Computer‐derived nuclear features compared with axillary lymph node status for breast carcinoma prognosis

• Published in: Cancer 1997-06, Vol.81 (3), p.172-179
• Main Authors: Wolberg, William H., Street, W. Nick, Mangasarian, Olvi L.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Biological and medical sciences; Biopsy, Needle; breast carcinoma; Breast Neoplasms - pathology; computer analysis; Female; Gynecology. Andrology. Obstetrics; Humans; Image Interpretation, Computer-Assisted; lymph nodes; Lymph Nodes - pathology; machine learning; Mammary gland diseases; Medical sciences; Neoplasm Invasiveness; Neoplasm Recurrence, Local; nuclei; Predictive Value of Tests; Prognosis; SEER Program; Survival Analysis; Tumors
• ISSN: 0008-543X; 1097-0142
• DOI: 10.1002/(SICI)1097-0142(19970625)81:3<172::AID-CNCR7>3.0.CO;2-T

BACKGROUND Both axillary lymph node involvement and tumor anaplasia, as expressed by visually assessed grade, have been shown to be prognostically important in breast carcinoma outcome. In this study, axillary lymph node involvement was used as the standard against which prognostic estimations based on computer‐derived nuclear features were gauged. METHODS The prognostic significance of nuclear morphometric features determined by computer‐based image analysis were analyzed in 198 consecutive preoperative samples obtained by fine‐needle aspiration (FNA) from patients with invasive breast carcinoma. A novel multivariate prediction method was used to model the time of distant recurrence as a function of the nuclear features. Prognostic predictions based on the nuclear feature data were cross‐validated to avoid overly optimistic conclusions. The estimated accuracy of these prognostic determinations was compared with determinations based on the extent of axillary lymph node involvement. RESULTS The predicted outcomes based on nuclear features were divided into three groups representing best, intermediate, and worst prognosis, and compared with the traditional TNM lymph node stratification. Nuclear feature stratification better separated the prognostically best from the intermediate group whereas lymph node stratification better separated the prognostically intermediate from the worst group. Prognostic accuracy was not increased by adding lymph node status or tumor size to the nuclear features. CONCLUSIONS Computer analysis of a preoperative FNA more accurately identified prognostically favorable patients than did pathologic examination of axillary lymph nodes and may obviate the need for routine axillary lymph node dissection. Cancer (Cancer Cytopathol) 1997; 81:172‐9. © 1997 American Cancer Society. Computer analysis of 198 preoperative fine‐needle aspirates obtained from patients with invasive breast carcinoma showed that nuclear features separated the prognostically best from the prognostically intermediate patients better than lymph node status. Prognostic accuracy was not increased by adding lymph node status or tumor size to the nuclear features.