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Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
Published on: April 9, 2019
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Clinically significant prostate cancer detection on MRI: A radiomic shape features study
Renato Cuocolo1, Arnaldo Stanzione1, Andrea Ponsiglione1
1Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
European Journal of Radiology
|June 3, 2019
Summary
The surface area to volume ratio (SAVR) from prostate MRI shows promise for detecting clinically significant prostate cancer (csPCa). This radiomic shape feature, particularly from ADC maps, is a strong predictor of csPCa presence.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Prostate multiparametric MRI (mpMRI) is crucial for detecting clinically significant prostate cancer (csPCa).
- Current diagnostic accuracy relies on lesion size and volume.
- Quantitative radiomics offers advanced shape features for improved detection.
Purpose of the Study:
- To evaluate the correlation between MRI-derived index lesion shape features and the presence of csPCa.
- To identify which radiomic shape features are most predictive of csPCa.
Main Methods:
- Retrospective analysis of 75 subjects undergoing 3T mpMRI.
- Segmentation of index lesions and extraction of ten shape features from T2-weighted and ADC maps.
- Univariable, multivariable logistic, and ROC analyses to assess feature significance and diagnostic performance.
Main Results:
- Most shape features showed significant differences between csPCa and non-csPCa groups in univariable analysis.
- Multivariable analysis identified surface area to volume ratio (SAVR) from ADC maps as the strongest independent predictor of csPCa.
- SAVR demonstrated high diagnostic accuracy, sensitivity, and specificity.
Conclusions:
- Radiomic shape feature SAVR, derived from segmented index lesions on ADC maps, is a promising tool for csPCa detection.
- SAVR enhances diagnostic capabilities beyond traditional size and volume metrics in mpMRI.
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