Evidence map›Paper›PMID 40988134›Full record

ArticleCancer medicine2025

Segment Anything Model for Gastric Cancer.

Lanlan Li, Chongyang Wang, Yi Geng, Tao Chen, Ziyue Wang, Kaixin Lin, Hongan Wei, Jianping Wang, Dabiao Wang, Decao Niu and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. [CGP-Net: Cross-modal guided prior network for precise gastric cancer segmentation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026
    Article
  3. 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

11 authors.

Lanlan LiCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Chongyang WangCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Yi GengCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Tao ChenCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Ziyue WangCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Kaixin LinDepartment of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Hongan WeiCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Jianping WangDepartment of Gastroenterology, Third People's Hospital, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Dabiao WangCollege of Chemical and Engineering, Fuzhou University, Fuzhou, China.
Decao NiuDepartment of Urology, Guangdong Second Provincial General Hospital, Guangzhou, China.
Juan LiDepartment of Endoscopic Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0000-0002-0536-3236

Funding

Guangdong Provincial Clinical Research Center for Digestive Diseases 2020B1111170004Guangzhou Municipal Science and Technology Project 2023A04J2240National Key Clinical Discipline
6 · The paper itself

Abstract

backgroundGastric cancer is a biologically aggressive disease, accounting for a substantial proportion of cancer-related deaths globally. Accurate localization of the lesion by artificial intelligence techniques helps timely and efficiently diagnose and treat. Segment Anything Model (SAM) has demonstrated considerable potential in medical image segmentation by displaying high performance in numerous image benchmark tests. However, its resource-intensive nature limits feasibility in embedded medical contexts.

methodsThis study proposed GC-SAM, a lightweight model for tumor segmentation. The architecture of GC-SAM is innovatively proposed, including a knowledge distillation image encoder, prompt encoder, and mask decoder, which effectively replaces the conventional fixed and computationally intensive network components.

resultsExtensive experiments demonstrate that GC-SAM significantly outperforms both classical segmentation models and recent state-of-the-art networks. On the internal test set, GC-SAM achieves 0.8186 Dice and 0.6504 mIoU, while reducing inference time and parameter count by over 80% compared to the original SAM. On the external dataset, GC-SAM maintains superior performance (Dice 0.8350), demonstrating excellent generalization.

conclusionsThe proposed GC-SAM model shows strong capability in segmenting gastric cancer tissue, while also demonstrating practical potential for deployment in embedded medical imaging devices.

Indexed as

Image Interpretation, Computer-AssistedImage Processing, Computer-AssistedStomach NeoplasmsAlgorithmsArtificial IntelligenceHumansfine‐tunegastric cancerimage segmentationknowledge distillationSAM

Identifiers

PMID40988134
PMCPMC12457216

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