Background: The purpose of this study was to investigate the diagnostic value of 3.0-T 1H magnetic resonance spectroscopy (1H MRS) in primary malignant hepatic tumors and to compare the effects of 1H MRS on the diagnostic accuracy of liver-occupying lesions between junior and experienced radiologists. Methods: This study included 50 healthy volunteers and 40 consecutive patients (50 lesions). Informed consent was obtained from each subject. Images were obtained on clinical whole-body 3.0-T MR system. Point -Resolved Spectroscopy was used to obtain the spectroscopy image. All conventional images were reviewed blindly by junior radiologist and experienced radiologist, respectively. The choline-containing compounds peak area (CCC-A) was measured with SAGE software, and the choline-containing compound ratio (ΔCCC) was calculated. The efficacy of CCC-A and ΔCCC in the diagnosis of primary malignant hepatic tumors was determined by plotting receiver operating characteristic (ROC) curves. We also compared the effects of MRS on the diagnostic accuracy of liver-occupying lesions with junior and experienced radiologist. Results: A significant increase in mean CCC-A was observed in malignant tumors compared with benign tumors. The ROC curve showed ΔCCC had a high discriminatory ability in diagnosing primary malignant hepatic tumors with a sensitivity and specificity of 94.3 and 93.3 %, respectively. The ΔCCC area under the curve (AUC) was 0.97 that was larger than that of both junior and experienced radiologist, while the significantly statistical difference was only obtained between ΔCCC and junior radiologist (P = 0.01). Conclusion: 1H MRS with ΔCCC demonstrates good efficacy in diagnosing primary malignant hepatic tumors. The technique improves the accuracy of diagnosing liver-occupying lesions, particularly for junior radiologists.
CITATION STYLE
Zhang, L., Zhao, X., Ouyang, H., Wang, S., & Zhou, C. (2016). Diagnostic value of 3.0T 1H MRS with choline-containing compounds ratio (ΔCCC) in primary malignant hepatic tumors. Cancer Imaging, 16(1). https://doi.org/10.1186/s40644-016-0082-4
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