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1.
Radiol Bras ; 50(5): 299-307, 2017.
Article in English | MEDLINE | ID: mdl-29085163

ABSTRACT

OBJECTIVE: To compare the predictions of dominant Gleason pattern ≥ 4 or non-organ confined disease with Prostate Imaging Reporting and Data System (PI-RADS v2) with or without proton magnetic resonance spectroscopic imaging (1H-MRSI). MATERIALS AND METHODS: Thirty-nine men underwent 3-tesla endorectal multiparametric MRI including 1H-MRSI and prostatectomy. Two radiologists assigned PI-RADS v2 and 1H-MRSI scores to index lesions. Statistical analyses used logistic regressions, receiver operating characteristic (ROC) curves, and 2x2 tables for diagnostic accuracies. RESULTS: The sensitivity and specificity of 1H-MRSI and PI-RADS v2 for high-grade prostate cancer (PCa) were 85.7% (57.1%) and 92.9% (100%), and 56% (68.0%) and 24.0% (24.0%). The sensitivity and specificity of 1H-MRSI and PI-RADS v2 for extra-prostatic extension (EPE) were 64.0% (40%) and 20.0% (48%), and 50.0% (57.1%) and 71.4% (64.3%). The area under the ROC curves (AUC) for prediction of high-grade prostate cancer were 0.65 and 0.61 for PI-RADS v2 and 0.72 and 0.70 when combined with 1H-MRSI (readers 1 and 2, p = 0.04 and 0.21). For prediction of EPE the AUC were 0.54 and 0.60 for PI-RADS v2 and 0.55 and 0.61 when combined with 1H-MRSI (p > 0.05). CONCLUSION: 1H-MRSI might improve the discrimination of high-grade prostate cancer when combined to PI-RADS v2, particularly for PI-RADS v2 score 4 lesions, but it does not affect the prediction of EPE.


OBJETIVO: Comparar as predições de tumor com padrão 4 de Gleason dominante ou de tumor com extensão extraprostática utilizando o sistema Prostate Imaging Reporting and Data System (PI-RADS v2), combinado ou não a espectroscopia por ressonância magnética (1H-ERM). MATERIAIS E MÉTODOS: Trinta e nove pacientes submeteram-se a RM de 3 tesla com bobina endorretal, incluindo 1H-ERM, e prostatectomia. Dois radiologistas classificaram as principais lesões identificadas em cada caso utilizando PI-RADS v2 e escores de 1H-ERM. As análises estatísticas incluíram regressões logísticas, curvas receiver operating characteristic (ROC) e tabelas 2x2 para acurácia diagnóstica. RESULTADOS: A sensibilidade e a especificidade da 1H-ERM e do PI-RADS v2 para a detecção de câncer de próstata de alto grau foram 85,7% (57,1%) e 92,9% (100%), e 56% (68%) e 24% (24%). A sensibilidade e a especificidade da 1H-ERM e do PI-RADS v2 para a detecção de extensão extraprostática (EEP) foram 64,0% (40%) e 20% (48%), e 50% (57,1%) e 71,4% (64,3%). As áreas das curvas ROC para a predição de câncer de alto grau foram 0,65 e 0,61 para PI-RADS v2 e 0,72 e 0,70 quando combinado com 1H-ERM (radiologistas 1 e 2, p = 0.04 e 0.21). Para a predição de EEP, as áreas das curvas ROC foram 0,54 e 0,60 para PI-RADS v2 e 0,55 e 0,61 quando combinado com 1H-ERM (p > 0.05). CONCLUSÃO: É possível que a 1H-ERM melhore a predição de câncer de alto grau quando combinada ao PI-RADS v2, em particular para lesões que recebem um escore PI-RADS v2 4; entretanto, ela não afeta a predição de EEP.

2.
Radiol. bras ; Radiol. bras;50(5): 299-307, Sept.-Oct. 2017. tab, graf
Article in English | LILACS | ID: biblio-896111

