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<p>This retrospective study aimed to develop a multimodal habitat-based model integrating clinicopathological factors, radiomics features, and intratumoral heterogeneity (ITH) score for predicting upstaging in patients with ductal carcinoma in situ (DCIS). A total of 389 DCIS patients from two institutions were included. Lesions were segmented on DCE-MRI, DWI, and ADC images with peritumoral expansion, and intratumoral and peritumoral habitats were identified using Gaussian mixture model (GMM) clustering. Radiomics features were extracted from each habitat, and ITH scores were calculated. Independent predictors of upstaging were identified using multivariate logistic regression, and multiple models were constructed and evaluated. Age, nuclear grade, palpable mass, and DCE-ITH score were independent risk factors for DCIS upstaging. Habitat-based models derived from intratumoral and peritumoral regions demonstrated good predictive performance, while the combined model integrating habitat features, ITH score, and clinicopathological variables achieved the best performance, with AUCs of 0.945 and 0.869 in the training and test cohorts, respectively. This multimodal combined model may facilitate individualized preoperative risk assessment and support clinical decision-making in DCIS patients.</p>

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Multimodal MRI-based quantification of tumoral spatial heterogeneity for preoperative upgrade prediction of ductal carcinoma in situ

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Nianshi Song,
  • Lingting Kong,
  • Binying Zhuo,
  • Yongyu An,
  • Zhen Fang,
  • Maosheng Xu,
  • Changyu Zhou,
  • Hua Qian

摘要

This retrospective study aimed to develop a multimodal habitat-based model integrating clinicopathological factors, radiomics features, and intratumoral heterogeneity (ITH) score for predicting upstaging in patients with ductal carcinoma in situ (DCIS). A total of 389 DCIS patients from two institutions were included. Lesions were segmented on DCE-MRI, DWI, and ADC images with peritumoral expansion, and intratumoral and peritumoral habitats were identified using Gaussian mixture model (GMM) clustering. Radiomics features were extracted from each habitat, and ITH scores were calculated. Independent predictors of upstaging were identified using multivariate logistic regression, and multiple models were constructed and evaluated. Age, nuclear grade, palpable mass, and DCE-ITH score were independent risk factors for DCIS upstaging. Habitat-based models derived from intratumoral and peritumoral regions demonstrated good predictive performance, while the combined model integrating habitat features, ITH score, and clinicopathological variables achieved the best performance, with AUCs of 0.945 and 0.869 in the training and test cohorts, respectively. This multimodal combined model may facilitate individualized preoperative risk assessment and support clinical decision-making in DCIS patients.