CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy.
Fengchang YangJiayi ZhangLiu ZhouWei XiaRui ZhangHaifeng WeiJinxue FengXingyu ZhaoJunming JianXin GaoShuanghu YuanPublished in: European radiology (2021)
The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646-0.846). The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- lymph node metastasis
- computed tomography
- decision making
- dual energy
- image quality
- magnetic resonance
- locally advanced
- small cell lung cancer
- artificial intelligence
- deep learning
- positron emission tomography
- squamous cell carcinoma
- advanced non small cell lung cancer
- optical coherence tomography
- genome wide
- radiation therapy
- machine learning
- chemotherapy induced
- gene expression
- brain metastases