Group Penalized Logistic Regressions Predict Ovarian Cancer

Objectives: Ovarian cancer ranks first among gynecological cancers in terms of the mortality rate. Accurately diagnosing ovarian benign tumors and malignant tumors is of immense important. The goal of this paper is to combine group LASSO/SCAD/MCP penalized logistic regression with machine learning procedure to further improve the prediction accuracy to ovarian benign tumors and malignant tumors prediction problem. Methods: We combine group LASSO/SCAD/MCP penalty with logistic regression, and propose group LASSO/SCAD/MCP penalized logistic regression to predict the benign and malignant ovarian cancer. Firstly, we select 349 ovarian cancer patients data and divide them into two sets: one is the training set for learning, and the other is the testing set for checking, and then choose 46 explanatory variables and divide them into 11 different groups. Secondly, we apply the training set and group coordinate descent algorithm to obtain group LASSO/SCAD/MCP estimator, and apply the testing set to compute confusion matrix, accuracy, sensitivity and specificity. Finally, we compare the prediction performance for group LASSO/SCAD/MCP penalized logistic regression with that for artificial neural network (ANN) and support vector machine (SVM).Results: Group LASSO/SCAD/MCP/ penalized logistic regression selects 6/4/1 groups. The prediction accuracy and AUC for group MCP/SCAD/LASSO penalized logistic regression/SVM/ANN is 93.33%/85.71%/82.26%/74.29%/72.38% and 0.892/0.852/0.823/0.639/0.789, respectively.Conclusions: Group MCP/SCAD/LASSO penalized logistic regression performs than SVM and ANN in terms of prediction accuracy and AUC. In particular, group MCP penalized logistic regression predicts the best. Therefore, we suggest group MCP penalized logistic regression to predict ovarian tumors.

Elements for Building Supervised Statistical Machine Learning Models

This chapter gives details of the linear multiple regression model including assumptions and some pros and cons, the maximum likelihood. Gradient descendent methods are described for learning the parameters under this model. Penalized linear multiple regression is derived under Ridge and Lasso penalties, which also emphasizes the estimation of the regularization parameter of importance for its successful implementation. Examples are given for both penalties (Ridge and Lasso) and but not for penalized regression multiple regression framework for illustrating the circumstances when the penalized versions should be preferred. Finally, the fundamentals of penalized and non-penalized logistic regression are provided under a gradient descendent framework. We give examples of logistic regression. Each example comes with the corresponding R codes to facilitate their quick understanding and use.

Group Penalized Logistic Regressions Predict Ovarian Cancer ∗

Objectives: Ovarian cancer ranks fifirst among gynecological cancers in terms of the mortality rate. Accurately diagnosing ovarian benign tumors and malignant tumors is of immense important. The goal of this paper is to combine group LASSO/SCAD/MCP penalized logistic regression with machine learning procedure to further improve the prediction accuracy to ovarian benign tumors and malignant tumors prediction problem. Methods: We combine group LASSO/SCAD/MCP penalty with logistic regression, and propose group LASSO/SCAD/MCP penalized logistic regression to predict the benign and malignant ovarian cancer. Firstly, we select 349 ovarian cancer patients data and divide them into two sets: one is the training set for learning, and the other is the testing set for checking, and then choose 46 explanatory variables and divide them into 11 difffferent groups. Secondly, we apply the training set and group coordinate descent algorithm to obtain group LASSO/SCAD/MCP estimator, and apply the testing set to compute confusion matrix, accuracy, sensitivity and specifificity. Finally, we compare the prediction performance for group LASSO/SCAD/MCP penalized logistic regression with that for artifificial neural network (ANN) and support vector machine (SVM). Results: Group LASSO/SCAD/MCP/ penalized logistic regression selects 6/4/1 groups. The prediction accuracy and AUC for group MCP/SCAD/LASSO penalized logistic regression/SVM/ANN is 93.33%/85.71%/82.26%/74.29%/72.38% and 0.892/0.852/0.823/0.639/0.789, respectively. Conclusions: Group MCP/SCAD/LASSO penalized logistic regression performs than SVM and ANN in terms of prediction accuracy and AUC. In particular, group MCP penalized logistic regression predicts the best. Therefore, we suggest group MCP penalized logistic regression to predict ovarian tumors.

