Open AccessA Potential Biomarker from Diffusion Weighted Imaging and Parametric Response Map Analysis for Treatment Response Prediction in Nasopharyngeal Cancer
Author(s) -
Titiya Jirawatwanith,
Thidaporn Tangyoosuk,
Chawalit Lertbutsayanukul,
Nutchawan Jittapiromsak,
Yothin Rakvongthai
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1505/1/012032
Subject(s) - effective diffusion coefficient , imaging biomarker , diffusion mri , voxel , medicine , biomarker , nasopharyngeal cancer , nuclear medicine , nasopharyngeal carcinoma , magnetic resonance imaging , radiology , radiation therapy , chemistry , biochemistry
Diffusion-weighted imaging (DWI) is an MRI technique which provides functional information of tissue by detecting microscopic motion of water molecules. The change of apparent diffusion coefficient (ADC) derived from DWI was used as an imaging biomarker for treatment response prediction in cancers [1]. However, it was based on whole-tumor analysis which did not reflect heterogeneity within the tumor. To overcome this limitation, a new method called parametric response map (PRM) analysis was proposed to evaluate response by quantifying voxel-wise changes in ADC [2]. Here we investigated the use of PRM analysis on ADC from DWI as an imaging biomarker for treatment response prediction in nasopharyngeal cancer (NPC) patients. We collected thirteen patient datasets including ten complete response (CR) patients and three partial response (PR) patients at King Chulalongkorn Memorial Hospital where one patient dataset consisted of DWI and ADC data acquired before (i.e. pre-treatment) and at five weeks after (i.e. mid-treatment) initiation of chemoradiation therapy. For each dataset, we compared pre-treatment ADC image with co-registered mid-treatment ADC image, and calculated the percentage of voxels with increased ADC values with respect to total voxels within the tumor ROI, defined as PRM+. To validate the feasibility of the PRM biomarker, we computed the mean and standard deviation (SD) of percentage change in tumor volume (%AVol ) and in ADC (%AADC) and PRM+ across CR and PR patients, where tumor response was from 6-month follow-up data using RECIST1.1 guideline. The results showed that %AVol as well as %AADC between both groups was not significantly different. In contrast, PRM+ was significantly different between both groups (p < 0.05, 82.7±7.8% in CR vs 66.7±6.5% in PR). Our results implied that the proposed PRM+ biomarker could be a potential biomarker for early treatment response prediction in NPC patients.