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Ultrasound-based radiomics score: a potential biomarker for the prediction of progression-free survival in ovarian epithelial cancer

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Abstract

Purpose

More than 80% of patients with ovarian epithelial cancer (OEC) show complete remission after initial treatment but eventually experience recurrence of the disease. This study aimed to develop a radiomics signature to identify a new prognostic indicator based on preoperative ultrasound imaging.

Methods

A total of 111 patients with OEC who underwent transvaginal ultrasound before surgery were included. Of these, 76 were divided into the training cohort and 35 into the test cohort. We defined the region of interest (ROI) of the tumor by manually drawing the tumor contour on the ultrasound image of the lesion. The radiomics features were extracted from ultrasound images. The radiomics score (Rad-Score) was constructed using the least absolute shrinkage and selection operator (LASSO) analysis and Cox regression. Combined with the ultrasound radiomics features, significant clinical variables were also used to establish predictive models for 5-year progression-free survival (PFS) prediction. The efficiency of the model was evaluated using the area under the curve (AUC). Kaplan–Meier analysis was used to evaluate the association between the Rad-Score and PFS.

Results

The combined model was superior to the clinical and Rad-Score models in estimating 5-year PFS and achieved an AUC of 0.868 (95%CI 0.766–0.971) in the training cohort. The Rad-Score was negatively correlated with prognosis in the training and test cohorts.

Conclusions

The combined model that incorporated both clinical parameters and ultrasound radiomics features achieved a good prognosis in patients with OEC, which might aid clinical decision-making.

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Abbreviations

AUC:

Area under the curve

CI:

Confidence interval

CT:

Computed tomography

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size zone matrix

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

OC:

Ovarian cancer

OEC:

Ovarian epithelial cancer

PFS:

Progression-free survival

Rad-Score:

Radiomics score

ROC:

Receiver-operating characteristic

ROI:

Region of interest

TC:

Serum total cholesterol

WBC:

White blood cells

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Funding

This study has received funding by the Project of China Natural Science Foundation (Grant Number 81803781) and Project of Zhejiang Provincial Natural Science Foundation (Grant Number LQ18H280007).

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Author notes

  1. Feng Lin and Li Lan contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Fei Yao, Jie Ding, Zhangyong Hu, Mengting Cai & Jinjin Liu

  2. Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Xiaowan Huang, Ruru Zheng & Feng Lin

  3. Department of Ultrasound Imaging, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Li Lan

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  1. Fei Yao
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Contributions

FY made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work; FL drafted the work or revised it critically for important intellectual content. LL agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. FL and LL contributed equally to this work, they are the co-corresponding author.

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Correspondence to Feng Lin or Li Lan.

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The retrospective study was approved by the institutional review Committee.

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Yao, F., Ding, J., Hu, Z. et al. Ultrasound-based radiomics score: a potential biomarker for the prediction of progression-free survival in ovarian epithelial cancer. Abdom Radiol 46, 4936–4945 (2021). https://doi.org/10.1007/s00261-021-03163-z

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