Evidence map›Paper›PMID 42621171›Full record

ReviewiScience2026

The application and progress of AI-based image analysis in tumor organoid research.

Yicheng Xu, Shuai Fan, Yuxin Chen, Wanting Xu, Haodong Lu, Yutong Zhou, Yuetong Liu, Qing Du, Wenyu Wang, Tonghui Yu and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in iScience, 2026. 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

11 authors.

Yicheng XuBeijing Key Laboratory of Gene Editing Therapy for Hereditary Neuromuscular Diseases, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), Aerospace Center Hospital, Tangshan Research Institute, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Shuai FanBeijing Key Laboratory of Gene Editing Therapy for Hereditary Neuromuscular Diseases, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), Aerospace Center Hospital, Tangshan Research Institute, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Yuxin ChenBeijing Key Laboratory of Gene Editing Therapy for Hereditary Neuromuscular Diseases, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), Aerospace Center Hospital, Tangshan Research Institute, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Wanting XuBeijing Key Laboratory of Gene Editing Therapy for Hereditary Neuromuscular Diseases, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), Aerospace Center Hospital, Tangshan Research Institute, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Haodong LuBeijing Key Laboratory of Gene Editing Therapy for Hereditary Neuromuscular Diseases, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), Aerospace Center Hospital, Tangshan Research Institute, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Yutong ZhouBeijing Key Laboratory of Gene Editing Therapy for Hereditary Neuromuscular Diseases, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), Aerospace Center Hospital, Tangshan Research Institute, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Yuetong LiuBeijing Key Laboratory of Gene Editing Therapy for Hereditary Neuromuscular Diseases, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), Aerospace Center Hospital, Tangshan Research Institute, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Qing DuBeijing Key Laboratory of Gene Editing Therapy for Hereditary Neuromuscular Diseases, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), Aerospace Center Hospital, Tangshan Research Institute, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Wenyu WangBeijing Key Laboratory of Gene Editing Therapy for Hereditary Neuromuscular Diseases, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), Aerospace Center Hospital, Tangshan Research Institute, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Tonghui YuInternational Center for Interdisciplinary Statistics, School of Mathematics, Harbin Institute of Technology, Harbin 150001, China.
Lei DongBeijing Key Laboratory of Gene Editing Therapy for Hereditary Neuromuscular Diseases, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), Aerospace Center Hospital, Tangshan Research Institute, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of preclinical models that recapitulate the physiological and pathological features of human tumors remains a central challenge in cancer research. Advances in cell biology have enabled the generation of three-dimensional tumor organoids, which closely mirror patient-specific therapeutic responses and facilitate the study of disease mechanisms. However, the trend of these models necessitates a shift from traditional, invasive analytical methods toward non-invasive, high-throughput imaging approaches. Here, we review the current state of tumor organoid culture and the emerging application of artificial intelligence (AI) in their evaluation. We discuss how AI-driven technologies are revolutionizing the analysis of fluorescence imaging, viability assessments, and dynamic cell tracking, thereby overcoming the limitations of manual interpretation. Finally, we provide a perspective on how integrating deep learning with organoid technology will enhance the precision and efficiency of drug discovery and personalized oncology.

Indexed as

artificial intelligencecancerdeep learningimage analysistumor organoid

Identifiers

PMID42621171
PMCPMC13486983

What Socratic holds

Textmetadata
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.