Evidence map›Paper›PMID 39985379›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Multimodal Artificial Intelligence-Based Virtual Biopsy for Diagnosing Abdominal Lavage Cytology-Positive Gastric Cancer.

Ping'an Ding, Jiaxuan Yang, Honghai Guo, Jiaxiang Wu, Haotian Wu, Tongkun Li, Renjun Gu, Lilong Zhang, Jinchen He, Peigang Yang and 6 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–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

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Characterization of acquired capecitabine resistance in MKN-45 gastric cancer cells reveals preserved apoptotic sensitivity.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026
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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

16 authors.

Ping'an DingThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
Jiaxuan YangThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
Honghai GuoThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
Jiaxiang WuThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
Haotian WuThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
Tongkun LiThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
Renjun GuSchool of Chinese Medicine & School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, China.
Lilong ZhangDepartment of General Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430065, China.
Jinchen HeThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
Peigang YangThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
Yuan TianThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
Ning MengDepartment of General Surgery, Shijiazhuang People's Hospital, Shijiazhuang, Hebei, 050050, China.
Xiaolong LiDepartment of General Surgery, Baoding Central Hospital, Baoding, Hebei, 071030, China.
Zhenjiang GuoGeneral Surgery Department, Hengshui People's Hospital, Hengshui, Hebei, 053099, China.
Lingjiao MengResearch Center and Tumor Research Institute, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
Qun ZhaoThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.ORCID https://orcid.org/0000-0003-1603-3002

Funding

Hebei Natural Science Foundation H2022206599Hebei Provincial Government-funded Clinical Talent Project ZF2023047Hebei Provincial Government-funded Clinical Talent Project ZF2025180S&T Program of Hebei 23297701Z
6 · The paper itself

Abstract

Gastric cancer with peritoneal dissemination remains a significant clinical challenge due to its poor prognosis and difficulty in early detection. This study introduces a multimodal artificial intelligence-based risk stratification assessment (RSA) model, integrating radiomic and clinical data to predict peritoneal lavage cytology-positive (GC-CY1) in gastric cancer patients. The RSA model is trained and validated across retrospective, external, and prospective cohorts. In the training cohort, the RSA model achieved an area under the curve (AUC) of 0.866, outperforming traditional clinical and radiomic feature models. External validation cohorts confirmed its robustness, with AUC values of 0.883 and 0.823 for predicting peritoneal metastasis and recurrence, respectively. In a prospective validation involving 152 patients, the model maintained superior predictive performance (AUC = 0.835). The RSA model also demonstrated significant clinical benefits by effectively identifying high-risk patients likely to benefit from specific treatments, such as paclitaxel-based conversion therapy. These findings suggest that the RSA model offers a reliable, non-invasive diagnostic tool for gastric cancer, capable of improving early detection and treatment outcomes. Further prospective studies are warranted to explore its full clinical potential.

Indexed as

Artificial IntelligencePeritoneal LavageStomach NeoplasmsAgedBiopsyFemaleHumansMaleMiddle AgedProspective StudiesRetrospective Studiesgastric cancermultimodal artificial intelligenceperitoneal lavage cytology‐positive (CY1)radiomicsvirtual biopsy

Identifiers

PMID39985379
PMCPMC12005817

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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.