Multi-institutional Clinical Tool for Predicting High-risk Lesions on 3Tesla Multiparametric Prostate Magnetic Resonance Imaging.
Eur Urol Oncol
; 2(3): 257-264, 2019 05.
Article
en En
| MEDLINE
| ID: mdl-31200839
ABSTRACT
BACKGROUND:
Multiparametric magnetic resonance imaging (mpMRI) for prostate cancer detection without careful patient selection may lead to excessive resource utilization and costs.OBJECTIVE:
To develop and validate a clinical tool for predicting the presence of high-risk lesions on mpMRI. DESIGN, SETTING, ANDPARTICIPANTS:
Four tertiary care centers were included in this retrospective and prospective study (BiRCH Study Collaborative). Statistical models were generated using 1269 biopsy-naive, prior negative biopsy, and active surveillance patients who underwent mpMRI. Using age, prostate-specific antigen, and prostate volume, a support vector machine model was developed for predicting the probability of harboring Prostate Imaging Reporting and Data System 4 or 5 lesions. The accuracy of future predictions was then prospectively assessed in 214 consecutive patients. OUTCOME MEASUREMENTS AND STATISTICALANALYSIS:
Receiver operating characteristic, calibration, and decision curves were generated to assess model performance. RESULTS ANDLIMITATIONS:
For biopsy-naïve and prior negative biopsy patients (n=811), the area under the curve (AUC) was 0.730 on internal validation. Excellent calibration and high net clinical benefit were observed. On prospective external validation at two separate institutions (n=88 and n=126), the machine learning model discriminated with AUCs of 0.740 and 0.744, respectively. The final model was developed on the Microsoft Azure Machine Learning platform (birch.azurewebsites.net). This model requires a prostate volume measurement as input.CONCLUSIONS:
In patients who are naïve to biopsy or those with a prior negative biopsy, BiRCH models can be used to select patients for mpMRI. PATIENTSUMMARY:
In this multicenter study, we developed and prospectively validated a calculator that can be used to predict prostate magnetic resonance imaging (MRI) results using patient age, prostate-specific antigen, and prostate volume as input. This tool can aid health care professionals and patients to make an informed decision regarding whether to get an MRI.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Próstata
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Técnicas de Apoyo para la Decisión
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Imágenes de Resonancia Magnética Multiparamétrica
Tipo de estudio:
Etiology_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
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Screening_studies
Límite:
Aged
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Eur Urol Oncol
Año:
2019
Tipo del documento:
Article
País de afiliación:
Estados Unidos