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Development and Validation of Models to Predict Pathological Outcomes of Radical Prostatectomy in Regional and National Cohorts

2021; Lippincott Williams & Wilkins; Volume: 207; Issue: 2 Linguagem: Inglês

10.1097/ju.0000000000002230

1527-3792

Erkin Ötleş, Brian T. Denton, Bo Qu, Adharsh Murali, Selin Merdan, Gregory B. Auffenberg, Spencer Hiller, Brian R. Lane, Arvin K. George, Karandeep Singh,

Health Systems, Economic Evaluations, Quality of Life

No AccessJournal of UrologyAdult Urology1 Feb 2022Development and Validation of Models to Predict Pathological Outcomes of Radical Prostatectomy in Regional and National CohortsThis article is commented on by the following:Editorial CommentEditorial Comment Erkin Ötleş, Brian T. Denton, Bo Qu, Adharsh Murali, Selin Merdan, Gregory B. Auffenberg, Spencer C. Hiller, Brian R. Lane, Arvin K. George, and Karandeep Singh Erkin ÖtleşErkin Ötleş http://orcid.org/0000-0003-3169-6832 Department of Industrial & Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan Medical Scientist Training Program, University of Michigan Medical School, Ann Arbor, Michigan , Brian T. DentonBrian T. Denton Department of Industrial & Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan , Bo QuBo Qu Department of Industrial & Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan , Adharsh MuraliAdharsh Murali Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan , Selin MerdanSelin Merdan Department of Industrial & Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan , Gregory B. AuffenbergGregory B. Auffenberg Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, Illinois , Spencer C. HillerSpencer C. Hiller Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan , Brian R. LaneBrian R. Lane Division of Urology, Spectrum Health, Grand Rapids, Michigan , Arvin K. GeorgeArvin K. George Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan , and Karandeep SinghKarandeep Singh †Correspondence: Department of Learning Health Sciences, University of Michigan Medical School, 1161H NIB, 300 N. Ingalls St., Ann Arbor, Michigan 48109 telephone: 734-936-1649; FAX: 734-647-3914; E-mail Address: [email protected] Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan School of Information, University of Michigan, Ann Arbor, Michigan for the Michigan Urological Surgery Improvement Collaborative View All Author Informationhttps://doi.org/10.1097/JU.0000000000002230AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail 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. References 1. : Validity of the patient-reported outcome measurement information system (PROMIS) sexual interest and satisfaction measures in men following radical prostatectomy. J Clin Oncol 2019; 37: 2017. Google Scholar 2. : Clinically localized prostate cancer: AUA/ASTRO/SUO guideline. Part I: risk stratification, shared decision making, and care options. J Urol 2018; 199: 683. Link, Google Scholar 3. National Comprehensive Cancer Network: NCCN guidelines prostate cancer version 4.2019. Available at https://www.nccn.org/professionals/physician_gls/pdf/prostate.pdf. Accessed August 11, 2021. Google Scholar 4. Memorial Sloan Kettering Cancer Center: MSKCC pre-radical prostatectomy nomogram. Available at https://www.mskcc.org/nomograms/prostate/pre_op. Accessed January 31, 2020. Google Scholar 5. Brady Urological Institute. Partin Tables Prostate Cancer Risk Assessment Tool. 2021. Available at https://www.hopkinsmedicine.org/brady-urology-institute/conditions_and_treatments/prostate_cancer/risk_assessment_tools/partin-tables.html. Accessed April 4, 2021. Google Scholar 6. : Development and internal validation of a novel model to identify the candidates for extended pelvic lymph node dissection in prostate cancer. Eur Urol 2017; 72: 632. Google Scholar 7. : Calibration: the Achilles heel of predictive analytics. BMC Med 2019; 17: 230. Google Scholar 8. : Validation of the partin nomogram for prostate cancer in a national sample. J Urol 2010; 183: 105. Link, Google Scholar 9. : Extent of pelvic lymph node dissection and the impact of standard template dissection on nomogram prediction of lymph node involvement. Eur Urol 2011; 60: 195. Google Scholar 10. Memorial Sloan Kettering Cancer Center Dynamic prostate cancer nomogram: coefficients. Available at https://www.mskcc.org/nomograms/prostate/pre_op/coefficients. Accessed April 4, 2021. Google Scholar 11. : Implementation of dynamically updated prediction models at the point of care at a major cancer center: making nomograms more like Netflix. Urology 2017; 102: 1. Google Scholar 12. : Validation of a nomogram predicting the probability of lymph node invasion based on the extent of pelvic lymphadenectomy in patients with clinically localized prostate cancer. BJU Int 2006; 98: 788. Google Scholar 13. : Updated nomogram predicting lymph node invasion in patients with prostate cancer undergoing extended pelvic lymph node dissection: the essential importance of percentage of positive cores. Eur Urol 2012; 61: 480. Google Scholar 14. : A novel nomogram to identify candidates for extended pelvic lymph node dissection among patients with clinically localized prostate cancer diagnosed with magnetic resonance imaging-targeted and systematic biopsies. Eur Urol 2019; 75: 506. Google Scholar 15. : GitHub AskMUSIC source code. Available at https://github.com/ML4LHS/askmusic. Accessed April 4, 2021. Google Scholar 16. Michigan Urological Surgery Improvement Collaborative: Ask MUSIC radical prostatectomy pathologic