Background: Combined MRI/Ultrasound fusion targeted biopsy (TBx) and systematic biopsy (SBx) results in better prostate cancer (PCa) detection relative to either TBx or SBx alone, while at the cost of higher number of biopsy cores and greater detection of clinically insignificant PCa. We therefore developed and evaluated a simple decision support scheme for optimizing prostate biopsy based on multiparametric (mp) MRI assessment.
Methods: Total 229 patients with suspicion of PCa underwent mpMRI before combined TBx/SBx were retrospectively included. Impacts of MRI characteristics such as Prostate Imaging-Reporting and Data System (PI-RADS) score, lesion size, zonal origination, and location on biopsy performance were evaluated. A clinically available decision support scheme relying on mpMRI assessment was subsequently developed as a triage test to optimize prostate biopsy process. Cost (downgrade, upgrade, and biopsy core)-to-Effectiveness (detection rate) was systemically compared.
Results: TBx achieved comparable detection rate to combined TBx/SBx in diagnosis of PCa and clinically significant PCa (csPCa) (PCa, 59% [135/229] vs 60.7% [139/229]; csPCa, 45.9% [105/229] vs 47.2% [108/229]; p-values > 0.05) and outperformed SBx (PCa, 42.4% [97/229]; csPCa, 28.4% [65/229]; p-values < 0.001). Specially, in personalized decision support scheme, TBx accurately detected all PCa (72.5% [74/102]) in PI-RADS 5 and larger (≥1 cm) PI-RADS 3-4 observations, adding SBx to TBx only resulted in 1.4% (1/74) upgrading csPCa. For smaller (<1 cm) PI-RADS 3-4 lesions, combined TBx/SBx resulted in relatively higher detection rate (51.2% [65/127] vs 48.0% [61/127]) and lower upgrading rate (30.6% [15/49] vs 36.7% [18/49]) than TBx.
Conclusions: The benefit of SBx added to TBx was largely restricted to smaller PI-RADS score 3-4 lesions. Using our personalized strategy of solo TBx for PI-RADS 5 and larger (≥1 cm) PI-RADS score 3-4 lesions would avoid excess SBx in 44.5% (102/229) of all biopsy-naïve patients without compromising detection rate.
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http://dx.doi.org/10.1038/s41391-021-00489-z | DOI Listing |
Ecohealth
January 2025
Health Services Academy, Chak Shahzad, Park Road, Islamabad, 44000, Pakistan.
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January 2025
Crop and Horticultural Science Research Department, Mazandaran Agricultural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Tajrish, Iran.
Plum fruit fresh weight (FW) estimation is crucial for various agricultural practices, including yield prediction, quality control, and market pricing. Traditional methods for estimating fruit weight are often destructive, time-consuming, and labor-intensive. In this study, we addressed the problem of predicting plum FW using artificial intelligence (AI) methods based on fruit dimensions.
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January 2025
Department of Orthopaedic and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Campus Grosshadern, Ludwig Maximilians University Munich, Munich, Germany.
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January 2025
Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Grønnegårdsvej 2, 1870, Frederiksberg C, Denmark.
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January 2025
Logistics Education (LEED) at Kühne Foundation, Hamburg, Germany.
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