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Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors
N Andres Parra1, Hong Lu1,2, Qian Li2
1Department of Cancer Physiology, H.L. Moffitt Cancer Center, Tampa, FL, USA.
Oncotarget
|January 17, 2019
Summary
This study introduces DCE-Habitats from multi-parametric MRI to improve prostate cancer diagnosis. These imaging features help distinguish clinically significant prostate tumors, enhancing patient management.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Prostate cancer diagnosis and treatment face challenges due to disease heterogeneity.
- Multi-parametric magnetic resonance imaging (mpMRI) offers improved resolution for disease detection and characterization.
Purpose of the Study:
- To assess the clinical significance of Dynamic Contrast Enhancement (DCE) derived perfusion curve patterns within tumor regions (DCE-Habitats).
- To develop and validate classifier models using DCE and ADC features to differentiate clinically significant from insignificant prostate lesions.
Main Methods:
- Quantification of DCE perfusion curves using seven features within identified tumor habitats.
- Building classifier models based on DCE and ADC features for lesion characterization.
- Multivariable analysis performed independently and validated across two institutions.
Main Results:
- Models achieved an intra-institution Area under the ROC Curve (AUC) of 0.82.
- Validation on an external cohort yielded an AUC of 0.82, with 0.68 sensitivity and 0.95 specificity.
- Identified key discriminating characteristics between significant and insignificant prostate lesions.
Conclusions:
- DCE-Habitats and quantitative perfusion features show promise in improving the accuracy of prostate cancer diagnosis.
- Multi-parametric MRI models incorporating DCE and ADC features are effective in differentiating lesion significance.
- Validated models can aid in more precise disease management and treatment planning.
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