Interval changes of histogram-derived diffusion indices predict treatment response to induction chemotherapy in head and neck cancer: a feasibility study

Kai-Lun Cheng; Hsueh-Ju Lu; Xi Lin; Hui-Yu Wang; Ying-Hsiang Chou; Yeu-Sheng Tyan; Ping-Huei Tsai

doi:10.21037/qims-22-263

Interval changes of histogram-derived diffusion indices predict treatment response to induction chemotherapy in head and neck cancer: a feasibility study

Quant Imaging Med Surg. 2022 Dec;12(12):5383-5393. doi: 10.21037/qims-22-263.

Authors

Kai-Lun Cheng^{1

2

3}, Hsueh-Ju Lu^{4

5}, Xi Lin², Hui-Yu Wang¹, Ying-Hsiang Chou^{2

6}, Yeu-Sheng Tyan^{1

2}, Ping-Huei Tsai^{1

2}

Affiliations

¹ Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung.
² Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung.
³ Department of Veterinary Medicine, National Chung Hsing University, Taichung.
⁴ Division of Hematology and Oncology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung.
⁵ School of Medicine, Chung Shan Medical University, Taichung.
⁶ Department of Radiation Oncology, Chung Shan Medical University Hospital, Taichung.

Abstract

Background: This retrospective study investigated whether the interval change of apparent diffusion coefficient (∆ADC) [baseline and after the first cycle of induction chemotherapy (ICT)] can be used as a valid predictive imaging biomarker of the treatment response to ICT in head and neck cancer (HNC).

Methods: A total of 19 consecutive patients with HNC who underwent diffusion-weighted magnetic resonance imaging (DWI) at baseline and after the first cycle of ICT were included. Whole-tumor ADC histogram parameters (mean, median, kurtosis, skewness, entropy, minimal, maximum, 25th percentile, and 75th percentile) were obtained. The correlations of ∆ADC histogram parameters, volume, T-stage, N-stage, and age with the treatment response were examined using the Mann-Whitney U test. The predictive value of histogram parameters was examined using receiver operating characteristic (ROC) curves.

Results: Responders showed significantly higher values of ∆ADC₂₅ (0.19±0.23) and ∆ADC_min (1.78±2.98) than non-responders (-0.09±0.15 and -0.73±0.36; P=0.035 and 0.009, respectively). When ∆ADC₂₅ and ∆ADC_min were used for predicting the treatment response, the area under the ROC curve was 0.850/0.933 with a sensitivity of 73.3%/80.0% and specificity of 100%/100% (P=0.036 and 0.009, respectively).

Conclusions: ∆ADC₂₅ and ∆ADC_min derived from whole-tumor histogram analysis are valuable imaging biomarkers for the early prediction of the ICT response in HNC.

Keywords: Head and neck cancer (HNC); diffusion; histogram; magnetic resonance imaging (MRI).