Optimizing Staging Imaging in Early-Stage Breast Cancer: Predictive Factors and Guideline Alignment in Oman.

OBJECTIVES

To evaluate the diagnostic yield of staging imaging in detecting distant metastases among women with early-stage breast cancer in Oman, assess alignment with international guidelines, and identify key patient factors that could guide selective imaging use.

METHODS

A retrospective cohort study was conducted at Sultan Qaboos University Hospital, Muscat, Oman, including women diagnosed with stage 0-II breast cancer between January 2014 and December 2019. Patient demographics, tumor characteristics, imaging modalities, and outcomes were reviewed. Staging imaging included computed tomography (CT) with bone scintigraphy and/or positron emission tomography (PET-CT). The primary outcome was the prevalence of confirmed metastatic disease (M1). Fisher's exact test was used to assess associations between clinicopathological factors and metastatic yield. The Number Needed to Image (NNI) was calculated to estimate the efficiency of imaging.

RESULTS

Among 207 patients, 187 (90.3%) underwent staging imaging. Suspicious findings were detected in 10 patients (5.3% of those imaged), but only six cases (3.2% of those imaged; 2.9% of the total cohort) were confirmed as true metastases. All confirmed metastases were identified using CT with bone scans, while PET-CT did not detect any additional cases. Lymph node status was the strongest predictor of metastases (p = 0.011). Node-positive patients had a 19.0% metastasis rate compared with 1.1% among node-negative patients. The NNI was 5 for node-positive versus 93 for node- negative patients, demonstrating the limited value of routine imaging in low-risk groups.

CONCLUSION

The overall yield of routine staging imaging in early-stage breast cancer is low, with the greatest benefit observed in node-positive patients. Adopting risk-based, guideline- aligned imaging strategies could reduce unnecessary investigations, patient anxiety, and healthcare costs while ensuring optimal use of resources.