AUTHOR=Shao Xian , An Li , Liu Hui , Feng Hui , Zheng Liyun , Dai Yongming , Yu Bin , Zhang Jin 

TITLE=Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings

JOURNAL=Frontiers in Oncology

VOLUME=12

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.851677

DOI=10.3389/fonc.2022.851677

ISSN=2234-943X

ABSTRACT=<sec><title>Objective</title><p>The objective of the study is to investigate the feasibility of using the fractional order calculus (FROC) model to reflect tumor subtypes and histological grades of cervical carcinoma.</p></sec><sec><title>Methods</title><p>Sixty patients with untreated cervical carcinoma underwent multi-b-value diffusion-weighted imaging (DWI) at 3.0T magnetic resonance imaging (MRI). The mono-exponential and the FROC models were fitted. The differences in the histological subtypes and grades were evaluated by the Mann–Whitney U test. Receiver operating characteristic (ROC) analyses were performed to assess the diagnostic performance and to determine the best predictor for both univariate analysis and multivariate analysis. Differences between ROC curves were tested using the Hanley and McNeil test, while the sensitivity, specificity, and accuracy were compared using the McNemar test. <italic>P</italic>-value &lt;0.05 was considered as significant difference. The Bonferroni corrections were applied to reduce problems associated with multiple comparisons.</p></sec><sec><title>Results</title><p>Only the parameter β, derived from the FROC model could differentiate cervical carcinoma subtypes (<italic>P</italic> = 0.03) and the squamous cell carcinoma (SCC) lesions exhibited significantly lower β than that in the adenocarcinoma (ACA) lesions. All the individual parameters, namely, ADC, β, <italic>D</italic>, and μ derived from the FROC model, could differentiate low-grade cervical carcinomas from high-grade ones (<italic>P</italic> = 0.022, 0.009, 0.004, and 0.015, respectively). The combination of all the FROC parameters showed the best overall performance, providing the highest sensitivity (81.2%) and AUC (0.829).</p></sec><sec><title>Conclusion</title><p>The parameters derived from the FROC model were able to differentiate the subtypes and grades of cervical carcinoma.</p></sec>