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CNN color-coded difference maps accurately display longitudinal changes in liver MRI-PDFF

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To assess the feasibility of a CNN-based liver registration algorithm to generate difference maps for visual display of spatiotemporal changes in liver PDFF, without needing manual annotations.

Methods

This retrospective exploratory study included 25 patients with suspected or confirmed NAFLD, who underwent PDFF-MRI at two time points at our institution. PDFF difference maps were generated by applying a CNN-based liver registration algorithm, then subtracting follow-up from baseline PDFF maps. The difference maps were post-processed by smoothing (5 cm2 round kernel) and applying a categorical color scale. Two fellowship-trained abdominal radiologists and one radiology resident independently reviewed difference maps to visually determine segmental PDFF change. Their visual assessment was compared with manual ROI-based measurements of each Couinaud segment and whole liver PDFF using intraclass correlation (ICC) and Bland-Altman analysis. Inter-reader agreement for visual assessment was calculated (ICC).

Results

The mean patient age was 49 years (12 males). Baseline and follow-up PDFF ranged from 2.0 to 35.3% and 3.5 to 32.0%, respectively. PDFF changes ranged from - 20.4 to 14.1%. ICCs against the manual reference exceeded 0.95 for each reader, except for segment 2 (2 readers ICC = 0.86–0.91) and segment 4a (reader 3 ICC = 0.94). Bland-Altman limits of agreement were within 5% across all three readers. Inter-reader agreement for visually assessed PDFF change (whole liver and segmental) was excellent (ICCs > 0.96), except for segment 2 (ICC = 0.93).

Conclusions

Visual assessment of liver segmental PDFF changes using a CNN-generated difference map strongly agreed with manual estimates performed by an expert reader and yielded high inter-reader agreement.

Key Points

• Visual assessment of longitudinal changes in quantitative liver MRI can be performed using a CNN-generated difference map and yields strong agreement with manual estimates performed by expert readers.

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Abbreviations

CNN:

Convolutional neural networks

ICC:

Intraclass correlation

MRI:

Magnetic resonance imaging

PDFF:

Proton density fat fraction

ROI:

Region of Interest

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Funding

The authors state that this work has not received any funding.

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Author notes

  1. Kyle Hasenstab and Guilherme Moura Cunha contributed equally to this work.

Authors and Affiliations

  1. Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA

    Kyle Hasenstab, Guilherme Moura Cunha, Soudabeh Fazeli Dehkordy, Alexandra Schlein, Yesenia Covarrubias, Claude B. Sirlin & Kathryn J. Fowler

  2. Department of Mathematics and Statistics, San Diego State University, San Diego, CA, USA

    Kyle Hasenstab

  3. Department of Radiology, University of Yamanashi, Yamanashi, Japan

    Shintaro Ichikawa

  4. Soonchunhyang University Bucheon Hospital, Gyeonggi-do, South Korea

    Min Hee Lee

  5. National Cancer Center, Republic of Korea, Gyeonggi-do, South Korea

    Soo Jin Kim

Authors
  1. Kyle Hasenstab
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  2. Guilherme Moura Cunha
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  3. Shintaro Ichikawa
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  4. Soudabeh Fazeli Dehkordy
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  5. Min Hee Lee
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  6. Soo Jin Kim
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  7. Alexandra Schlein
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  8. Yesenia Covarrubias
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  9. Claude B. Sirlin
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  10. Kathryn J. Fowler
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Corresponding author

Correspondence to Kyle Hasenstab.

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Guarantor

All authors take public responsibility for the content of this work. The data is also available by request to Dr Kyle A Hasenstab, PhD, the scientific guarantor of this publication.

Conflict of interest

All authors or institutions involved in this work have no conflicts of interest or industry support to disclose with regard to the current manuscript. This includes financial or personal relationships that inappropriately influence his or her actions within 3 years of the work beginning submitted.

Statistics and biometry

One of the authors (KH) has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective, cross-sectional observational study performed at one institution

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Hasenstab, K., Cunha, G.M., Ichikawa, S. et al. CNN color-coded difference maps accurately display longitudinal changes in liver MRI-PDFF. Eur Radiol 31, 5041–5049 (2021). https://doi.org/10.1007/s00330-020-07649-0

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  • DOI: https://doi.org/10.1007/s00330-020-07649-0

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