BREASTAID: Clinical results from early development of a clinical decision rule for palpable solid breast masses.
OBJECTIVE
To develop a clinical decision rule (entitled BREASTAID) that will predict the probability of malignancy in women with palpable solid breast masses.


BACKGROUND
Currently, 80% of open breast biopsies are benign, resulting in excessive economic, psychologic, and physical morbidity.


METHODS
A total of 452 solid breast masses were evaluated in a surgical breast clinic between November 1994 and February 1998. Breast cancer status was defined histologically as ductal carcinoma in situ or invasive cancer. Noncancer status included benign histology, mass resolution, or stability at 12-month follow-up. Data were collected on risk factors, clinical breast examination, mammography, and cytology results. Three multiple logistic regression models were used to generate the probability of cancer at 3 logical steps in the workup; Bayes' theorem was applied in a stepwise fashion to generate a final probability of cancer.


RESULTS
A model incorporating only clinical breast examination and mammography resulted in an excessive number of either missed cases or biopsies compared with one that included cytology. Using a cut-point of 4%, this latter BREASTAID model had 97.6% sensitivity and 85.1% specificity. Compared with triple diagnosis, BREASTAID would have reduced the open biopsy rate from 39.8% (180 of 452) to 22.3% (101 of 452), improving the diagnostic yield from 22.7% to 40.6%.


CONCLUSIONS
This study convincingly demonstrates that at minimum, clinical, radiologic, and cytologic evaluations are required to accurately evaluate a solid breast mass. BREASTAID has the potential to minimize the number of open biopsies performed while allowing safe triage to follow-up. Before widespread application, further validation studies are required.
Osuch
Janet Rose
JR
Department of Epidemiology, Michigan State University, 4660 South Hagadorn, Suite 600, East Lansing, MI 48823, USA. janet.osuch@ht.msu.edu
Reeves
Mathew J
MJ
Pathak
Dorothy R
DR
Kinchelow
Tosca
T
D001499:Bayes Theorem; D044963:Biopsy, Fine-Needle; D001943:Breast Neoplasms / Q000473:pathology*; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D016015:Logistic Models; D012372:ROC Curve; D018570:Risk Assessment; D012680:Sensitivity and Specificity