Applying an explainable machine learning model might reduce the number of negative appendectomies in pediatric patients with a high probability of acute appendicitis.

The diagnosis of acute appendicitis and concurrent surgery referral is primarily based on clinical presentation, laboratory and radiological imaging. However, utilizing such an approach results in as much as 10-15% of negative appendectomies. Hence, in the present study, we aimed to develop a machine learning (ML) model designed to reduce the number of negative appendectomies in pediatric patients with a high clinical probability of acute appendicitis. The model was developed and validated on a registry of 551 pediatric patients with suspected acute appendicitis that underwent surgical treatment. Clinical, anthropometric, and laboratory features were included for model training and analysis. Three machine learning algorithms were tested (random forest, eXtreme Gradient Boosting, logistic regression) and model explainability was obtained. Random forest model provided the best predictions achieving mean specificity and sensitivity of 0.17 ± 0.01 and 0.997 ± 0.001 for detection of acute appendicitis, respectively. Furthermore, the model outperformed the appendicitis inflammatory response (AIR) score across most sensitivity-specificity combinations. Finally, the random forest model again provided the best predictions for discrimination between complicated appendicitis, and either uncomplicated acute appendicitis or no appendicitis at all, with a joint mean sensitivity of 0.994 ± 0.002 and specificity of 0.129 ± 0.009. In conclusion, the developed ML model might save as much as 17% of patients with a high clinical probability of acute appendicitis from unnecessary surgery, while missing the needed surgery in only 0.3% of cases. Additionally, it showed better diagnostic accuracy than the AIR score, as well as good accuracy in predicting complicated acute appendicitis over uncomplicated and negative cases bundled together. This may be useful in centers that advocate for the conservative treatment of uncomplicated appendicitis. Nevertheless, external validation is needed to support these findings.

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy.

Objective.To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model.Approach.Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds.Main results.Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively.Significance.Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.

Improved PAA algorithm for breast mass detection in mammograms.

Mammography screening is instrumental in the early detection and diagnosis of breast cancer by identifying masses in mammograms. With the rapid development of deep learning, numerous deep learning-based object detection algorithms have been explored for mass detection studies. However, these methods often yield a high false positive rate per image (FPPI) while achieving a high true positive rate (TPR). To maintain a higher TPR while also ensuring lower FPPI, we improved the Probability Anchor Assignment (PAA) algorithm to enhance the detection capability for mammographic characteristics with our previous work. We considered three dimensions: the backbone network, feature fusion module, and dense detection heads. The final experiment showed the effectiveness of the proposed method, and the TPR/FPPI values of the final improved PAA algorithm were 0.96/0.56 on the INbreast datasets. Compared to other methods, our method stands distinguished with its effectiveness in addressing the imbalance between positive and negative classes in cases of single lesion detection.

CureAuxSP: An R package for estimating mixture cure models with auxiliary survival probabilities.

BACKGROUND AND OBJECTIVE: There is a rising interest in exploiting aggregate information from external medical studies to enhance the statistical analysis of a modestly sized internal dataset. Currently available software packages for analyzing survival data with a cure fraction ignore the potentially available auxiliary information. This paper aims at filling this gap by developing a new R package CureAuxSP that can include subgroup survival probabilities extracted outside into an interested internal survival dataset. METHODS: The newly developed R package CureAuxSP provides an efficient approach for information synthesis under the mixture cure models, including Cox proportional hazards mixture cure model and the accelerated failure time mixture cure model as special cases. It focuses on synthesizing subgroup survival probabilities at multiple time points and the underlying method development lies in the control variate technique. Evaluation of homogeneity assumption based on a test statistic can be automatically carried out by our package and if heterogeneity does exist, the original outputs can be further refined adaptively. RESULTS: The R package CureAuxSP provides a main function SMC.AxuSP() that helps us adaptively incorporate external subgroup survival probabilities into the analysis of an internal survival data. We also provide another function Print.SMC.AuxSP() for printing the results with a better presentation. Detailed usages are described, and implementations are illustrated with numerical examples, including a simulated dataset with a well-designed data generating process and a real breast cancer dataset. Substantial efficiency gain can be observed by our results. CONCLUSIONS: Our R package CureAuxSP can make the wide applications of utilizing auxiliary information possible. It is anticipated that the performance of mixture cure models can be improved for the survival data with a cure fraction, especially for those with small sample sizes.

