Radiomic analysis offers a powerful tool for the extraction of clinically relevant information
from radiologic imaging. Radiomics can be used to predict patient outcome through …

Radiological imaging is an integral component of cancer care, including diagnosis, staging,
and treatment response monitoring. It contains rich information about tumor phenotypes that …

Radiomics refers to the high-throughput extraction of quantitative features from radiological
scans and is widely used to search for imaging biomarkers for the prediction of clinical …

Purpose: Accurate prognostic stratification of patients with oropharyngeal squamous cell
carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning–based …

There is no consensus regarding optimal interpretative criteria (IC) for Fluorine-18
fluorodeoxyglucose (FDG) Positron Emission Tomography–Computed Tomography (PET …

Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial
intelligence (AI) has achieved tremendous success in medical image analysis. This paper …

Purpose This study aimed to investigate the impact of several ComBat harmonization
strategies, intra-tumoral sub-volume characterization, and automatic segmentations for …

Survival models exist to study relationships between biomarkers and treatment effects. Deep
learning-powered survival models supersede the classical Cox proportional hazards …

Background Translation of predictive and prognostic image‐based learning models to
clinical applications is challenging due in part to their lack of interpretability. Some deep …

The efficacy of quantitative ultrasound (QUS) multi-parametric imaging in conjunction with
unsupervised classification algorithms was investigated for the first time in characterizing …