Search

Background: Apparent diffusion coefficient (ADC) value is an important quantitative parameter in the research of cervical cancer, affected by some factors. Purpose: To investigate the effect of pathological type and menstrual status on the ADC value of cervical cancer. Material and Methods: A total of 352 individuals with pathologically confirmed cervical cancer between January 2015 to December 2017 were retrospectively enrolled in this study, including 317 cases with squamous cell carcinomas (SCC) and 35 cases with adenocarcinomas (AC); 177 patients were non-menopausal and 175 were menopausal. All patients underwent a routine 3.0-T magnetic resonance imaging (MRI) scan and diffusion-weighted imaging (DWI) examination using b-values of 0, 800, and 1000 s/mm². Three parameters including mean ADC (ADCmean), maximum ADC (ADCmax), and minimum ADC (ADCmin) of cervical cancer lesions were measured and retrospectively analyzed. Independent samples t-test was used to compare the difference of ADC values in different menstrual status and pathological types. Results: In all menopausal and non-menopausal patients, the ADCmean and ADCmin values of SCC were lower than those of AC (P<0.05), the ADCmax of two pathological types showed no statistical difference (P > 0.05). In menopausal patients, the ADCmean, ADCmax, and ADCmin values of SCC were not statistically different compared with those of AC (P > 0.05). The ADCmean, ADCmax, and ADCmin values of different pathological types cervical cancers in non-menopausal patients were all higher than those in menopausal patients (P<0.05). Conclusion: The ADC values of the cervical cancers were different in different pathological types and were also affected by menstrual status. [ABSTRACT FROM AUTHOR]

To investigate the prediction value of a radiomics model based on apparent diffusion coefficient (ADC) maps for pelvic lymph node metastasis (PLNM) in patients with stage IB–IIA cervical squamous cell carcinoma (CSCC). A total of 153 stage IB–IIA CSCC patients who underwent preoperative MRI including DWI from January 2015 to October 2017 were retrospectively studied and divided into a training cohort (n = 102) and a validation cohort (n = 51). Radiomics features were extracted from the ADC maps. The one-way ANOVA method, Mann-Whitney U test and Pearson's correlation analysis were used for selecting radiomics features. Logistic regression analyses were used to develop the model. ROC analyses were used to evaluate the prediction performance of the model. Clinical stage, tumor diameter, and MR-reported lymph node (LN) status were significantly associated with LN status (p < 0.05 for both the training and validation cohorts). The radiomics model, which incorporated clinical stage, MR-reported LN status, and grey-level non-uniformity, showed good predictive performance in the training group (AUC 0.864; 95% CI, 0.782 – 0.924) and the validation group (AUC 0.870; 95% CI, 0.747 – 0.948). The performance of the radiomics model was significantly better than that of each predictive factor alone. The presented radiomics model, a non-invasive preoperative prediction tool, has the potential to have more predictive efficacy than clinical and radiological factors for differentiating between metastatic and non-metastatic lymph nodes. A radiomics model derived from the ADC maps of primary lesions demonstrated good performance for predicting PLNM in stage IB-IIA CSCC patients and may help to improve clinical decision-making. [ABSTRACT FROM AUTHOR]