Skip to main content

Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

Publication ,  Journal Article
Jesneck, JL; Nolte, LW; Baker, JA; Floyd, CE; Lo, JY
Published in: Med Phys
August 2006

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.

Duke Scholars

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

August 2006

Volume

33

Issue

8

Start / End Page

2945 / 2954

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Reproducibility of Results
  • Quality Control
  • Nuclear Medicine & Medical Imaging
  • Information Storage and Retrieval
  • Humans
  • Diagnosis, Computer-Assisted
  • Decision Support Systems, Clinical
  • Databases, Factual
  • Database Management Systems
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Jesneck, J. L., Nolte, L. W., Baker, J. A., Floyd, C. E., & Lo, J. Y. (2006). Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis. Med Phys, 33(8), 2945–2954. https://doi.org/10.1118/1.2208934
Jesneck, Jonathan L., Loren W. Nolte, Jay A. Baker, Carey E. Floyd, and Joseph Y. Lo. “Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.Med Phys 33, no. 8 (August 2006): 2945–54. https://doi.org/10.1118/1.2208934.
Jesneck JL, Nolte LW, Baker JA, Floyd CE, Lo JY. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis. Med Phys. 2006 Aug;33(8):2945–54.
Jesneck, Jonathan L., et al. “Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.Med Phys, vol. 33, no. 8, Aug. 2006, pp. 2945–54. Pubmed, doi:10.1118/1.2208934.
Jesneck JL, Nolte LW, Baker JA, Floyd CE, Lo JY. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis. Med Phys. 2006 Aug;33(8):2945–2954.

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

August 2006

Volume

33

Issue

8

Start / End Page

2945 / 2954

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Reproducibility of Results
  • Quality Control
  • Nuclear Medicine & Medical Imaging
  • Information Storage and Retrieval
  • Humans
  • Diagnosis, Computer-Assisted
  • Decision Support Systems, Clinical
  • Databases, Factual
  • Database Management Systems