Featured Application Computational models based on artificial intelligence (AI) variants
have been developed and applied successfully in many areas, both inside and outside of …

Cancer remains a substantial worldwide health issue that requires careful and exact
classification to plan treatment in its early stages. Classical methods of cancer diagnosis …

Objectives Optimizing a machine learning (ML) pipeline for radiomics analysis involves
numerous choices in data set composition, preprocessing, and model selection. Objective …

In this study, we tested and compared radiomics and deep learning-based approaches on
the public LUNG1 dataset, for the prediction of 2-year overall survival (OS) in non-small cell …

Currently, lung cancer has become one of the most common and deadliest types of cancer.
Due to its severity, many countries are now encouraging their at-risk citizens to test and treat …

This study aimed to systematically summarize the performance of the machine learning-
based radiomics models in the prediction of microsatellite instability (MSI) in patients with …

Non-invasive diagnostic method based on radiomic features in patients with non-small cell
lung cancer (NSCLC) has attracted attention. This study aimed to develop a CT image …

Breast Cancer (BC) is the most common cancer among women worldwide and is
characterized by intra-and inter-tumor heterogeneity that strongly contributes towards its …

Background Prompt diagnosis of early gastric cancer (EGC) is crucial for improving patient
survival. However, most previous computer-aided-diagnosis (CAD) systems did not …

Lung cancer is the leading cause of cancer-related deaths worldwide. Medical imaging
technologies such as computed tomography (CT) and positron emission tomography (PET) …