Improved detection of metastases on magnetic resonance images by digital tissue recognition: validation using VX-2
Bradley T Wyman1, Chris L Stork, Justin P Smith
1Confirma, Inc., Kirkland, Washington 98033, USA. info@confirma.com
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 (FSTIR), 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.
