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AI-driven bayesian deep learning for lung cancer prediction: precision decision support in big data health informatics
Merkouris, Christos Amasiad, N Aslani-Gkotzamanidou, M Theodorakopoulos, L Theodoropoulou, A Krimpas, G, A Karras, A
Merkouris, Christos
Amasiad, N
Aslani-Gkotzamanidou, M
Theodorakopoulos, L
Theodoropoulou, A
Krimpas, G, A
Karras, A
Abstract
Lung-cancer incidence is projected to rise by 50% by 2035, underscoring the need for accurate yet accessible risk-stratification tools. We trained a Bayesian neural network on 300 annotated chest-CT scans from the public LIDC–IDRI cohort, integrating clinical metadata. Hamiltonian Monte-Carlo sampling (10 000 posterior draws) captured parameter uncertainty; performance was assessed with stratified five-fold cross-validation and on three independent multi-centre cohorts. On the locked internal test set, the model achieved 99.0% accuracy, AUC = 0.990 and macro-F1 = 0.987. External validation across 824 scans yielded a mean AUC of 0.933 and an expected calibration error <0.034
, while eliminating false positives for benign nodules and providing voxel-level uncertainty maps. Uncertainty-aware Bayesian deep learning delivers state-of-the-art, well-calibrated lung-cancer risk predictions from a single CT scan, supporting personalised screening intervals and safe deployment in clinical workflows.
MIDER Authors
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Date
2025-07-09
Type
Article
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Citation
Amasiadi N, Aslani-Gkotzamanidou M, Theodorakopoulos L, Theodoropoulou A, Krimpas GA, Merkouris C, Karras A. AI-Driven Bayesian Deep Learning for Lung Cancer Prediction: Precision Decision Support in Big Data Health Informatics. BioMedInformatics. 2025; 5(3):39. https://doi.org/10.3390/biomedinformatics5030039