Interventional probability of causation (IPoC) with epidemiological and partial mechanistic evidence: benzene vs. formaldehyde and acute myeloid leukemia (AML).

INTRODUCTION: Causal epidemiology for regulatory risk analysis seeks to evaluate how removing or reducing exposures would change disease occurrence rates. We define interventional probability of causation (IPoC) as the change in probability of a disease (or other harm) occurring over a lifetime or other specified time interval that would be caused by a specified change in exposure, as predicted by a fully specified causal model. We define the closely related concept of causal assigned share (CAS) as the predicted fraction of disease risk that would be removed or prevented by a specified reduction in exposure, holding other variables fixed. Traditional approaches used to evaluate the preventable risk implications of epidemiological associations, including population attributable fraction (PAF) and the Bradford Hill considerations, cannot reveal whether removing a risk factor would reduce disease incidence. We argue that modern formal causal models coupled with causal artificial intelligence (CAI) and realistically partial and imperfect knowledge of underlying disease mechanisms, show great promise for determining and quantifying IPoC and CAS for exposures and diseases of practical interest. METHODS: We briefly review key CAI concepts and terms and then apply them to define IPoC and CAS. We present steps to quantify IPoC using a fully specified causal Bayesian network (BN) model. Useful bounds for quantitative IPoC and CAS calculations are derived for a two-stage clonal expansion (TSCE) model for carcinogenesis and illustrated by applying them to benzene and formaldehyde based on available epidemiological and partial mechanistic evidence. RESULTS: Causal BN models for benzene and risk of acute myeloid leukemia (AML) incorporating mechanistic, toxicological and epidemiological findings show that prolonged high-intensity exposure to benzene can increase risk of AML (IPoC of up to 7e-5, CAS of up to 54%). By contrast, no causal pathway leading from formaldehyde exposure to increased risk of AML was identified, consistent with much previous mechanistic, toxicological and epidemiological evidence; therefore, the IPoC and CAS for formaldehyde-induced AML are likely to be zero. CONCLUSION: We conclude that the IPoC approach can differentiate between likely and unlikely causal factors and can provide useful upper bounds for IPoC and CAS for some exposures and diseases of practical importance. For causal factors, IPoC can help to estimate the quantitative impacts on health risks of reducing exposures, even in situations where mechanistic evidence is realistically incomplete and individual-level exposure-response parameters are uncertain. This illustrates the strength that can be gained for causal inference by using causal models to generate testable hypotheses and then obtaining toxicological data to test the hypotheses implied by the models-and, where necessary, refine the models. This virtuous cycle provides additional insight into causal determinations that may not be available from weight-of-evidence considerations alone.

Pollution levels and probability risk assessment of potential toxic elements in soil of Pb-Zn smelting areas.

Soil pollution around Pb-Zn smelters has attracted widespread attention around the world. In this study, we compiled a database of eight potentially toxic elements (PTEs) Pb, Zn, Cd, As, Cr, Ni, Cu, and Mn in the soil of Pb-Zn smelting areas by screening the published research papers from 2000 to 2023. The pollution assessment and risk screening of eight PTEs were carried out by geo-accumulation index (Igeo), potential ecological risk index (PERI) and health risk assessment model, and Monte Carlo simulation employed to further evaluate the probabilistic health risks. The results suggested that the mean values of the eight PTEs all exceeded the corresponding values in the upper crust, and more than 60% of the study sites had serious Pb and Cd pollution (Igeo > 4), with Brazil, Belgium, China, France and Slovenia having higher levels of pollution than other regions. Besides, PTEs in smelting area caused serious ecological risk (PERI = 10912.12), in which Cd was the main contributor to PREI (86.02%). The average hazard index (HI) of the eight PTEs for adults and children was 7.19 and 9.73, respectively, and the average value of total carcinogenic risk (TCR) was 4.20 × 10-3 and 8.05 × 10-4, respectively. Pb and As are the main contributors to non-carcinogenic risk, while Cu and As are the main contributors to carcinogenic risk. The probability of non-carcinogenic risk in adults and children was 84.05% and 97.57%, while carcinogenic risk was 92.56% and 79.73%, respectively. In summary, there are high ecological and health risks of PTEs in the soil of Pb-Zn smelting areas, and Pb, Cd, As and Cu are the key elements that cause contamination and risk, which need to be paid attention to and controlled. This study is expected to provide guidance for soil remediation in Pb-Zn smelting areas.

