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. 2023 Aug;28(8):1033-1042.
doi: 10.1007/s10147-023-02356-4. Epub 2023 May 31.

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

Jun Matsushima  1   2   3 , Tamotsu Sato  4 , Yuichiro Yoshimura  2 , Hiroyuki Mizutani  4 , Shinichiro Koto  4 , Keisuke Matsusaka  5 , Jun-Ichiro Ikeda  3 , Taiki Sato  1 , Akiko Fujii  1 , Yuko Ono  6 , Takashi Mitsui  7 , Shinichi Ban  1 , Hisahiro Matsubara  8 , Hideki Hayashi  9   10
Affiliations

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

Jun Matsushima et al. Int J Clin Oncol. 2023 Aug.

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.

Keywords: Convolutional neural network; Isolated tumor cells; Machine learning; Micrometastasis; Whole-slide image.

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