Evidence map›Paper›PMID 40443544›Full record

ArticleOpen life sciences2025

Research on individualized drug sensitivity detection technology based on bio-3D printing technology for precision treatment of gastrointestinal stromal tumors.

Yang-Yang Tu, Zhong-Ting Huang, Zhi-Yong Zhang, Dong-Liang Liu, Hai-Long Qian

Abstract read
In one paragraph

Article in Open life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yang-Yang TuDepartment of Gastrointestinal Surgery, Ningbo Medical Center Li Hui Li Hospital, Ningbo, Zhejiang, 315000, China.
Zhong-Ting HuangDepartment of Gastrointestinal Surgery, Ningbo Medical Center Li Hui Li Hospital, Ningbo, Zhejiang, 315000, China.
Zhi-Yong ZhangDepartment of Gastrointestinal Surgery, Ningbo Medical Center Li Hui Li Hospital, Ningbo, Zhejiang, 315000, China.
Dong-Liang LiuDepartment of Gastrointestinal Surgery, Ningbo Medical Center Li Hui Li Hospital, Ningbo, Zhejiang, 315000, China.
Hai-Long QianDepartment of Gastrointestinal Surgery, Ningbo Medical Center Li Hui Li Hospital, Ningbo, Zhejiang, 315000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to analyze and explore whether tumor biological three-dimensional printing (3DP) models can serve as reliable preclinical model research tools and assist in the personalized treatment of gastrointestinal stromal tumor (GIST) patients. Ten GIST cases admitted to our hospital from May 2024 to September 2024 were included in the personalized treatment group. Patient-derived GIST 3DP models were established, and treatment plans were selected based on the results of drug sensitivity tests. The progression-free survival (PFS) of the personalized treatment group was compared with that of GIST patients who had progressed after first-line treatment and were admitted to our hospital before the study. Treatment safety was also assessed. Immunofluorescence staining technology was used to observe tumor markers in the 3DP tumor models and their corresponding parent tumor tissues, revealing a high degree of consistency, which indicates that the 3DP tumor models highly retain the histological characteristics of the parent tumor tissues. The median PFS of patients in the personalized treatment group was 6.1 months, compared to 5.3 months in the previous treatment group, with a statistically significant difference (

Indexed as

bio-3D printing technologydrug sensitivity testinggastrointestinal stromal tumor

Identifiers

PMID40443544
PMCPMC12120404

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.