Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study.
Guanchao YeGuangyao WuYu QiKuo LiMingliang WangChunyang ZhangFeng LiLeonard WeeAndre DekkerChu HanZaiyi LiuYongde LiaoZhen-Wei ShiPublished in: Journal for immunotherapy of cancer (2024)
By extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.
Keyphrases
- contrast enhanced
- deep learning
- diffusion weighted
- magnetic resonance imaging
- rectal cancer
- magnetic resonance
- computed tomography
- diffuse large b cell lymphoma
- locally advanced
- lymph node
- diffusion weighted imaging
- artificial intelligence
- dual energy
- convolutional neural network
- small cell lung cancer
- high resolution
- machine learning
- wastewater treatment
- pain management
- advanced non small cell lung cancer
- radiation therapy
- image quality
- chronic pain