Does Specialty Bias Trump Evidence in the Management of High-risk Prostate Cancer?

Abstract

Objective: 

The objective was to query how specialty influences treatment recommendations for high-risk prostate cancer in 3 clinical settings: upfront management, postoperative management, and management of biochemical recurrences (BCRs) after radiotherapy (RT). We hypothesized that specialty bias would manifest in all settings, trumping available evidence.

Methods: 

A survey of practicing urologists and radiation oncologists was distributed through electronic mail. Questions pertained to upfront management, postoperative treatment, and local salvage for postradiation BCRs. The associations between 26 selected categorical responses and specialty were assessed using multivariate logistic regression. Training level/expertise, practice setting, percentage of consultation caseload consisting of prostate cancer, and nationality were set as effect modifiers.

Results: 

One thousand two hundred fifty-three physicians (846 radiation oncologists and 407 urologists) completed the survey. Radiation oncologists were more likely to recommend adjuvant RT and consider it to be underutilized, and more likely to recommend salvage RT at lower prostate-specific antigen thresholds (P<0.0001). Urologists were more likely to recommend salvage radical prostatectomy or cryoablation for local salvage after RT, whereas radiation oncologists were more likely to recommend RT-based modalities and more likely to report that local salvage was underutilized after RT (P<0.0001). Urologists were more likely to report that upfront radical prostatectomy was a better definitive treatment (P<0.0001), whereas radiation oncologists were more likely to report the opposite (P=0.005).

Conclusions: 

Specialty biases permeate recommendations for upfront management and management in the postoperative and post-RT BCR setting, irrespective of available evidence. These data reveal the critical need for multidisciplinary clinics and cross-specialty training as potential solutions for overcoming specialty bias.