Improving breast cancer diagnosis with computer-aided diagnosis

Acad Radiol. 1999 Jan;6(1):22-33. doi: 10.1016/s1076-6332(99)80058-0.

Abstract

Rationale and objectives: The purpose of this study was to test whether computer-aided diagnosis (CAD) can improve radiologists' performance in breast cancer diagnosis.

Materials and methods: The computer classification scheme used in this study estimates the likelihood of malignancy for clustered microcalcifications based on eight computer-extracted features obtained from standard-view mammograms. One hundred four histologically verified cases of microcalcifications (46 malignant, 58 benign) in a near-consecutive biopsy series were used in this study. Observer performance was measured on 10 radiologists who read the original standard- and magnification-view mammograms. The computer aid provided a percentage estimate of the likelihood of malignancy. Comparison was made between computer-aided performance and unaided (routine clinical) performance by using receiver operating characteristic (ROC) analysis and by comparing biopsy recommendations.

Results: The average ROC curve area (Az) increased from 0.61 without aid to 0.75 with the computer aid (P < .0001). On average, with the computer aid, each observer recommended 6.4 additional biopsies for cases with malignant lesions (P = .0006) and 6.0 fewer biopsies for cases with benign lesions (P = .003). This improvement corresponded to increases in sensitivity (from 73.5% to 87.4%), specificity (from 31.6% to 41.9%), and hypothetical positive biopsy yield (from 46% to 55%).

Conclusion: CAD can be used to improve radiologists' performance in breast cancer diagnosis.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Area Under Curve
  • Biopsy
  • Breast Neoplasms / classification
  • Breast Neoplasms / diagnostic imaging*
  • Breast Neoplasms / pathology
  • Calcinosis / diagnostic imaging
  • Calcinosis / pathology
  • Diagnosis, Computer-Assisted*
  • Female
  • Fibroadenoma / diagnostic imaging
  • Fibroadenoma / pathology
  • Fibrocystic Breast Disease / diagnostic imaging
  • Fibrocystic Breast Disease / pathology
  • Follow-Up Studies
  • Humans
  • Likelihood Functions
  • Mammography* / classification
  • Neural Networks, Computer
  • Observer Variation
  • ROC Curve
  • Radiology
  • Sensitivity and Specificity
  • Time Factors