A New Model Including AMH Cut-off Levels to Predict Post-treatment Ovarian Function in Early Breast Cancer: A Prospective Cohort Study.

BACKGROUND: Breast cancer (BC) treatment decreases fertility capacity, but unnecessary fertility preservation procedures in women who would not be infertile after treatment would be a waste of time and resources and could cause the unwarranted exposure of cancer cells to exogenous sex hormones. It has been largely shown that post-treatment ovarian reserve is directly associated with pre-treatment anti-mullerian hormone levels (AMH0). A threshold for AMH0, or a model including AMH0 and patient characteristics that could distinguish the patients who will be infertile after treatments, still needs to be defined. Accordingly, this study was performed to specifically target this high-priority concern. METHODS: Women≤45 years old with newly diagnosed non-metastatic BC were entered in this multicenter prospective cohort study. AMH0 and two-year post-treatment AMH (AMH2) were measured, and hormonal patient features were recorded as well. Receiver operating characteristic (ROC) curve analysis, decision tree (DT), and random forest analyses were performed to find a cut-off point for AMH0 and define a model involving related features for the prediction of AMH2. RESULTS: The data from 84 patients were analyzed. ROC curve analysis revealed that AMH0>3 ng/mL (Area under the curve=0.69, 95% CI: 0.54‒0.84) was the best indicator for predicting AMH2≥0.7 (sensitivity=79%, specificity=60%). The best model detected by DT and random forest for predicting an AMH2>0.7 with a probability of 93% consisted of a combination of AMH0>3.3, menarche age<14, and age<31. CONCLUSION: This combination model can be used to withhold fertility preservation procedures in BC patients. Performing larger studies is suggested to further test this model.

Comparing surgical and endoscopic resection approaches for colorectal neuroendocrine tumors within the diameter range of 10-20mm: an inverse probability weighting analysis based on the SEER database.

Background: Currently, the primary treatment modalities for colorectal neuroendocrine tumors (CRNET) with a diameter between 10mm and 20mm are surgical resection (SR) and endoscopic resection (ER). However, it remains unclear which surgical approach yields the greatest survival benefit for patients. Methods: This study included data from patients diagnosed with CRNET with tumor diameters ranging from 10mm to 20mm between the years 2004 and 2019, obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were categorized into ER and SR groups based on the respective surgical approaches. Inverse probability weighting (IPTW) was employed to mitigate selection bias. Kaplan-Meier analysis and log-rank tests were utilized to estimate overall survival (OS) and cancer-specific survival (CSS). Cox regression analysis (univariate and multivariate) was performed to evaluate potential factors influencing survival. Results: A total of 292 CRNET patients were included in this study (ER group: 108 individuals, SR group: 184 individuals). Prior to IPTW adjustment, Kaplan-Meier analysis and Cox proportional hazard regression analysis demonstrated that the OS and CSS of the SR group were inferior to those of the ER group. However, after IPTW adjustment, no statistically significant differences in prognosis were observed between the two groups. Subgroup analyses revealed that patients with muscular invasion, positive lymph nodes, or distant metastasis derived greater survival benefits from SR. Significant differences in OS and CSS between the two groups were also observed across different age groups. Conclusion: For patients with mucosal-limited lesions and without local lymph node or distant metastasis, ER is the preferred surgical approach. However, for patients with muscular invasion or positive lymph nodes/distant metastasis, SR offers a better prognosis. The choice of surgical approach should be based on the specific clinical characteristics of patients within different subgroups.

Exposure Assessment of Pesticides in Surface Waters of Ontario, Canada Reveals Low Probability of Exceeding Acute Regulatory Thresholds.

