Improved detection of metastases on magnetic resonance images by digital tissue recognition: Validation using VX-2 tumor in the rabbit

Bradley T. Wyman, Chris L. Stork, Justin P. Smith, Roger E. Price, Patrick R. Gavin, Russell L. Tucker, Erik R Wisner, John S. Mattoon, John D. Hazle

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Purpose: To evaluate the ability of a prototype digital tissue recognition (DTR) system to improve the accuracy of detection of metastases on magnetic resonance (MR) images in the rabbit VX-2 tumor model. Materials and Methods: Multiple MR imaging (MRI) sequences, including pre-contrast and post-contrast enhanced T1-weighted, T2-weighted, proton-density, and fast short inversion time inversion recovery were acquired for six rabbits implanted with VX-2 adenocarcinoma. For each rabbit, DTR used the MR intensity characteristics of a known tumor site to highlight other areas suspicious for tumor. Three independent veterinary radiologists with extensive experience in animal MRI interpreted the images for tumor both without and with the results of DTR. The conventional and DTR-assisted interpretations were compared to pathology. Results: Using DTR, the radiologists found an average of 13.2% more true positive sites with a 10.3% reduction in false positives compared to unassisted interpretation. The improvement for the radiologists was statistically significant (McNemar's test, P = 0.0004). The agreement between radiologists using DTR was consistently higher than for their conventional interpretations (kappa statistic). Conclusion: Compared with conventional interpretation of MR images, the use of DTR provided a statistically significant improvement in the accuracy of locating more and smaller sites of tumor. This improvement was achieved without the benefit of post-contrast images.

Original languageEnglish (US)
Pages (from-to)232-241
Number of pages10
JournalJournal of Magnetic Resonance Imaging
Volume18
Issue number2
DOIs
StatePublished - Aug 1 2003
Externally publishedYes

Keywords

  • Animal model
  • Histopathology
  • Magnetic resonance imaging
  • Metastases
  • Tissue segmentation
  • Validation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

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