Evidence map›Paper›PMID 39991097›Full record

ArticleThe EPMA journal2025

Prediction of risk for isolated incomplete lateral meniscal injury using a dynamic nomogram based on MRI-derived anatomic radiomics and physical activity: a proof-of-concept study in 3PM-guided management.

Chao Xie, Jingle Chen, Hantao Chen, Zhijie Zuo, Yucong Li, Lijun Lin

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Article in The EPMA journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Chao Xie *Department of Joint and Orthopedics, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong People's Republic of China.
Jingle Chen *Department of Joint and Orthopedics, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong People's Republic of China.
Hantao Chen *Department of Joint and Orthopedics, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong People's Republic of China.
Zhijie ZuoDepartment of Joint and Orthopedics, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong People's Republic of China.
Yucong LiDepartment of Joint and Orthopedics, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong People's Republic of China.
Lijun LinDepartment of Joint and Orthopedics, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The 3PM framework revolutionizes disease management by facilitating early risk prediction, disease prevention, and personalized treatment. For isolated incomplete lateral meniscal injuries (IILMI), where early diagnosis is challenging due to non-specific symptoms, 3PM's proactive approach is beneficial in preventing knee joint disease progression and maintaining patients' quality of life. Aims: This study aimed to develop a predictive model within the 3PM framework, integrating knee MRI anatomical features with individual physical activity (PA) patterns to enhance early IILMI detection and treatment efficacy, improving patient outcomes and quality of life. Methods: The training dataset comprised 254 patients. Using logistic regression analyses and least absolute shrinkage and selection operator (LASSO), IILMI was identified among various preoperative factors containing knee MRI and PA features. A dynamic nomogram was constructed and subjected to internal and external validations (91 patients). Validation encompassed C-index, receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves. ROC analysis determined the risk stratification cut-off. Results: Six independent IILMI factors were identified, including PA intensity, PA type, degree of PA intensity, and MRI-derived anatomical parameters. The dynamic nomogram showed high predictive accuracy (C-index, 0.829 in training, 0.906 in validation). IILMI patients were divided into low-risk, medium-risk, and high-risk groups according to the cut-off value. Conclusion: In 3PM-guided management, the dynamic nomogram enables early IILMI diagnosis in patients while promoting IILMI stratification making personalized treatment feasible. With further development, it holds promise for effectively predicting IILMI risk, preventing severe knee pathologies, and enhancing the quality of life for at-risk patients. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-025-00399-3.

Indexed as

3PM-guided health and disease management3PM-guided managementDecision-making supportDynamic nomogramIsolated incomplete lateral meniscal injuriesKnee anatomic characteristicsLife qualityMRIPatient stratificationPhysical activityPredictive preventive personalised medicine (PPPM / 3PM)Risk assessment

Identifiers

PMID39991097
PMCPMC11842652

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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.