Result: ViMDT: a clinical data visual analysis tool for multi-disciplinary treatment of lung cancer.

Title:
ViMDT: a clinical data visual analysis tool for multi-disciplinary treatment of lung cancer.
Authors:
Zhu W; University of Science and Technology of China, Hefei, 230026, Anhui, China.; Institute of Intelligent Machines/Zhongqi AI joint Lab, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, Anhui, China., Jiang X; Medical Oncology Department, The First Affiliated Hospital of University of Science and Technology of China, Hefei, 230001, Anhui, China., Zhang L; Division of Life Sciences and Medicine, Department of Pharmacy, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China., Zhou P; School of Life Science, Hefei Normal University, Hefei, 230601, Anhui, China., Xie X; School of Mathematics and Physics, Anhui Jianzhu University, Hefei, 230031, Anhui, China., Wang H; Institute of Intelligent Machines/Zhongqi AI joint Lab, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, Anhui, China. hqwang126@126.com.
Source:
Clinical and experimental medicine [Clin Exp Med] 2026 Jan 12; Vol. 26 (1), pp. 105. Date of Electronic Publication: 2026 Jan 12.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer-Verlag Italia Country of Publication: Italy NLM ID: 100973405 Publication Model: Electronic Cited Medium: Internet ISSN: 1591-9528 (Electronic) Linking ISSN: 15918890 NLM ISO Abbreviation: Clin Exp Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: Milano, Italy: Springer-Verlag Italia, c2001-
MeSH Terms:
Lung Neoplasms*/therapy , Lung Neoplasms*/drug therapy , Lung Neoplasms*/diagnosis , Carcinoma, Non-Small-Cell Lung*/drug therapy , Data Visualization*, Humans ; Clinical Decision-Making/methods ; Antineoplastic Agents/therapeutic use ; Precision Medicine/methods
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Grant Information:
202304295107020050 Anhui Clinical Medical Research Transformation Special Project; 52373160 National Natural Science Foundation of China; 61973295 National Natural Science Foundation of China; 2024AH051569 Natural Science Foundation of Higher Education in Anhui Province; 60423018 Research Initiation Fund Project of Introduced High-level Talents; YCSJ2024ZR02 Laboratory of Operations Research and Data Science of Anhui Jianzhu University; 201904a07020092 Anhui Province's key Research and Development Project; KJ2021A0633 University Science Research Project of the Education Department of Anhui Province; 2022AH050247 Anhui Provincial Natural Science Foundation
Contributed Indexing:
Keywords: Clinical data visualization and analysis; Electronic medical records; LDA; MDT
Substance Nomenclature:
0 (Antineoplastic Agents)
Entry Date(s):
Date Created: 20260112 Date Completed: 20260120 Latest Revision: 20260123
Update Code:
20260123
PubMed Central ID:
PMC12819497
DOI:
10.1007/s10238-025-01898-3
PMID:
41524897
Database:
MEDLINE

Further Information

Multi-disciplinary treatment (MDT) has become a routine practice in clinical cancer diagnosis and treatment, playing an indispensable role in clinical decision-making. By integrating expertise from multiple disciplines, MDT provides patients with individualized diagnosis and treatment strategies. However, there is not yet a specialized clinical data visualization tool for MDT. This paper develops a novel clinical data analysis visualization tool for MDT, which analyzes in-depth and displays patient data comprehensively. Specifically, this tool designs a latent Dirichlet allocation (LDA)-based visualization model for clinical unstructured data, and Z-Score-3σ transformation and hierarchical strategies for clinical structural data. Moreover, we propose to predict personalized anti-tumor drug efficacy based on topic keywords. The results showed that, compared with users who did not use the tool, the time cost in MDT decision-making for users who used the tool was reduced by 26.17%. Furthermore, the proposed drug efficacy prediction method achieved an accuracy rate of 71.08% on a dataset of 958 patients with non-small cell cancer treated with anti-tumor drugs. The proposed tool is potentially helpful for doctors in MDT tasks by vividly visualizing the large-scale complex clinical data and improving the MDT efficiency.
(© 2026. The Author(s).)

Declarations. Conflict of interest: The authors declare that they have no competing interests. Ethical approval: This study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Institutional Research Ethics Committee of Anhui Chest Hospital (NO. KJ2024-018). Consent to participate: Informed consent was obtained from all study participants. Furthermore, all data underwent anonymization procedures (e.g., removal of names, identification card numbers, telephone numbers, and other directly identifiable information) to address GDPR/HIPAA compliance. Consent for publication: Not applicable.