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Background <p>The cross-modal correlations between radiomics and pathomics, as well as their clinical translational applications in oral tumors and cysts, remain unclear. Here, we proposed a novel radiomic feature selection strategy guided by radio–pathomic correlations in Ameloblastoma (AM) and Odontogenic Keratocyst (OKC) to enhance their preoperative differentiation.</p> Methods <p>We automatically extracted radiomic and pathomic features from CBCT scans and multi-resolution (25, 50, 100, 200&#xa0;μm) whole slide image-derived cell density maps, respectively. Subsequently, radio-pathomic associations were evaluated by correlation analysis at two levels, directly between features, and between latent factors derived from features via factor analysis, separately in AM and OKC. Additionally, we compared the diagnostic performance of machine learning models using our proposed pathomics-guided feature selection strategy against traditional selection approaches.</p> Results <p>At the feature level, correlation analysis identified one and seven significant feature pairs in AM and OKC, respectively (all |ρ| &gt; 0.50, q &lt; 0.05), suggesting that radiomic morphological feature weres strongly correlated with pathomic textural features reflecting tissue complexity. At the factor level, one significant factor pair was revealed in AM (ρ = 0.50, q = 0.007) and another in OKC (ρ = − 0.41, q = 0.04). Additionally, classification performance was enhanced by our proposed strategy across all six models, with an average area under the receiver operating characteristic curve (AUROC) improvement of 0.036 and individual gains ranging from 0.016 in Logistic Regression to 0.063 in Lasso.</p> Conclusion <p>Significant cross-modal correlations between radiomic and pathomic features were identified in AM and OKC. Leveraging these associations, our proposed pathomics-guided radiomics showed the potential to improve the accuracy of preoperative differentiation between the two lesions, although these improvements reached statistical significance only in some models and still require further validation before clinical translation.</p>

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Enhancing the preoperative differentiation of ameloblastoma and odontogenic keratocyst using pathomics-guided radiomics: a pilot study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Liang Li,
  • Shengfu Huang,
  • Kai Yang,
  • Zimo Huang,
  • Yuhong Li

摘要

Background

The cross-modal correlations between radiomics and pathomics, as well as their clinical translational applications in oral tumors and cysts, remain unclear. Here, we proposed a novel radiomic feature selection strategy guided by radio–pathomic correlations in Ameloblastoma (AM) and Odontogenic Keratocyst (OKC) to enhance their preoperative differentiation.

Methods

We automatically extracted radiomic and pathomic features from CBCT scans and multi-resolution (25, 50, 100, 200 μm) whole slide image-derived cell density maps, respectively. Subsequently, radio-pathomic associations were evaluated by correlation analysis at two levels, directly between features, and between latent factors derived from features via factor analysis, separately in AM and OKC. Additionally, we compared the diagnostic performance of machine learning models using our proposed pathomics-guided feature selection strategy against traditional selection approaches.

Results

At the feature level, correlation analysis identified one and seven significant feature pairs in AM and OKC, respectively (all |ρ| > 0.50, q < 0.05), suggesting that radiomic morphological feature weres strongly correlated with pathomic textural features reflecting tissue complexity. At the factor level, one significant factor pair was revealed in AM (ρ = 0.50, q = 0.007) and another in OKC (ρ = − 0.41, q = 0.04). Additionally, classification performance was enhanced by our proposed strategy across all six models, with an average area under the receiver operating characteristic curve (AUROC) improvement of 0.036 and individual gains ranging from 0.016 in Logistic Regression to 0.063 in Lasso.

Conclusion

Significant cross-modal correlations between radiomic and pathomic features were identified in AM and OKC. Leveraging these associations, our proposed pathomics-guided radiomics showed the potential to improve the accuracy of preoperative differentiation between the two lesions, although these improvements reached statistical significance only in some models and still require further validation before clinical translation.