Deep Reinforcement Learning for Early Diagnosis of Lung Cancer
DOI:
https://doi.org/10.1609/aaai.v38i20.30248Keywords:
GeneralAbstract
Lung cancer remains the leading cause of cancer-related death worldwide, and early diagnosis of lung cancer is critical for improving the survival rate of patients. Performing annual low-dose computed tomography (LDCT) screening among high-risk populations is the primary approach for early diagnosis. However, after each screening, whether to continue monitoring (with follow-up screenings) or to order a biopsy for diagnosis remains a challenging decision to make. Continuing with follow-up screenings may lead to delayed diagnosis but ordering a biopsy without sufficient evidence incurs unnecessary risk and cost. In this paper, we tackle the problem by an optimal stopping approach. Our proposed algorithm, called EarlyStop-RL, utilizes the structure of the Snell envelope for optimal stopping, and model-free deep reinforcement learning for making diagnosis decisions. Through evaluating our algorithm on a commonly used clinical trial dataset (the National Lung Screening Trial), we demonstrate that EarlyStop-RL has the potential to greatly enhance risk assessment and early diagnosis of lung cancer, surpassing the performance of two widely adopted clinical models, namely the Lung-RADS and the Brock model.Downloads
Published
2024-03-24
How to Cite
Wang, Y., Zhang, Q., Ying, L., & Zhou, C. (2024). Deep Reinforcement Learning for Early Diagnosis of Lung Cancer. Proceedings of the AAAI Conference on Artificial Intelligence, 38(20), 22410-22419. https://doi.org/10.1609/aaai.v38i20.30248
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Section
AAAI Technical Track on AI for Social Impact Track