Calibrated Regression Models Based on the Risk of Clinical Nodal Metastasis Should be Used as Decision Aids for Prostate Cancer Staging to Reduce Unnecessary Imaging

Mitchell Hayes, Yun Yu, Solange Bassale, Nicholas Chakiryan, Yiyi Chen, Shangyuan Ye, Mark Garzotto, Ryan Kopp

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Radionuclide imaging will change the role of computed tomography and magnetic resonance imaging (CT/MRI) for prostate cancer (CaP) staging. Current guidelines recommend abdominopelvic imaging for new cases of CaP categorized as unfavorable intermediate risk (UIR) or higher. We assessed the performance characteristics of CT/MRI based on the National Comprehensive Cancer Network (NCCN) guidelines and developed a model that predicts cN1 disease using conventional imaging. Patients and Methods: We selected patients in the National Cancer Database diagnosed with CaP from 2010 to 2016 with available age, prostate specific antigen, clinical locoregional staging, biopsy Gleason grading, and core information. Multivariate logistic regression (MLR) was used on a undersampled training dataset using cN1 as the outcome. Performance characteristics were compared to those of the three most recent versions of the NCCN guidelines. Results: A total of 443,640 men were included, and 2.5% had cN1 disease. Using CT/MRI only, the current NCCN guidelines have a sensitivity of 99%, and the number needed to image (NNI) is 24. At the same sensitivity, the cN1 risk was 1.6% using the MLR. The NNI for UIR alone is 341. Using the MLR model and a threshold of 10%, the PPV is 10.3% and 64% of CTs/MRIs could be saved at a cost of missing 6% of cN1 patients (or 0.15% of all patients). Conclusion: The NCCN guidelines are sensitive for detecting cN1 with CT/MRI, however, the number needed to image is 24. Obtaining CT/MRI for nodal staging when patients have a cN1 risk of 10% would reduce total imaging while still remaining sensitive. As novel PET tracers becomes increasingly used for initial CaP staging, well calibrated prediction models trained on the outcome of interest should be developed as decision aids for obtaining imaging.

Original languageEnglish (US)
JournalClinical Genitourinary Cancer
DOIs
StateAccepted/In press - 2022

Keywords

  • Cancer guidelines
  • Computed tomography
  • MRI
  • Oligometastatic
  • Predictive modeling

ASJC Scopus subject areas

  • Oncology
  • Urology

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