[Water mobility of diffusion MRI in prediction of response to chemoembolization in liver cancer]
Zheng Yuan1, Xiao-dan Ye, Sheng Dong
1Department of Diagnostic Radiology, Changzheng Hospital, Second Military Medical University, Shanghai 200003, China.
Objective:
To investigate the value of pretreatment and posttreatment changes of apparent diffusion coefficients (ADCs) in predicting response to chemoembolization in liver cancer.
Methods:
Patients with liver cancer were examined with diffusion-weighted MRI at two b values (0 and 500 s/mm(2)) before and after chemoemblization. Quantitative ADC maps were calculated using images under b values of 0 and 500 s/mm(2). The mean ADC values of lesions before and after chemoemblization were compared. The correlation of response to chemoembolization with ADC value was analyzed.
Results:
The mean value of pretreatment ADC in non-responding lesions were significantly higher than that in the responding lesions (1.687 x 10(-3) mm(2)/s vs. 1.278 x 10(-3) mm(2)/s, P < 0.05). The results of receiver operator characteristic (ROC) analysis showed that when a threshold ADC value was set on 1.618 x 10(-3) mm(2)/s, the sensitivity and specificity for identification of non-responding lesions were 96.0% and 77.8%, respectively. After transarterial chemoembolization, the responding lesions had a significant increase in ADC values than non-responding lesions (32.6% vs. 5.2%, P = 0.025). The results of ROC analysis indicated that when the changes of ADC value for identification of responding lesions before and after transarterial chemoembolization was > or = 16.2%, the sensitivity and specificity were 72% and 100%, respectively. However, no significant change was observed in normal liver parenchyma and spleen (P > 0.05).
Conclusion:
Pretreatment mean ADC value can be used to predict the response to chemoembolization, and for selection of therapy in liver cancer. A significant increase in mean ADC can be observed if the lesions responds to chemoembolization.
More Related Videos
15:48Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
10:25A Dorsal Skinfold Window Chamber Tumor Mouse Model for Combined Intravital Microscopy and Magnetic Resonance Imaging in Translational Cancer Research
Published on: April 12, 2024
