Machine Learning Principles for Radiology Investigators

Borstelmann, Stephen M.

doi:10.1016/j.acra.2019.07.030

Cited by 49 publications

(37 citation statements)

References 40 publications

(47 reference statements)

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“…The logistic regression model uses the maximum likelihood method to estimate and determine the regression coefficient and accurately predict the probability of dichotomy. SVM is a supervised learning algorithm that can clearly identify high-dimensional boundaries and solve dichotomy problems ( 21 ). Ensemble algorithms include random forest and eXtreme Gradient Boosting.…”

Section: Methodsmentioning

confidence: 99%

“…Random forest is an integrated algorithm that combines multiple decision trees together by voting to discriminate and classify data ( 22 ). eXtreme Gradient Boosting integrates many weak classifiers into a strong classifier, which is an optimized extreme gradient promotion to improve the predictive power ( 21 , 23 ). We also attempted NB, an efficient algorithm based on the Bayesian principle that uses the knowledge of probability in statistics to classify data sets ( 24 ).…”

Section: Methodsmentioning

confidence: 99%

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Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

et al. 2021

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PurposeTo evaluate isocitrate dehydrogenase (IDH) status in clinically diagnosed grade II~IV glioma patients using the 2016 World Health Organization (WHO) classification based on MRI parameters.Materials and MethodsOne hundred and seventy-six patients with confirmed WHO grade II~IV glioma were retrospectively investigated as the study set, including lower-grade glioma (WHO grade II, n = 64; WHO grade III, n = 38) and glioblastoma (WHO grade IV, n = 74). The minimum apparent diffusion coefficient (ADCmin) in the tumor and the contralateral normal-appearing white matter (ADCn) and the rADC (ADCmin to ADCn ratio) were defined and calculated. Intraclass correlation coefficient (ICC) analysis was carried out to evaluate interobserver and intraobserver agreement for the ADC measurements. Interobserver agreement for the morphologic categories was evaluated by Cohen’s kappa analysis. The nonparametric Kruskal-Wallis test was used to determine whether the ADC measurements and glioma subtypes were related. By univariable analysis, if the differences in a variable were significant (P<0.05) or an image feature had high consistency (ICC >0.8; κ >0.6), then it was chosen as a predictor variable. The performance of the area under the receiver operating characteristic curve (AUC) was evaluated using several machine learning models, including logistic regression, support vector machine, Naive Bayes and Ensemble. Five evaluation indicators were adopted to compare the models. The optimal model was developed as the final model to predict IDH status in 40 patients with glioma as the subsequent test set. DeLong analysis was used to compare significant differences in the AUCs.ResultsIn the study set, six measured variables (rADC, age, enhancement, calcification, hemorrhage, and cystic change) were selected for the machine learning model. Logistic regression had better performance than other models. Two predictive models, model 1 (including all predictor variables) and model 2 (excluding calcification), correctly classified IDH status with an AUC of 0.897 and 0.890, respectively. The test set performed equally well in prediction, indicating the effectiveness of the trained classifier. The subgroup analysis revealed that the model predicted IDH status of LGG and GBM with accuracy of 84.3% (AUC = 0.873) and 85.1% (AUC = 0.862) in the study set, and with the accuracy of 70.0% (AUC = 0.762) and 70.0% (AUC = 0.833) in the test set, respectively.ConclusionThrough the use of machine-learning algorithms, the accurate prediction of IDH-mutant versus IDH-wildtype was achieved for adult diffuse gliomas via noninvasive MR imaging characteristics, including ADC values and tumor morphologic features, which are considered widely available in most clinical workstations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

et al. 2021

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“…The field of machine learning (ML) cuts across multiple statistics-based techniques useful for radiologists in disease diagnosis which complements the currently adopted deep learning (DL) approach (7). The incorporation of ML into deep learning and artificial intelligence (AI) has shown great potentials in assisting decision-making for assessing severity and prediction of clinical outcomes of disease in COVID-19 patients (8,9).…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis

Komolafe¹,

Cao²,

Nguchu³

et al. 2021

Academic Radiology

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…Random forest and XGBoost are two of the most popular ensemble learning techniques that combine several machine learning techniques (e.g., bagging and boosting) to decrease variance, bias, and improve predictions [33]. Although both algorithms use multiple decision trees, the XGBoost utilizes gradient boosting, wherein it builds one tree at a time and takes into account the errors made by all previously built trees [34].…”

Section: Analytic Approachmentioning

confidence: 99%

A Machine Learning Approach to Identify Predictors of Potentially Inappropriate Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Use in Older Adults with Osteoarthritis

Patel

Ladani

Sambamoorthi

et al. 2020

IJERPH

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Evidence from some studies suggest that osteoarthritis (OA) patients are often prescribed non-steroidal anti-inflammatory drugs (NSAIDs) that are not in accordance with their cardiovascular (CV) or gastrointestinal (GI) risk profiles. However, no such study has been carried out in the United States. Therefore, we sought to examine the prevalence and predictors of potentially inappropriate NSAIDs use in older adults (age > 65) with OA using machine learning with real-world data from Optum De-identified Clinformatics® Data Mart. We identified a retrospective cohort of eligible individuals using data from 2015 (baseline) and 2016 (follow-up). Potentially inappropriate NSAIDs use was identified using the type (COX-2 selective vs. non-selective) and length of NSAIDs use and an individual’s CV and GI risk. Predictors of potentially inappropriate NSAIDs use were identified using eXtreme Gradient Boosting. Our study cohort comprised of 44,990 individuals (mean age 75.9 years). We found that 12.8% individuals had potentially inappropriate NSAIDs use, but the rate was disproportionately higher (44.5%) in individuals at low CV/high GI risk. Longer duration of NSAIDs use during baseline (AOR 1.02; 95% CI:1.02–1.02 for both non-selective and selective NSAIDs) was associated with a higher risk of potentially inappropriate NSAIDs use. Additionally, individuals with low CV/high GI (AOR 1.34; 95% CI:1.20–1.50) and high CV/low GI risk (AOR 1.61; 95% CI:1.34–1.93) were also more likely to have potentially inappropriate NSAIDs use. Heightened surveillance of older adults with OA requiring NSAIDs is warranted.

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Machine Learning Principles for Radiology Investigators

Cited by 49 publications

References 40 publications

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis

A Machine Learning Approach to Identify Predictors of Potentially Inappropriate Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Use in Older Adults with Osteoarthritis

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