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.
Keywords
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Article info
Publication history
Published online: May 07, 2022
Accepted:
May 5,
2022
Received in revised form:
May 4,
2022
Received:
March 28,
2022
Identification
Copyright
© 2022 Elsevier Inc. All rights reserved.
