicoradiological data and computed tomography (CT)-radiomics for differentiating between benign and maligna
NIAID Data Ecosystem60
DataSheet_1_Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades.docx
PurposeTo evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of patholog
NIAID Data Ecosystem30
DataSheet_1_Preoperative prediction of residual back pain after vertebral augmentation for osteoporotic vertebral compression fractures: Initial application of a radiomics score based nomogram.docx
BackgroundMost patients with osteoporotic vertebral compression fracture (OVCF) obtain pain relief after vertebral augmentation, but some will experience residual back pain (RBP) after surgery. Althou
NIAID Data Ecosystem10
SUPPLEMENTARY MATERIAL.docx
A concise statement of the major contents The study developed and validated a nomogram combining BMUS radiomics and the ACR TI-RADS. The predictive performance and stability of the nomogram was asse
NIAID Data Ecosystem30
Data_Sheet_1_Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma.docx
The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to
NIAID Data Ecosystem20
