We developed a radiomic-based reinforcement learning (R-RL) model for the early diagnosis of lung cancer. We formulated the classification of malignant and benign lung nodules with multiple years of screening as a Markov decision process. The reinforcement learning method learned a policy mapping from the set of states (patients’ clinical conditions) of the environment (patients) to the set of possible actions (decisions). The customary mapping between the two sets was based on a value function with the expected reward designed to be associated with lung cancer risk which was increased when the patient was diagnosed with lung cancer and vice versa in the Markov chains. The trained model can be deployed to a single baseline CT scan for early diagnosis of malignant nodules. 215 NLST cases including 108 positive and 107 negative cases with 431 LDCT scans collected from 3 years of screening were used as the training set and another 70 cases with 35 positive and 35 negative cases were used as the independent test set. For each screen-detected nodule in a CT exam, forty-three texture features were extracted and used as the state in reinforcement learning. An offline model-free value iteration method was used to build the R-RL model. Our R-RL model trained with 3 years of serial CT exams achieved an AUC of 0.824 ± 0.003 when deployed to the first year CT exams of the test set. In comparison, the R-RL model trained with only the first year CT scans achieved a significantly (P<0.05) lower test AUC of 0.736 ± 0.004. Our study demonstrated that the R-RL model built with serial CT scans has the potential to improve early diagnosis of indeterminate lung nodules in screening programs, thus reducing follow-up exams or unnecessary biopsy and the associated costs.
This research investigates the fundamental limits and trade-space of quantum semiconductor photodetectors using the Schrödinger equation and the laws of thermodynamics.We envision that, to optimize the metrics of single photon detection, it is critical to maximize the optical absorption in the minimal volume and minimize the carrier transit process simultaneously. Integration of photon management with quantum charge transport/redistribution upon optical excitation can be engineered to maximize the quantum efficiency (QE) and data rate and minimize timing jitter at the same time. Due to the ultra-low capacitance of these quantum devices, even a single photoelectron transfer can induce a notable change in the voltage, enabling non-avalanche single photon detection at room temperature as has been recently demonstrated in Si quanta image sensors (QIS). In this research, uniform III-V quantum dots (QDs) and Si QIS are used as model systems to test the theory experimentally. Based on the fundamental understanding, we also propose proof-of-concept, photon-managed quantum capacitance photodetectors. Built upon the concepts of QIS and single electron transistor (SET), this novel device structure provides a model system to synergistically test the fundamental limits and tradespace predicted by the theory for semiconductor detectors.
This project is sponsored under DARPA/ARO's DETECT Program: Fundamental Limits of Quantum Semiconductor Photodetectors.
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