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Objective <p>To differentiate cribriform (GP4Crib+) from non-cribriform growth and Gleason 3 patterns (GP4Crib-/GP3) using MRI.</p> Methods <p>Two hundred and ninety-one operated prostate cancer men with pre-treatment MRI and whole-mount prostate histology were retrospectively included. T2-weighted, apparent diffusion coefficient (ADC) and fractional blood volume maps from 1.5/3T MRI systems were used. 592 histological GP3, GP4Crib- and GP4Crib+ regions were segmented on whole-mount specimens and manually co-registered to MRI sequences/maps. Radiomics features were extracted, and an erosion process was applied to minimize the impact of delineation uncertainties. A logistic regression model was developed to differentiate GP4Crib+ from GP3/GP4Crib- in the 465 remaining regions. The differences in balanced accuracy between the model and baseline (where all regions are labeled as GP3/GP4Crib-) and 95% confidence intervals (CI) for all metrics were assessed using bootstrapping.</p> Results <p>The logistic regression model, using the 90th percentile ADC feature with a negative coefficient, showed a balanced accuracy of 0.65 (95% CI: 0.48–0.79), receiver operating characteristic area under the curve (AUC) of 0.75 (95% CI: 0.54–0.92), a precision–recall AUC of 0.35 (95% CI: 0.14–0.68).</p> Conclusion <p>The radiomics MRI-based model, trained on Gleason sub-patterns segmented on whole-mount specimen, was able to differentiate GP4Crib+ from GP3/GP4Crib- patterns with moderate accuracy. The most dominant feature was the 90th percentile ADC<i>.</i> This exploratory study highlights 90th percentile ADC as a potential biomarker for cribriform growth differentiation, providing insights into future MRI-based risk assessment strategies.</p>

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Beyond Gleason grading: MRI radiomics to differentiate cribriform growth from non-cribriform growth in prostate cancer men

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月29日

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  • Mar Fernandez Salamanca,
  • Rita Simões,
  • Malgorzata Deręgowska-Cylke,
  • Pim J. van Leeuwen,
  • Henk G. van der Poel,
  • Elise Bekers,
  • Marcos A. S. Guimaraes,
  • Uulke A. van der Heide,
  • Ivo G. Schoots

摘要

Objective

To differentiate cribriform (GP4Crib+) from non-cribriform growth and Gleason 3 patterns (GP4Crib-/GP3) using MRI.

Methods

Two hundred and ninety-one operated prostate cancer men with pre-treatment MRI and whole-mount prostate histology were retrospectively included. T2-weighted, apparent diffusion coefficient (ADC) and fractional blood volume maps from 1.5/3T MRI systems were used. 592 histological GP3, GP4Crib- and GP4Crib+ regions were segmented on whole-mount specimens and manually co-registered to MRI sequences/maps. Radiomics features were extracted, and an erosion process was applied to minimize the impact of delineation uncertainties. A logistic regression model was developed to differentiate GP4Crib+ from GP3/GP4Crib- in the 465 remaining regions. The differences in balanced accuracy between the model and baseline (where all regions are labeled as GP3/GP4Crib-) and 95% confidence intervals (CI) for all metrics were assessed using bootstrapping.

Results

The logistic regression model, using the 90th percentile ADC feature with a negative coefficient, showed a balanced accuracy of 0.65 (95% CI: 0.48–0.79), receiver operating characteristic area under the curve (AUC) of 0.75 (95% CI: 0.54–0.92), a precision–recall AUC of 0.35 (95% CI: 0.14–0.68).

Conclusion

The radiomics MRI-based model, trained on Gleason sub-patterns segmented on whole-mount specimen, was able to differentiate GP4Crib+ from GP3/GP4Crib- patterns with moderate accuracy. The most dominant feature was the 90th percentile ADC. This exploratory study highlights 90th percentile ADC as a potential biomarker for cribriform growth differentiation, providing insights into future MRI-based risk assessment strategies.