Abstract Least Angle Regression is a promising technique for variable selection
applications, offering a nice alternative to stepwise regression. It provides an explanation for …

In recent years, breakthroughs in biomedical technology have led to a wealth of data in
which the number of features (for instance, genes on which expression measurements are …

Purpose: We aimed to evaluate the value of deep learning on positron emission tomography
with computed tomography (PET/CT)–based radiomics for individual induction …

Purpose To develop a radiomics signature to estimate disease-free survival (DFS) in
patients with early-stage (stage I–II) non–small cell lung cancer (NSCLC) and assess its …

Background TP53 mutation is the most common mutation in hepatocellular carcinoma
(HCC), and it affects the progression and prognosis of HCC. We investigated how TP53 …

Purpose: To identify MRI-based radiomics as prognostic factors in patients with advanced
nasopharyngeal carcinoma (NPC). Experimental Design: One-hundred and eighteen …

We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by
convex combinations of l 1 and l 2 penalties (elastic net). Our algorithm fits via cyclical …

Ordinary least squares and stepwise selection are widespread in behavioral science
research; however, these methods are well known to encounter overfitting problems such …

Rationale: The incidence of hepatocellular carcinoma is rising worldwide. It is predicted that
nearly half of the early-stage hepatocellular carcinoma (E-HCC) patients will develop …

Background Pseudoprogression is a diagnostic challenge in early posttreatment
glioblastoma. We therefore developed and validated a radiomics model using …