Andreas G Wibmer1, Michael W Kattan2, Francesco Alessandrino3, Alexander D J Baur4, Lars Boesen5, Felipe Boschini Franco3, David Bonekamp6, Riccardo Campa7, Hannes Cash4,8, Violeta Catalá9,10, Sebastien Crouzet11, Sounil Dinnoo12, James Eastham13, Fiona M Fennessy3, Kamyar Ghabili14, Markus Hohenfellner15, Angelique W Levi16, Xinge Ji2, Vibeke Løgager5, Daniel J Margolis17, Paul C Moldovan11, Valeria Panebianco7, Tobias Penzkofer4,18, Philippe Puech12, Jan Philipp Radtke6,15, Olivier Rouvière11,19, Heinz-Peter Schlemmer6, Preston C Sprenkle14, Clare M Tempany3, Joan C Vilanova20, Jeffrey Weinreb21, Hedvig Hricak1, Amita Shukla-Dave1. 1. Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA. 2. Department of Quantitative Health Sciences in the Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA. 3. Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. 4. Charité University Hospital, 10117 Berlin, Germany. 5. Herlev Gentofte University Hospital, 2730 Herlev, Denmark. 6. DKFZ German Cancer Research Center, 69120 Heidelberg, Germany. 7. Department of Radiological Sciences, Oncology & Pathology, Sapienza University of Rome, 00185 Rome, Italy. 8. Department of Urology, University Magdeburg, 39120 Magdeburg, Germany. 9. Department of Radiology, Fundació Puigvert, 08025 Barcelona, Spain. 10. Department of Uro-Radiology, Creu Blanca, 08034 Barcelona, Spain. 11. Hospices Civils de Lyon, Hôpital Edouard Herriot, 69003 Lyon, France. 12. Genitourinary and Women's Imaging Departments, Lille University Hospital, 59037 Lille, France. 13. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 14. Department of Urology, Yale School of Medicine, New Haven, CT 06510, USA. 15. Department of Urology, University Hospital of Heidelberg, 69120 Heidelberg, Germany. 16. Department of Pathology, Yale School of Medicine, New Haven, CT 06510, USA. 17. Weill Cornell Medicine, Weill Cornell Imaging, New York-Presbyterian Hospital, New York, NY 10021, USA. 18. Berlin Institute of Health (BIH), 10178 Berlin, Germany. 19. Faculté de Médecine Lyon Est, Université de Lyon, 69003 Lyon, France. 20. Clínica Girona, Institute Catalan of Health-IDI, University of Girona, 17004 Girona, Spain. 21. Department of Radiology, Yale School of Medicine, New Haven, CT 06510, USA.
Abstract
BACKGROUND: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data. METHODS: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens. The performance of the statistical model was evaluated by bootstrap resampling and cross validation and compared with the performance of benchmark models that do not incorporate MRI findings. RESULTS: Data from 840 patients were analyzed; pathologic EPE was found in 320/840 (31.8%). The nomogram model included patient age, prostate-specific antigen density, side-specific biopsy data (i.e., Gleason grade group, percent positive cores, tumor extent), and side-specific MRI features (i.e., presence of a PI-RADSv2 4 or 5 lesion, level of suspicion for EPE, length of capsular contact). The area under the receiver operating characteristic curve of the new, MRI-inclusive model (0.828, 95% confidence limits: 0.805, 0.852) was significantly higher than that of any of the benchmark models (p < 0.001 for all). CONCLUSIONS: In an international, multi-site study, we developed an MRI-inclusive nomogram for the side-specific prediction of EPE of prostate cancer that demonstrated significantly greater accuracy than clinical benchmark models.
BACKGROUND: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data. METHODS: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens. The performance of the statistical model was evaluated by bootstrap resampling and cross validation and compared with the performance of benchmark models that do not incorporate MRI findings. RESULTS: Data from 840 patients were analyzed; pathologic EPE was found in 320/840 (31.8%). The nomogram model included patient age, prostate-specific antigen density, side-specific biopsy data (i.e., Gleason grade group, percent positive cores, tumor extent), and side-specific MRI features (i.e., presence of a PI-RADSv2 4 or 5 lesion, level of suspicion for EPE, length of capsular contact). The area under the receiver operating characteristic curve of the new, MRI-inclusive model (0.828, 95% confidence limits: 0.805, 0.852) was significantly higher than that of any of the benchmark models (p < 0.001 for all). CONCLUSIONS: In an international, multi-site study, we developed an MRI-inclusive nomogram for the side-specific prediction of EPE of prostate cancer that demonstrated significantly greater accuracy than clinical benchmark models.
Entities:
Keywords:
clinical staging; extraprostatic tumor extension; magnetic resonance imaging; nomogram; prostate cancer
Authors: Timothy D McClure; Daniel J A Margolis; Robert E Reiter; James W Sayre; M Albert Thomas; Rajakumar Nagarajan; Mittul Gulati; Steven S Raman Journal: Radiology Date: 2012-01-24 Impact factor: 11.105
Authors: Thomas Steuber; Markus Graefen; Alexander Haese; Andreas Erbersdobler; Felix K-H Chun; Thorsten Schlom; Paul Perrotte; Hartwig Huland; Pierre I Karakiewicz Journal: J Urol Date: 2006-03 Impact factor: 7.450
Authors: Kareem N Rayn; Jonathan B Bloom; Samuel A Gold; Graham R Hale; Joseph A Baiocco; Sherif Mehralivand; Marcin Czarniecki; Vikram K Sabarwal; Vladimir Valera; Bradford J Wood; Maria J Merino; Peter Choyke; Baris Turkbey; Peter A Pinto Journal: J Urol Date: 2018-05-29 Impact factor: 7.450
Authors: Andrew B Rosenkrantz; Luke A Ginocchio; Daniel Cornfeld; Adam T Froemming; Rajan T Gupta; Baris Turkbey; Antonio C Westphalen; James S Babb; Daniel J Margolis Journal: Radiology Date: 2016-04-01 Impact factor: 11.105
Authors: Are Losnegård; Lars A R Reisæter; Ole J Halvorsen; Jakub Jurek; Jörg Assmus; Jarle B Arnes; Alfred Honoré; Jan A Monssen; Erling Andersen; Ingfrid S Haldorsen; Arvid Lundervold; Christian Beisland Journal: Acta Radiol Date: 2020-02-28 Impact factor: 1.990
Authors: John B Eifler; Zhaoyang Feng; Brian M Lin; Michael T Partin; Elizabeth B Humphreys; Misop Han; Jonathan I Epstein; Patrick C Walsh; Bruce J Trock; Alan W Partin Journal: BJU Int Date: 2012-07-26 Impact factor: 5.588
Authors: Kevin C Zorn; Andrea Gallina; Georg C Hutterer; Jochen Walz; Arieh L Shalhav; Gregory P Zagaja; Luc Valiquette; Ofer N Gofrit; Marcelo A Orvieto; Jerome B Taxy; Pierre I Karakiewicz Journal: J Endourol Date: 2007-11 Impact factor: 2.942
Authors: Andreas G Wibmer; Ines Nikolovski; Joshua Chaim; Yulia Lakhman; Robert A Lefkowitz; Evis Sala; Sigrid V Carlsson; Samson W Fine; Michael W Kattan; Hedvig Hricak; Hebert Alberto Vargas Journal: Radiology Date: 2021-12-21 Impact factor: 11.105