Diagnostic Accuracy of PET Imaging for Lymph Node Detection in Breast cancer patients undergoing Upfront Surgery: A Single-Institution Analysis of Breast Cancer Patients
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background The role of PET imaging in lymph node staging for breast cancer patients proceeding to upfront surgery remains incompletely characterized. We evaluated the diagnostic accuracy of PET imaging in immediate surgical patients. Methods A retrospective cohort of 70 breast cancer patients undergoing upfront surgery was analyzed. PET imaging findings were compared with histopathological examination (HPE) results. Diagnostic accuracy metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratios were calculated. Results Among 70 surgical patients (mean age 53.9 ± 11.5 years), 26 (37.14%) had pathologically confirmed lymph node involvement. PET imaging demonstrated sensitivity of 69.23% (95% CI: 48.27-85.67), specificity of 81.82% (95% CI: 67.27-91.60), PPV of 69.23%, and NPV of 81.82%. The test correctly identified 18 of 26 patients with nodal disease while avoiding false positives in 36 of 44 node-negative patients. Positive likelihood ratio was 3.81, and negative likelihood ratio was 0.38. The F1-score was 0.6923, indicating good balance between precision and sensitivity for surgical planning. Conclusions PET imaging demonstrates clinically meaningful sensitivity (69.23%) and high negative predictive value (81.82%) in upfront surgical patients, supporting its utility for identifying patients with nodal involvement and confidently excluding nodal disease when negative. These performance characteristics suggest that PET can inform surgical extent and staging accuracy in immediate surgical patients, though positive findings warrant confirmatory assessment when staging determines operative planning.
Source: F1000Research
Institution: Yenepoya University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,350
OpenAlex ID: W7130367650