Long-Term (10-Year) Gastrointestinal and Genitourinary Toxicity after Treatment with External Beam Radiotherapy, Radical Prostatectomy, or Brachytherapy for Prostate Cancer

Objective.To examine gastrointestinal (GI) and genitourinary (GU) toxicity profiles of patients treated in 1999 with external beam radiotherapy (RT), prostate interstitial brachytherapy (PI) or radical prostatectomy (RP). Methods. TThe records of 525 patients treated in 1999 were reviewed to evaluate toxicity. Late GI and GU morbidities were graded according to the RTOG late morbidity criteria. Other factors examined were patient age, BMI, smoking history, and medical co-morbidities. Due to the low event rate for late GU and GI toxicities, a competing risk regression (CRR) analysis was done with death as the competing event. Results. Median follow-up time was 8.5 years. On CRR univariate analysis, only the presence of DM was significantly associated with GU toxicity grade >2 (P = 0.43, HR 2.35, 95% Cl = 1.03–5.39). On univariate analysis, RT and DM were significantly associated with late GI toxicity. On multivariable analysis, both variables remained significant (RT: P = 0.038, HR = 4.71, CI = 1.09–20.3; DM: P = 0.008, HR = 3.81, 95% Cl = 1.42–10.2). Conclusions. Late effects occur with all treatment modalities. The presence of DM at the time of treatment was significantly associated with worse late GI and GU toxicity. RT was significantly associated with worse late GI toxicity compared to PI and RP

Efficacy of Endoscopic Submucosal Dissection for Superficial Gastric Neoplasia in a Large Cohort in North America

Background & Aims Endoscopic submucosal dissection (ESD) is a widely accepted treatment option for superficial gastric neoplasia in Asia, but there are few data on outcomes of gastric ESD from North America. We aimed to evaluate the safety and efficacy of gastric ESD in North America. Methods We analyzed data from 347 patients who underwent gastric ESD at 25 centers, from 2010 through 2019. We collected data on patient demographics, lesion characteristics, procedure details and related adverse events, treatment outcomes, local recurrence, and vital status at the last follow up. For the 277 patients with available follow-up data, the median interval between initial ESD and last clinical or endoscopic evaluation was 364 days. The primary endpoint was the rate of en bloc and R0 resection. Secondary outcomes included curative resection, rates of adverse events and recurrence, and gastric cancer-related death. Results Ninety patients (26%) had low-grade adenomas or dysplasia, 82 patients (24%) had high-grade dysplasia, 139 patients (40%) had early gastric cancer, and 36 patients (10%) had neuroendocrine tumors. Proportions of en bloc and R0 resection for all lesions were 92%/82%, for early gastric cancers were 94%/75%, for adenomas and low-grade dysplasia were 93%/ 92%, for high-grade dysplasia were 89%/ 87%, and for neuroendocrine tumors were 92%/75%. Intraprocedural perforation occurred in 6.6% of patients; 82% of these were treated successfully with endoscopic therapy. Delayed bleeding occurred in 2.6% of patients. No delayed perforation or procedure-related deaths were observed. There were local recurrences in 3.9% of cases; all occurred after non-curative ESD resection. Metachronous lesions were identified in 14 patients (6.9%). One of 277 patients with clinical follow up died of metachronous gastric cancer that occurred 2.5 years after the initial ESD. Conclusions ESD is a highly effective treatment for superficial gastric neoplasia and should be considered as a viable option for patients in North America. The risk of local recurrence is low and occurs exclusively after non-curative resection. Careful endoscopic surveillance is necessary to identify and treat metachronous lesions

Spatial Sampling for Model Selection

Many applications in sensor networks require the estimation of spatial environmental fields. We focus on the applications where the estimation is done by fitting a parametric model to the field. We study the case when the parametric model structure is unknown. Instead of assuming a particular structure, we introduce uncertainty by assuming that the spatial field is one of multiple plausible models. We then set a likelihood test for the model selection and find a spatial sampling strategy that optimizes the test. The strategy finds the locations that result in a minimum probability of error in the selection of the correct model structure. The strategy is introduced by Atkinson and Fedorov and is called T-design. We present as well the benefit over the passive (random strategy) data collection

Uncertainties in Modeling of Natural Phenomena (MAS 4)

Natural phenomena are difficult to model because there are many real-world complications. These complications in turn affect the sensor network experimental design which aims to answer a scientific question of interest. For example, a light field is affected by the position of the sun, the height of the tree canopy, and the strength of the wind blowing the leaves, among other things.Instead of using equations which relate all these variables to the field, we can use a statistical model which captures our beliefs of what the field will be like. We can take prior information, which may come from our knowledge or from previous experimental experience, and combine it with uncertanties in unknown quantities using the bayesian statistical framework. We learn the parameters of our models from measured data, and use these models to inform our experimental design.If we use a model for the field which is incorrect to design our sensor network experiements, for example to place the sensors, then our final result will not be what we desired. This project explores ways in which these model choices affect the performance of the learning process

Spatial Sampling for Model Selection

Many applications in sensor networks require the estimation of spatial environmental fields. We focus on the applications where the estimation is done by fitting a parametric model to the field. We study the case when the parametric model structure is unknown. Instead of assuming a particular structure, we introduce uncertainty by assuming that the spatial field is one of multiple plausible models. We then set a likelihood test for the model selection and find a spatial sampling strategy that optimizes the test. The strategy finds the locations that result in a minimum probability of error in the selection of the correct model structure. The strategy is introduced by Atkinson and Fedorov and is called T-design. We present as well the benefit over the passive (random strategy) data collection

Uncertainties in Modeling of Natural Phenomena (MAS 4)

Natural phenomena are difficult to model because there are many real-world complications. These complications in turn affect the sensor network experimental design which aims to answer a scientific question of interest. For example, a light field is affected by the position of the sun, the height of the tree canopy, and the strength of the wind blowing the leaves, among other things.Instead of using equations which relate all these variables to the field, we can use a statistical model which captures our beliefs of what the field will be like. We can take prior information, which may come from our knowledge or from previous experimental experience, and combine it with uncertanties in unknown quantities using the bayesian statistical framework. We learn the parameters of our models from measured data, and use these models to inform our experimental design.If we use a model for the field which is incorrect to design our sensor network experiements, for example to place the sensors, then our final result will not be what we desired. This project explores ways in which these model choices affect the performance of the learning process