ObjectivesThe aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone.MethodsThis retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG >= 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated.ResultsMRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73-98%) and NPV of 93% (95%CI 82-98%), and for csPCa identification, with sensitivity of 91% (95%CI 72-99%) and NPV of 95% (95%CI 84-99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone.ConclusionMRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naive patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results.Clinical relevance statementThe use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa.Key Points center dot Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA.center dot The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance.center dot Results of this study were in line with previous works on MRI and microRNA.Key Points center dot Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA.center dot The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance.center dot Results of this study were in line with previous works on MRI and microRNA.Key Points center dot Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA.center dot The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance.center dot Results of this study were in line with previous works on MRI and microRNA.

Development and validation of a clinical decision support system based on PSA, microRNAs, and MRI for the detection of prostate cancer

Mazzetti, Simone;Defeudis, Arianna;Nicoletti, Giulia;Chiorino, Giovanna;Faletti, Riccardo;Gatti, Marco;Gontero, Paolo;Porpiglia, Francesco;Regge, Daniele;Giannini, Valentina
2024-01-01

Abstract

ObjectivesThe aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone.MethodsThis retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG >= 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated.ResultsMRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73-98%) and NPV of 93% (95%CI 82-98%), and for csPCa identification, with sensitivity of 91% (95%CI 72-99%) and NPV of 95% (95%CI 84-99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone.ConclusionMRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naive patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results.Clinical relevance statementThe use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa.Key Points center dot Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA.center dot The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance.center dot Results of this study were in line with previous works on MRI and microRNA.Key Points center dot Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA.center dot The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance.center dot Results of this study were in line with previous works on MRI and microRNA.Key Points center dot Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA.center dot The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance.center dot Results of this study were in line with previous works on MRI and microRNA.
2024
Epub ahead of print.
Epub ahead of print.
Clinical decision support system; Detection; Magnetic resonance imaging; Prostate cancer; microRNA
Mazzetti, Simone; Defeudis, Arianna; Nicoletti, Giulia; Chiorino, Giovanna; De Luca, Stefano; Faletti, Riccardo; Gatti, Marco; Gontero, Paolo; Manfred...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1989330
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