BMC Medical Imaging (Aug 2020)

Potentialities of multi-b-values diffusion-weighted imaging for predicting efficacy of concurrent chemoradiotherapy in cervical cancer patients

  • Bing Liu,
  • Wan-Ling Ma,
  • Guang-Wen Zhang,
  • Zhen Sun,
  • Meng-Qi Wei,
  • Wei-Huan Hou,
  • Bing-Xin Hou,
  • Li-Chun Wei,
  • Yi Huan

DOI
https://doi.org/10.1186/s12880-020-00496-x
Journal volume & issue
Vol. 20, no. 1
pp. 1 – 9

Abstract

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Abstract Background To testify whether multi-b-values diffusion-weighted imaging (DWI) can be used to ultra-early predict treatment response of concurrent chemoradiotherapy (CCRT) in cervical cancer patients and to assess the predictive ability of concerning parameters. Methods Fifty-three patients with biopsy proved cervical cancer were retrospectively recruited in this study. All patients underwent pelvic multi-b-values DWI before and at the 3rd day during treatment. The apparent diffusion coefficient (ADC), true diffusion coefficient (Dslow), perfusion-related pseudo-diffusion coefficient (Dfast), perfusion fraction (f), distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index(α) were generated by mono-exponential, bi-exponential and stretched exponential models. Treatment response was assessed based on Response Evaluation Criteria in Solid Tumors (RECIST v1.1) at 1 month after the completion of whole CCRT. Parameters were compared using independent t test or Mann-Whitney U test as appropriate. Receiver operating characteristic (ROC) curves was used for statistical evaluations. Results ADC-T0 (p = 0.02), Dslow-T0 (p < 0.01), DDC-T0 (p = 0.03), ADC-T1 (p < 0.01), Dslow-T1 (p < 0.01), ΔADC (p = 0.04) and Δα (p < 0.01) were significant lower in non-CR group patients. ROC analyses showed that ADC-T1 and Δα exhibited high prediction value, with area under the curves of 0.880 and 0.869, respectively. Conclusions Multi-b-values DWI can be used as a noninvasive technique to assess and predict treatment response in cervical cancer patients at the 3rd day of CCRT. ADC-T1 and Δα can be used to differentiate good responders from poor responders.

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