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Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To investigate the feasibility of a noninvasive detection of lymph node metastasis (LNM) for early-stage cervical cancer (ECC) patients with radiomics methods based on the textural features from ultrasound images.

Methods

One hundred seventy-two ECC patients between January 2014 and September 2018 with pathologically confirmed lymph node status (LNS) and preoperative ultrasound images were retrospectively reviewed. Regions of interest (ROIs) were delineated by a senior radiologist in the ultrasound images. LIFEx was applied to extract textural features for radiomics study. Least absolute shrinkage and selection operator (LASSO) regression was applied for dimension reduction and for selection of key features. A multivariable logistic regression analysis was adopted to build the radiomics signature. The Mann–Whitney U test was applied to investigate the correlation between radiomics and LNS for both training and validation cohorts. Receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of the radiomics prediction models.

Results

A total of 152 radiomics features were extracted from ultrasound images, in which 6 features were significantly associated with LNS (p < 0.05). The radiomics signatures demonstrated a good discrimination between patients with LNM and non-LNM groups. The best radiomics performance model achieved an area under the curve (AUC) of 0.79 (95% confidence interval (CI), 0.71–0.88) in the training cohort and 0.77 (95% CI, 0.65–0.88) in the validation cohort.

Conclusions

The feasibility of radiomics features from ultrasound images for the prediction of LNM in ECC was investigated. This noninvasive prediction method may be used to facilitate preoperative identification of LNS in patients with ECC.

Key Points

• Few studied had investigated the feasibility of radiomics based on ultrasound images for cervical cancer, even though it is the most common practice for gynecological cancer diagnosis and treatment.

• The radiomics signatures based on ultrasound images demonstrated a good discrimination between patients with and without lymph node metastasis with an area under the curve (AUC) of 0.79 and 0.77 in the training and validation cohorts, respectively.

• The radiomics model based on preoperative ultrasound images has the potential ability to predict lymph node status noninvasively in patients with early-state cervical cancer, so as to reduce the impact of invasive examination and to optimize the treatment choices.

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Abbreviations

AUC:

Area under the curve

CE:

Contrast-enhanced

CI:

Confidence interval

CT:

Computed tomography

ECC:

Early-stage cervical cancer

ECCR:

Ethics Committee in Clinical Research

FIGO:

International Federation of Gynecology and Obstetrics

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run length matrix

GLZLM:

Grey-level zone length matrix

LASSO:

least absolute shrinkage and selection operator

LNM:

Lymph node metastasis

LNS:

Lymph node status

MRI:

Magnetic resonance imaging

NGLDM:

Neighborhood gray-level different matrix

PACS:

Picture archiving and communication system

PET/CT:

Positron emission tomography/computed tomography

ROC:

Receiver operating characteristic

ROI:

Region of interest

SLN:

Sentinel lymph node

SVM:

Support vector machine

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Funding

This work was partially funded by the National Natural Science Foundation of China (under Grant No. 11675122) and the Wenzhou Municipal Science and Technology Bureau (Nos. Y20190183 and 2018ZY016).

Author information

Author notes

  1. Xiance Jin and Yao Ai contributed equally to this work.

Authors and Affiliations

  1. Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People’s Republic of China

    Xiance Jin, Yao Ai, Ji Zhang & Congying Xie

  2. Department of Gynecology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People’s Republic of China

    Haiyan Zhu

  3. Department of Gynecology, Shanghai First Maternal and Infant Hospital, Tongji University School of Medicine, Shanghai, 200126, People’s Republic of China

    Haiyan Zhu

  4. Department of Medical Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People’s Republic of China

    Juebin Jin

  5. Department of Ultrasound imaging, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People’s Republic of China

    Yinyan Teng & Bin Chen

Authors
  1. Xiance Jin
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  2. Yao Ai
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  3. Ji Zhang
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  4. Haiyan Zhu
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  5. Juebin Jin
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  7. Bin Chen
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  8. Congying Xie
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Corresponding authors

Correspondence to Bin Chen or Congying Xie.

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The scientific guarantor of this publication is Congying Xie.

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The authors declare that they have no conflicts of interest.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was waived by the institutional review board.

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Institutional review board approval was obtained.

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• Retrospective

• Observational

• Performed at one institution

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Jin, X., Ai, Y., Zhang, J. et al. Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images. Eur Radiol 30, 4117–4124 (2020). https://doi.org/10.1007/s00330-020-06692-1

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  • DOI: https://doi.org/10.1007/s00330-020-06692-1

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