Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs.
Hyewon ChoiHyung Jin KimWonju HongJongsoo ParkEui Jin HwangChang Min ParkYoung Tae KimJin Mo GooPublished in: European radiology (2020)
• The preoperative CT-based deep learning model demonstrated an expert-level diagnostic performance for the presence of visceral pleural invasion in early-stage lung cancer. • Radiologists had a tendency toward highly sensitive, but not specific diagnoses for the visceral pleural invasion.
Keyphrases
- deep learning
- cell migration
- early stage
- insulin resistance
- artificial intelligence
- image quality
- computed tomography
- dual energy
- contrast enhanced
- convolutional neural network
- machine learning
- positron emission tomography
- magnetic resonance imaging
- metabolic syndrome
- squamous cell carcinoma
- magnetic resonance
- radiation therapy
- mass spectrometry
- living cells
- rectal cancer
- neoadjuvant chemotherapy
- structural basis
- solid phase extraction