ABSTRACT
In this paper, we present an efficient statistical method (denoted as 'Adaptive Resources Allocation CUSUM') to robustly and efficiently detect the hotspot with limited sampling resources. Our main idea is to combine the multi-arm bandit (MAB) and change-point detection methods to balance the exploration and exploitation of resource allocation for hotspot detection. Further, a Bayesian weighted update is used to update the posterior distribution of the infection rate. Then, the upper confidence bound (UCB) is used for resource allocation and planning. Finally, CUSUM monitoring statistics to detect the change point as well as the change location. For performance evaluation, we compare the performance of the proposed method with several benchmark methods in the literature and showed the proposed algorithm is able to achieve a lower detection delay and higher detection precision. Finally, this method is applied to hotspot detection in a real case study of county-level daily positive COVID-19 cases in Washington State WA) and demonstrates the effectiveness with very limited distributed samples.
ABSTRACT
High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications. However, one specific challenge is that it is often not easy to obtain complete measurements due to limited sensing powers and resource constraints. Furthermore, distinct failure patterns may exist in the systems, and it is necessary to identify the true failure pattern. This work focuses on the online adaptive monitoring of high-dimensional data in resource-constrained environments with multiple potential failure modes. To achieve this, we propose to apply the Shiryaev-Roberts procedure on the failure mode level and utilize the multi-arm bandit to balance the exploration and exploitation. We further discuss the theoretical property of the proposed algorithm to show that the proposed method can correctly isolate the failure mode. Finally, extensive simulations and two case studies demonstrate that the change point detection performance and the failure mode isolation accuracy can be greatly improved.
ABSTRACT
PURPOSE: Recent studies of radiotherapy (RT) for stage III non-small-cell lung cancer (NSCLC) have associated high dose to the heart with cardiac toxicity and decreased overall survival (OS). We used advanced statistical techniques to account for correlations between dosimetric variables and more accurately determine the range of heart doses which are associated with reduced OS in patients receiving RT for stage III NSCLC. METHODS: From 2006 to 2013, 119 patients with stage III NSCLC received definitive RT at our institution. OS data was obtained from institutional tumor registry. We used multivariate Cox model to determine patient specific covariates predictive for reduced overall survival. We examined age, prescription dose, mean lung dose, lung V20, RT technique, stage, chemotherapy, tumor laterality, tumor volume, and tumor site as candidate covariates. We subsequently used novel statistical techniques within multivariate Cox model to systematically search the whole heart dose-volume histogram (DVH) for dose parameters associated with OS. RESULTS: Patients were followed until death or 2.5 to 81.2 months (median 30.4 months) in those alive at last follow up. On multivariate analysis of whole heart DVH, the dose of 51 Gy was identified as a threshold dose above which the dose volume relationship becomes predictive for OS. We identified V55Gy (percentage of the whole heart volume receiving at least 55 Gy) as the best single DVH index which can be used to set treatment optimization constraints (Hazard Ratio = 1.044 per 1% increase in heart volume exposed to at least 55 Gy, P = 0.03). Additional characteristics correlated with OS on multivariate analysis were age, stage (IIIA/IIIB), and administration of chemotherapy. CONCLUSION: Doses above 51 Gy, applied to small volumes of the heart, are associated with worse OS in stage III NSCLC patients treated with definitive RT. Higher stage, older age and lack of chemotherapy were also associated with reduced OS.
ABSTRACT
PURPOSE: To examine possible association between heart irradiation and Overall Survival (OS) in lung SBRT patients and to compare observed associations with cardiac toxicity models previously derived in LA-NSCLC patient studies. MATERIALS AND METHODS: 197 Patients treated with lung SBRT at Mayo Clinic Arizona were selected for this IRB-approved study. Multivariate Cox model with Akaike Information Criterion (AIC) was used to select patient specific covariates associated with OS. Heart dosimetry was represented by VD indices, which is a percentage of volume exposed to dose D or greater. Multivariate Cox models with patient specific covariates and single VD index per model was used to find a range of doses which were predictive for OS. A digital subdivision of the heart was further used to determine the spatial distribution of doses which were predictive for OS. A coarse subdivision divided heart into 4 segments, while the fine subdivision divided heart into 64 segments. Knowledge constrained Fused Lasso operator was used to derive a more complete model which correlated heart dosimetry with OS. Results of statistical analysis were compared to predictions of a model of cardiac toxicity in LA-NSCLC patients. RESULTS: Higher age (p < 0.001), higher stage (p < 0.001) and squamous cell histology (p = 0.001) were associated with reduced OS. Whole heart DVH analysis did not reveal associations between heart irradiation and reduced OS. Coarse subdivision of the heart into four segments revealed that the irradiation of two inferior segments of the heart with low doses was associated with reduced OS, V 2Gy in the right-inferior segment (HR = 1.012/1%, p = 0.02), and V 1Gy in the left-inferior segment (HR = 1.01/1%, p = 0.04). Maximum dose in the right-inferior segment of the heart was also associated with reduced OS (HR = 1.02/Gy, p = 0.02). Fine subdivision of the heart into 64 segments revealed that approximately 25% of heart volume in the inferior part of the heart (15/64 segments), when irradiated to doses in the 1 Gy - 5 Gy range, were predictive for reduced OS (HR = 1.01/1%, p = 0.01). A previously derived model of cardiac toxicity in LA-NSCLC patients did not predict a reduction of OS due to heart irradiation in lung SBRT patients, because of relatively low doses to the heart in most lung SBRT patients. CONCLUSIONS: Doses lower than 5 Gy in the inferior segments of the heart may be associated with reduced overall survival in patients treated for lung lesions with SBRT. Stage and histology of the disease, as well as patients' age, were also associated with overall survival. Comparisons of cardiac toxicity patterns in LA-NSCLC patients and lung SBRT patients suggest different etiology of cardiac toxicity in the two groups.
