The value of AI in the Diagnosis, Treatment, and Prognosis of Malignant Lung Cancer
Open Access
- 6 May 2022
- journal article
- review article
- Published by Frontiers Media SA in Frontiers in Radiology
Abstract
Malignant tumors is a serious public health threat. Among them, lung cancer, which has the highest fatality rate globally, has significantly endangered human health. With the development of artificial intelligence (AI) and its integration with medicine, AI research in malignant lung tumors has become critical. This article reviews the value of CAD, computer neural network deep learning, radiomics, molecular biomarkers, and digital pathology for the diagnosis, treatment, and prognosis of malignant lung tumors.Keywords
This publication has 76 references indexed in Scilit:
- Pembrolizumab for the Treatment of Non–Small-Cell Lung CancerThe New England Journal of Medicine, 2015
- Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary NodulesJournal of Digital Imaging, 2014
- Computer-aided Diagnosis: How to Move from the Laboratory to the ClinicRadiology, 2011
- Incidence and Risk Factors for Chest Wall Toxicity After Risk-Adapted Stereotactic Radiotherapy for Early-Stage Lung CancerJournal of Thoracic Oncology, 2011
- Stereotactic Body Radiation Therapy for Inoperable Early Stage Lung CancerJAMA, 2010
- Non-Small Cell Lung Cancer: Epidemiology, Risk Factors, Treatment, and SurvivorshipMayo Clinic Proceedings, 2008
- Non-Small Cell Lung Cancer: Epidemiology, Risk Factors, Treatment, and SurvivorshipMayo Clinic Proceedings, 2008
- Reducing the Dimensionality of Data with Neural NetworksScience, 2006
- A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public databaseMedical Image Analysis, 2006
- Processing X-ray images to eliminate irrelevant structures that mask important featuresComputerized Medical Imaging and Graphics, 2004