Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …

Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI)
field. Modern radiation oncology is based on the exploitation of advanced computational …

Radiomics research is rapidly growing in recent years, but more concerns on radiomics
reliability are also raised. This review attempts to update and overview the current status of …

The application of radiogenomics in oncology has great prospects in precision medicine.
Radiogenomics combines large volumes of radiomic features from medical digital images …

Recent advances in computer-aided diagnosis, treatment response and prognosis in
radiomics and deep learning challenge radiology with requirements for world-wide …

Purpose To investigate the impact of the following three methodological pitfalls on model
generalizability:(a) violation of the independence assumption,(b) model evaluation with an …

Abstract Background and Objectives Artificial intelligence (AI) models trained on multi-
centric and multi-device studies can provide more robust insights and research findings …

Radiomic approaches in precision medicine are promising, but variation associated with
image acquisition factors can result in severe biases and low generalizability. Multicenter …

With the development of genome sequencing, the role of molecular targeted therapy in
cancer is becoming increasingly important. However, genetic testing remains expensive …

Radiomics has become a research field that involves the process of converting standard
nursing images into quantitative image data, which can be combined with other data …