Introduction Identifying high-risk lung cancer individuals at an early disease stage is the most effective way of improving survival. The landmark National Lung Screening Trial (NLST) demonstrated the utility of low-dose computed tomography (LDCT) imaging to reduce mortality (relative to x-ray screening). As a result of the NLST and other studies, imaging-based lung cancer screening programs are now being implemented. However, LDCT interpretation results in a high number of false positives. A set of dynamic Bayesian networks (DBN) were designed and evaluated to provide insight into how longitudinal data can be used to help inform lung cancer screening decisions. Methods The LDCT arm of the NLST dataset was used to build and explore five DBNs for high-risk individuals. Three of these DBNs were built using a backward construction process, and two using structure learning methods. All models employ demographic, smoking status, cancer history, family lung cancer history, exposure risk factors, comorbidities related to lung cancer, and LDCT screening outcome information. Given the uncertainty arising from lung cancer screening, a cancer state-space model based on lung cancer staging was utilized to characterize the cancer status of an individual over time. The models were evaluated on balanced training and test sets of cancer and non-cancer cases to deal with data imbalance and overfitting. Results Results were comparable to expert decisions. The average area under the curve (AUC) of the receiver operating characteristic (ROC) for the three intervention points of the NLST trial was higher than 0.75 for all models. Evaluation of the models on the complete LDCT arm of the NLST dataset (N = 25, 486) demonstrated satisfactory generalization. Consensus of predictions over similar cases is reported in concordance statistics between the models’ and the physicians’ predictions. The models’ predictive ability with respect to missing data was also evaluated with the sample of cases that missed the second screening exam of the trial (N = 417). The DBNs outperformed comparison models such as logistic regression and naïve Bayes. Conclusion The lung cancer screening DBNs demonstrated high discrimination and predictive power with the majority of cancer and non-cancer cases.
Globally, lung cancer is responsible for nearly one in five cancer deaths. The National Lung Screening Trial (NLST) demonstrated the efficacy of low-dose computed tomography (LDCT) to identify early-stage disease, setting the basis for widespread implementation of lung cancer screening programs. However, the specificity of LDCT lung cancer screening is suboptimal, with a significant false positive rate. Representing this imaging-based screening process as a sequential decision making problem, we combined multiple machine learning-based methods to learn a partially-observable Markov decision process that simultaneously optimizes lung cancer detection while enhancing test specificity. Using NLST data, we trained a dynamic Bayesian network as an observational model and used inverse reinforcement learning to discover a rewards function based on experts' decisions. Our resultant predictive model decreased the false positive rate while maintaining a high true positive rate at a level comparable to human experts. Our model also detected a number of lung cancers earlier. INDEX TERMS Early disease prediction, dynamic Bayesian networks, lung cancer screening, partially observable Markov decision processes, QMDP algorithm.
A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson’s chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE.
Cancer screening can benefit from individualized decision-making tools that decrease overdiagnosis. The heterogeneity of cancer screening participants advocates the need for more personalized methods. Partially observable Markov decision processes (POMDPs), when defined with an appropriate reward function, can be used to suggest optimal, individualized screening policies. However, determining an appropriate reward function can be challenging. Here, we propose the use of inverse reinforcement learning (IRL) to form rewards functions for lung and breast cancer screening POMDPs. Using experts (physicians) retrospective screening decisions for lung and breast cancer screening, we developed two POMDP models with corresponding reward functions. Specifically, the maximum entropy (MaxEnt) IRL algorithm with an adaptive step size was employed to learn rewards more efficiently; and combined with a multiplicative model to learn state-action pair rewards for a POMDP. The POMDP screening models were evaluated based on their ability to recommend appropriate screening decisions before the diagnosis of cancer. The reward functions learned with the MaxEnt IRL algorithm, when combined with POMDP models in lung and breast cancer screening, demonstrate performance comparable to experts. The Cohen's Kappa score of agreement between the POMDPs and physicians' predictions was high in breast cancer and had a decreasing trend in lung cancer.
Background Complement factor H-related protein 5 (CFHR5) nephropathy is an inherited renal disease characterized by microscopic and synpharyngitic macroscopic haematuria, C3 glomerulonephritis and renal failure. It is caused by an internal duplication of exons 2–3 within the CFHR5 gene resulting in dysregulation of the alternative complement pathway. The clinical characteristics and outcomes of transplanted patients with this rare familial nephropathy remain unknown. Methods This is a retrospective case series study of 17 kidney transplant patients with the established founder mutation, followed-up over a span of 30 years. Results The mean (±SD) age of patients at the time of the study and at transplantation was 58.6 ± 9.9 and 46.7 ± 8.8 years, respectively. The 10- and 15-year patient survival rates were 100 and 77.8%, respectively. Proteinuria was present in 33.3% and microscopic haematuria in 58.3% of patients with a functional graft. Serum complement levels were normal in all. ‘Confirmed’ and ‘likely’ recurrence of CFHR5 nephropathy were 16.6 and 52.9%, respectively; however, 76.5% of patients had a functional graft after a median of 120 months post-transplantation. Total recurrence was not associated with graft loss (P = 0.171), but was associated with the presence of microscopic haematuria (P = 0.001) and proteinuria (P = 0.018). Graft loss was associated with the presence of proteinuria (P = 0.025). Conclusions We describe for the first time the clinical characteristics and outcome of patients with CFHR5 nephropathy post-transplantation. Despite the recurrence of CFHR5 nephropathy, we provide evidence for a long-term favourable outcome and support the continued provision of kidney transplantation as a renal replacement option in patients with CFHR5 nephropathy.
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