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Deep Learning-Fuzzy Inference (DeLFI): an interpretable model for detecting indirect immunofluorescence patterns associated with nasopharyngeal cancer

Published:June 21, 2022DOI:https://doi.org/10.1016/j.ajpath.2022.05.010
Deep Learning-Fuzzy Inference (DeLFI): an interpretable model for detecting indirect immunofluorescence patterns associated with nasopharyngeal cancer
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      Abstract

      The detection of serum antibodies to Epstein-Barr Virus proteins by immunofluorescence assay (IFA) is considered the gold standard for nasopharyngeal cancer (NPC) screening in high-risk populations. Given the high survival rates of early asymptomatic patients compared to the poor prognosis for late-stage NPC, IFA has tremendous life-saving potential for general population screening. However, IFA requires visual interpretation of cellular staining patterns by trained pathology staff, making it labor-intensive and hence unscalable. This study demonstrates that an automated Fuzzy Inference (FI) system achieved high agreement (κ=0.82) with a human IFA expert in identifying cellular patterns associated with NPC. Integrating a deep learning module into FI further improved FI’s performance (κ=0.90) and reduced the number of uncertain cases which required manual evaluation. The resulting hybrid model, termed deep learning-fuzzy inference (DeLFI), was then evaluated with a separate testing set of clinical samples. In this clinical validation, DeLFI outperformed human evaluation on the Area Under the Curve (0.926 vs 0.821) and closely matched human performance on Youden’s J index (0.81 vs 0.80). We conclude that DeLFI’s combination of deep learning with fuzzy inference has the potential to improve the scalability and accuracy of NPC detection.
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      Article Info

      Publication History

      Accepted: May 18, 2022
      Received in revised form: May 12, 2022
      Received: February 14, 2022

      Publication stage

      In Press Journal Pre-Proof

      Footnotes

      Competing interests: S.H.C. and I.C. hold equity in Pathnova Laboratories Pte Ltd. S.S., M.S., J.H., S.H.C. and I.C. are co-inventors on a patent application based on the subject matter of this study.

      Funding sources: Temasek Life Sciences Laboratory Core Funding (I.C.), National Medical Research Council Singapore, Transition Award TA20nov-0025 (J.K.T.), National Medical Research Council Singapore, Individual Research Grant CIRG18nov-0045 (K.S.L.), Pathnova Laboratories Pte. Ltd.

      Identification

      DOI: https://doi.org/10.1016/j.ajpath.2022.05.010

      Copyright

      © 2022 Published by Elsevier Inc. on behalf of the American Society for Investigative Pathology.

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