Diagnostic value of MRI-based 3D texture analysis for tissue characterisation and discrimination of low-grade chondrosarcoma from enchondroma: a pilot study

Lisson, Catharina Silvia; Lisson, Christoph; Flosdorf, K; Mayer-Steinacker, Regine; Schultheiß, Markus; Baer, Alexandra von; Barth, Thomas F.E.; Beer, Ambros; Baumhauer, Matthias; Meier, Reinhard; Beer, Meinrad; Schmidt, Stefan A.

doi:10.1007/s00330-017-5014-6

Cited by 69 publications

(61 citation statements)

References 58 publications

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“…A recent publication evaluating the diagnostic value of 3-dimensional (3D)-based TA for differentiation of enchondroma from low-grade chondrosarcoma on 22 patients also found significant differences between TA features after univariate analysis. 46 However, in…”

Section: Discussionmentioning

confidence: 99%

Magnetic Resonance Imaging–Based Grading of Cartilaginous Bone Tumors

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Magnetic Resonance Imaging–Based Grading of Cartilaginous Bone Tumors

et al. 2018

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“…Recent literature dedicated to musculoskeletal radiology shows increasing interest in the application of machine learning and other computer techniques, for example, in the analysis of benign and malignant vertebral compression fractures [28], skeletal maturity [29], and differentiation between benign and malignant cartilaginous bone tumors [30]. However, to our best knowledge, there is no previous study dedicated to SpA SIJ inflammation.…”

Section: Discussionmentioning

confidence: 99%

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging

et al. 2020

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Background: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologists remains subject to significant intrapersonal and interpersonal variation. This encouraged us to use machine-learning methods for this task. Methods: In this retrospective study including 56 sacroiliac joint MRI exams, 24 patients had positive and 32 had negative findings for inflammatory sacroiliitis according to the ASAS group criteria. The dataset was randomly split with~80% (46 samples, 20 positive and 26 negative) as training and~20% as external test (10 samples, 4 positive and 6 negative). After manual segmentation of the images by a musculoskeletal radiologist, multiple features were extracted. The classifiers used were the Support Vector Machine, the Multilayer Perceptron (MLP), and the Instance-Based Algorithm, combined with the Relief and Wrapper methods for feature selection. Results: Based on 10-fold cross-validation using the training dataset, the MLP classifier obtained the best performance with sensitivity = 100%, specificity = 95.6% and accuracy = 84.7%, using 6 features selected by the Wrapper method. Using the test dataset (external validation) the same MLP classifier obtained sensitivity = 100%, specificity = 66.7% and accuracy = 80%. Conclusions: Our results show the potential of machine learning methods to identify SIJ subchondral bone marrow edema in axSpA patients and are promising to aid in the detection of active inflammatory sacroiliitis on MRI STIR sequences. Multilayer Perceptron (MLP) achieved the best results.

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“…The nature of breast tumors is definitively obtained from the histopathological examination. Interestingly, the texture information extracted from the DCE‐MRIs may act as a bridge between the MRI images and histopathological types 19,33‐35 . If the histopathological type of a breast tumor can be accurately inferred from breast MRI, individualized treatment can be performed early.…”

Section: Discussionmentioning

confidence: 99%

“…Several kinds of quantitative methods have been applied to the differentiation of benign from malignant breast lesions based on DCE‐MRI 28‐32 . However, most previous studies showed that heterogeneity investigations based on post‐contrast images were commonly separate from analyses based on kinetic parametric maps 33‐35 …”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE‐MRI in classifying benign from malignant lesions

Jiang

Yin

2020

Journal of Surgical Oncology

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Background and Objectives: To investigate the performance of texture analysis based on enhancement kinetic parametric maps derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in discriminating benign from malignant tumors.Methods: A total of 192 cases confirmed by histopathology were retrospectively selected from our Picture Archiving and Communication System, including 93 benign and 99 malignant tumors. Lesion areas were delineated semi-automatically, and six kinetic parametric maps reflecting the perfusion information were generated, namely the maximum slope of increase (MSI), slope of signal intensity (SI slope ), initial percentage of peak enhancement (E initial ), percentage of peak enhancement (E peak ), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) maps. A total of 286 texture features were extracted from those quantitative parametric maps. The Student t test or Mann-Whitney U test was used to select features that were statistically significantly different between the benign and malignant groups. A support vector machine was employed with a leave-one-out crossvalidation method to establish the classification model. Classification performance was evaluated according to the receiver operating characteristic (ROC) theory. Results:The areas under ROC curves (AUCs) indicating the diagnostic performance were 0.925 for MSI, 0.854 for SI slope , 0.756 for E initial , 0.923 for E peak , 0.871 for ESER and 0.881 for SEP. Significant differences in AUCs were found between E initial vs MSI, E initial vs E peak and E initial vs SEP (P < .05). There were no significant differences in other pairwise comparisons.Conclusion: Texture analysis of the kinetic parametric maps derived from breast DCE-MRI can contribute to the discrimination between malignant and benign lesions. It can be considered as a supplementary tool for breast diagnosis. K E Y W O R D S breast cancer, kinetics, machine learning, magnetic resonance imaging, receiver operating characteristic

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Diagnostic value of MRI-based 3D texture analysis for tissue characterisation and discrimination of low-grade chondrosarcoma from enchondroma: a pilot study

Abstract: • MRI texture analysis may assist in differentiating low-grade chondrosarcoma from enchondroma. • Kurtosis in the contrast-enhanced T1w has the highest power of discrimination. • Tools provide insight into tumour characterisation as a non-invasive imaging biomarker.

Cited by 69 publications

References 58 publications

Magnetic Resonance Imaging–Based Grading of Cartilaginous Bone Tumors

Magnetic Resonance Imaging–Based Grading of Cartilaginous Bone Tumors

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging

Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE‐MRI in classifying benign from malignant lesions

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