The objectives of this study were to: (1) characterize the exposure of aquatic ecosystems in Southern Ontario, Canada to pesticides between 2002 and 2016 by constructing environmental exposure distributions (EEDs), including censored data; and (2) predict the probability of exceeding acute regulatory guidelines. Surface water samples were collected over a 15-year period by Environment and Climate Change Canada. The dataset contained 167 compounds, sampled across 114 sites, with a total of 2,213 samples. There were 67,920 total observations of which 55,058 were non-detects (81%), and 12,862 detects (19%). The most commonly detected compound was atrazine, with a maximum concentration of 18,600 ngL- 1 and ~ 4% chance of exceeding an acute guideline (1,000 ngL- 1) in rivers and streams. Using Southern Ontario as a case study, this study provides insight into the risk that pesticides pose to aquatic ecosystems and the utility of EEDs that include censored data for the purpose of risk assessment.

Preoperative risk calculator for the probability of completing nephron sparing for kidney cancer.

PURPOSE: In absence of predictive models, preoperative estimation of the probability of completing partial (PN) relative to radical nephrectomy (RN) is invariably inaccurate and subjective. We aimed to develop an evidence-based model to assess objectively the probability of PN completion based on patients' characteristics, tumor's complexity, urologist expertise and surgical approach. DESIGN, SETTING AND PARTICIPANTS: 675 patients treated with PN or RN for cT1-2 cN0 cM0 renal mass by seven surgeons at one single experienced centre from 2000 to 2019. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSES: The outcome of the study was PN completion. We used a multivariable logistic regression (MVA) model to investigate predictors of PN completion. We used SPARE score to assess tumor complexity. We used a bootstrap validation to compute the model's predictive accuracy. We investigated the relationship between the outcomes and specific predictors of interest such as tumor's complexity, approach and experience. RESULTS: Of 675 patients, 360 (53%) were treated with PN vs. 315 (47%) with RN. Smaller tumors [Odds ratio (OR): 0.52, 95%CI 0.44-0.61; P < 0.001], lower SPARE score (OR: 0.67, 95%CI 0.47-0.94; P = 0.02), more experienced surgeons (OR: 1.01, 95%CI 1.00-1.02; P < 0.01), robotic (OR: 10; P < 0.001) and open (OR: 36; P < 0.001) compared to laparoscopic approach resulted associated with higher probability of PN completion. Predictive accuracy of the model was 0.94 (95% CI 0.93-0.95). CONCLUSIONS: The probability of PN completion can be preoperatively assessed, with optimal accuracy relaying on routinely available clinical information. The proposed model might be useful in preoperative decision-making, patient consensus, or during preoperative counselling. PATIENT SUMMARY: In patients with a renal mass the probability of completing a partial nephrectomy varies considerably and without a predictive model is invariably inaccurate and subjective. In this study we build-up a risk calculator based on easily available preoperative variables that can predict with optimal accuracy the probability of not removing the entire kidney.

On variance estimation of the inverse probability-of-treatment weighting estimator: A tutorial for different types of propensity score weights.

Propensity score methods, such as inverse probability-of-treatment weighting (IPTW), have been increasingly used for covariate balancing in both observational studies and randomized trials, allowing the control of both systematic and chance imbalances. Approaches using IPTW are based on two steps: (i) estimation of the individual propensity scores (PS), and (ii) estimation of the treatment effect by applying PS weights. Thus, a variance estimator that accounts for both steps is crucial for correct inference. Using a variance estimator which ignores the first step leads to overestimated variance when the estimand is the average treatment effect (ATE), and to under or overestimated estimates when targeting the average treatment effect on the treated (ATT). In this article, we emphasize the importance of using an IPTW variance estimator that correctly considers the uncertainty in PS estimation. We present a comprehensive tutorial to obtain unbiased variance estimates, by proposing and applying a unifying formula for different types of PS weights (ATE, ATT, matching and overlap weights). This can be derived either via the linearization approach or M-estimation. Extensive R code is provided along with the corresponding large-sample theory. We perform simulation studies to illustrate the behavior of the estimators under different treatment and outcome prevalences and demonstrate appropriate behavior of the analytical variance estimator. We also use a reproducible analysis of observational lung cancer data as an illustrative example, estimating the effect of receiving a PET-CT scan on the receipt of surgery.

A probabilistic knowledge graph for target identification.

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.

Bayesian two-stage modeling of longitudinal and time-to-event data with an integrated fractional Brownian motion covariance structure.

