Computerized breast cancer diagnosis and prognosis from fine-needle aspirates
W. H. Wolberg, W. N. Street, D. M. Heisey and O. L. Mangasarian
Department of Surgery, University of Wisconsin, Madison, USA.
OBJECTIVE: To use digital image analysis and machine learning to (1)
improve breast mass diagnosis based on fine-needle aspirates and (2)
improve breast cancer prognostic estimations. DESIGN: An interactive
computer system evaluates, diagnoses, and determines prognosis based on
cytologic features derived from a digital scan of fine-needle aspirate
slides. SETTING: The University of Wisconsin (Madison) Departments of
Computer Science and Surgery and the University of Wisconsin Hospital and
Clinics. PATIENTS: Five hundred sixty-nine consecutive patients (212 with
cancer and 357 with benign masses) provided the data for the diagnostic
algorithm, and an additional 118 (31 with malignant masses and 87 with
benign masses) consecutive, new patients tested the algorithm. One hundred
ninety of these patients with invasive cancer and without distant
metastases were used for prognosis. INTERVENTIONS: Surgical biopsy
specimens were taken from all cancers and some benign masses. The remaining
cytologically benign masses were followed up for a year and surgical biopsy
specimens were taken if they changed in size or character. Patients with
cancer received standard treatment. OUTCOME MEASURES: Cross validation was
used to project the accuracy of the diagnostic algorithm and to determine
the importance of prognostic features. In addition, the mean errors were
calculated between the actual times of distant disease occurrence and the
times predicted using various prognostic features. Statistical analyses
were also done. RESULTS: The predicted diagnostic accuracy was 97% and the
actual diagnostic accuracy on 118 new samples was 100%. Tumor size and
lymph node status were weak prognosticators compared with nuclear features,
in particular those measuring nuclear size. Compared with the actual time
for recurrence, the mean error of predicted times for recurrence with the
nuclear features was 17.9 months and was 20.1 months with tumor size and
lymph node status (P = .11). CONCLUSION: Computer technology will improve
breast fine-needle aspirate accuracy and prognostic estimations.
