Evidence map›Paper›PMID 41109897›Full record

ArticleSurgery today2026

Utility of multimodal deep learning model to diagnose lymph node metastasis in esophageal cancer using computed tomography and positron emission tomography images.

Yasuharu Shinozaki, Hirotaka Ishida, Tomofumi Kaneno, Eichi Takaya, Tomoya Kobayashi, Yusuke Taniyama, Chiaki Sato, Hiroshi Okamoto, Yohei Ozawa, Takuya Ueda and 1 more

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In one paragraph

Article in Surgery today, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

11 authors.

Yasuharu ShinozakiDepartment of Surgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.
Hirotaka Ishida *Department of Surgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan. h-ishida@tohoku.ac.jp.
Tomofumi KanenoDepartment of Diagnostic Imaging, Tohoku University Graduate School of Medicine, Sendai, Japan.
Eichi TakayaAI Lab, Tohoku University Hospital, Sendai, Japan.
Tomoya KobayashiDepartment of Diagnostic Imaging, Tohoku University Graduate School of Medicine, Sendai, Japan.
Yusuke TaniyamaDepartment of Surgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.
Chiaki SatoDepartment of Surgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.
Hiroshi OkamotoDepartment of Surgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.
Yohei OzawaDepartment of Surgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.
Takuya UedaDepartment of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Sendai, Japan.
Takashi KameiDepartment of Surgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.

Funding

CREST Grant No. JPMJCR15D1JSPS KAKENHI JP20 K16687
6 · The paper itself

Abstract

purposeThis study aimed to assess the performance of a deep learning model using multimodal imaging for detecting lymph node metastasis in esophageal cancer in comparison to expert assessments.

methodsA retrospective analysis was performed for 521 lymph nodes from 167 patients with esophageal cancer who underwent esophagectomy. Deep learning models were developed based on multimodal imaging, including non-contrast-enhanced computed tomography, contrast-enhanced computed tomography, and positron emission tomography imaging. The diagnostic performance was evaluated and compared with expert assessments using a receiver operating characteristic curve analysis.

resultsThe area under the receiver operating characteristic curve values for the deep learning model were 0.81 with multimodal imaging, 0.73 with non-contrast-enhanced computed tomography, 0.72 with contrast-enhanced computed tomography, and 0.75 with positron emission tomography were calculated. The area under the curve of the deep learning model (0.81) demonstrated diagnostic performance comparable to that of experienced experts (area under the curve, 0.84; P = 0.62, DeLong's test).

conclusionThe multimodal deep learning model using computed tomography and positron emission tomography demonstrated performance comparable to that of experts in diagnosing the presence of lymph node metastasis, a key prognostic factor in esophageal cancer, suggesting its potential clinical utility.

Indexed as

Deep LearningEsophageal NeoplasmsLymphatic MetastasisLymph NodesMultimodal ImagingPositron-Emission TomographyTomography, X-Ray ComputedAgedAged, 80 and overEsophagectomyFemaleHumansMaleMiddle AgedRetrospective StudiesROC CurveComputed tomographyEsophageal cancerLymph node metastasisMultimodal deep learning modelPositron emission tomography

Identifiers

PMID41109897
PMCPMC13013182

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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.