La resonancia magnética como herramienta para el diagnóstico del cáncer de próstata: nuevas evidencias y posicionamiento de la ESUT (EAU Section of Uro-Technology)

Maestro, M. Álvarez; Rivas, Juan Gómez; Olarte, J. Quesada; Carrión, D.M.; Guzmán, C. Trelles; Ballesteros, C.; Quintana, Luis Miguel; Bazán, Alfredo Aguilera; Martínez‐Piñeiro, Luis; Liatsikos, Evangelos; Barret, Éric

doi:10.1016/j.acuro.2019.08.003

Cited by 2 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiparametric MRI (mpMRI) is considered a standard tool for diagnostic evaluations of PCa and can help reduce unnecessary biopsies by a quarter [ 13 , 14 ]. The accurate assessment of PCa by mpMRI before RP can help clinicians distinguish extraprostatic disease, identify risk factors associated with PSM, and evaluate intraoperative complications and functional recovery after surgery [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins

Wang

et al. 2021

Cancer Imaging

View full text Add to dashboard Cite

Purpose To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction. Methods and materials In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets. Results The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively. Conclusions The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.

show abstract

Section: Introductionmentioning

confidence: 99%

MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins

Wang

et al. 2021

Cancer Imaging

View full text Add to dashboard Cite

show abstract

68Ga-PSMA PET/CT-based multivariate model for highly accurate and noninvasive diagnosis of clinically significant prostate cancer in the PSA gray zone

Yang,

Li,

Xiao

et al. 2023

Cancer Imaging

View full text Add to dashboard Cite

Background The prostate-specific antigen (PSA) has been widely used in screening and early diagnosis of prostate cancer (PCa). However, in the PSA grey zone of 4–10 ng/ml, the sensitivity and specificity for diagnosing PCa are limited, resulting in considerable number of unnecessary and invasive prostate biopsies, which may lead to potential overdiagnosis and overtreatment. We aimed to predict clinically significant PCa (CSPCa) by combining the maximal standardized uptake value (SUVmax) based on 68Ga‑PSMA PET/CT and clinical indicators in men with gray zone PSA levels. Methods 81 patients with suspected PCa based on increased serum total PSA (TPSA) levels of 4 − 10 ng/mL who underwent transrectal ultrasound/magnetic resonance imaging (MRI)/PET fusion-guided biopsy were enrolled. Among them, patients confirmed by histopathology were divided into the CSPCa group and the non-CSPCa group, and data on PSA concentration, prostate volume (PV), PSA density (PSAD), free PSA (FPSA)/TPSA, Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) score, 68Ga-PSMA PET/CT imaging evaluation results and SUVmax were compared. Multivariate logistic regression analysis was performed to identify the independent predictors for CSPCa, thereby establishing a predictive model based on SUVmax that was evaluated by analyzing the receiver operating characteristic (ROC) curve and decision curve analysis. Results Compared to non-CSPCa, CSPCa patients had smaller PVs (median, 31.40 mL), lower FPSA/TPSA (median, 0.12), larger PSADs (median, 0.21 ng/mL2) and higher PI-RADS scores (P < 0.05). The prediction model comprising 68Ga-PSMA PET/CT maximal standardized uptake value, PV and FPSA/TPSA had the highest AUC of 0.927 compared with that of other predictors alone (AUCs of 0.585 for PSA, 0.652 for mpMRI and 0.850 for 68Ga-PSMA PET/CT). The diagnostic sensitivity and specificity of the prediction model were 86.21% and 86.54%, respectively. Conclusion Given the low diagnostic accuracy of regular PSA tests, a new prediction model based on the 68Ga-PSMA PET/CT SUVmax, PV and FPSA/TPSA was developed and validated, and this model could provide a more satisfactory predictive accuracy for CSPCa. This study provides a noninvasive prediction model with high accuracy for the diagnosis of CSPCa in the PSA gray zone, thus may be better avoiding unnecessary biopsy procedures.

show abstract

La resonancia magnética como herramienta para el diagnóstico del cáncer de próstata: nuevas evidencias y posicionamiento de la ESUT (EAU Section of Uro-Technology)

Cited by 2 publications

References 37 publications

MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins

MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins

68Ga-PSMA PET/CT-based multivariate model for highly accurate and noninvasive diagnosis of clinically significant prostate cancer in the PSA gray zone

Contact Info

Product

Resources

About