The classification of benign and malignant pulmonary nodules can provide an important aid
to the diagnosis of lung cancer. However, high-performance classification models are still …

Radiology is integral to cancer care. Compared to molecular assays, imaging has its
advantages. Imaging as a noninvasive tool can assess the entirety of tumor unbiased by …

Topological data analysis provides tools to capture wide-scale structural shape information
in data. Its main method, persistent homology, has found successful applications to various …

Abstract Background Brain metastases (BMs) are common in small cell lung cancer (SCLC),
and the efficacy of immune checkpoint inhibitors (ICIs) in these patients is uncertain. In this …

Purpose We aim to evaluate the performance of radiomic biopsy (RB), best-fit bounding box
(BB), and a deep-learning-based segmentation method called no-new-U-Net (nnU-Net) …

The era of high-throughput techniques created big data in the medical field and research
disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how …

This study explores a multi-modal machine-learning-based approach to classify solitary
pulmonary nodules (SPNs). Non-small cell lung cancer (NSCLC), presenting primarily as …