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<p>This study aims to explore the prognostic value of regionally modulated radiomics for patients with head and neck cancer (HNC) in positron emission tomography/computed tomography (PET/CT) imaging. The dataset included 224 HNC patients who underwent PET/CT imaging at five different centers. The primary tumor was manually contoured by experienced radiologists. For introducing regionally modulated radiomics, we developed four fuzzy masks by applying Gaussian filter, and four peritumor-included masks by applying morphological operations. For each patient, a total of 326 radiomic features were extracted from each of nine masks. Multivariate Cox proportional hazards model with ensemble strategy was adopted to construct classical, fuzzy, and peritumoral based prognostic models, respectively, for predicting progression-free survival. ComBat harmonization was applied to adjust for multicenter variability. A consistent modelling approach was employed to ensure the independence and comparability of these models. The models were evaluated by C-index, log-rank test, and the area under the time-dependent ROC curve (tAUC). The fuzzy radiomics model applied with 5&#xa0;mm FWHM of Gaussian filter demonstrated superior performance compared to classical radiomics model (Testing C-index, 0.735 vs. 0.685; log-rank test, <i>p</i> &lt; 0.007 vs. <i>p</i> &lt; 0.035). Peritumoral radiomics models showed slightly improved performance compared to classical radiomics model (Testing C-index, 0.727 vs. 0.685; log-rank test, <i>p</i> &lt; 0.014 vs. <i>p</i> &lt; 0.035). The tAUC demonstrated consistent findings with the C-index. The harmonization strategy showed further improved performance for both fuzzy and peritumoral models. These results showed that regionally modulated radiomics analysis was superior for estimating prognosis in this multicenter HNC cohort when compared to classical radiomics. This demonstrated the potentially prognostic values by considering regional variations in radiomics analysis.</p>

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Regionally modulated radiomics analysis in PET/CT imaging: application to prognosis prediction of head and neck cancer

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Yuan Sheng,
  • Guoping Shan,
  • Xue Bai,
  • Binbing Wang,
  • Yue Feng,
  • Chong Xu,
  • Yihao Li,
  • Guoping Zuo,
  • Hui Xu

摘要

This study aims to explore the prognostic value of regionally modulated radiomics for patients with head and neck cancer (HNC) in positron emission tomography/computed tomography (PET/CT) imaging. The dataset included 224 HNC patients who underwent PET/CT imaging at five different centers. The primary tumor was manually contoured by experienced radiologists. For introducing regionally modulated radiomics, we developed four fuzzy masks by applying Gaussian filter, and four peritumor-included masks by applying morphological operations. For each patient, a total of 326 radiomic features were extracted from each of nine masks. Multivariate Cox proportional hazards model with ensemble strategy was adopted to construct classical, fuzzy, and peritumoral based prognostic models, respectively, for predicting progression-free survival. ComBat harmonization was applied to adjust for multicenter variability. A consistent modelling approach was employed to ensure the independence and comparability of these models. The models were evaluated by C-index, log-rank test, and the area under the time-dependent ROC curve (tAUC). The fuzzy radiomics model applied with 5 mm FWHM of Gaussian filter demonstrated superior performance compared to classical radiomics model (Testing C-index, 0.735 vs. 0.685; log-rank test, p < 0.007 vs. p < 0.035). Peritumoral radiomics models showed slightly improved performance compared to classical radiomics model (Testing C-index, 0.727 vs. 0.685; log-rank test, p < 0.014 vs. p < 0.035). The tAUC demonstrated consistent findings with the C-index. The harmonization strategy showed further improved performance for both fuzzy and peritumoral models. These results showed that regionally modulated radiomics analysis was superior for estimating prognosis in this multicenter HNC cohort when compared to classical radiomics. This demonstrated the potentially prognostic values by considering regional variations in radiomics analysis.