ABSTRACT

Abstract Objective: To compare the predictions of dominant Gleason pattern ≥ 4 or non-organ confined disease with Prostate Imaging Reporting and Data System (PI-RADS v2) with or without proton magnetic resonance spectroscopic imaging (1H-MRSI). Materials and Methods: Thirty-nine men underwent 3-tesla endorectal multiparametric MRI including 1H-MRSI and prostatectomy. Two radiologists assigned PI-RADS v2 and 1H-MRSI scores to index lesions. Statistical analyses used logistic regressions, receiver operating characteristic (ROC) curves, and 2x2 tables for diagnostic accuracies. Results: The sensitivity and specificity of 1H-MRSI and PI-RADS v2 for high-grade prostate cancer (PCa) were 85.7% (57.1%) and 92.9% (100%), and 56% (68.0%) and 24.0% (24.0%). The sensitivity and specificity of 1H-MRSI and PI-RADS v2 for extra-prostatic extension (EPE) were 64.0% (40%) and 20.0% (48%), and 50.0% (57.1%) and 71.4% (64.3%). The area under the ROC curves (AUC) for prediction of high-grade prostate cancer were 0.65 and 0.61 for PI-RADS v2 and 0.72 and 0.70 when combined with 1H-MRSI (readers 1 and 2, p = 0.04 and 0.21). For prediction of EPE the AUC were 0.54 and 0.60 for PI-RADS v2 and 0.55 and 0.61 when combined with 1H-MRSI (p > 0.05). Conclusion: 1H-MRSI might improve the discrimination of high-grade prostate cancer when combined to PI-RADS v2, particularly for PI-RADS v2 score 4 lesions, but it does not affect the prediction of EPE.


Resumo Objetivo: Comparar as predições de tumor com padrão 4 de Gleason dominante ou de tumor com extensão extraprostática utilizando o sistema Prostate Imaging Reporting and Data System (PI-RADS v2), combinado ou não a espectroscopia por ressonância magnética (1H-ERM). Materiais e Métodos: Trinta e nove pacientes submeteram-se a RM de 3 tesla com bobina endorretal, incluindo 1H-ERM, e prostatectomia. Dois radiologistas classificaram as principais lesões identificadas em cada caso utilizando PI-RADS v2 e escores de 1H-ERM. As análises estatísticas incluíram regressões logísticas, curvas receiver operating characteristic (ROC) e tabelas 2x2 para acurácia diagnóstica. Resultados: A sensibilidade e a especificidade da 1H-ERM e do PI-RADS v2 para a detecção de câncer de próstata de alto grau foram 85,7% (57,1%) e 92,9% (100%), e 56% (68%) e 24% (24%). A sensibilidade e a especificidade da 1H-ERM e do PI-RADS v2 para a detecção de extensão extraprostática (EEP) foram 64,0% (40%) e 20% (48%), e 50% (57,1%) e 71,4% (64,3%). As áreas das curvas ROC para a predição de câncer de alto grau foram 0,65 e 0,61 para PI-RADS v2 e 0,72 e 0,70 quando combinado com 1H-ERM (radiologistas 1 e 2, p = 0.04 e 0.21). Para a predição de EEP, as áreas das curvas ROC foram 0,54 e 0,60 para PI-RADS v2 e 0,55 e 0,61 quando combinado com 1H-ERM (p > 0.05). Conclusão: É possível que a 1H-ERM melhore a predição de câncer de alto grau quando combinada ao PI-RADS v2, em particular para lesões que recebem um escore PI-RADS v2 4; entretanto, ela não afeta a predição de EEP.

3.
AJR Am J Roentgenol ; 197(6): 1369-74, 2011 Dec.
Article in English | MEDLINE | ID: mdl-22109291

ABSTRACT

OBJECTIVE: The purpose of this study was to determine the frequency and incremental prognostic importance of gross locally advanced disease seen at endorectal MRI in patients with prostate cancer. MATERIALS AND METHODS: We retrospectively identified the cases of all patients with biopsy-proven prostate cancer who underwent pretreatment endorectal MRI over a 6-year period (n = 1777). Three experienced radiologists identified by consensus patients with gross locally advanced disease, defined as unequivocal extracapsular extension or unequivocal seminal vesicle invasion. Outcome among these patients was compared with that in a control group without gross locally advanced disease matched by D'Amico risk stratification. RESULTS: Sixty-six of 1777 (3.7%) patients had gross locally advanced disease. One of 1085 (0.1%) patients had low-risk disease, 25 of 489 (5.1%) had intermediate-risk disease, and 40 of 203 (19.7%) had high-risk disease. Follow-up data were available for 44 of these 66 patients. During a median follow-up period of 79 months, biochemical failure and metastasis had developed in 17 and 6 of these 44 patients compared with 9 and none of the 65 patients in the control group (p < 0.001). CONCLUSION: Almost 4% of patients with prostate cancer, particularly those with intermediate- and high-risk disease, have gross locally advanced disease at endorectal MRI and have a significantly worse prognosis than matched controls. These patients may be candidates for more aggressive treatment.


Subject(s)
Magnetic Resonance Imaging/methods , Prostatic Neoplasms/pathology , Biopsy , Humans , Image Processing, Computer-Assisted , Logistic Models , Male , Middle Aged , Prognosis , Prostate-Specific Antigen/blood , Rectum , Retrospective Studies , Risk Assessment , Statistics, Nonparametric , Survival Analysis , Treatment Outcome
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