Two Novel Ceramide-Like Molecules and miR-5100 Levels as Biomarkers Improve Prediction of Prostate Cancer in Gray-Zone PSA

Reliable liquid biopsy-based tools able to accurately discriminate prostate cancer (PCa) from benign prostatic hyperplasia (BPH), when PSA is within the “gray zone” (PSA 4–10), are still urgent. We analyzed plasma samples from a cohort of 102 consecutively recruited patients with PSA levels between 4 and 16 ng/ml, using the SANIST-Cloud Ion Mobility Metabolomic Mass Spectrometry platform, combined with the analysis of a panel of circulating microRNAs (miR). By coupling CIMS ion mobility technology with SANIST, we were able to reveal three new structures among the most differentially expressed metabolites in PCa vs. BPH. In particular, two were classified as polyunsaturated ceramide ester-like and one as polysaturated glycerol ester-like. Penalized logistic regression was applied to build a model to predict PCa, using six circulating miR, seven circulating metabolites, and demographic/clinical variables, as covariates. Four circulating metabolites, miR-5100, and age were selected by the model, and the corresponding prediction score gave an AUC of 0.76 (C.I. = 0.66–0.85). At a specified cut-off, no high-risk tumor was misclassified, and 22 out of 53 BPH were correctly identified, reducing by 40% the false positives of PSA. We developed and applied a novel, minimally invasive, liquid biopsy-based powerful tool to characterize novel metabolites and identified new potential non-invasive biomarkers to better predict PCa, when PSA is uninformative as a tool for precision medicine in genitourinary cancers.

482. SARS-CoV-2 Prevalence in Feces of Very Young Children, A Longitudinal Study

Background Understanding the disease burden of SARS- CoV-2 in young children has been challenging as the majority are asymptomatic or experience mild symptoms and were rarely tested. SARS-CoV-2 is traditionally detected through respiratory secretions but has also been reported in feces where shedding may continue for weeks after respiratory samples show resolution. We examined the prevalence of SARS-CoV-2 in already collected fecal samples from young children through the pandemic as well as associated demographic factors. Methods As part of an ongoing longitudinal microbiome study in Northern Virginia, serial stools samples were collected from infants before and throughout the Covid-19 pandemic. Reverse transcription quantitative-PCR detecting SARS-CoV-2 nucleocapsid gene in the N1 and N2 regions was performed. Penalized logistic regression models were developed to evaluate the association between fecal positivity and potential risk factors. Results The overall prevalence of SARS-CoV-2 in infant feces was 1.69 % (13 samples) with a prevalence at delivery, 2, 6, 12 and 24 months of 0, 0, 2.56, 1.96, and 0.85 % respectively. Fecal positivity was first detected 31 days before the reported first case of Covid-19 in Northern Virginia; prevalence rates peaked in September at 4.5% (Figure 1). Only one infant who tested positive was symptomatic with COVID-19 21 days before his stool was collected. Of the 13 positive samples, 8 reported Hispanic ethnicity and 7 reported an essential worker (Table 1). Penalized logistic regression model showed association between Hispanic ethnicity and testing positive (OR 5.04 (95% CI 1.7 – 15.0)) that remained after controlling for the presences of an essential worker (OR 4.7 (95% CI 1.6 – 14.0)). Conclusion Prevalence of SARS- CoV-2 in infant stool correlated with the prevalence of COVID-19 during the pandemic, with higher rates in those of Hispanic ethnicity corelating with regional trends. Fecal positivity in asymptomatic infants even before quarantine restrictions supports the early but silent transmission of SARS-CoV-2. This study likely underestimates true prevalence rates as stool samples were stored without viral preservative. There are many socioeconomic factors that predispose to disease while ethnicity may be a mediating or confounding factor Disclosures All Authors: No reported disclosures

A comparison of prediction approaches for identifying prodromal Parkinson disease

Identifying people with Parkinson disease during the prodromal period, including via algorithms in administrative claims data, is an important research and clinical priority. We sought to improve upon an existing penalized logistic regression model, based on diagnosis and procedure codes, by adding prescription medication data or using machine learning. Using Medicare Part D beneficiaries age 66–90 from a population-based case-control study of incident Parkinson disease, we fit a penalized logistic regression both with and without Part D data. We also built a predictive algorithm using a random forest classifier for comparison. In a combined approach, we introduced the probability of Parkinson disease from the random forest, as a predictor in the penalized regression model. We calculated the receiver operator characteristic area under the curve (AUC) for each model. All models performed well, with AUCs ranging from 0.824 (simplest model) to 0.835 (combined approach). We conclude that medication data and random forests improve Parkinson disease prediction, but are not essential.

