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Background <p>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.</p> Aims <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>In 3PM-guided&#xa0;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.</p>

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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

  • 期刊论文

  • 发表日期：

    2025年1月27日

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

摘要

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.