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Related Concept Videos

The Tumor Microenvironment02:17

The Tumor Microenvironment

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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Related Experiment Video

Updated: Jun 11, 2026

Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment
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Published on: June 2, 2023

Association Between Functional Multiparametric MRI Features and Tumour Immune Microenvironment Phenotypes in Prostate

Peng Wang1, Enhao Wang1, Guanghai Ji1

  • 1Department of Radiology, The First Affiliated Hospital of Yangtze University, The First People's Hospital of Jingzhou, 434000 Jingzhou, Hubei, China.

Archivos Espanoles De Urologia
|June 10, 2026
PubMed
Summary

Multiparametric MRI (mpMRI) can predict prostate cancer (PCa) tumor immune microenvironment (TIME) phenotypes. Elevated PSA and low ADC_mean indicate immune-inflamed TIME, while low PI-RADS suggests immune-desert TIME.

Keywords:
PI-RADSimmune phenotypempMRIprostate cancertumour immune microenvironment

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Published on: February 8, 2018

Area of Science:

  • Oncology
  • Radiology
  • Immunology

Background:

  • The tumor immune microenvironment (TIME) significantly influences prostate cancer (PCa) progression and treatment response.
  • Understanding TIME phenotypes (immune-inflamed, immune-excluded, immune-desert) is crucial for personalized immunotherapy.
  • Non-invasive methods to assess TIME phenotypes in PCa are highly desirable.

Purpose of the Study:

  • To investigate the association between functional multiparametric magnetic resonance imaging (mpMRI) features and TIME phenotypes in PCa.
  • To develop a multiclass prediction model for PCa TIME phenotypes using mpMRI.
  • To explore the potential of mpMRI as a non-invasive imaging surrogate for TIME assessment.

Main Methods:

  • Retrospective study of 228 PCa patients who underwent mpMRI.
  • Categorization of patients into three TIME phenotypes based on immune cell infiltration.
  • Collection of functional mpMRI features (ADC_mean, ADC_min, PI-RADS score, lesion diameter) and clinicopathological indices.
  • Multinomial logistic regression and 10-fold cross-validation for model development and performance evaluation.

Main Results:

  • Significant differences in PSA, ISUP grade, and mpMRI features were observed across TIME phenotypes (p < 0.05).
  • Immune-inflamed phenotype associated with higher PSA and lower ADC_mean.
  • Immune-desert phenotype more common in lesions with low PI-RADS scores.
  • ROC analysis yielded AUC values of 0.654 (immune-inflamed), 0.742 (immune-excluded), and 0.802 (immune-desert).

Conclusions:

  • Functional mpMRI features are significantly associated with TIME phenotypes in PCa.
  • Elevated PSA and low ADC_mean may indicate immune-inflamed TIME.
  • Low PI-RADS score may suggest an immune-desert TIME.
  • mpMRI shows promise as a non-invasive tool for assessing PCa TIME phenotypes, aiding precision immunotherapy.