Multivariable model integrating PHI and mpMRI for detecting csPCa in biopsy‐naïve men
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Wiley
2025
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| author | Mario Dominguez Esteban Ester Fernandez Guzman Enrique Ramos Barselo Ernesto Herrero Blanco Sergio Zubillaga Guerrero Roberto Ballestero Diego Alejandro Fernandez Florez Jose Javier Gomez Roman Jaime Garcia Herrero Marina Sanchez Gil Guillermo Velilla Diez Felix Campos Juanatey Maria Teresa Garcia Unzueta Jose Luis Gutierrez Baños |
| author_facet | Mario Dominguez Esteban Ester Fernandez Guzman Enrique Ramos Barselo Ernesto Herrero Blanco Sergio Zubillaga Guerrero Roberto Ballestero Diego Alejandro Fernandez Florez Jose Javier Gomez Roman Jaime Garcia Herrero Marina Sanchez Gil Guillermo Velilla Diez Felix Campos Juanatey Maria Teresa Garcia Unzueta Jose Luis Gutierrez Baños Mario Dominguez Esteban Ester Fernandez Guzman Enrique Ramos Barselo Ernesto Herrero Blanco Sergio Zubillaga Guerrero Roberto Ballestero Diego Alejandro Fernandez Florez Jose Javier Gomez Roman Jaime Garcia Herrero Marina Sanchez Gil Guillermo Velilla Diez Felix Campos Juanatey Maria Teresa Garcia Unzueta Jose Luis Gutierrez Baños |
| collection | Wiley Open Access |
| contents | Multivariable model integrating PHI and mpMRI for detecting csPCa in biopsy‐naïve men Mario Dominguez Esteban Ester Fernandez Guzman Enrique Ramos Barselo Ernesto Herrero Blanco Sergio Zubillaga Guerrero Roberto Ballestero Diego Alejandro Fernandez Florez Jose Javier Gomez Roman Jaime Garcia Herrero Marina Sanchez Gil Guillermo Velilla Diez Felix Campos Juanatey Maria Teresa Garcia Unzueta Jose Luis Gutierrez Baños BJUI Compass Abstract Background The integration of blood‐based biomarkers and multiparametric magnetic resonance imaging (mpMRI) has been proposed to improve prostate cancer (PCa) diagnosis. However, few validated models combine both tools to support risk‐adapted clinical decision‐making. Objective The study's aim is to evaluate and internally validate a multivariable model integrating clinical, analytical and imaging parameters—including the Prostate Health Index (PHI) and mpMRI—for predicting clinically significant prostate cancer (csPCa) in biopsy‐naïve men. Design, setting and participants This prospective observational study included 183 biopsy‐naïve men aged 50–75 years with PSA levels of 4–10 ng/mL and/or abnormal digital rectal examination. All patients underwent PHI testing, and 47.5% received prebiopsy mpMRI. All underwent systematic biopsy; targeted cognitive fusion biopsy was performed for PIRADS ≥ 3 lesions. Outcome measurements and statistical analysis A multivariable logistic regression model was constructed using PHI, PSA density, PSA free/total ratio, PIRADS score and age. The model was internally validated with bootstrap resampling and converted into a clinical nomogram. Diagnostic accuracy (AUC, sensitivity, specificity, NPV and PPV) was assessed and compared with simplified strategies using PHI or PIRADS alone, as well as a sequential approach (PHI → PIRADS). Results and limitations The model achieved an AUC of 0.841 (95% CI 0.76–0.91), with 100% sensitivity and 66.7% specificity for csPCa in the mpMRI cohort at the optimal 17% risk threshold (65.5 points). It safely avoided 49.4% of biopsies without missing any csPCa cases. Simpler strategies using PHI or PIRADS alone showed lower efficiency, particularly in balancing sensitivity and biopsy reduction. As an additional analysis, the PHI–mpMRI nomogram by Siddiqui et al. (2023) was externally validated in our cohort, confirming robust diagnostic accuracy (AUC 0.89, 95% CI 0.82–0.95). Limitations include the modest size of the mpMRI cohort and the historical nature of recruitment (2014–2018), although PHI and mpMRI remain standard in contemporary practice. Conclusions This model accurately predicts csPCa and outperforms individual tools such as PHI or PIRADS alone. Its application may improve diagnostic efficiency and reduce unnecessary procedures. Patient summary A model combining a blood test (PHI) and MRI can help avoid unnecessary prostate biopsies while reliably detecting aggressive cancers. 