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Background <p>Thymomas are primarily assessed using chest computed tomography (CT); however, this method has inherent limitations such as an overlap between the semantic features of thymomas presenting as localized lesions and other histological subtypes. Therefore, this study aimed to develop a combined radiomics model, based on non-contrast CT, and investigate its potential clinical utility in preoperatively differentiating between the thymoma risk classification.</p> Methods <p>We retrospectively analyzed the clinical and imaging data of 436 patients with pathologically confirmed thymomas between January 2010 and October 2023. The cohort comprised 272 low-risk cases (types A, AB, and B1) and 164 high-risk cases (types B2 and B3), which were randomly divided into training (<i>n</i> = 306) and validation (<i>n</i> = 130) sets in a 7:3 ratio. Radiomic features were extracted from the volume of interest on non-contrast CT images. Feature selection was performed using principal component analysis, correlation analysis, least absolute shrinkage, and selection operator algorithms to identify the most discriminative features. A combined radiomics nomogram was developed by integrating significant clinical factors with radiomics scores. The discriminative performance of the model was assessed using receiver operating characteristic curve analysis.</p> Results <p>Two clinicoradiological and 11 radiomics features were identified and used to construct a radiomics nomogram. The diagnostic performance of the nomogram for thymoma risk stratification surpassed that of any single model. The nomogram yielded an area under the curve of 0.753 (accuracy, 71.2%; sensitivity, 62.4%; specificity, 76.7%) in the training cohort and 0.735 (accuracy, 70.8%; sensitivity, 58.8%; specificity, 78.5%) in the validation cohort.</p> Conclusion <p>The nomogram model integrating clinical factors and radiomics features accurately differentiated the histological subtypes of thymoma. This tool may be helpful in formulating personalized treatment plans in clinical practice and is worthy of clinical promotion and application.</p>

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Preoperative prediction of thymoma risk classification with machine learning-based computed tomography radiomics features

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Kai Zhao,
  • Yiming Liu,
  • Honghao Xu,
  • Wenhan Cai,
  • Jiamei Jin,
  • Leilei Shen,
  • Zirui Zhu,
  • Herui Han,
  • Mingchuan Hu,
  • Qiang Lu,
  • Zhiqiang Xue

摘要

Background

Thymomas are primarily assessed using chest computed tomography (CT); however, this method has inherent limitations such as an overlap between the semantic features of thymomas presenting as localized lesions and other histological subtypes. Therefore, this study aimed to develop a combined radiomics model, based on non-contrast CT, and investigate its potential clinical utility in preoperatively differentiating between the thymoma risk classification.

Methods

We retrospectively analyzed the clinical and imaging data of 436 patients with pathologically confirmed thymomas between January 2010 and October 2023. The cohort comprised 272 low-risk cases (types A, AB, and B1) and 164 high-risk cases (types B2 and B3), which were randomly divided into training (n = 306) and validation (n = 130) sets in a 7:3 ratio. Radiomic features were extracted from the volume of interest on non-contrast CT images. Feature selection was performed using principal component analysis, correlation analysis, least absolute shrinkage, and selection operator algorithms to identify the most discriminative features. A combined radiomics nomogram was developed by integrating significant clinical factors with radiomics scores. The discriminative performance of the model was assessed using receiver operating characteristic curve analysis.

Results

Two clinicoradiological and 11 radiomics features were identified and used to construct a radiomics nomogram. The diagnostic performance of the nomogram for thymoma risk stratification surpassed that of any single model. The nomogram yielded an area under the curve of 0.753 (accuracy, 71.2%; sensitivity, 62.4%; specificity, 76.7%) in the training cohort and 0.735 (accuracy, 70.8%; sensitivity, 58.8%; specificity, 78.5%) in the validation cohort.

Conclusion

The nomogram model integrating clinical factors and radiomics features accurately differentiated the histological subtypes of thymoma. This tool may be helpful in formulating personalized treatment plans in clinical practice and is worthy of clinical promotion and application.