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Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis

Abstract

Objectives

Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS disease burden and activity on MRI.

Methods

A single-institutional dataset with 334 MS patients (334 MRI exams) was used to develop and train an ANN for automated identification and volumetric segmentation of T2/FLAIR-hyperintense and contrast-enhancing (CE) lesions. Independent testing was performed in a single-institutional longitudinal dataset with 82 patients (266 MRI exams). We evaluated lesion detection performance (F1 scores), lesion segmentation agreement (DICE coefficients), and lesion volume agreement (concordance correlation coefficients [CCC]). Independent evaluation was performed on the public ISBI-2015 challenge dataset.

Results

The F1 score was maximized in the training set at a detection threshold of 7 mm3 for T2/FLAIR lesions and 14 mm3 for CE lesions. In the training set, mean F1 scores were 0.867 for T2/FLAIR lesions and 0.636 for CE lesions, as compared to 0.878 for T2/FLAIR lesions and 0.715 for CE lesions in the test set. Using these thresholds, the ANN yielded mean DICE coefficients of 0.834 and 0.878 for segmentation of T2/FLAIR and CE lesions in the training set (fivefold cross-validation). Corresponding DICE coefficients in the test set were 0.846 for T2/FLAIR lesions and 0.908 for CE lesions, and the CCC was ≥ 0.960 in each dataset.

Conclusions

Our results highlight the capability of ANN for quantitative state-of-the-art assessment of volumetric lesion load on MRI and potentially enable a more accurate assessment of disease burden in patients with MS.

Key Points

• Artificial neural networks (ANN) can accurately detect and segment both T2/FLAIR and contrast-enhancing MS lesions in MRI data.

• Performance of the ANN was consistent in a clinically derived dataset, with patients presenting all possible disease stages in MRI scans acquired from standard clinical routine rather than with high-quality research sequences.

• Computer-aided evaluation of MS with ANN could streamline both clinical and research procedures in the volumetric assessment of MS disease burden as well as in lesion detection.

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Abbreviations

ANN:

Artificial neural networks

CCC:

Concordance correlation coefficients

CE:

Contrast enhancing

FLAIR:

Fluid-attenuated inversion recovery

GT:

Ground truth

LPPV:

Lesion-wise positive predictive value

LTPR:

Lesion-wise true positive rate

MRI:

Magnetic resonance imaging

MS:

Multiple sclerosis

PD-w:

Proton density weighted

T2-w:

T2 weighted

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Funding

Dr. Kickingereder, MBA was supported by the Medical Faculty Heidelberg Postdoc-Program and the Else Kröner-Fresenius Foundation (Else-Kröner Memorial Scholarship).

Author information

Correspondence to Philipp Kickingereder.

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Guarantor

The scientific guarantor of this publication is Dr. Philipp Kickingereder, MBA.

Conflict of interest

The authors declare that they have no competing interests.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained at the local ethics committee of the medical faculty of the University of Heidelberg.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Brugnara, G., Isensee, F., Neuberger, U. et al. Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis. Eur Radiol (2020). https://doi.org/10.1007/s00330-019-06593-y

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Keywords

  • Multiple sclerosis
  • Magnetic resonance imaging
  • Neural networks (computer)
  • Diagnosis, computer-assisted
  • Artificial intelligence