A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography.
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| Title: | A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. |
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| Authors: | Baltzer, Pascal1 pascal.baltzer@meduniwien.ac.at, Dietzel, Matthias2, Kaiser, Werner3 |
| Source: | European Radiology. Aug2013, Vol. 23 Issue 8, p2051-2060. 10p. 3 Black and White Photographs, 1 Diagram, 2 Charts, 2 Graphs. |
| Subjects: | Decision trees, Magnetic resonance mammography, Sensitivity & specificity (Statistics), Mammograms, Chi-squared test |
| Abstract: | Objectives: In the face of multiple available diagnostic criteria in MR-mammography (MRM), a practical algorithm for lesion classification is needed. Such an algorithm should be as simple as possible and include only important independent lesion features to differentiate benign from malignant lesions. This investigation aimed to develop a simple classification tree for differential diagnosis in MRM. Methods: A total of 1,084 lesions in standardised MRM with subsequent histological verification (648 malignant, 436 benign) were investigated. Seventeen lesion criteria were assessed by 2 readers in consensus. Classification analysis was performed using the chi-squared automatic interaction detection (CHAID) method. Results include the probability for malignancy for every descriptor combination in the classification tree. Results: A classification tree incorporating 5 lesion descriptors with a depth of 3 ramifications (1, root sign; 2, delayed enhancement pattern; 3, border, internal enhancement and oedema) was calculated. Of all 1,084 lesions, 262 (40.4 %) and 106 (24.3 %) could be classified as malignant and benign with an accuracy above 95 %, respectively. Overall diagnostic accuracy was 88.4 %. Conclusions: The classification algorithm reduced the number of categorical descriptors from 17 to 5 (29.4 %), resulting in a high classification accuracy. More than one third of all lesions could be classified with accuracy above 95 %. Key Points: • A practical algorithm has been developed to classify lesions found in MR-mammography. • A simple decision tree consisting of five criteria reaches high accuracy of 88.4 %. • Unique to this approach, each classification is associated with a diagnostic certainty. • Diagnostic certainty of greater than 95 % is achieved in 34 % of all cases. [ABSTRACT FROM AUTHOR] |
| Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Baltzer%2C+Pascal%22">Baltzer, Pascal</searchLink><relatesTo>1</relatesTo><i> pascal.baltzer@meduniwien.ac.at</i><br /><searchLink fieldCode="AR" term="%22Dietzel%2C+Matthias%22">Dietzel, Matthias</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Kaiser%2C+Werner%22">Kaiser, Werner</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Aug2013, Vol. 23 Issue 8, p2051-2060. 10p. 3 Black and White Photographs, 1 Diagram, 2 Charts, 2 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+mammography%22">Magnetic resonance mammography</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+%26+specificity+%28Statistics%29%22">Sensitivity & specificity (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Mammograms%22">Mammograms</searchLink><br /><searchLink fieldCode="DE" term="%22Chi-squared+test%22">Chi-squared test</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objectives: In the face of multiple available diagnostic criteria in MR-mammography (MRM), a practical algorithm for lesion classification is needed. Such an algorithm should be as simple as possible and include only important independent lesion features to differentiate benign from malignant lesions. This investigation aimed to develop a simple classification tree for differential diagnosis in MRM. Methods: A total of 1,084 lesions in standardised MRM with subsequent histological verification (648 malignant, 436 benign) were investigated. Seventeen lesion criteria were assessed by 2 readers in consensus. Classification analysis was performed using the chi-squared automatic interaction detection (CHAID) method. Results include the probability for malignancy for every descriptor combination in the classification tree. Results: A classification tree incorporating 5 lesion descriptors with a depth of 3 ramifications (1, root sign; 2, delayed enhancement pattern; 3, border, internal enhancement and oedema) was calculated. Of all 1,084 lesions, 262 (40.4 %) and 106 (24.3 %) could be classified as malignant and benign with an accuracy above 95 %, respectively. Overall diagnostic accuracy was 88.4 %. Conclusions: The classification algorithm reduced the number of categorical descriptors from 17 to 5 (29.4 %), resulting in a high classification accuracy. More than one third of all lesions could be classified with accuracy above 95 %. Key Points: • A practical algorithm has been developed to classify lesions found in MR-mammography. • A simple decision tree consisting of five criteria reaches high accuracy of 88.4 %. • Unique to this approach, each classification is associated with a diagnostic certainty. • Diagnostic certainty of greater than 95 % is achieved in 34 % of all cases. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00330-013-2804-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 2051 Subjects: – SubjectFull: Decision trees Type: general – SubjectFull: Magnetic resonance mammography Type: general – SubjectFull: Sensitivity & specificity (Statistics) Type: general – SubjectFull: Mammograms Type: general – SubjectFull: Chi-squared test Type: general Titles: – TitleFull: A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Baltzer, Pascal – PersonEntity: Name: NameFull: Dietzel, Matthias – PersonEntity: Name: NameFull: Kaiser, Werner IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 09387994 Numbering: – Type: volume Value: 23 – Type: issue Value: 8 Titles: – TitleFull: European Radiology Type: main |
| ResultId | 1 |
