AI RESEARCH
Time-driven Survival Analysis from FDG-PET/CT in Non-Small Cell Lung Cancer
arXiv CS.CV
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ArXi:2604.06885v1 Announce Type: new Purpose: Automated medical image-based prediction of clinical outcomes, such as overall survival (OS), has great potential in improving patient prognostics and personalized treatment planning. We developed a deep regression framework using tissue-wise FDG-PET/CT projections as input, along with a temporal input representing a scalar time horizon (in days) to predict OS in patients with Non-Small Cell Lung Cancer (NSCLC). Methods: The proposed framework employed a ResNet-50 backbone to process input images and generate corresponding image embeddings.