It is difficult to characterize complex variations of biological processes, often longitudinally measured using biomarkers that yield noisy data. While joint modeling with a longitudinal submodel for the biomarker measurements and a survival submodel for assessing the hazard of events can alleviate measurement error issues, the continuous longitudinal submodel often uses random intercepts and slopes to estimate both between- and within-patient heterogeneity in biomarker trajectories. To overcome longitudinal submodel challenges, we replace random slopes with scaled integrated fractional Brownian motion (IFBM). As a more generalized version of integrated Brownian motion, IFBM reasonably depicts noisily measured biological processes. From this longitudinal IFBM model, we derive novel target functions to monitor the risk of rapid disease progression as real-time predictive probabilities. Predicted biomarker values from the IFBM submodel are used as inputs in a Cox submodel to estimate event hazard. This two-stage approach to fit the submodels is performed via Bayesian posterior computation and inference. We use the proposed approach to predict dynamic lung disease progression and mortality in women with a rare disease called lymphangioleiomyomatosis who were followed in a national patient registry. We compare our approach to those using integrated Ornstein-Uhlenbeck or conventional random intercepts-and-slopes terms for the longitudinal submodel. In the comparative analysis, the IFBM model consistently demonstrated superior predictive performance.

Application of inverse weighting analysis to assess the association of youth perceptions with the age of initiation of tobacco products.

Introduction: To examine if perceptions of harmfulness and addictiveness of hookah and cigarettes impact the age of initiation of hookah and cigarettes, respectively, among US youth. Youth (12-17 years old) users and never users of hookah and cigarettes during their first wave of PATH participation were analyzed by each tobacco product (TP) independently. The effect of perceptions of (i) harmfulness and (ii) addictiveness at the first wave of PATH participation on the age of initiation of ever use of hookah was estimated using interval-censoring Cox proportional hazards models. Methods: Users and never users of hookah at their first wave of PATH participation were balanced by multiplying the sampling weight and the 100 balance repeated replicate weights with the inverse probability weight (IPW). The IPW was based on the probability of being a user in their first wave of PATH participation. A Fay's factor of 0.3 was included for variance estimation. Crude hazard ratios (HR) and 95% confidence intervals (CIs) are reported. A similar process was repeated for cigarettes. Results: Compared to youth who perceived each TP as "a lot of harm", youth who reported perceived "some harm" had younger ages of initiation of these tobacco products, HR: 2.53 (95% CI: 2.87-4.34) for hookah and HR: 2.35 (95% CI: 2.10-2.62) for cigarettes. Similarly, youth who perceived each TP as "no/little harm" had an earlier age of initiation of these TPs compared to those who perceived them as "a lot of harm", with an HR: 2.23 (95% CI: 1.82, 2.71) for hookah and an HR: 1.85 (95% CI: 1.72, 1.98) for cigarettes. Compared to youth who reported each TP as "somewhat/very likely" as their perception of addictiveness, youth who reported "neither likely nor unlikely" and "very/somewhat unlikely" as their perception of addictiveness of hookah had an older age of initiation, with an HR: 0.75 (95% CI: 0.67-0.83) and an HR: 0.55 (95% CI: 0.47, 0.63) respectively. Discussion: Perceptions of the harmfulness and addictiveness of these tobacco products (TPs) should be addressed in education campaigns for youth to prevent early ages of initiation of cigarettes and hookah.

Towards a Clearer Causal Question Underlying the Association Between Cancer and Dementia.