outcomes app. Available at https://shiny.med.umich.edu/apps/kdpsingh/askmusic_prostate_path_outcomes/. Accessed April 4, 2021. Google Scholar 17. : Comparison between Briganti, Partin and MSKCC tools in predicting positive lymph nodes in prostate cancer: a systematic review and meta-analysis. Scand J Urol 2017; 51: 345. Google Scholar 18. : Evaluation of prostate cancer risk calculators for shared decision making across diverse urology practices in Michigan. Urology 2017; 104: 137. Google Scholar Funding: EÖ was supported by NIH grant T32GM007863. BD and BQ were supported by the National Science Foundation, Grant No. CMMI-1536444. The Michigan Urological Surgery Improvement Collaborative is funded by the Blue Cross Blue Shield of Michigan. Author Contributions: Erkin Ötleş: Conceptualization, validation, draft writing, critical revisions. Brian Denton: Conceptualization, supervision, critical revisions. Bo Qu: Conceptualization, data analysis, critical revisions. Adharsh Murali: Data curation, data analysis, critical revisions. Selin Merdan: Conceptualization, methodology, supervision, draft writing. Gregory Auffenberg: Conceptualization, supervision, critical revisions. Spencer Hiller: Conceptualization, critical revisions. Brian R. Lane: Conceptualization, critical revisions. Arvin K. George: Conceptualization, supervision, critical revisions. Karandeep Singh: Conceptualization, data curation, data analysis, draft writing, critical revisions, methodology, project administration, supervision. Each MUSIC practice obtained an exemption or approval for collaborative participation from a local institutional review board. Editor's Note: This article is the third of 5 published in this issue for which category 1 CME credits can be earned. Instructions for obtaining credits are given with the questions on pages 481 and 482. © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsCited BySiemens D (2021) This Month in Adult UrologyJournal of Urology, VOL. 207, NO. 2, (259-260), Online publication date: 1-Feb-2022.Related articlesJournal of UrologyNov 17, 2021, 12:00:00 AMEditorial CommentJournal of UrologyNov 17, 2021, 12:00:00 AMEditorial Comment Volume 207Issue 2February 2022Page: 358-366Supplementary Materials Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.Keywordsclinical decision rulesprostatic neoplasmsprostatectomyAcknowledgmentsThe corresponding author thanks all the support staff from the Michigan Urological Surgery Improvement Collaborative. Since the initial submission of this manuscript, our beloved co-author Adharsh Murali unexpectedly passed away. We give our love to Adharsh's family and friends.MetricsAuthor Information Erkin Ötleş Department of Industrial & Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan Medical Scientist Training Program, University of Michigan Medical School, Ann Arbor, Michigan More articles by this author Brian T. Denton Department of Industrial & Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan More articles by this author Bo Qu Department of Industrial & Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan More articles by this author Adharsh Murali Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan Deceased. More articles by this author Selin Merdan Department of Industrial & Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan More articles by this author Gregory B. Auffenberg Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, Illinois More articles by this author Spencer C. Hiller Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan More articles by this author Brian R. Lane Division of Urology, Spectrum Health, Grand Rapids, Michigan More articles by this author Arvin K. George Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan More articles by this author Karandeep Singh Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan School of Information, University of Michigan, Ann Arbor, Michigan †Correspondence: Department of Learning Health Sciences, University of Michigan Medical School, 1161H NIB, 300 N. Ingalls St., Ann Arbor, Michigan 48109 telephone: 734-936-1649; FAX: 734-647-3914; E-mail Address: [email protected] More articles by this author Expand All Funding: EÖ was supported by NIH grant T32GM007863. BD and BQ were supported by the National Science Foundation, Grant No. CMMI-1536444. The Michigan Urological Surgery Improvement Collaborative is funded by the Blue Cross Blue Shield of Michigan. Author Contributions: Erkin Ötleş: Conceptualization, validation, draft writing, critical revisions. Brian Denton: Conceptualization, supervision, critical revisions. Bo Qu: Conceptualization, data analysis, critical revisions. Adharsh Murali: Data curation, data analysis, critical revisions. Selin Merdan: Conceptualization, methodology, supervision, draft writing. Gregory Auffenberg: Conceptualization, supervision, critical revisions. Spencer Hiller: Conceptualization, critical revisions. Brian R. Lane: Conceptualization, critical revisions. Arvin K. George: Conceptualization, supervision, critical revisions. Karandeep Singh: Conceptualization, data curation, data analysis, draft writing, critical revisions, methodology, project administration, supervision. Each MUSIC practice obtained an exemption or approval for collaborative participation from a local institutional review board. Editor's Note: This article is the third of 5 published in this issue for which category 1 CME credits can be earned. Instructions for obtaining credits are given with the questions on pages 481 and 482. Advertisement Loading ...