Non-small cell lung cancer (NSCLC) is a serious disease and has a high recurrence rate
after surgery. Recently, many machine learning methods have been proposed for …

Objective To develop and validate the radiomics nomogram on the discrimination of lung
invasive adenocarcinoma from'non-invasive'lesion manifesting as ground glass nodule …

Objectives: In radiomics, high-throughput algorithms extract objective quantitative features
from medical images. In this study, we evaluated CT-based radiomics features, clinical …

Objectives To analyze whether CT image normalization can improve 3-year recurrence-free
survival (RFS) prediction performance in patients with non-small cell lung cancer (NSCLC) …

Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need
for a streamlined process that can allow radiologists to provide diagnosis with greater …

Background As an emerging technology, radiomics is being widely used in the diagnosis of
early lung cancer due to its excellent diagnostic performance. However, there is a lack of …

Purpose The purpose of this study was to develop a model for predicting chemoradiation
response in non-small cell lung cancer (NSCLC) patients by integrating radiomics and deep …

Objectives To explore the diagnostic value of radiomics in differentiating between lung
adenocarcinomas appearing as ground‐glass opacity nodules (GGO) with high‐and low Ki …

Introduction In the field of personalized medicine, radiomics has shown its potential to
support treatment decisions. However, the limited feature interpretability hampers its …

Objective. Epidermal growth factor receptor (EGFR) mutation genotyping plays a pivotal role
in targeted therapy for non-small cell lung cancer (NSCLC). We aimed to develop a …