BACKGROUND: Several observational studies have described an inverse association between cancer diagnosis and subsequent dementia risk. Multiple biologic mechanisms and potential biases have been proposed in attempts to explain this association. One proposed explanation is the opposite expression of Pin1 in cancer and dementia, and we use this explanation and potential drug target to illustrate the required assumptions and potential sources of bias for inferring an effect of Pin1 on dementia risk from analyses measuring cancer diagnosis as a proxy for Pin1 expression. METHODS: We used data from the Rotterdam Study, a population-based cohort. We estimate the association between cancer diagnosis (as a proxy for Pin1) and subsequent dementia diagnosis using two different proxy methods and with confounding and censoring for death addressed with inverse probability weights. We estimate and compare the complements of a weighted Kaplan-Meier survival estimator at 20 years of follow-up. RESULTS: Out of 3634 participants, 899 (25%) were diagnosed with cancer, of whom 53 (6%) had dementia, and 567 (63%) died. Among those without cancer, 15% (411) were diagnosed with dementia, and 667 (24%) died over follow-up. Depending on the confounding and selection bias control, and the way in which cancer was used as a time-varying proxy exposure, the risk ratio for dementia diagnosis ranged from 0.71 (95% confidence interval [CI] = 0.49, 0.95) to 1.1 (95% CI = 0.79, 1.3). CONCLUSION: Being explicit about the underlying mechanism of interest is key to maximizing what we can learn from this cancer-dementia association given available or readily collected data, and to defining, detecting, and preventing potential biases.

Temporal Variation and Persistence of Methane Emissions from Shallow Water Oil and Gas Production in the Gulf of Mexico.

Methane emissions from the oil and gas supply chain can be intermittent, posing challenges for monitoring and mitigation efforts. This study examines shallow water facilities in the US Gulf of Mexico with repeat atmospheric observations to evaluate temporal variation in site-specific methane emissions. We combine new and previous observations to develop a longitudinal study, spanning from days to months to almost five years, evaluating the emissions behavior of sites over time. We also define and determine the chance of subsequent detection (CSD): the likelihood that an emitting site will be observed emitting again. The average emitting central hub in the Gulf has a 74% CSD at any time interval. Eight facilities contribute 50% of total emissions and are over 80% persistent with a 96% CSD above 100 kg/h and 46% persistent with a 42% CSD above 1000 kg/h, indicating that large emissions are persistent at certain sites. Forward-looking infrared (FLIR) footage shows many of these sites exhibiting cold venting. This suggests that for offshore, a low sampling frequency over large spatial coverage can capture typical site emissions behavior and identify targets for mitigation. We further demonstrate the preliminary use of space-based observations to monitor offshore emissions over time.

Development of the multivariate administrative data cystectomy model and its impact on misclassification bias.

BACKGROUND: Misclassification bias (MB) is the deviation of measured from true values due to incorrect case assignment. This study compared MB when cystectomy status was determined using administrative database codes vs. predicted cystectomy probability. METHODS: We identified every primary cystectomy-diversion type at a single hospital 2009-2019. We linked to claims data to measure true association of cystectomy with 30 patient and hospitalization factors. Associations were also measured when cystectomy status was assigned using billing codes and by cystectomy probability from multivariate logistic regression model with covariates from administrative data. MB was the difference between measured and true associations. RESULTS: 500 people underwent cystectomy (0.12% of 428 677 hospitalizations). Sensitivity and positive predictive values for cystectomy codes were 97.1% and 58.6% for incontinent diversions and 100.0% and 48.4% for continent diversions, respectively. The model accurately predicted cystectomy-incontinent diversion (c-statistic [C] 0.999, Integrated Calibration Index [ICI] 0.000) and cystectomy-continent diversion (C:1.000, ICI 0.000) probabilities. MB was significantly lower when model-based predictions was used to impute cystectomy-diversion type status using for both incontinent cystectomy (F = 12.75; p < .0001) and continent cystectomy (F = 11.25; p < .0001). CONCLUSIONS: A model using administrative data accurately returned the probability that cystectomy by diversion type occurred during a hospitalization. Using this model to impute cystectomy status minimized MB. Accuracy of administrative database research can be increased by using probabilistic imputation to determine case status instead of individual codes.

Probability of normal tissue complications for hematologic and gastrointestinal toxicity in postoperative whole pelvic radiotherapy for gynecologic malignancies using intensity-modulated proton therapy with robust optimization.

