We developed multi-modality radiomic models by integrating information extracted from 18 F-
FDG PET and CT images using feature-and image-level fusions, toward improved prognosis …

Aims Despite the promising results achieved by radiomics prognostic models for various
clinical applications, multiple challenges still need to be addressed. The two main limitations …

To characterize intra-tumor heterogeneity comprehensively, we propose a multi-level fusion
strategy to combine PET and CT information at the image-, matrix-and feature-levels towards …

Purpose To explore prognostic and predictive values of a novel quantitative feature set
describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and …

AIM To assess the prognostic value of 2-[18 F]-fluoro-2-deoxy-d-glucose (FDG) positron-
emission tomography (PET)-based radiomics using a machine learning approach in patients …

Objectives To develop and validate the imbalanced data correction based PET/CT radiomics
model for predicting lymph node metastasis (LNM) in clinical stage T1 lung adenocarcinoma …

Purpose To evaluate the usefulness of surrogate biomarkers as predictors of histopathologic
tumor grade and aggressiveness using radiomics data from dual-energy computed …

Objectives This study investigates the ability of machine learning (ML) models trained on
clinical data and 2-deoxy-2-[18F] fluoro-D-glucose (FDG) positron emission …

Purpose To evaluate the ability of CT and PET radiomics features to classify lung lesions as
primary or metastatic, and secondly to differentiate histological subtypes of primary lung …

Machine learning (ML) algorithms for selecting and combining radiomic features into
multiparametric prediction models have become popular; however, it has been shown that …