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters.

Therapeutic Decision-Making and Outcomes in Elderly Patients With Severe Symptomatic Aortic Stenosis: Prognostic Implications of Elderly Patients' Initial Decisions

Background: Despite clear indications for intervention, therapeutic decision-making for elderly patients with severe symptomatic aortic stenosis (AS) remains a complex issue due to the wide variation in individual risk profiles and the involvement of patients' subjective preferences. We aimed to investigate the reasons leading to the decisions against intervention and the consequences thereof on survival.Methods: Data were derived from the China Elderly Valve Disease (China-DVD) Cohort Study on patients aged ≥60-year-old with severe symptomatic AS consecutively enrolled between September to December 2016. Patients were analyzed according to the initial therapeutic decisions made by consensus between patients and physicians at the time of the index evaluation: intervention group (patients who were evaluated as suitable for intervention and accepted the treatment proposal); patient-refusal group (patients who were evaluated as suitable for intervention but refused due to subjective preferences); physician-deny group (patients who were denied intervention by physicians after evaluation). The least absolute shrinkage and selection operator (LASSO)-penalized logistic regression model was used to identify the factors associated with physicians' decisions against intervention. Twelve-month survival was analyzed using Cox proportional hazards models, with multivariate adjustment using inverse probability weighting (IPW).Results: Among the enrolled 456 elderly patients with severe symptomatic AS, 52 (11.4%) patients refused intervention and 49 (10.7%) patients were denied intervention by their physicians. LASSO-penalized logistic regression model identified that reduced left ventricular ejection fraction and increased EuroSCORE-II were strongly associated with physicians' decisions against intervention. At 12-month follow-up, only 8 (15.4%) patients who initially refused the intervention proposal underwent the subsequent intervention, with an average delay of 195 days. Patients' initial decisions against intervention were significantly associated with 12-month mortality, even after IPW adjustment (Hazard ratio: 2.61; 95% confidence interval: 1.09–6.20; P = 0.031).Conclusions: The decision against intervention was taken in about one-fifth of elderly patients with symptomatic severe AS, half of which were due to patients' subjective preferences. Surgical risk remains the primary concern for physicians when making therapeutic decisions. Elderly patients' initial decisions against intervention have a profound impact on subsequent intervention rates and prognosis, and therefore should be treated as a “risk factor” at the subjective level.Clinical Trial Registration:clinicaltrials.gov/ct2/show/NCT02865798, China elDerly Valve Disease (China-DVD) cohort study (NCT02865798).

Predictors of Successful Yttrium-90 Radioembolization Bridging or Downstaging in Patients with Hepatocellular Carcinoma

Purpose. This study aims to identify clinical and imaging prognosticators associated with the successful bridging or downstaging to liver transplantation (LT) in patients undergoing Yttrium-90 radioembolization (Y90-RE) for hepatocellular carcinoma (HCC). Methods. Retrospectively, patients with Y90-RE naïve HCC who were candidates or potential candidates for LT and underwent Y90-RE were included. Patients were then divided into favorable (maintained or achieved Milan criteria (MC) eligibility) or unfavorable (lost eligibility or unchanged MC ineligibility) cohorts based on changes to their MC eligibility after Y90-RE. Penalized logistic regression analysis was performed to identify the significant baseline prognosticators. Results. Between 2013 and 2018, 135 patients underwent Y90-RE treatment. Among the 59 (42%) patients within MC, LT eligibility was maintained in 49 (83%) and lost in 10 (17%) patients. Within the 76 (56%) patients outside MC, eligibility was achieved in 32 (42%) and unchanged in 44 (58%). Among the 81 (60%) patients with a favorable response, 16 (20%) went on to receive LT. Analysis of the baseline characteristics revealed that lower Albumin-Bilirubin score, lower Child–Pugh class, lower Barcelona Clinic Liver Cancer stage, HCC diagnosis using dynamic contrast-enhanced imaging on CT or MRI, normal/higher albumin levels, decreased severity of tumor burden, left lobe HCC disease, and absence of HBV-associated cirrhosis, baseline abdominal pain, or fatigue were all associated with a higher likelihood of bridging or downstaging to LT eligibility ( p ’s < 0.05). Conclusion. Certain baseline clinical and tumor characteristics are associated with the successful bridging or downstaging of potential LT candidates with HCC undergoing Y90-RE.