BACKGROUND AND PURPOSE: ADC provides a measure of water molecule diffusion in tissue. The aim of this study was to evaluate whether the fractional change in ADC during therapy can be used as a valid predictive indicator of treatment response in head and neck squamous cell carcinoma treated with chemoradiotherapy. MATERIALS AND METHODS: Forty patients underwent DWI at pretreatment and 3 weeks after the start of treatment. The pretreatment ADC, fractional change in ADC, tumor regression rate, and other clinical variables were compared with locoregional control and locoregional failure and were analyzed by using logistic regression analysis and receiver operating characteristic analysis. Furthermore, progression-free survival curves divided by the corresponding threshold value were compared by means of the log-rank test. RESULTS: The fractional change in ADCprimary, the fractional change in ADCnode, primary tumor volume, nodal volume, tumor regression ratenode, N stage, and tumor location revealed significant differences between locoregional failure and locoregional control (P < .05). In univariate analysis, the fractional change in ADCprimary, fractional change in ADCnode, tumor regression ratenode, N stage, and tumor location showed significant association with locoregional control (P < .05). In multivariate analysis, however, only the fractional change in ADCprimary was identified as a significant and independent predictor of locoregional control (P = .04). A threshold fractional change in ADCprimary of 0.24 revealed a sensitivity of 100%, specificity of 78.7%, and overall accuracy of 84.8% for the prediction of locoregional control. Progression-free survival of the 2 groups divided by the fractional change in ADCprimary at 0.24 showed a significant difference (P < .05). CONCLUSIONS: The results suggest that the fractional change in ADCprimary is a valid imaging biomarker for predicting treatment response in head and neck squamous cell carcinoma treated with chemoradiotherapy.
BACKGROUND AND PURPOSE:ADC provides a measure of water molecule diffusion in tissue. The aim of this study was to evaluate whether the fractional change in ADC during therapy can be used as a valid predictive indicator of treatment response in head and neck squamous cell carcinoma treated with chemoradiotherapy. MATERIALS AND METHODS: Forty patients underwent DWI at pretreatment and 3 weeks after the start of treatment. The pretreatment ADC, fractional change in ADC, tumor regression rate, and other clinical variables were compared with locoregional control and locoregional failure and were analyzed by using logistic regression analysis and receiver operating characteristic analysis. Furthermore, progression-free survival curves divided by the corresponding threshold value were compared by means of the log-rank test. RESULTS: The fractional change in ADCprimary, the fractional change in ADCnode, primary tumor volume, nodal volume, tumor regression ratenode, N stage, and tumor location revealed significant differences between locoregional failure and locoregional control (P < .05). In univariate analysis, the fractional change in ADCprimary, fractional change in ADCnode, tumor regression ratenode, N stage, and tumor location showed significant association with locoregional control (P < .05). In multivariate analysis, however, only the fractional change in ADCprimary was identified as a significant and independent predictor of locoregional control (P = .04). A threshold fractional change in ADCprimary of 0.24 revealed a sensitivity of 100%, specificity of 78.7%, and overall accuracy of 84.8% for the prediction of locoregional control. Progression-free survival of the 2 groups divided by the fractional change in ADCprimary at 0.24 showed a significant difference (P < .05). CONCLUSIONS: The results suggest that the fractional change in ADCprimary is a valid imaging biomarker for predicting treatment response in head and neck squamous cell carcinoma treated with chemoradiotherapy.
Authors: Kee H Wong; Rafal Panek; Shreerang A Bhide; Christopher M Nutting; Kevin J Harrington; Katie L Newbold Journal: Br J Radiol Date: 2017-03 Impact factor: 3.039
Authors: B K H Law; A D King; K S Bhatia; A T Ahuja; M K M Kam; B B Ma; Q Y Ai; F K F Mo; J Yuan; D K W Yeung Journal: AJNR Am J Neuroradiol Date: 2016-05-05 Impact factor: 3.825
Authors: S Connor; C Sit; M Anjari; M Lei; T Guerrero-Urbano; T Szyszko; G Cook; P Bassett; V Goh Journal: J Cancer Res Clin Oncol Date: 2021-06-22 Impact factor: 4.553
Authors: Rasheed Zakaria; Kumar Das; Mark Radon; Maneesh Bhojak; Philip R Rudland; Vanessa Sluming; Michael D Jenkinson Journal: BMC Med Imaging Date: 2014-08-03 Impact factor: 1.930