Evidence map›Paper›PMID 37256523›Full record

ArticleInternational journal of clinical oncology2023

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

Jun Matsushima, Tamotsu Sato, Yuichiro Yoshimura, Hiroyuki Mizutani, Shinichiro Koto, Keisuke Matsusaka, Jun-Ichiro Ikeda, Taiki Sato, Akiko Fujii, Yuko Ono and 4 more

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Article in International journal of clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Jun MatsushimaDepartment of Pathology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, Japan.
Tamotsu SatoToshiba Digital Solutions Corporation, 72-34 Horikawa-Cho, Saiwai-Ku, Kawasaki, Kanagawa, Japan.
Yuichiro YoshimuraCenter for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-Ku, Chiba, Chiba, 263-8522, Japan.
Hiroyuki MizutaniToshiba Digital Solutions Corporation, 72-34 Horikawa-Cho, Saiwai-Ku, Kawasaki, Kanagawa, Japan.
Shinichiro KotoToshiba Digital Solutions Corporation, 72-34 Horikawa-Cho, Saiwai-Ku, Kawasaki, Kanagawa, Japan.
Keisuke MatsusakaDepartment of Pathology, Chiba University Hospital, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, Japan.
Jun-Ichiro IkedaDepartment of Diagnostic Pathology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, Japan.
Taiki SatoDepartment of Pathology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, Japan.
Akiko FujiiDepartment of Pathology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, Japan.
Yuko OnoDepartment of Diagnostic Pathology, Dokkyo Medical University, 880 Kitakobayashi, Shimotusugagun, Mibu, Tochigi, Japan.
Takashi MitsuiDepartment of Surgery, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Japan.
Shinichi BanDepartment of Pathology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, Japan.
Hisahiro MatsubaraDepartment of Frontier Surgery, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, Japan.
Hideki HayashiCenter for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-Ku, Chiba, Chiba, 263-8522, Japan. hhayashi@faculty.chiba-u.jp.ORCID http://orcid.org/0000-0001-5122-6008

Funding

JSPS KAKENHI JP20K09027Toshiba Digital Solutions Corporation J20KK00041
6 · The paper itself

Abstract

backgroundAdvances 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.

methodsWe 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.

resultsNo 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.

conclusionsA 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.

Indexed as

AdenocarcinomaStomach NeoplasmsAlgorithmsArtificial IntelligenceHumansLymphatic MetastasisLymph NodesNeoplasm MicrometastasisConvolutional neural networkIsolated tumor cellsMachine learningMicrometastasisWhole-slide image

Identifiers

PMID37256523

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.