Mutual Information as an Index of Diagnostic Test Performance

Benish, William A.

doi:10.1055/s-0038-1634358

Cited by 26 publications

(37 citation statements)

References 17 publications

(23 reference statements)

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“…In the life sciences, Shannon entropy has been used to measure cellular diversity (12,13) and phylogenetic variation (14), and to model molecular interactions (15). This concept has previously been applied to singleanalyte laboratory testing by Rudolph (16)(17)(18) and, more recently, by Benish (19)(20)(21)(22)(23) and Vollmer (24). However, their approaches have not been widely adopted or disseminated and, in particular, have not been applied to NGS.…”

mentioning

confidence: 98%

Diagnostic yield of targeted next generation sequencing in various cancer types: An information-theoretic approach

et al. 2015

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mentioning

confidence: 98%

Diagnostic yield of targeted next generation sequencing in various cancer types: An information-theoretic approach

et al. 2015

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“…When the base of the logarithm in Equation (1) is two, the unit of measurement is bits (binary digits) (3,4). Shannon entropy can be used to quantify diagnostic uncertainty.…”

Section: Methodsmentioning

confidence: 99%

“…The difference between the a priori uncertainty and the expected value of the a posteriori uncertainty gives the gain in information provided by the diagnostic test. It is referred to as the diagnostic information or IC of the diagnostic test (4)(5)(6). This difference is also called mutual information.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Diagnostic Tests Using Information Theory for Multi-Class Diagnostic Problems and its Application for the Detection of Occlusal Caries Lesions

Arslan¹,

Karaağaoğlu²,

Özkan³

et al. 2014

Balkan Med J

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Diagnostic tests are widely used in medical science for diagnosing the presence or absence of a disease. These tests may not discriminate between subjects with and without the disease or disorder with perfect accuracy. Several methods are available to evaluate the performance of diagnostic tests when the purpose of the test is to discriminate between two possible disease states. Different performance indices have been proposed to determine the accuracy of diagnostic tests when both the disease state and the test result are binary. However, in some cases, the disease state may have more than two categories. For this reason, there is a need for other methods to characterize the accuracy of diagnostic tests. In this study, we propose a method which is useful in terms of selecting the most appropriate diagnostic test when the disease state and test results have three or more classes. In this study, information content (IC) and proportional reduction in diagnostic uncertainty (PRDU) were introduced as measures of diagnostic test accuracy. To demonstrate the applicability of these statistics, we evaluated the diagnostic performance of four different diagnostic methods which are used for dental caries detection: visual inspection, radiography, laser fluorescence (LF) and micro-computed tomography (M-CT) examination.Background: Several methods are available to evaluate the performance of the tests when the purpose of the diagnostic test is to discriminate between two possible disease states. However multi-class diagnostic problems frequently appear in many areas of medical science. Hence, there is a need for methods which will enable us to characterize the accuracy of diagnostic tests when there are more than two possible disease states. Aims: To show that two information theory measures, information content (IC) and proportional reduction in diagnostic uncertainty (PRDU), can be used for the evaluation of the performance of diagnostic tests for multi-class diagnostic problems that may appear in different areas of medical science. Study Design: Diagnostic accuracy study. Methods: Sixty freshly extracted permanent human molar and premolar teeth suspected to have occlusal caries lesions were selected for the study and were assessed by two experienced examiners. Each examiner performed two evaluations. Histological examination was used as the gold standard. The scores of the histological examination were defined as sound (n=11), enamel caries (n=22) and dentin caries (n=27). Diagnostic performance of i) visual inspection, ii) radiography, iii) laser fluorescence (LF) and iv) micro-computed tomography (M-CT) caries detection methods was evaluated by calculating IC and PRDU. Results: Micro-computed tomography examination was the best method among the diagnostic techniques for the diagnosis of occlusal caries in terms of both IC and PRDU. M-CT examination supplied the maximum diagnostic information about the diagnosis of occlusal caries in the first (IC: 1.056; p<0.05), (PRDU: 70.5%) and second evaluation (IC: 1.105; p<0.05),...

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“…In this study, we use a measure of predictive accuracy for this domain called "Mutual Information" (MI), which is a measure of the information that one variable provides about the other [13,14]. MI does not exhibit the limitations of PPV and logistic regression because it can quantify not only the relationship between descriptors and breast cancer risk, but also the relationship between imaging observation features (including all descriptors belonging to that feature) and risk.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Methodology for Determining the Most Informative Mammographic Features

Alagöz

Ayvaci³

et al. 2013

J Digit Imaging

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This study aims to determine the most informative mammographic features for breast cancer diagnosis using mutual information (MI) analysis. Our Health Insurance Portability and Accountability Act-approved database consists of 44,397 consecutive structured mammography reports for 20,375 patients collected from 2005 to 2008. The reports include demographic risk factors (age, family and personal history of breast cancer, and use of hormone therapy) and mammographic features from the Breast Imaging Reporting and Data System lexicon. We calculated MI using Shannon's entropy measure for each feature with respect to the outcome (benign/malignant using a cancer registry match as reference standard). In order to evaluate the validity of the MI rankings of features, we trained and tested naïve Bayes classifiers on the feature with tenfold cross-validation, and measured the predictive ability using area under the ROC curve (AUC). We used a bootstrapping approach to assess the distributional properties of our estimates, and the DeLong method to compare AUC. Based on MI, we found that mass margins and mass shape were the most informative features for breast cancer diagnosis. Calcification morphology, mass density, and calcification distribution provided predictive information for distinguishing benign and malignant breast findings. Breast composition, associated findings, and special cases provided little information in this task. We also found that the rankings of mammographic features with MI and AUC were generally consistent. MI analysis provides a framework to determine the value of different mammographic features in the pursuit of optimal (i.e., accurate and efficient) breast cancer diagnosis.

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Mutual Information as an Index of Diagnostic Test Performance

Cited by 26 publications

References 17 publications

Diagnostic yield of targeted next generation sequencing in various cancer types: An information-theoretic approach

Diagnostic yield of targeted next generation sequencing in various cancer types: An information-theoretic approach

Evaluation of Diagnostic Tests Using Information Theory for Multi-Class Diagnostic Problems and its Application for the Detection of Occlusal Caries Lesions

A Comprehensive Methodology for Determining the Most Informative Mammographic Features

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