Whole solid tumor volume histogram parameters for predicting the recurrence in patients with epithelial ovarian carcinoma: a feasibility study on quantitative DCE-MRI

Hai Ming Li & Jin Wei Qiang et al. · 2020-01-19

Background

Preoperative prediction of the recurrence of epithelial ovarian carcinoma (EOC) can guide the clinical treatment and improve the prognosis. However, there are still no reliable predictive biomarkers.

Purpose

To evaluate whether whole solid tumor volume histogram parameters measured from quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the recurrence in patients with EOC.

Material and Methods

We followed up 56 patients with surgical and histopathologically diagnosed EOC who underwent quantitative DCE-MRI scans. The differences of the histogram parameters between patients with and without recurrence were compared. Mann–Whitney U test, Pearson’s Chi-squared test, or Fisher’s exact test, and receiver operating characteristic (ROC) curves were used for statistical analysis.

Results

All histogram parameters of Ktrans, kep, and ve were not significantly different between EOC patients with and without recurrence ( P>0.05). For 30 patients with high-grade serous ovarian carcinoma (HGSOC), the histogram parameters of Ktrans (mean and 5th, 10th, 25th, 50th, 75th percentiles) and kep (mean and 50th percentile) in 12 patients with recurrence were significantly lower than those in 18 patients without recurrence (all P<0.05). ROC curves showed that the 5th percentile of Ktrans had the largest area under the curve (AUC) of 0.792 for predicting the recurrence in patients with HGSOC. When the threshold value was ≤0.0263/min, the sensitivity, specificity, and accuracy were 100%, 66.7%, and 80%, respectively.

Conclusion

Instead of predicting the recurrence of EOC, whole solid tumor volume quantitative DCE-MRI histogram parameters could predict the recurrence of HGSOC and may be potential biomarkers for the prediction of HGSOC recurrence.

Authors
Hai Ming Li, Wei Tang, Feng Feng, Shu Hui Zhao, Wei Yong Gu, Guo Fu Zhang, Jin Wei Qiang
Funding

Shanghai Health and Family Planning Commission Youth Fund Project

20174Y0242

Shanghai Municipal Commission of Science and Technology

No.19411972000

Imaging Foundation of Fudan University Shanghai Cancer Center

20174Y0242

National Natural Science Foundations of China

YX201803

Shanghai Municipal Health Commission

No. ZK2019B01