Evidence map›Paper›PMID 41772669›Full record

ArticleCardio-oncology (London, England)2026

Temporal trends in myocardial ischemia risk estimated from 12-lead electrocardiograms using deep learning in individuals with suspected cancer during health checkups.

Ken Kurisu, Maiko Fujimori, Kohei Takeshita, Akira Fukui, Kyoko Ito, Keitaro Yokoyama, Tomohiro Kato, Tatsuo Akechi, Kazuhiro Yoshiuchi, Yosuke Uchitomi

Abstract read
In one paragraph

Article in Cardio-oncology (London, England), 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. Article
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

10 authors.

Ken KurisuDepartment of Cancer Survivorship and Digital Medicine, The Jikei University School of Medicine, Tokyo, Japan. chrischris0801@gmail.com.
Maiko FujimoriDivision of Survivorship Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan.
Kohei TakeshitaDepartment of Innovation for Medical Information Technology, The Jikei University School of Medicine, Tokyo, Japan.
Akira FukuiDivision of Nephrology and Hypertension, Department of Internal Medicine, The Jikei University School of Medicine, Tokyo, Japan.
Kyoko ItoCenter for Preventive Medicine, The Jikei University Hospital, Tokyo, Japan.
Keitaro YokoyamaHarumi Triton Clinic of Jikei University Hospital, The Jikei University School of Medicine, Tokyo, Japan.
Tomohiro KatoDepartment of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan.
Tatsuo AkechiDepartment of Psychiatry and Cognitive-Behavioral Medicine, Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan.
Kazuhiro YoshiuchiDepartment of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Yosuke UchitomiDepartment of Cancer Survivorship and Digital Medicine, The Jikei University School of Medicine, Tokyo, Japan.

Funding

Health Labor Sciences Research Grant 23EA1028JSPS KAKENHI 24K23742
6 · The paper itself

Abstract

backgroundPrevious studies have suggested the potential effect of psychological stress related to cancer diagnosis on cardiovascular mortality. This study aimed to investigate the temporal trends of cardiovascular risk before and after cancer diagnosis using a deep learning model applied to 12-lead electrocardiograms (ECGs).

methodsWe developed a deep learning model using a publicly available large-scale dataset to quantify myocardial ischemia risk from 12-lead ECGs. We collected ECG records from individuals diagnosed with cancer at a university hospital who also underwent an ECG as part of a health checkup within 90 days prior to cancer diagnosis. The deep learning model was then applied to the ECGs of individuals with cancer, and the temporal trend of cardiovascular risk was examined.

resultsThe deep learning model demonstrated high predictive performance, with an area under the receiver operating characteristic curve of 0.930 (95% confidence interval = 0.920–0.941). The model was then applied to 523 ECG records of 89 individuals with cancer. The estimated probability of ECG-indicated myocardial ischemia increased until cancer diagnosis, peaked shortly after diagnosis, and then declined.

conclusionsThese findings support the immediate effect of psychological stress related to cancer diagnosis on increased cardiovascular risks.

Indexed as

cancercardiovascular diseasedeep learningmyocardial infarctionpsychological distress

Identifiers

PMID41772669
PMCPMC13059618

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.