Background This study classifies lung adenocarcinoma (ADC) and squamous cell
carcinoma (SCC) using subregion-based radiomics features extracted from positron …

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 …

Aims We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC)
histopathological subtypes through a dual-centre dataset and comprehensively evaluate the …

Purpose To evaluate a new radiomics strategy that incorporates intratumoral and
peritumoral features extracted from lung CT images with ensemble learning for pretreatment …

Background Radiomics provides promising opportunities in cancer diagnosis, endowing
medical imaging with an increasingly important role in analyzing tumor phenotypes. Positron …

Purpose We sought to distinguish lung adenocarcinoma (ADC) from squamous cell
carcinoma using a machine-learning algorithm with PET-based radiomic features. Methods …

Purpose Classifying the subtypes of non‐small cell lung cancer (NSCLC) is essential for
clinically adopting optimal treatment strategies and improving clinical outcomes, but the …

Noninvasive differentiation between the squamous cell carcinoma (SCC) and
adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit …

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 …

Purpose Histological subtypes of non‐small cell lung cancer (NSCLC) are crucial for
systematic treatment decisions. However, the current studies which used noninvasive …