This retrospective treatment-planning study was conducted to determine whether intensity-modulated proton therapy with robust optimization (ro-IMPT) reduces the risk of acute hematologic toxicity (H-T) and acute and late gastrointestinal toxicity (GI-T) in postoperative whole pelvic radiotherapy for gynecologic malignancies when compared with three-dimensional conformal radiation therapy (3D-CRT), intensity-modulated X-ray (IMXT) and single-field optimization proton beam (SFO-PBT) therapies. All plans were created for 13 gynecologic-malignancy patients. The prescribed dose was 45 GyE in 25 fractions for 95% planning target volume in 3D-CRT, IMXT and SFO-PBT plans and for 99% clinical target volume (CTV) in ro-IMPT plans. The normal tissue complication probability (NTCP) of each toxicity was used as an in silico surrogate marker. Median estimated NTCP values for acute H-T and acute and late GI-T were 0.20, 0.94 and 0.58 × 10-1 in 3D-CRT; 0.19, 0.65 and 0.24 × 10-1 in IMXT; 0.04, 0.74 and 0.19 × 10-1 in SFO-PBT; and 0.06, 0.66 and 0.15 × 10-1 in ro-IMPT, respectively. Compared with 3D-CRT and IMXT plans, the ro-IMPT plan demonstrated significant reduction in acute H-T and late GI-T. The risk of acute GI-T in ro-IMPT plan is equivalent with IMXT plan. The ro-IMPT plan demonstrated potential clinical benefits for reducing the risk of acute H-T and late GI-T in the treatment of gynecologic malignances by reducing the dose to the bone marrow and bowel bag while maintaining adequate dose coverage to the CTV. Our results indicated that ro-IMPT may reduce acute H-T and late GI-T risk with potentially improving outcomes for postoperative gynecologic-malignancy patients with concurrent chemotherapy.

A Bayesian Bernoulli-Exponential joint model for binary longitudinal outcomes and informative time with applications to bladder cancer recurrence data.

BACKGROUND: A variety of methods exist for the analysis of longitudinal data, many of which are characterized with the assumption of fixed visit time points for study individuals. This, however is not always a tenable assumption. Phenomenon that alter subject visit patterns such as adverse events due to investigative treatment administered, travel or any other emergencies may result in unbalanced data and varying individual visit time points. Visit times can be considered informative, because subsequent or current subject outcomes can change or be adapted due to previous subject outcomes. METHODS: In this paper, a Bayesian Bernoulli-Exponential model for analyzing joint binary outcomes and exponentially distributed informative visit times is developed. Via statistical simulations, the influence of controlled variations in visit patterns, prior and sample size schemes on model performance is assessed. As an application example, the proposed model is applied to a Bladder Cancer Recurrence data. RESULTS AND CONCLUSIONS: Results from the simulation analysis indicated that the Bayesian Bernoulli-Exponential joint model converged in stationarity, and performed relatively better for small to medium sample size scenarios with less varying time sequences regardless of the choice of prior. In larger samples, the model performed better for less varying time sequences. This model's application to the bladder cancer data showed a statistically significant effect of prior tumor recurrence on the probability of subsequent recurrences.

Theoretical evaluation of the impact of diverse treatment conditions by calculation of the tumor control probability (TCP) of simulated cervical cancer Hyperthermia-Radiotherapy (HT-RT) treatments in-silico.

INTRODUCTION: Hyperthermia (HT) induces various cellular biological processes, such as repair impairment and direct HT cell killing. In this context, in-silico biophysical models that translate deviations in the treatment conditions into clinical outcome variations may be used to study the extent of such processes and their influence on combined hyperthermia plus radiotherapy (HT + RT) treatments under varying conditions. METHODS: An extended linear-quadratic model calibrated for SiHa and HeLa cell lines (cervical cancer) was used to theoretically study the impact of varying HT treatment conditions on radiosensitization and direct HT cell killing effect. Simulated patients were generated to compute the Tumor Control Probability (TCP) under different HT conditions (number of HT sessions, temperature and time interval), which were randomly selected within margins based on reported patient data. RESULTS: Under the studied conditions, model-based simulations suggested a treatment improvement with a total CEM43 thermal dose of approximately 10 min. Additionally, for a given thermal dose, TCP increased with the number of HT sessions. Furthermore, in the simulations, we showed that the TCP dependence on the temperature/time interval is more correlated with the mean value than with the minimum/maximum value and that comparing the treatment outcome with the mean temperature can be an excellent strategy for studying the time interval effect. CONCLUSION: The use of thermoradiobiological models allows us to theoretically study the impact of varying thermal conditions on HT + RT treatment outcomes. This approach can be used to optimize HT treatments, design clinical trials, and interpret patient data.