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Objectives <p>Early diagnosis of knee osteoarthritis (KOA) remains challenging, particularly in distinguishing between Kellgren–Lawrence (KL) grades 1 and 2 on standard radiographs. This study aimed to develop a radiomics-based model using digital radiography (DR) to facilitate early identification of radiographic KOA (RKOA).</p> Materials and methods <p>A total of 859 patients with KL grade 1 or 2 were retrospectively enrolled and randomly divided into a training set (n = 601) and a validation set (n = 258). From anteroposterior and lateral DR images, 2,632 radiomics features were extracted per patient. Features were filtered using intraclass correlation coefficient (ICC ≥ 0.75), correlation analysis (threshold &gt; 0.9), and least absolute shrinkage and selection operator (LASSO) regression, yielding 38 features. Five machine learning models were constructed and compared. Logistic regression (LR), which demonstrated the best generalizability, was used to compute a radiomics score (Radscore). A combined nomogram incorporating Radscore and age was developed and evaluated.</p> Results <p>In the validation set, the LR model achieved the highest area under the curve (AUC) of 0.821. The combined nomogram model outperformed the Radscore model alone, with AUCs of 0.914 vs. 0.908 in the training set (<i>P</i> = 0.0067), and 0.833 vs. 0.823 in the validation set (<i>P</i> = 0.0041). Calibration curves confirmed model goodness-of-fit, and decision curve analysis showed higher clinical net benefit for the nomogram.</p> Conclusion <p>Digital radiography-based radiomics combined with age enables accurate early KOA diagnosis and demonstrates strong potential for clinical application.</p>

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Radiomics-based nomogram using digital radiography for early diagnosis of knee osteoarthritis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Hongbiao Sun,
  • Chenyuanying Long,
  • Yanqing Ma,
  • Tianyi Xing,
  • Shuwen Dong,
  • Sixuan Guo,
  • Yuanyuan Cui,
  • Qinling Jiang,
  • Shiyuan Liu,
  • Yi Xiao

摘要

Objectives

Early diagnosis of knee osteoarthritis (KOA) remains challenging, particularly in distinguishing between Kellgren–Lawrence (KL) grades 1 and 2 on standard radiographs. This study aimed to develop a radiomics-based model using digital radiography (DR) to facilitate early identification of radiographic KOA (RKOA).

Materials and methods

A total of 859 patients with KL grade 1 or 2 were retrospectively enrolled and randomly divided into a training set (n = 601) and a validation set (n = 258). From anteroposterior and lateral DR images, 2,632 radiomics features were extracted per patient. Features were filtered using intraclass correlation coefficient (ICC ≥ 0.75), correlation analysis (threshold > 0.9), and least absolute shrinkage and selection operator (LASSO) regression, yielding 38 features. Five machine learning models were constructed and compared. Logistic regression (LR), which demonstrated the best generalizability, was used to compute a radiomics score (Radscore). A combined nomogram incorporating Radscore and age was developed and evaluated.

Results

In the validation set, the LR model achieved the highest area under the curve (AUC) of 0.821. The combined nomogram model outperformed the Radscore model alone, with AUCs of 0.914 vs. 0.908 in the training set (P = 0.0067), and 0.833 vs. 0.823 in the validation set (P = 0.0041). Calibration curves confirmed model goodness-of-fit, and decision curve analysis showed higher clinical net benefit for the nomogram.

Conclusion

Digital radiography-based radiomics combined with age enables accurate early KOA diagnosis and demonstrates strong potential for clinical application.