Rationale and Objectives To build a risk stratification by incorporating PET/CT-based deep
learning features and whole-body metabolic tumor volume (MTV wb), which was to make …

Background The study was aimed to develop a deep learning model for predicting overall
survival (OS) in lung cancer based on radiomic features, standard uptake value (SUV) and …

Background Machine-learning and radiomics are promising approaches to improve the
clinical management of NSCLC. However the additive value of FDG-PET based radiomic …

Rationale and Objectives To develop an end-to-end deep learning (DL) model for non-
invasively predicting non-small cell lung cancer (NSCLC) pathological subtypes based on …

Summary Background Pre-treatment FDG-PET/CT scans were analyzed with machine
learning to predict progression of lung malignancies and overall survival (OS). Methods A …

Objectives: Radiomics and machine learning are innovative approaches to improve the
clinical management of NSCLC. However, there is less information about the additive value …

Purpose: Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer.
Patient survival prediction using machine learning and radiomics analysis proved to provide …

Purposes To evaluate the capability of PET/CT images for differentiating the histologic
subtypes of non-small cell lung cancer (NSCLC) and to identify the optimal model from …

In a secondary analysis of American College of Radiology Imaging Network (ACRIN)
6668/RTOG 0235, high pretreatment metabolic tumor volume (MTV) on 18F-FDG PET was …

329 Objectives: Baseline positron emission tomography with fluorodeoxyglucose (FDG-PET)
based radiomics are of increasing interest for lung cancer prognostic studies. However …