Erkin Ötleş1,2, Brian T Denton1,3, Bo Qu1, Adharsh Murali4, Selin Merdan1, Gregory B Auffenberg5, Spencer C Hiller3, Brian R Lane6, Arvin K George3, Karandeep Singh3,4,7,8. 1. Department of Industrial & Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan. 2. Medical Scientist Training Program, University of Michigan Medical School, Ann Arbor, Michigan. 3. Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan. 4. Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan. 5. Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, Illinois. 6. Division of Urology, Spectrum Health, Grand Rapids, Michigan. 7. Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan. 8. School of Information, University of Michigan, Ann Arbor, Michigan.
Abstract
PURPOSE: Prediction models are recommended by national guidelines to support clinical decision making in prostate cancer. Existing models to predict pathological outcomes of radical prostatectomy (RP)-the Memorial Sloan Kettering (MSK) models, Partin tables, and the Briganti nomogram-have been developed using data from tertiary care centers and may not generalize well to other settings. MATERIALS AND METHODS: Data from a regional cohort (Michigan Urological Surgery Improvement Collaborative [MUSIC]) were used to develop models to predict extraprostatic extension (EPE), seminal vesicle invasion (SVI), lymph node invasion (LNI), and nonorgan-confined disease (NOCD) in patients undergoing RP. The MUSIC models were compared against the MSK models, Partin tables, and Briganti nomogram (for LNI) using data from a national cohort (Surveillance, Epidemiology, and End Results [SEER] registry). RESULTS: We identified 7,491 eligible patients in the SEER registry. The MUSIC model had good discrimination (SEER AUC EPE: 0.77; SVI: 0.80; LNI: 0.83; NOCD: 0.77) and was well calibrated. While the MSK models had similar discrimination to the MUSIC models (SEER AUC EPE: 0.76; SVI: 0.80; LNI: 0.84; NOCD: 0.76), they overestimated the risk of EPE, LNI, and NOCD. The Partin tables had inferior discrimination (SEER AUC EPE: 0.67; SVI: 0.76; LNI: 0.69; NOCD: 0.72) as compared to other models. The Briganti LNI nomogram had an AUC of 0.81 in SEER but overestimated the risk. CONCLUSIONS: New models developed using the MUSIC registry outperformed existing models and should be considered as potential replacements for the prediction of pathological outcomes in prostate cancer.
PURPOSE: Prediction models are recommended by national guidelines to support clinical decision making in prostate cancer. Existing models to predict pathological outcomes of radical prostatectomy (RP)-the Memorial Sloan Kettering (MSK) models, Partin tables, and the Briganti nomogram-have been developed using data from tertiary care centers and may not generalize well to other settings. MATERIALS AND METHODS: Data from a regional cohort (Michigan Urological Surgery Improvement Collaborative [MUSIC]) were used to develop models to predict extraprostatic extension (EPE), seminal vesicle invasion (SVI), lymph node invasion (LNI), and nonorgan-confined disease (NOCD) in patients undergoing RP. The MUSIC models were compared against the MSK models, Partin tables, and Briganti nomogram (for LNI) using data from a national cohort (Surveillance, Epidemiology, and End Results [SEER] registry). RESULTS: We identified 7,491 eligible patients in the SEER registry. The MUSIC model had good discrimination (SEER AUC EPE: 0.77; SVI: 0.80; LNI: 0.83; NOCD: 0.77) and was well calibrated. While the MSK models had similar discrimination to the MUSIC models (SEER AUC EPE: 0.76; SVI: 0.80; LNI: 0.84; NOCD: 0.76), they overestimated the risk of EPE, LNI, and NOCD. The Partin tables had inferior discrimination (SEER AUC EPE: 0.67; SVI: 0.76; LNI: 0.69; NOCD: 0.72) as compared to other models. The Briganti LNI nomogram had an AUC of 0.81 in SEER but overestimated the risk. CONCLUSIONS: New models developed using the MUSIC registry outperformed existing models and should be considered as potential replacements for the prediction of pathological outcomes in prostate cancer.
Authors: Nnenaya Q Agochukwu; Daniela Wittmann; Nicholas R Boileau; Rodney L Dunn; James E Montie; Tae Kim; David C Miller; James Peabody; Noelle E Carlozzi Journal: J Clin Oncol Date: 2019-06-20 Impact factor: 44.544
Authors: Guilherme Godoy; Kian Tai Chong; Angel Cronin; Andrew Vickers; Vincent Laudone; Karim Touijer; Bertrand Guillonneau; James A Eastham; Peter T Scardino; Jonathan A Coleman Journal: Eur Urol Date: 2011-01-18 Impact factor: 20.096
Authors: Giorgio Gandaglia; Nicola Fossati; Emanuele Zaffuto; Marco Bandini; Paolo Dell'Oglio; Carlo Andrea Bravi; Giuseppe Fallara; Francesco Pellegrino; Luigi Nocera; Pierre I Karakiewicz; Zhe Tian; Massimo Freschi; Rodolfo Montironi; Francesco Montorsi; Alberto Briganti Journal: Eur Urol Date: 2017-04-12 Impact factor: 20.096
Authors: Gregory B Auffenberg; Selin Merdan; David C Miller; Karandeep Singh; Benjamin R Stockton; Khurshid R Ghani; Brian T Denton Journal: Urology Date: 2017-02-22 Impact factor: 2.649
Authors: Ben Van Calster; David J McLernon; Maarten van Smeden; Laure Wynants; Ewout W Steyerberg Journal: BMC Med Date: 2019-12-16 Impact factor: 8.775
Authors: Martin G Sanda; Jeffrey A Cadeddu; Erin Kirkby; Ronald C Chen; Tony Crispino; Joann Fontanarosa; Stephen J Freedland; Kirsten Greene; Laurence H Klotz; Danil V Makarov; Joel B Nelson; George Rodrigues; Howard M Sandler; Mary Ellen Taplin; Jonathan R Treadwell Journal: J Urol Date: 2017-12-15 Impact factor: 7.450