Dynamic Functional Mammoscopy: A Non-Ionizing Imaging Technique Enhancing Early Detection of Breast Cancer

Abstract

During the third and final year of this study, we focused on improving the imaging technology of Dynamic Functional Optical Mammoscopy (DFOM) and focused on technique on refining existing weaknesses in specificy identified during the second year of study. In addition, we summarize the case studies and analysis of data obtained during the first two years. This data included optical imaging of patients scheduled for biopsy of breast lesions. These patients were recommended for core or excisional breast biopsy on the basis of equivocal mammographic and ancillary clinical findings within ACR BI-RADS(TM) categories 3 or 4. Analysis of test results of 117 patients showed that DFOM modality detected cancer in 13 of the 15 patients in whom biopsies confirmed malignant lesions, giving a sensitivity of 87%. DFOM also correctly identified 79 of 102 benign lesions giving a specificity of 77%. In clinical practice, the adjunctive use of DFOM would have decreased the percentage of biopsies that turn out to be benign from 102/117 (87%) to 23/117 (20%). The negative predictive value, the chance that a negative DFOM result truly indicates a benign lesion, was 79/81(98%) for the cases included thus far. While encouraging, these results suggest the need for further patient studies on_specificity.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2003
Accession Number
ADA421983

Entities

People

  • Suzanne J. Smith

Organizations

  • Columbia University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Absorption Coefficients
  • Abstracts
  • Algorithms
  • Breast Cancer
  • Case Studies
  • Coefficients
  • Data Analysis
  • Detection
  • Imaging Techniques
  • Intensity
  • Light Sources
  • Mathematical Models
  • New York
  • Optical Properties
  • Scattering
  • Sensitivity
  • Standards

Fields of Study

  • Medicine

Readers

  • Medical Imaging.
  • Oncology and Biomarker-Based Cancer Detection.