Prediction of the response of ocular adnexal lymphoma to chemotherapy using combined pretreatment dynamic contrast-enhanced and diffusion-weighted MRI

Abstract

Background

There are no established biomarkers predictive of the efficacy of treatment for ocular adnexal lymphoma (OAL).

Purpose

To evaluate the effectiveness of pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in predicting the response of OAL to chemotherapy.

Material and Methods

Twenty-one patients, who were pathologically diagnosed with OAL, were retrospectively analyzed. According to the National Comprehensive Cancer Network (NCCN) response evaluation criteria for non-Hodgkin’s lymphoma, patients were divided into responders (n = 14) and non-responders (n = 7). The volume transfer constant (K^trans), rate constant (K_ep), extracellular extravascular volume fraction (Ve), and apparent diffusion coefficient (ADC) were computed. Two independent-sample tests were applied for statistical analysis. For significantly different parameters, receiver-operator characteristics curve analysis was performed.

Results

The K^trans value (min⁻¹), K_ep value (min⁻¹), and ADC value (10⁻³mm²/s) were 0.76 ± 0.36 vs. 0.47 ± 0.18 (mean ± SD), 4.43 ± 1.29 vs. 3.14 ± 1.37, and 0.51 ± 0.12 vs. 0.66 ± 0.15, respectively, in the responders and non-responders groups. Significant differences were found between the two groups regarding these parameters (P < 0.05). However, no significant difference was observed in Ve (min⁻¹) between the groups (P > 0.05). K^trans, K_ep, and ADC had a moderately predictive sensitivity or specificity. When K^trans and ADC or the three parameters were combined, a considerably higher sensitivity (85.7%) and specificity (85.7%) with a significant discriminative accuracy (area under the curve = 0.929; P = 0.002) was found.

Conclusion

K^trans, K_ep, and ADC could potentially predict OAL response to chemotherapy. A combination of these DWI and DCE-MRI quantitative parameters might increase sensitivity and specificity.

Keywords

Diffusion-weighted imaging (DWI)dynamic contrast-enhanced imaging (DCE-MRI)lymphoma magnetic resonance imaging (MRI)

Get full access to this article

View all access options for this article.

References

Shields

Scartozzi

. Survey of 1264 patients with orbital tumors and simulating lesions: The 2002 Montgomery Lecture, part 1. Ophthalmology 2004; 111: 997–1008.

Azim

Santoro

Bociek

. High dose intensity doxorubicin in aggressive non-Hodgkin’s lymphoma: a literature-based meta-analysis. Ann Oncol 2010; 21: 1064–1071.

Holzapfel

Duetsch

Fauser

. Value of diffusion-weighted MR imaging in the differentiation between benign and malignant cervical lymph nodes. Eur J Radiol 2009; 72: 381–387.

Kim

Loevner

Quon

. Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck. Clin Cancer Res 2009; 15: 986–994.

Martincich

Deantoni

Bertotto

. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol 2012; 22: 1519–1528.

Kim

Loevner

Quon

. Prediction of response to chemoradiation therapy in squamous cell carcinomas of the head and neck using dynamic contrast-enhanced MR imaging. Am J Neuroradiol 2010; 31: 262–268.

Zhou

. Quantitative magnetic resonance and optical imaging biomarkers of melanoma metastatic potential. Proc Natl Acad Sci U S A 2009; 106: 6608–6613.

Jung

Heo

Kim

. Predicting response to neoadjuvant chemoradiation therapy in locally advanced rectal cancer: diffusion-weighted 3 Tesla MR imaging. J Magn Reson Imaging 2012; 35: 110–116.

Shukla-Dave

Lee

Jansen

. Dynamic contrast-enhanced magnetic resonance imaging as a predictor of outcome in head-and-neck squamous cell carcinoma patients with nodal metastases. Int J Radiat Oncol Biol Phys 2012; 82: 1837–1844.

10.

Zelenetz

Wierda

Abramson

. Non-Hodgkin’s lymphomas, version 1.2013. J Natl Compr Canc Netw 2013; 11: 257–272. quiz 273.

11.

Yankeelov

Luci

Lepage

. Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model. Magn Reson Imaging 2005; 23: 519–529.

12.

Dzik-Jurasz

Domenig

George

. Diffusion MRI for prediction of response of rectal cancer to chemoradiation. Lancet 2002; 360: 307–308.

13.

Ingrisch

Sourbron

Morhard

. Quantification of perfusion and permeability in multiple sclerosis: dynamic contrast-enhanced MRI in 3D at 3T. Invest Radiol 2012; 47: 252–258.

14.

Chawla

Kim

Dougherty

. Pretreatment diffusion-weighted and dynamic contrast-enhanced MRI for prediction of local treatment response in squamous cell carcinomas of the head and neck. Am J Roentgenol 2013; 200: 35–43.

15.

Yeo

Jung

. Correlation of dynamic contrast-enhanced MRI perfusion parameters with angiogenesis and biologic aggressiveness of rectal cancer: Preliminary results. J Magn Reson Imaging 2015; 41: 474–480.

16.

Guo

Reddick

Glass

. Dynamic contrast-enhanced magnetic resonance imaging as a prognostic factor in predicting event-free and overall survival in pediatric patients with osteosarcoma. Cancer 2012; 118: 3776–3785.

17.

Gollub

Gultekin

Akin

. Dynamic contrast enhanced-MRI for the detection of pathological complete response to neoadjuvant chemotherapy for locally advanced rectal cancer. Eur Radiol 2012; 22: 821–831.

18.

Nerisho

Dastidar

. Comparison of different MRI sequences in lesion detection and early response evaluation of diffuse large B-cell lymphoma–a whole-body MRI and diffusion-weighted imaging study. NMR Biomed 2013; 26: 1186–1194.

19.

Liu

Diwanji

Zhang

. SU-F-303-05: DCE-MRI Before and during treatment for prediction of concurrent chemotherapy and radiation therapy response in head and neck cancer. Med Phys 2015; 42: 3538–3538.