Indeterminate fine-needle aspiration of the breast : Image analysis-assisted diagnosis

• Published in: Cancer 1997-04, Vol.81 (2), p.129-135
• Main Authors: TEAGUE, M. W, WOLBERG, W. H, STREET, W. N, MANGASARIAN, O. L, LAMBREMONT, S, PAGE, D. L
• Format: Article
• Language: English
• Subjects: Adult; Aged; Biological and medical sciences; Biopsy, Needle; Breast Neoplasms - pathology; Breast Neoplasms - ultrastructure; Female; Genital system. Mammary gland; Gynecology. Andrology. Obstetrics; Humans; Image Cytometry; Image Processing, Computer-Assisted; Investigative techniques, diagnostic techniques (general aspects); Mammary gland diseases; Medical sciences; Middle Aged; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Predictive Value of Tests; Tumors
• ISSN: 0008-543X; 1097-0142
• DOI: 10.1002/(SICI)1097-0142(19970425)81:2<129::AID-CNCR7>3.0.CO;2-N

Fine-needle aspiration (FNA) of the breast, although effective for the diagnosis of breast carcinoma, has a significant drawback. A minority of cases cannot be classified as benign or malignant. These FNAs are assigned an inconclusive diagnosis, often prompting surgical biopsy. Surgery is justified in some of these cases, but many of these lesions are benign. If these inconclusive FNAs could be accurately diagnosed as benign or malignant, many of these patients might avoid having to undergo surgical biopsy. An image analysis and an automated learning system that was developed at the University of Wisconsin (Xcyt) was used to categorize 56 (37 benign and 19 malignant) breast FNAs diagnosed as "indeterminate" and the computer diagnosis compared with the surgical biopsy. For each case, an operator chose a group of cells within a single field on the FNA slide and digitized this image using a video camera. The outline of each nucleus was manually outlined, and the exact border was delineated by the computer. Based on the analysis of three nuclear features (area, texture, and smoothness), the Xcyt system computed a benign or malignant diagnosis and a corresponding probability of malignancy for each case. Probabilities of malignancy for the respective cases ranged from 0.0-1.0. Benign cases were defined as those having probabilities of malignancy < 0.3; those with probabilities above this limit were considered malignant. Using these criteria, the computer identified 33 cases as benign and 23 cases as malignant. When compared with the surgical biopsy, 42 of the cases (75%) were correctly classified with a sensitivity and specificity of 73.7% and 75.7%, respectively. There were only 5 false-negative cases with a false-negative rate of 13.5% and a predictive value of a negative test of 84.8%. When faced with inconclusive diagnoses of FNAs of breast masses, the authors believe that image analysis may be used as an aid in the further classification of such lesions, thereby providing a more appropriate triage for surgical biopsy.