Bing Liu

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 3^rd day during treatment. The apparent diffusion coefficient (ADC), true diffusion coefficient (D_slow), perfusion-related pseudo-diffusion coefficient (D_fast), 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), D_slow-T0 (pslow-T1 (pConclusions: Multi-b-values DWI can be used as a noninvasive technique to assess and predict treatment response in cervical cancer patients at the 3^rd day of CCRT. ADC-T1 and Δα can be used to differentiate good responders from poor responders.

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 (D_slow), perfusion-related pseudo-diffusion coefficient (D_fast), 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 independentttest or Mann-WhitneyUtest as appropriate. Receiver operating characteristic (ROC) curves was used for statistical evaluations.Results: ADC-T0 (p = 0.02), D_slow-T0 (p < 0.01), DDC-T0 (p = 0.03), ADC-T1 (p < 0.01), D_slow-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.

Purpose: To testify whether multi-b-values DWI can be used to ultra-early predict treatment response of CCRT in cervical cancer patients and to assess the predictive ability of concerning parameters. Methods: 53 patients with biopsy proved cervical cancer were retrospectively recruited in this study. All patients underwent pelvic multi-b-values DWI before and at the 3 rd day during treatment. Tumor diffusion parameters were calculated by mono-exponential (apparent diffusion coefficient, ADC), bi-exponential (D slow , D fast and f p ) and stretched exponential (distributed diffusion coefficient, DDC and α) models. Treatment response was assessed based on 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), D slow -T0 (p<0.01), DDC-T0 (p=0.03), ADC-T1 (p<0.01), D slow -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. Conclusion: Multi-b-values DWI can be used as a noninvasive technique to assess and predict treatment response in cervical cancer patients at the 3 rd day of CCRT. ADC-T1 and Δα can be used to differentiate good responders from poor responders.

Purpose: To testify whether multi-b-values DWI can be used to ultra-early predict treatment response of CCRT in cervical cancer patients and to assess the predictive ability of concerning parameters. Methods: 53 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. Tumor diffusion parameters were calculated by mono-exponential (apparent diffusion coefficient, ADC), bi-exponential (Dslow, Dfast and fp) and stretched exponential (distributed diffusion coefficient, DDC and α) models. Treatment response was assessed based on 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. Conclusion: 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.