Axillary fine needle aspiration cytology for pre-operative staging of patients with screen-detected invasive breast carcinoma.

INTRODUCTION: Fine needle aspiration cytology (FNAC) of radiologically abnormal axillary lymph nodes in patients with breast cancer can identify patients suitable for primary axillary clearance (AC) rather than sentinel node biopsy, enabling surgical axillary staging by a single operation. This study assessed the accuracy of FNAC in predicting positive axillary lymph nodes. METHODS: 161 patients with screen-detected invasive carcinoma and who had pre-operative FNAC of a radiologically abnormal axillary lymph node were identified from two screening units, The axillary FNAC reports were correlated with sentinel node biopsy and AC reports, and sensitivity, specificity, positive (PPV) and negative (NPV) predictive values were calculated. RESULTS: FNAC had a moderate sensitivity (66.3%) and NPV (71.8%), and a high specificity (98.7%) and PPV (98.3%). Most patients (86%) had a single axillary operation. The sensitivity was highest in grade 3 (81.8%) and ductal type (77.8%) tumours. The sensitivity was lower in tumours of special type (34.8%), grade 1 tumours (50%) and those without lymphovascular invasion (LVI) (55.9%). The NPV was highest in pT1 (86.7%) and in grade 1 (84.5%) tumours, and lowest (44%) in tumours with LVI. The PPV was 100% in grade 1 and 3 tumours, stage pT2 and pT3 tumours and those without LVI, and was high (>96%) in all other groups. In lymph-node-positive patients, the mean number of lymph nodes involved was higher in the case of a positive (6.4) than negative FNAC (4.4). CONCLUSIONS: FNAC of ultrasonically abnormal axillary lymph nodes achieved surgical staging by a single operation in most patients with screen-detected invasive breast carcinoma, with moderate sensitivity and high specificity.

Quality initiatives: anatomy and pathophysiology of errors occurring in clinical radiology practice.

The Joint Commission requires development of comprehensive error detection systems that incorporate root cause analyses for all sentinel events. To prevent medical errors from occurring, there is a need for a readily available and easy-to-implement system for detecting, classifying, and managing mistakes. The wide spectrum of interrelated contributing factors makes the classification of errors difficult. Contributors to and causes of radiologic errors can be classified under latent and active failures. Latent failures include technical and system-related failures, with a radiology-specific subgroup of communication failures that includes documentation, inaccurate or incomplete information, and communication loop failures. Active failures may be ascribed to human failures (more specifically failure of execution of a task, inadequate planning, or behavior-related failures), patient-based failures, and external failures. Classification of an error should also include the impact of the error on the patient, staff, other customers, and radiology practice. Further considerations should include nonmedical impact of the error, including legal, social, and economic effects on both the patient and the system. Rather than focusing the investigation on blaming individuals for active failures, the primary effort should be to discover latent system failures that can be remedied at a departmental level. Such an error classification system will decrease the likelihood of future errors and diminish their adverse impact.