Habitats in DCE-MRI to Predict Clinically Significant Prostate Cancers

Area of Science:

• Radiology
• Medical Imaging
• Oncology

Background:

• Prostate cancer identification and clinical significance assessment remain challenging.
• Multiparametric MRI aids in evaluating disease progression.
• Current radiological assessment (PIRADS v2) for dynamic contrast-enhanced (DCE) imaging has limitations due to inconsistency and non-quantitative nature.

Purpose of the Study:

• To develop a systematic methodology for quantifying perfusion dynamics in DCE imaging.
• To localize 7 distinct perfusion habitats within targeted prostate cancer lesions.
• To correlate these quantitative perfusion metrics with clinical significance.

Main Methods:

• A systematic methodology was developed to quantify perfusion dynamics from DCE imaging data.
• Seven subregions, termed 'perfusion habitats,' were identified within targeted lesions.
• Quantitative features of these habitats were analyzed for predictive value.

Main Results:

• Quantitative features derived from perfusion dynamics were used to characterize 7 distinct 'perfusion habitats.'
• The late area under the DCE time-activity curve emerged as a significant predictor of clinical significance.
• The best predictive feature achieved an Area Under the Curve (AUC) of 0.82 (95% CI [0.81-0.83]).

Conclusions:

• Quantitative analysis of DCE perfusion dynamics offers a robust method for prostate cancer assessment.
• The identified 'perfusion habitats' and their quantitative features can predict clinical significance.
• This approach enhances the utility of DCE-MRI in evaluating prostate cancer progression and significance.