Lung cancer is the second most common cancer and the leading cause of cancer-related
death worldwide. Molecular imaging using [18 F] fluorodeoxyglucose Positron Emission …

This study aimed to investigate the diagnostic performance of machine learning-based
radiomics analysis to diagnose coronary artery disease status and risk from rest/stress …

Lung cancer accounts for more deaths worldwide than any other cancer disease. In order to
provide patients with the most effective treatment for these aggressive tumours, multimodal …

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 …

Recently, lung cancer prediction based on imaging genomics has attracted great attention.
However, such studies often have many challenges, such as small sample size, high …

Purpose This paper aims to critically evaluate the role of advanced artificial intelligence (AI)-
enhanced image fusion techniques in lung cancer diagnostics within the context of AI-driven …

Results In the head and neck, chest, chest-abdomen interval, abdomen, and pelvis regions,
the best models achieved area under the curve, accuracy, sensitivity, and specificity of [0.97 …

To develop radiomics models with the optimum performance at survival prognostication, two
main challenges including the selection of imaging modality that reflects the most relevant …

A body of literature has reported the promising performance of deep learning models when
applied to mammograms for different clinical tasks. However, a major pitfall of deep learning …

This study aimed to develop an interpretable prognostic model with a nomogram for Non-
Small Cell Lung Cancer (NSCLC) recurrence prediction following complete resection, using …