Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta.

Introduction: In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult. Methods: By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost. Results: Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.

Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.

OBJECTIVES: Artificial intelligence (AI) has the potential to impact clinical practice and healthcare delivery. AI is of particular significance in radiology due to its use in automatic analysis of image characteristics. This scoping review examines stakeholder perspectives on AI use in radiology, the benefits, risks, and challenges to its integration. METHODS: A search was conducted from 1960 to November 2019 in EMBASE, PubMed/MEDLINE, Web of Science, Cochrane Library, CINAHL, and grey literature. Publications reflecting stakeholder attitudes toward AI were included with no restrictions. RESULTS: Commentaries (n = 32), surveys (n = 13), presentation abstracts (n = 8), narrative reviews (n = 8), and a social media study (n = 1) were included from 62 eligible publications. These represent the views of radiologists, surgeons, medical students, patients, computer scientists, and the general public. Seven themes were identified (predicted impact, potential replacement, trust in AI, knowledge of AI, education, economic considerations, and medicolegal implications). Stakeholders anticipate a significant impact on radiology, though replacement of radiologists is unlikely in the near future. Knowledge of AI is limited for non-computer scientists and further education is desired. Many expressed the need for collaboration between radiologists and AI specialists to successfully improve patient care. CONCLUSIONS: Stakeholder views generally suggest that AI can improve the practice of radiology and consider the replacement of radiologists unlikely. Most stakeholders identified the need for education and training on AI, as well as collaborative efforts to improve AI implementation. Further research is needed to gain perspectives from non-Western countries, non-radiologist stakeholders, on economic considerations, and medicolegal implications. KEY POINTS: Stakeholders generally expressed that AI alone cannot be used to replace radiologists. The scope of practice is expected to shift with AI use affecting areas from image interpretation to patient care. Patients and the general public do not know how to address potential errors made by AI systems while radiologists believe that they should be "in-the-loop" in terms of responsibility. Ethical accountability strategies must be developed across governance levels. Students, residents, and radiologists believe that there is a lack in AI education during medical school and residency. The radiology community should work with IT specialists to ensure that AI technology benefits their work and centres patients.

Prediction of preterm birth in nulliparous women using logistic regression and machine learning.

OBJECTIVE: To predict preterm birth in nulliparous women using logistic regression and machine learning. DESIGN: Population-based retrospective cohort. PARTICIPANTS: Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20-42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014. METHODS: We used data during the first and second trimesters to build logistic regression and machine learning models in a "training" sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent "validation" sample. RESULTS: During the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23-5.42; Type II: AOR: 2.68; 95% CI: 2.05-3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80-2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58-62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21-13.90). During the second trimester, the AUC increased to 65% (95% CI: 63-66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79-81%) with artificial neural networks. All models yielded 94-97% negative predictive values for spontaneous PTB during the first and second trimesters. CONCLUSION: Although artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.

Clinical risk models for preterm birth less than 28 weeks and less than 32 weeks of gestation using a large retrospective cohort.

OBJECTIVE: To develop risk prediction models for singleton preterm birth (PTB) < 28 weeks and <32 weeks. METHODS: Using a retrospective cohort of 267,226 singleton births in Ontario hospitals, we included variables from the first and second trimester in multivariable logistic regression models to predict overall and spontaneous PTB < 28 weeks and <32 weeks. RESULTS: During the first trimester, the area under the curve (AUC) for prediction of PTB < 28 weeks for nulliparous and multiparous women was 68.5% (95% CI: 63.5-73.6%) and 73.4% (68.6-78.2%), respectively, while for PTB < 32 weeks it was 68.9% (65.5-72.3%) and 75.5% (72.3-78.7%), respectively. AUCs for second-trimester models were 72.4% (95% CI: 69.7-75.1%) and 78.2% (95% CI: 75.8-80.5%), respectively, in nulliparous and multiparous women. Predicted probabilities were well-calibrated within a wide range around expected base prevalence for the study outcomes. CONCLUSIONS: Our prediction models generated acceptable AUCs for PTB < 28 weeks and <32 weeks with good calibration during the first and second trimester.

Identifying Potential miRNA Biomarkers for Gastric Cancer Diagnosis Using Machine Learning Variable Selection Approach.

Aim: This study aimed to accurately identification of potential miRNAs for gastric cancer (GC) diagnosis at the early stages of the disease. Methods: We used GSE106817 data with 2,566 miRNAs to train the machine learning models. We used the Boruta machine learning variable selection approach to identify the strong miRNAs associated with GC in the training sample. We then validated the prediction models in the independent sample GSE113486 data. Finally, an ontological analysis was done on identified miRNAs to eliciting the relevant relationships. Results: Of those 2,874 patients in the training the model, there were 115 (4%) patients with GC. Boruta identified 30 miRNAs as potential biomarkers for GC diagnosis and hsa-miR-1343-3p was at the highest ranking. All of the machine learning algorithms showed that using hsa-miR-1343-3p as a biomarker, GC can be predicted with very high precision (AUC; 100%, sensitivity; 100%, specificity; 100% ROC; 100%, Kappa; 100) using with the cut-off point of 8.2 for hsa-miR-1343-3p. Also, ontological analysis of 30 identified miRNAs approved their strong relationship with cancer associated genes and molecular events. Conclusion: The hsa-miR-1343-3p could be introduced as a valuable target for studies on the GC diagnosis using reliable biomarkers.

Stein-type shrinkage estimators in gamma regression model with application to prostate cancer data.

Gamma regression is applied in several areas such as life testing, forecasting cancer incidences, genomics, rainfall prediction, experimental designs, and quality control. Gamma regression models allow for a monotone and no constant hazard in survival models. Owing to the broad applicability of gamma regression, we propose some novel and improved methods to estimate the coefficients of gamma regression model. We combine the unrestricted maximum likelihood (ML) estimators and the estimators that are restricted by linear hypothesis, and we present Stein-type shrinkage estimators (SEs). We then develop an asymptotic theory for SEs and obtain their asymptotic quadratic risks. In addition, we conduct Monte Carlo simulations to study the performance of the estimators in terms of their simulated relative efficiencies. It is evident from our studies that the proposed SEs outperform the usual ML estimators. Furthermore, some tabular and graphical representations are given as proofs of our assertions. This study is finally ended by appraising the performance of our estimators for a real prostate cancer data.