Transition zone prostate cancer: Logistic regression and machine-learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis.
benign prostatic hyperplasia
magnetic resonance imaging
medical imaging
prostate
prostate cancer
Journal
Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850
Informations de publication
Date de publication:
09 2019
09 2019
Historique:
received:
05
11
2018
revised:
11
01
2019
accepted:
11
01
2019
pubmed:
1
2
2019
medline:
22
10
2020
entrez:
1
2
2019
Statut:
ppublish
Résumé
The limitation of diagnosis of transition zone (TZ) prostate cancer (PCa) using subjective assessment of multiparametric (mp) MRI with PI-RADS v2 is related to overlapping features between cancers and stromal benign prostatic hyperplasia (BPH) nodules, particularly in small lesions. To evaluate modeling of quantitative apparent diffusion coefficient (ADC), texture, and shape features using logistic regression (LR) and support vector machine (SVM) models for the diagnosis of transition zone PCa. Retrospective. Ninety patients; 44 consecutive TZ PCa were compared with 61 consecutive BPH nodules (26 glandular/35 stromal). 3 T/T A radiologist manually segmented lesions on axial images for quantitative ADC (mean, 10 Quantitative features were selected a priori and were compared using univariate and multivariate analysis. LR and SVM models of statistically significant features were constructed and evaluated using receiver operator characteristic (ROC) analysis. Subgroup analysis of TZ PCa vs. only stromal BPH and in lesions measuring <15 mm was performed. Agreement in measurements was assessed using the Dice similarity coefficient (DSC). Mean, 25 LR and SVM models incorporating previously described quantitative ADC, shape and texture analysis features are highly accurate for the diagnosis of TZ PCa and remained accurate when comparing TZ PCa with stromal BPH and in smaller lesions. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:940-950.
Sections du résumé
BACKGROUND
The limitation of diagnosis of transition zone (TZ) prostate cancer (PCa) using subjective assessment of multiparametric (mp) MRI with PI-RADS v2 is related to overlapping features between cancers and stromal benign prostatic hyperplasia (BPH) nodules, particularly in small lesions.
PURPOSE
To evaluate modeling of quantitative apparent diffusion coefficient (ADC), texture, and shape features using logistic regression (LR) and support vector machine (SVM) models for the diagnosis of transition zone PCa.
STUDY TYPE
Retrospective.
POPULATION
Ninety patients; 44 consecutive TZ PCa were compared with 61 consecutive BPH nodules (26 glandular/35 stromal).
FIELD STRENGTH/SEQUENCE
3 T/T
ASSESSMENT
A radiologist manually segmented lesions on axial images for quantitative ADC (mean, 10
STATISTICAL TESTS
Quantitative features were selected a priori and were compared using univariate and multivariate analysis. LR and SVM models of statistically significant features were constructed and evaluated using receiver operator characteristic (ROC) analysis. Subgroup analysis of TZ PCa vs. only stromal BPH and in lesions measuring <15 mm was performed. Agreement in measurements was assessed using the Dice similarity coefficient (DSC).
RESULTS
Mean, 25
DATA CONCLUSION
LR and SVM models incorporating previously described quantitative ADC, shape and texture analysis features are highly accurate for the diagnosis of TZ PCa and remained accurate when comparing TZ PCa with stromal BPH and in smaller lesions.
LEVEL OF EVIDENCE
3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:940-950.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
940-950Informations de copyright
© 2019 International Society for Magnetic Resonance in Medicine.