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<p>Breast cancer is a highly heterogeneous disease, and accurate prediction of pathological complete response (pCR) at an early stage of neoadjuvant chemotherapy (NAC) is of great clinical importance. This study aims to predict pCR to NAC in breast cancer using longitudinal MRI-based fractal features, while exploring their biological relevance. Fractal dimension and fractal abundance were calculated from pre-treatment and early-treatment MRI scans of 911 patients across three institutions. A nomogram integrating delta fractal features and clinicopathologic variables was developed and validated in three cohorts, achieving AUCs of 0.781–0.807. Delta fractal features were independent predictors of pCR. Transcriptomic analysis in a genomics cohort revealed associations between delta fractal features and immune infiltration, particularly activated natural killer cells and naïve CD4 + T cells. These findings suggest that longitudinal fractal analysis provides two noninvasive biomarkers for early prediction of treatment response and may reflect the tumor immune microenvironment in breast cancer.</p>

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Fractal analysis of longitudinal MRI for predicting response to neoadjuvant chemotherapy in breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Yao Huang,
  • Hao Gong,
  • Wanjun Hu,
  • Huanhuan Ren,
  • Ying Cao,
  • Xiaosong Lan,
  • Huifang Chen,
  • Lu Wang,
  • Xiaoxia Wang,
  • Jiuquan Zhang

摘要

Breast cancer is a highly heterogeneous disease, and accurate prediction of pathological complete response (pCR) at an early stage of neoadjuvant chemotherapy (NAC) is of great clinical importance. This study aims to predict pCR to NAC in breast cancer using longitudinal MRI-based fractal features, while exploring their biological relevance. Fractal dimension and fractal abundance were calculated from pre-treatment and early-treatment MRI scans of 911 patients across three institutions. A nomogram integrating delta fractal features and clinicopathologic variables was developed and validated in three cohorts, achieving AUCs of 0.781–0.807. Delta fractal features were independent predictors of pCR. Transcriptomic analysis in a genomics cohort revealed associations between delta fractal features and immune infiltration, particularly activated natural killer cells and naïve CD4 + T cells. These findings suggest that longitudinal fractal analysis provides two noninvasive biomarkers for early prediction of treatment response and may reflect the tumor immune microenvironment in breast cancer.