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Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...
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Advanced diffusion-weighted imaging biomarkers for non-invasive assessment of tumor microenvironment in rectal

Jie Yuan1, Yiqun Sun2,3, Kun Liu4

  • 1Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.

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|February 11, 2025
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Summary
This summary is machine-generated.

Restricted spectrum imaging (RSI) parameters non-invasively assess the rectal cancer microenvironment (TME). This approach shows potential for improved diagnosis of tumor stroma, Ki67 expression, and differentiation, aiding patient management.

Keywords:
DiffusionMagnetic resonance imagingRectal neoplasmsTumor microenvironment

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Area of Science:

  • Radiology and Oncologic Imaging
  • Biomarker Discovery
  • Cancer Microenvironment Research

Background:

  • The rectal cancer microenvironment (TME) significantly influences treatment response and patient outcomes.
  • Accurate, non-invasive characterization of the TME is crucial for personalized rectal cancer management.
  • Current methods for TME assessment often require invasive procedures, limiting their utility.

Purpose of the Study:

  • To explore the heterogeneity of the rectal cancer microenvironment (TME) using restricted spectrum imaging (RSI).
  • To investigate the association between RSI-derived parameters and key histopathologic indicators: tumor stroma and Ki67 expression.
  • To evaluate the diagnostic performance of quantitative imaging biomarkers for TME characterization.

Main Methods:

  • A prospective study involving 66 rectal cancer patients undergoing pretreatment MRI with RSI.
  • Application of a three-compartment RSI model (RSI3), with optimal model selection via Bayesian Information Criterion (BIC).
  • Correlation of RSI3-derived parameters (RSI3-C1, RSI3-C2, RSI3-C3) and ADC values with stroma status, Ki67 expression, and clinicopathological features, assessed using ROC analysis.

Main Results:

  • The RSI3 model effectively characterized rectal cancer.
  • RSI3 parameters demonstrated significant differences between low- and high-stroma groups (P < 0.05), with RSI3-C2 showing high accuracy (AUC=0.800) for stroma status.
  • All RSI3 parameters and ADC values significantly differentiated low- and high-Ki67 groups (P < 0.05), with RSI3-C1 achieving the highest accuracy (AUC=0.824) for Ki67 status.
  • RSI3-C3 and ADC values showed significant differences for tumor differentiation (P < 0.05), with RSI3-C3 demonstrating the highest accuracy (AUC=0.721).

Conclusions:

  • RSI3-derived parameters show promise as non-invasive biomarkers for evaluating the rectal cancer TME.
  • This quantitative imaging approach may enhance diagnostic capabilities for rectal cancer.
  • The findings suggest that RSI could improve clinical decision-making and patient outcomes in rectal cancer management.