Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics.
Tiening ZhangZhihan XuGuixue LiuBeibei JiangGeertruida H de BockHarry J M GroenRozemarijn VliegenthartXueqian XiePublished in: Cancers (2021)
Machine learning-derived 3D radiomics can simultaneously discriminate the presence of EGFR, KRAS, ERBB2, and TP53 mutations in patients with NSCLC. This noninvasive and low-cost approach may be helpful in screening patients before invasive sampling and NGS testing.
Keyphrases
- machine learning
- small cell lung cancer
- tyrosine kinase
- low cost
- end stage renal disease
- epidermal growth factor receptor
- chronic kidney disease
- newly diagnosed
- ejection fraction
- artificial intelligence
- lymph node metastasis
- prognostic factors
- peritoneal dialysis
- advanced non small cell lung cancer
- wild type
- deep learning
- magnetic resonance imaging
- magnetic resonance