10.1002/bco2.70101 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/bco2.70101 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_bco2_70101 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Multivariable model integrating PHI and mpMRI for detecting csPCa in biopsy‐naïve men Mario Dominguez Esteban Ester Fernandez Guzman Enrique Ramos Barselo Ernesto Herrero Blanco Sergio Zubillaga Guerrero Roberto Ballestero Diego Alejandro Fernandez Florez Jose Javier Gomez Roman Jaime Garcia Herrero Marina Sanchez Gil Guillermo Velilla Diez Felix Campos Juanatey Maria Teresa Garcia Unzueta Jose Luis Gutierrez Baños BJUI Compass Multivariable model integrating PHI and mpMRI for detecting csPCa in biopsy‐naïve men Mario Dominguez Esteban Ester Fernandez Guzman Enrique Ramos Barselo Ernesto Herrero Blanco Sergio Zubillaga Guerrero Roberto Ballestero Diego Alejandro Fernandez Florez Jose Javier Gomez Roman Jaime Garcia Herrero Marina Sanchez Gil Guillermo Velilla Diez Felix Campos Juanatey Maria Teresa Garcia Unzueta Jose Luis Gutierrez Baños BJUI Compass Abstract Background The integration of blood‐based biomarkers and multiparametric magnetic resonance imaging (mpMRI) has been proposed to improve prostate cancer (PCa) diagnosis. However, few validated models combine both tools to support risk‐adapted clinical decision‐making. Objective The study's aim is to evaluate and internally validate a multivariable model integrating clinical, analytical and imaging parameters—including the Prostate Health Index (PHI) and mpMRI—for predicting clinically significant prostate cancer (csPCa) in biopsy‐naïve men. Design, setting and participants This prospective observational study included 183 biopsy‐naïve men aged 50–75 years with PSA levels of 4–10 ng/mL and/or abnormal digital rectal examination. All patients underwent PHI testing, and 47.5% received prebiopsy mpMRI. All underwent systematic biopsy; targeted cognitive fusion biopsy was performed for PIRADS ≥ 3 lesions. Outcome measurements and statistical analysis A multivariable logistic regression model was constructed using PHI, PSA density, PSA free/total ratio, PIRADS score and age. The model was internally validated with bootstrap resampling and converted into a clinical nomogram. Diagnostic accuracy (AUC, sensitivity, specificity, NPV and PPV) was assessed and compared with simplified strategies using PHI or PIRADS alone, as well as a sequential approach (PHI → PIRADS). Results and limitations The model achieved an AUC of 0.841 (95% CI 0.76–0.91), with 100% sensitivity and 66.7% specificity for csPCa in the mpMRI cohort at the optimal 17% risk threshold (65.5 points). It safely avoided 49.4% of biopsies without missing any csPCa cases. Simpler strategies using PHI or PIRADS alone showed lower efficiency, particularly in balancing sensitivity and biopsy reduction. As an additional analysis, the PHI–mpMRI nomogram by Siddiqui et al. (2023) was externally validated in our cohort, confirming robust diagnostic accuracy (AUC 0.89, 95% CI 0.82–0.95). Limitations include the modest size of the mpMRI cohort and the historical nature of recruitment (2014–2018), although PHI and mpMRI remain standard in contemporary practice. Conclusions This model accurately predicts csPCa and outperforms individual tools such as PHI or PIRADS alone. Its application may improve diagnostic efficiency and reduce unnecessary procedures. Patient summary A model combining a blood test (PHI) and MRI can help avoid unnecessary prostate biopsies while reliably detecting aggressive cancers. 10.1002/bco2.70101 http://creativecommons.org/licenses/by/4.0/ |
| title | Multivariable model integrating PHI and mpMRI for detecting csPCa in biopsy‐naïve men |
| topic | BJUI Compass |
| url | https://bjui-journals.onlinelibrary.wiley.com/doi/10.1002/bco2.70101 |