Integrative tumor characterization linking radiomic profiles to corresponding gene expression profiles has the potential to identify specific genetic alterations based on non-invasive radiomic profiling in cancer. The aim of this study was to develop and validate a radiomic prognostic index (RPI) based on preoperative magnetic resonance imaging (MRI) and assess possible associations between the RPI and gene expression profiles in endometrial cancer patients. Tumor texture features were extracted from preoperative 2D MRI in 177 endometrial cancer patients. The RPI was developed using least absolute shrinkage and selection operator (LASSO) Cox regression in a study cohort (n = 95) and validated in an MRI validation cohort (n = 82). Transcript...
Abstract To evaluate whether radiomic features from contrast-enhanced computed tomography (CE-CT) ca...
Objectives To explore the diagnostic accuracy of preoperative magnetic resonance imaging (MRI)-deri...
Rationale and Objectives: To evaluate an MRI radiomics-powered machine learning (ML) model's perform...
Integrative tumor characterization linking radiomic profiles to corresponding gene expression profil...
Objective: To develop and validate magnetic resonance (MR) imaging-based radiomics models for high-r...
Background In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, whi...
ObjectiveTo develop and validate a multiparametric MRI-based radiomics model for prediction of micro...
Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for ...
Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk fac...
Purpose To investigate radiomics and machine learning (ML) as possible tools to enhance MRI-based r...
Background: Endometrial cancer is the most common gynecological cancer in highdeveloped regions of t...
High- and low-risk endometrial carcinoma (EC) differ in whether or not a lymphadenectomy is performe...
Objectives: To evaluate the ability of MRI in predicting histological grade of endometrial cancer (E...
Abstract Background To identify predictive value of apparent diffusion coefficient (ADC) values and ...
Purpose. To identify mRNA expression-based stemness index- (mRNAsi-) related genes and build an mRNA...
Abstract To evaluate whether radiomic features from contrast-enhanced computed tomography (CE-CT) ca...
Objectives To explore the diagnostic accuracy of preoperative magnetic resonance imaging (MRI)-deri...
Rationale and Objectives: To evaluate an MRI radiomics-powered machine learning (ML) model's perform...
Integrative tumor characterization linking radiomic profiles to corresponding gene expression profil...
Objective: To develop and validate magnetic resonance (MR) imaging-based radiomics models for high-r...
Background In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, whi...
ObjectiveTo develop and validate a multiparametric MRI-based radiomics model for prediction of micro...
Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for ...
Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk fac...
Purpose To investigate radiomics and machine learning (ML) as possible tools to enhance MRI-based r...
Background: Endometrial cancer is the most common gynecological cancer in highdeveloped regions of t...
High- and low-risk endometrial carcinoma (EC) differ in whether or not a lymphadenectomy is performe...
Objectives: To evaluate the ability of MRI in predicting histological grade of endometrial cancer (E...
Abstract Background To identify predictive value of apparent diffusion coefficient (ADC) values and ...
Purpose. To identify mRNA expression-based stemness index- (mRNAsi-) related genes and build an mRNA...
Abstract To evaluate whether radiomic features from contrast-enhanced computed tomography (CE-CT) ca...
Objectives To explore the diagnostic accuracy of preoperative magnetic resonance imaging (MRI)-deri...
Rationale and Objectives: To evaluate an MRI radiomics-powered machine learning (ML) model's perform...