Multimodality diagnosis of liver tumors: Feature analysis with CT, liver-specific and contrast-enhanced MR, and a computer model

Steven E. Seltzer, David J. Getty, Ronald M. Pickett, John A. Swets, Gregory Sica, Jeffrey Brown, Sanjay Saini, Robert F. Mattrey, Ben Harmon, Isaac R. Francis, Judith Chezmar, Mitchell O. Schnall, Evan S. Siegelman, Rocco Ballerini, Sandeep Bhat

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Rationale and Objectives. The purpose of this study was to measure and to clarify the diagnostic contributions of image based features in differentiating benign from malignant and hepatocyte-containing from non-hepatocyte-containing liver lesions. Materials and Methods. Six experienced abdominal radiologists each read images from 146 cases (including a contrast material-enhanced computed tomographic [CT] scan and contrast-enhanced and unenhanced magnetic resonance [MR] images) following a checklist-questionnaire requiring them to rate quantitatively each of as many as 131 image features and then reported on each of the two differentiations. The diagnostic value of each feature was assessed, and linear discriminant analysis was used to develop statistical prediction rules (SPRs) for merging feature data into computerized "second opinions." For the two differentiations, accuracy (area under the receiver operating characteristic curve [Az]) was then determined for the radiologists' readings by themselves and for each of three SPRs. Results. Thirty-seven candidate features had diagnostic value for each of the two differentiations (a slightly different feature set for each). Radiologists' performance at both differentiations was excellent (Az = 0.929 [benign vs malignant] and 0.926 [hepatocyte-containing vs non-hepatocyte-containing]). Performance of the SPR that operated on the features from all modalities together was better than that of radiologists (Az = 0.936 [benign vs malignant] and 0.951 [hepatocyte-containing vs non-hepatocyte-containing]), but this difference was of marginal statistical significance (P = .11). Contrast-enhanced MR imaging and contrast-enhanced CT each made significant adjunctive contributions to accuracy compared with unenhanced MR imaging alone. Conclusion. Many CT- and MR imaging-based features have diagnostic value in differentiating benign from malignant and hepatocyte-containing from non-hepatocyte-containing liver lesions. Radiologists could also benefit from the fully informed SPR's "second opinions."

Original languageEnglish (US)
Pages (from-to)256-269
Number of pages14
JournalAcademic radiology
Volume9
Issue number3
DOIs
StatePublished - Jan 1 2002

Keywords

  • Computer-aided diagnosis
  • Liver, tumors

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Multimodality diagnosis of liver tumors: Feature analysis with CT, liver-specific and contrast-enhanced MR, and a computer model'. Together they form a unique fingerprint.

  • Cite this

    Seltzer, S. E., Getty, D. J., Pickett, R. M., Swets, J. A., Sica, G., Brown, J., Saini, S., Mattrey, R. F., Harmon, B., Francis, I. R., Chezmar, J., Schnall, M. O., Siegelman, E. S., Ballerini, R., & Bhat, S. (2002). Multimodality diagnosis of liver tumors: Feature analysis with CT, liver-specific and contrast-enhanced MR, and a computer model. Academic radiology, 9(3), 256-269. https://doi.org/10.1016/S1076-6332(03)80368-9