Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions.
PI-RADS
clinically significant prostate cancer
prostate MRI
radiomics
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:
11 2021
11 2021
Historique:
revised:
25
04
2021
received:
24
01
2021
accepted:
27
04
2021
pubmed:
11
5
2021
medline:
16
10
2021
entrez:
10
5
2021
Statut:
ppublish
Résumé
While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions typically warrant prostate biopsy and PI-RADS 1 and 2 lesions may be safely observed, PI-RADS 3 lesions are equivocal. To construct and cross-validate a machine learning model based on radiomics features from T Single-center retrospective study. A total of 240 patients were included (training cohort, n = 188, age range 43-82 years; test cohort, n = 52, age range 41-79 years). Eligibility criteria were 1) magnetic resonance imaging (MRI)-targeted biopsy between 2015 and 2020; 2) PI-RADS 3 index lesion identified on multiparametric MRI; (3) biopsy performed within 1 year of MRI. The percentages of csPCa lesions were 10.6% and 15.4% in the training and test cohorts, respectively. A 3 T; T Multislice volumes-of-interest (VOIs) were drawn in the PI-RADS 3 index lesions on T A random forest classifier using the radiomics features as input was trained and validated for prediction of csPCa. The performance of the machine learning classifier, prostate specific antigen (PSA) density, and prostate volume for csPCa prediction was evaluated using receiver operating characteristic (ROC) analysis. The trained random forest classifier constructed from the T The machine learning classifier based on T 4 TECHNICAL EFFICACY: 2.
Sections du résumé
BACKGROUND
While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions typically warrant prostate biopsy and PI-RADS 1 and 2 lesions may be safely observed, PI-RADS 3 lesions are equivocal.
PURPOSE
To construct and cross-validate a machine learning model based on radiomics features from T
STUDY TYPE
Single-center retrospective study.
POPULATION
A total of 240 patients were included (training cohort, n = 188, age range 43-82 years; test cohort, n = 52, age range 41-79 years). Eligibility criteria were 1) magnetic resonance imaging (MRI)-targeted biopsy between 2015 and 2020; 2) PI-RADS 3 index lesion identified on multiparametric MRI; (3) biopsy performed within 1 year of MRI. The percentages of csPCa lesions were 10.6% and 15.4% in the training and test cohorts, respectively.
FIELD STRENGTH/SEQUENCE
A 3 T; T
ASSESSMENT
Multislice volumes-of-interest (VOIs) were drawn in the PI-RADS 3 index lesions on T
STATISTICAL TESTS
A random forest classifier using the radiomics features as input was trained and validated for prediction of csPCa. The performance of the machine learning classifier, prostate specific antigen (PSA) density, and prostate volume for csPCa prediction was evaluated using receiver operating characteristic (ROC) analysis.
RESULTS
The trained random forest classifier constructed from the T
CONCLUSION
The machine learning classifier based on T
EVIDENCE LEVEL
4 TECHNICAL EFFICACY: 2.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1466-1473Informations de copyright
© 2021 International Society for Magnetic Resonance in Medicine.
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