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Clinical utility of artificial intelligence assistance in histopathologic review of lymph node metastasis for gastric adenocarcinoma

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International Journal of Clinical Oncology Aims and scope Submit manuscript

Abstract

Background

Advances in whole-slide image capture and computer image analyses using deep learning technologies have enabled the development of computer-assisted diagnostics in pathology. Herein, we built a deep learning algorithm to detect lymph node (LN) metastasis on whole-slide images of LNs retrieved from patients with gastric adenocarcinoma and evaluated its performance in clinical settings.

Methods

We randomly selected 18 patients with gastric adenocarcinoma who underwent surgery with curative intent and were positive for LN metastasis at Chiba University Hospital. A ResNet-152-based assistance system was established to detect LN metastases and to outline regions that are highly probable for metastasis in LN images. Reference standards comprising 70 LN images from two different institutions were reviewed by six pathologists with or without algorithm assistance, and their diagnostic performances were compared between the two settings.

Results

No statistically significant differences were observed between these two settings regarding sensitivity, review time, or confidence levels in classifying macrometastases, isolated tumor cells, and metastasis-negative. Meanwhile, the sensitivity for detecting micrometastases significantly improved with algorithm assistance, although the review time was significantly longer than that without assistance. Analysis of the algorithm’s sensitivity in detecting metastasis in the reference standard indicated an area under the curve of 0.869, whereas that for the detection of micrometastases was 0.785.

Conclusions

A wide variety of histological types in gastric adenocarcinoma could account for these relatively low performances; however, this level of algorithm performance could suffice to help pathologists improve diagnostic accuracy.

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Data availability

The datasets supporting the conclusions of this article are included within the article.

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Acknowledgements

The authors thank Kiyotaka Onodera of the Department of Pathology, Chiba University Hospital and Takuya Okamura of the Department of Pathology, Dokkyo Medical University Saitama Medical Center for their technical assistance.

Funding

This study was partially supported by the Chiba University Strategic Priority Research Promotion Program “Multimodal Medical Engineering and KAKENHI (the Grants-in-Aid for Scientific Research) (C) 20K09027.

Author information

Authors and Affiliations

  1. Department of Pathology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, Japan

    Jun Matsushima, Taiki Sato, Akiko Fujii & Shinichi Ban

  2. Toshiba Digital Solutions Corporation, 72-34 Horikawa-Cho, Saiwai-Ku, Kawasaki, Kanagawa, Japan

    Tamotsu Sato, Hiroyuki Mizutani & Shinichiro Koto

  3. Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-Ku, Chiba, Chiba, 263-8522, Japan

    Jun Matsushima, Yuichiro Yoshimura & Hideki Hayashi

  4. Department of Pathology, Chiba University Hospital, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, Japan

    Keisuke Matsusaka

  5. Department of Diagnostic Pathology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, Japan

    Jun Matsushima & Jun-ichiro Ikeda

  6. Department of Diagnostic Pathology, Dokkyo Medical University, 880 Kitakobayashi, Shimotusugagun, Mibu, Tochigi, Japan

    Yuko Ono

  7. Department of Surgery, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Japan

    Takashi Mitsui

  8. Department of Frontier Surgery, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, Japan

    Hisahiro Matsubara & Hideki Hayashi

Authors
  1. Jun Matsushima
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  2. Tamotsu Sato
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  3. Yuichiro Yoshimura
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  4. Hiroyuki Mizutani
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  7. Jun-ichiro Ikeda
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  8. Taiki Sato
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  9. Akiko Fujii
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  10. Yuko Ono
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  11. Takashi Mitsui
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  12. Shinichi Ban
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  13. Hisahiro Matsubara
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  14. Hideki Hayashi
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Contributions

JM: Methodology, data curation, writing and original draft. TS: Methodology, software, visualization, and formal analysis. YY: supervision. HM: Project administration. SK: supervision. KM: validation. JI: Resources and writing−the review. TS: validation. AF: validation. YO: validation. TM: resources; SB: resources, supervision, and validation. HM: Resources and supervision. HH: conceptualization, formal analysis, writing, reviewing, and editing.

Corresponding author

Correspondence to Hideki Hayashi.

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Conflict of interest

The research leading to these results received funding from Toshiba Digital Solutions Corporation under Grant Agreement No. J20KK00041. No other relationships or activities appear to have influenced the submitted work.

Ethical approval

This study was approved by the Research Ethics Committee of the Graduate School of Medicine, Chiba University (#4122) and the Research Ethics Committee of Saitama Medical Center, Dokkyo Medical University (#21016).

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Matsushima, J., Sato, T., Yoshimura, Y. et al. Clinical utility of artificial intelligence assistance in histopathologic review of lymph node metastasis for gastric adenocarcinoma. Int J Clin Oncol 28, 1033–1042 (2023). https://doi.org/10.1007/s10147-023-02356-4

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  • DOI: https://doi.org/10.1007/s10147-023-02356-4

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