Histogram analysis of apparent diffusion coefficient from whole-body diffusion-weighted MRI to predict early response to chemotherapy in patients with metastatic colorectal cancer: preliminary results

2018 ◽  
Vol 73 (9) ◽  
pp. 832.e9-832.e16 ◽  
Author(s):  
I. Lavdas ◽  
A.G. Rockall ◽  
E. Daulton ◽  
K. Kozlowski ◽  
L. Honeyfield ◽  
...  
Author(s):  
Nada Gamal El-Husseiny ◽  
Sayed Mohamed Mehana ◽  
Sherif Farouk El Zawawy

Abstract Background Colorectal cancer is considered one of the most common causes of cancer-related deaths worldwide. We aim to evaluate the efficacy of DWI-MRI in predicting response to chemotherapy in this cohort. The study included 30 lesions in 20 biopsy proven-colorectal cancer patients with hepatic metastasis larger than 1 cm. All patients underwent both triphasic CT with intravenous contrast, pre-chemotherapy MRI (axial T2 and DW sequences) which was repeated 21 days following chemotherapy. A follow-up CT was done 2 months later. The response of the lesions was evaluated using the RESCIST criteria. On MRI, the lesions corresponding to the ones chosen on CT were identified and the apparent diffusion coefficient (ADC) values of pre- and post-chemotherapy images were recorded and correlated with the CT results. Results In the study, 17 (56.7%) of the lesions showed response to chemotherapy while 13 (43.3%) were non-responding. There was no significant difference in pretreatment ADC values between responding and non-responding lesions (p = 0.14). The mean percentage increase in ADC values in responding lesions was 42% compared to 18% in non-responding lesions (p < 0.001). Lesions that showed less than 18% increase were all found to be non-responsive Conclusion DWI-MRI has an emerging role in early assessment of early treatment response that can be detected before morphological response for patients with hepatic metastasis from colorectal cancer. Based on our study, the use of 25 % as the cutoff point of percent difference in ADC for detection of non-responding lesions proved to be successful only 21 days after the 1st chemotherapy cycle.


2019 ◽  
Vol 18 ◽  
pp. 153303381984294 ◽  
Author(s):  
Genji Bai ◽  
Yating Wang ◽  
Yan Zhu ◽  
Lili Guo

Objective: To determine whether change in apparent diffusion coefficient value could predict early response to chemotherapy in breast cancer liver metastases. Materials and Methods: We retrospectively studied 42 patients (86 lesions) with breast cancer liver metastases who had undergone conventional magnetic resonance imaging and diffusion-weighted imaging (b = 0.700 s/mm2) before and after chemotherapy. Maximum diameter and mean apparent diffusion coefficient value (×10−3 mm2/s) of liver metastases from breast cancer were evaluated. The grouping reference was based on magnetic resonance imaging according to Response Evaluation Criteria in Solid Tumors (RECIST). Analysis of variance and receiver–operating characteristic analyses were performed. Results: Eighty-six metastases were classified as 40 responders and 46 nonresponders. A statistically significant correlation was found between prechemotherapy and postchemotherapy apparent diffusion coefficient values in responders, which were 0.9 ± 0.16 × 10−3 mm2/s, 1.05 ± 0.12 × 10−3 mm2/s, 1.26 ± 0.12 × 10−3 mm2/s, and 1.33 ± 0.87 × 10−3 mm2/s, respectively. No statistically significant difference was found between prechemotherapy and postchemotherapy apparent diffusion coefficient values in nonresponders. Differences were statistically significant between responders and nonresponders at prechemotherapy, 2 weeks after chemotherapy, and 4 weeks after chemotherapy ( P = 0.014, P = .001, and P = .000, respectively). Receiver operating characteristic curves showed that apparent diffusion coefficient values could predict treatment response early at 2 weeks after chemotherapy with 64.5% sensitivity and 91.8% specificity. Conclusion: The change in apparent diffusion coefficient value may be a sensitive indicator to predict early response to chemotherapy in breast cancer liver metastases.


2017 ◽  
Vol 28 (4) ◽  
pp. 1687-1691 ◽  
Author(s):  
Jessica M. Winfield ◽  
Gabriele Poillucci ◽  
Matthew D. Blackledge ◽  
David J. Collins ◽  
Vallari Shah ◽  
...  

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