Purpose The aim of this systematic review was to analyse literature on artificial intelligence
(AI) and radiomics, including all medical imaging modalities, for oncological and non …

Radiomics is a technique that extracts quantitative features from medical images using data‐
characterization algorithms. Radiomic features can be used to identify tissue characteristics …

Purpose To evaluate ability of radiomic (computer-extracted imaging) features to distinguish
non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials …

No predictive biomarkers can robustly identify patients with non–small cell lung cancer
(NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a …

Computed tomography (CT) screening is an effective way for early detection of lung cancer
in order to improve the survival rate of such a deadly disease. For more than two decades …

Purpose To identify the role of radiomics texture features both within and outside the nodule
in predicting (a) time to progression (TTP) and overall survival (OS) as well as (b) response …

Neuro-oncology largely consists of malignancies of the brain and central nervous system
including both primary as well as metastatic tumors. Currently, a significant clinical …

Objective The use of a neoadjuvant chemoradiation followed by surgery in patients with
stage IIIA NSCLC is controversial and the benefit of surgery is limited. There are currently no …

Objectives Pulmonary granulomatous nodule (GN) with spiculated or lobulated appearance
are indistinguishable from solid lung adenocarcinoma (SADC) based on CT morphological …

Objectives To evaluate whether combining stability and discriminability criteria in building
radiomic classifiers will improve the prognosis of cancer recurrence in early stage non-small …