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

European Radiology volume 30, pages 4117–4124 (2020)Cite this article

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

References

  1. Torre LA, Bray F, Siegel RL, Ferlay J, LortetTieulent J, Jemal A (2015) Global cancer statistics, 2012. CA Cancer J Clin 65:87–108

  2. Biewenga P, van der Velden J, Mol BW et al (2011) Prognostic model for survival in patients with early stage cervical cancer. Cancer 117:768–776

    Article  Google Scholar 

  3. Bourgioti C, Chatoupis K, Moulopoulos LA (2016) Current imaging strategies for the evaluation of uterine cervical cancer. World J Radiol 8:342

    Article  Google Scholar 

  4. Plante M, Renaud MC, Têtu B, Harel F, Roy M (2003) Laparoscopic sentinel node map-ping in early-stage cervical cancer. Gynecol Oncol 91:494–503

    CAS  Article  Google Scholar 

  5. Matsuura Y, Kawagoe T, Toki N, Tanaka M, Kashimura M (2010) Long-standing complications after treatment for cancer of the uterine cervix clinical significance of medical examination at 5 years after treatment. Int J Gynecol Cancer 16:294–297

  6. Gien LT, Covens A (2009) Lymph node assessment in cervical cancer: prognostic and therapeutic implications. J Surg Oncol 99:242–247

    CAS  Article  Google Scholar 

  7. Ferrandina G, Anchora LP, Gallotta V et al (2017) Can we define the risk of lymph node metastasis in early-stage cervical cancer patients? A large-scale, retrospective study. Ann Surg Oncol 24:2311–2318

    Article  Google Scholar 

  8. Macdonald MC, Tidy JA (2016) Can we be less radical with surgery for early cervical cancer? Curr Oncol Rep 18:16

    Article  Google Scholar 

  9. Diaz JP, Gemignani ML, Pandit-Taskar N et al (2011) Sentinel lymph node biopsy in the management of early-stage cervical carcinoma. Gynecol Oncol 120:347–352

    Article  Google Scholar 

  10. Lecuru F, Mathevet P, Querleu D et al (2011) Bilateral negative sentinel nodes accurately predict absence of lymph node metastasis in early cervical cancer: results of the SENTICOL study. J Clin Oncol 29:1686–1691

    Article  Google Scholar 

  11. Cross MJ (2007) Different criteria for radioactive sentinel lymph nodes has different impact on sentinel node biopsy in breast cancer patients. J Surg Oncol 95:616–617

    Article  Google Scholar 

  12. Liu J, Wang Z, Shao H, Qu D, Liu J, Yao L (2018) Improving CT detection sensitivity for nodal metastases in oesophageal cancer with combination of smaller size and lymph node axial ratio. Eur Radiol 28:188–195

    Article  Google Scholar 

  13. Williams AD, Cousins C, Soutter WP et al (2001) Detection of pelvic lymph node metastases in gynecologic malignancy: a comparison of CT, MR imaging, and positron emission tomography. AJR Am J Roentgenol 177:343–348

    CAS  Article  Google Scholar 

  14. Gillies RJ, Kinahan PE, Hricak H (2015) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  15. Han L, Zhu Y, Liu Z et al (2019) Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer. Eur Radiol 29:3820–3829

    Article  Google Scholar 

  16. Wu S, Zheng J, Li Y et al (2018) Development and validation of an MRI-based radiomics signature for the preoperative prediction of lymph node metastasis in bladder cancer. EBioMedicine. 34:76–84

    Article  Google Scholar 

  17. Mu W, Chen Z, Liang Y et al (2015) Staging of cervical cancer based on tumor heterogeneity characterized by texture features on 18F-FDG PET images. Phys Med Biol 60:5123

    Article  Google Scholar 

  18. Liu Y, Zhang Y, Cheng R et al (2019) Radiomics analysis of apparent diffusion coefficient in cervical cancer: a preliminary study on histological grade evaluation. J Magn Reson Imaging 49:280–290

    Article  Google Scholar 

  19. Lucia F, Visvikis D, Vallières M et al (2019) External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging Res 46:864–877

    Article  Google Scholar 

  20. Ho JC, Allen PK, Bhosale PR et al (2017) Diffusion-weighted magnetic resonance imaging as a predictor of outcome in cervical cancer after chemoradiation. Int J Radiat Oncol Biol Phys 97:546–553

    Article  Google Scholar 

  21. Pascual MA, Graupera B, Hereter L, Rotili A, Rodriguez I, Alcazar JL (2011) Intra-and interobserver variability of 2D and 3D transvaginal sonography in the diagnosis of benign versus malignant adnexal masses. J Clin Ultrasound 39:316–321

  22. Nioche C, Orlhac F, Boughdad S et al (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789

    CAS  Article  Google Scholar 

  23. Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1

    Article  Google Scholar 

  24. Han X, Wen H, Ju X et al (2017) Predictive factors of para-aortic lymph nodes metastasis in cervical cancer patients: a retrospective analysis based on 723 para-aortic lymphadenectomy cases. Oncotarget 8:51840–51847

    Article  Google Scholar 

  25. Li D, Cai J, Kuang Y et al (2012) Surgical-pathologic risk factors of pelvic lymph node metastasis in stage Ib1-IIb cervical cancer. Acta Obstet Gynecol Scand 91:802–809

    Article  Google Scholar 

  26. Cibula D, Zikan M, Slama J et al (2016) Risk of micrometastases in non-sentinel pelvic lymph nodes in cervical cancer. Gynecol Oncol 143:83–86

    CAS  Article  Google Scholar 

  27. Shen WC, Chen SW, Liang JA, Hsieh TC, Yen KY, Kao CH (2017) [18] Fluorodeoxyglucose positron emission tomography for the textural features of cervical cancer associated with lymph node metastasis and histological type. Eur J Nucl Med Mol Imaging 44:1721–1731

    Article  Google Scholar 

  28. Kan Y, Dong D, Zhang Y et al (2019) Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer. J Magn Reson Imaging 49:304–310

    Article  Google Scholar 

  29. Wang T, Gao T, Yang J et al (2019) Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging. Eur J Radiol 114:128–135

    Article  Google Scholar 

  30. Li K, Sun H, Lu Z et al (2018) Value of [18F] FDG PET radiomic features and VEGF expression in predicting pelvic lymphatic metastasis and their potential relationship in early-stage cervical squamous cell carcinoma. Eur J Radiol 106:160–166

    Article  Google Scholar 

  31. Liu T, Zhou S, Yu J et al (2019) Prediction of lymph node metastasis in patients with papillary thyroid carcinoma: a radiomics method based on preoperative ultrasound images. Technol Cancer Res Treat 18:1533033819831713

    PubMed  PubMed Central  Google Scholar 

  32. Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ (2013) Quantifying tumour heterogeneity in 18 F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging Res 40:133–140

    Article  Google Scholar 

  33. Yu YY, Zhang R, Dong RT et al (2019) Feasibility of an ADC-based radiomics model for predicting pelvic lymph node metastases in patients with stage IB–IIA cervical squamous cell carcinoma. Br J Radiol 92(1097):20180986

    Article  Google Scholar 

  34. Becker AS, Ghafoor S, Marcon M et al (2017) MRI texture features may predict differentiation and nodal stage of cervical cancer: a pilot study. Acta Radiol Open 6:2058460117729574

    PubMed  PubMed Central  Google Scholar 

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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.

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.

Methodology

• 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

Keywords

  • Uterine cervical neoplasms
  • Ultrasonography
  • Lymph nodes
  • Radiomics