000291445 001__ 291445
000291445 005__ 20241218111628.0
000291445 0247_ $$2doi$$a10.1007/s00330-024-10818-0
000291445 0247_ $$2pmid$$apmid:38955845
000291445 0247_ $$2ISSN$$a0938-7994
000291445 0247_ $$2ISSN$$a1432-1084
000291445 0247_ $$2ISSN$$a1613-3749
000291445 0247_ $$2ISSN$$a1613-3757
000291445 0247_ $$2ISSN$$a(ISSN
000291445 0247_ $$2ISSN$$aDES
000291445 0247_ $$2ISSN$$aSUPPLEMENTS)
000291445 0247_ $$2altmetric$$aaltmetric:165067995
000291445 037__ $$aDKFZ-2024-01408
000291445 041__ $$aEnglish
000291445 082__ $$a610
000291445 1001_ $$0P:(DE-He78)b524425cb0469141913534458d441006$$aSchrader, Adrian$$b0$$eFirst author$$udkfz
000291445 245__ $$aProstate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.
000291445 260__ $$aHeidelberg$$bSpringer$$c2024
000291445 3367_ $$2DRIVER$$aarticle
000291445 3367_ $$2DataCite$$aOutput Types/Journal article
000291445 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1732099161_11919
000291445 3367_ $$2BibTeX$$aARTICLE
000291445 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000291445 3367_ $$00$$2EndNote$$aJournal Article
000291445 500__ $$a#EA:E010#LA:E010# /  Volume 34, pages 7909–7920, (2024)
000291445 520__ $$aRisk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS.One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis.Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49% [252/517] of biopsies while maintaining the negative predictive value (94%), compared to PI-RADS ≥ 4 cut-off which spared 37% [190/517] (p < 0.001).Incorporating DL as an independent diagnostic marker for RCs can improve patient stratification before biopsy, as there is complementary information in DL features and clinical PI-RADS assessment.For patients with positive prostate screening results, a comprehensive diagnostic workup, including prostate MRI, DL analysis, and individual classification using nomograms can identify patients with minimal prostate cancer risk, as they benefit less from the more invasive biopsy procedure.The current MRI-based nomograms result in many negative prostate biopsies. The addition of DL to nomograms with clinical data and PI-RADS improves patient stratification before biopsy. Fully automatic DL can be substituted for PI-RADS without sacrificing the quality of nomogram predictions. Prostate nomograms show cancer detection ability comparable to previous validation studies while being suitable for the addition of DL analysis.
000291445 536__ $$0G:(DE-HGF)POF4-315$$a315 - Bildgebung und Radioonkologie (POF4-315)$$cPOF4-315$$fPOF IV$$x0
000291445 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
000291445 650_7 $$2Other$$aDeep learning
000291445 650_7 $$2Other$$aNomograms
000291445 650_7 $$2Other$$aProstatic neoplasms, Multiparametric magnetic resonance imaging
000291445 650_7 $$2Other$$aRisk assessment
000291445 7001_ $$0P:(DE-He78)32c69a3bed6c75b378ef19ad39a74572$$aNetzer, Nils$$b1$$udkfz
000291445 7001_ $$0P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f$$aHielscher, Thomas$$b2$$udkfz
000291445 7001_ $$0P:(DE-He78)0f26d76d27427945f14f0e874d824aa6$$aGörtz, Magdalena$$b3$$udkfz
000291445 7001_ $$0P:(DE-He78)b542df279437ced507cda1a8c93a2d4d$$aZhang, Kevin Sun$$b4$$udkfz
000291445 7001_ $$aSchütz, Viktoria$$b5
000291445 7001_ $$aStenzinger, Albrecht$$b6
000291445 7001_ $$aHohenfellner, Markus$$b7
000291445 7001_ $$0P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec$$aSchlemmer, Heinz-Peter$$b8$$udkfz
000291445 7001_ $$0P:(DE-He78)ea098e4d78abeb63afaf8c25ec6d6d93$$aBonekamp, David$$b9$$eLast author$$udkfz
000291445 773__ $$0PERI:(DE-600)1472718-3$$a10.1007/s00330-024-10818-0$$p7909–7920$$tEuropean radiology$$v34$$x0938-7994$$y2024
000291445 8564_ $$uhttps://inrepo02.dkfz.de/record/291445/files/s00330-024-10818-0.pdf
000291445 8564_ $$uhttps://inrepo02.dkfz.de/record/291445/files/s00330-024-10818-0.pdf?subformat=pdfa$$xpdfa
000291445 909CO $$ooai:inrepo02.dkfz.de:291445$$pVDB
000291445 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)b524425cb0469141913534458d441006$$aDeutsches Krebsforschungszentrum$$b0$$kDKFZ
000291445 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)32c69a3bed6c75b378ef19ad39a74572$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ
000291445 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f$$aDeutsches Krebsforschungszentrum$$b2$$kDKFZ
000291445 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)0f26d76d27427945f14f0e874d824aa6$$aDeutsches Krebsforschungszentrum$$b3$$kDKFZ
000291445 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)b542df279437ced507cda1a8c93a2d4d$$aDeutsches Krebsforschungszentrum$$b4$$kDKFZ
000291445 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec$$aDeutsches Krebsforschungszentrum$$b8$$kDKFZ
000291445 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)ea098e4d78abeb63afaf8c25ec6d6d93$$aDeutsches Krebsforschungszentrum$$b9$$kDKFZ
000291445 9131_ $$0G:(DE-HGF)POF4-315$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vBildgebung und Radioonkologie$$x0
000291445 9141_ $$y2024
000291445 915__ $$0StatID:(DE-HGF)3002$$2StatID$$aDEAL Springer$$d2023-08-19$$wger
000291445 915__ $$0StatID:(DE-HGF)3002$$2StatID$$aDEAL Springer$$d2023-08-19$$wger
000291445 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-08-19
000291445 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-08-19
000291445 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-08-19
000291445 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-08-19
000291445 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-08-19
000291445 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-08-19
000291445 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2023-08-19
000291445 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bEUR RADIOL : 2022$$d2023-08-19
000291445 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2023-08-19
000291445 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2023-08-19
000291445 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bEUR RADIOL : 2022$$d2023-08-19
000291445 9202_ $$0I:(DE-He78)E010-20160331$$kE010$$lE010 Radiologie$$x0
000291445 9201_ $$0I:(DE-He78)E010-20160331$$kE010$$lE010 Radiologie$$x0
000291445 9201_ $$0I:(DE-He78)C060-20160331$$kC060$$lC060 Biostatistik$$x1
000291445 9201_ $$0I:(DE-He78)E250-20160331$$kE250$$lNWG KKE Multiparametrische Methoden zur Früherkennung des Prostatakarzinoms$$x2
000291445 9200_ $$0I:(DE-He78)E010-20160331$$kE010$$lE010 Radiologie$$x0
000291445 980__ $$ajournal
000291445 980__ $$aVDB
000291445 980__ $$aI:(DE-He78)E010-20160331
000291445 980__ $$aI:(DE-He78)C060-20160331
000291445 980__ $$aI:(DE-He78)E250-20160331
000291445 980__ $$aUNRESTRICTED