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Computed tomography–based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To compare computed tomography (CT)–based radiomics for preoperatively differentiating type I and II epithelial ovarian cancers (EOCs) using different machine learning classifiers and to construct and validate the best diagnostic model.

Methods

A total of 470 patients with EOCs were included retrospectively. Patients were divided into a training dataset (N = 329) and a test dataset (N = 141). A total of 1316 radiomics features were extracted from the portal venous phase of contrast-enhanced CT images for each patient, followed by dimension reduction of the features. The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), naïve Bayes (NB), logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) classifiers were trained to obtain the radiomics signatures. The performance of each radiomics signature was evaluated and compared by the area under the receiver operating characteristic curve (AUC) and relative standard deviation (RSD). The best radiomics signature was selected and combined with clinical and radiological features to establish a combined model. The diagnostic value of the combined model was assessed.

Results

The LR-based radiomics signature performed well in the test dataset, with an AUC of 0.879 and an accuracy of 0.773. The combined model performed best in both the training and test datasets, with AUCs of 0.900 and 0.934 and accuracies of 0.848 and 0.823, respectively.

Conclusion

The combined model showed the best diagnostic performance for distinguishing between type I and II EOCs preoperatively. Therefore, it can be a useful tool for clinical individualized EOC classification.

Key Points

• Radiomics features extracted from computed tomography (CT) could be used to differentiate type I and II epithelial ovarian cancers (EOCs).

• Machine learning can improve the performance of differentiating type I and II EOCs.

• The combined model exhibited the best diagnostic capability over the other models in both the training and test datasets.

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Abbreviations

EOCs:

Epithelial ovarian cancers

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size zone matrix

ICC:

Intraclass correlation coefficient

KNN:

K-nearest neighbor

LR:

Logistic regression

NB:

Naïve Bayes

NGTDM:

Neighboring gray-tone difference matrix

OC:

Ovarian cancer

RF:

Random forest

RSD:

Relative standard deviation

SVM:

Support vector machines

VOI:

Volume of interest

XGBoost:

eXtreme Gradient Boosting

References

  1. Torre LA, Trabert B, DeSantis CE et al (2018) Ovarian cancer statistics, 2018. CA Cancer J Clin 68:284–296

    Article  PubMed  PubMed Central  Google Scholar 

  2. Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer statistics, 2021. CA Cancer J Clin 71:7–33

    Article  PubMed  Google Scholar 

  3. Kurman RJ, Shih Ie M (2008) Pathogenesis of ovarian cancer: lessons from morphology and molecular biology and their clinical implications. Int J Gynecol Pathol 27:151–160

    PubMed  PubMed Central  Google Scholar 

  4. Kurman RJ, Shih Ie M (2016) The dualistic model of ovarian carcinogenesis: revisited, revised, and expanded. Am J Pathol 186:733–747

    Article  PubMed  PubMed Central  Google Scholar 

  5. Cree IA, White VA, Indave BI, Lokuhetty D (2020) Revising the WHO classification: female genital tract tumours. Histopathology 76:151–156

    Article  PubMed  Google Scholar 

  6. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  PubMed  Google Scholar 

  7. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  PubMed  Google Scholar 

  8. Chiappa V, Bogani G, Interlenghi M et al (2021) The Adoption of Radiomics and machine learning improves the diagnostic processes of women with Ovarian MAsses (the AROMA pilot study). J Ultrasound 24:429–437

    Article  PubMed  Google Scholar 

  9. Gu D, Hu Y, Ding H et al (2019) CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol 29:6880–6890

    Article  PubMed  Google Scholar 

  10. Cui X, Wang N, Zhao Y et al (2019) Preoperative prediction of axillary lymph node metastasis in breast cancer using radiomics features of DCE-MRI. Sci Rep 9:2240

    Article  PubMed  PubMed Central  Google Scholar 

  11. Zhou Y, He L, Huang Y et al (2017) CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY) 42:1695–1704

    Article  PubMed  Google Scholar 

  12. Qian L, Ren J, Liu A et al (2020) MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes. Eur Radiol 30:5815–5825

    Article  CAS  PubMed  Google Scholar 

  13. Jian J, Li Y, Pickhardt PJ et al (2021) MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers. Eur Radiol 31:403–410

    Article  PubMed  Google Scholar 

  14. Zhang H, Mao Y, Chen X et al (2019) Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study. Eur Radiol 29:3358–3371

    Article  PubMed  Google Scholar 

  15. Forstner R, Sala E, Kinkel K, Spencer JA (2010) ESUR guidelines: ovarian cancer staging and follow-up. Eur Radiol 20:2773–2780

    Article  PubMed  Google Scholar 

  16. An H, Wang Y, Wong EMF et al (2021) CT texture analysis in histological classification of epithelial ovarian carcinoma. Eur Radiol 31:5050–5058

    Article  PubMed  Google Scholar 

  17. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zhang Y, Zhang B, Liang F et al (2019) Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types. Eur Radiol 29:2157–2165

    Article  PubMed  Google Scholar 

  19. McCluggage WG (2011) Morphological subtypes of ovarian carcinoma: a review with emphasis on new developments and pathogenesis. Pathology 43:420–432

    Article  CAS  PubMed  Google Scholar 

  20. Masoodi T, Siraj S, Siraj AK et al (2020) Genetic heterogeneity and evolutionary history of high-grade ovarian carcinoma and matched distant metastases. Br J Cancer 122:1219–1230

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Liu D, Zhang L, Indima N et al (2017) CT and MRI findings of type I and type II epithelial ovarian cancer. Eur J Radiol 90:225–233

    Article  PubMed  Google Scholar 

  22. Papanikolaou N, Matos C, Koh DM (2020) How to develop a meaningful radiomic signature for clinical use in oncologic patients. Cancer Imaging 20:33

    Article  PubMed  PubMed Central  Google Scholar 

  23. Xie XJ, Liu SY, Chen JY et al (2021) Development of unenhanced CT-based imaging signature for BAP1 mutation status prediction in malignant pleural mesothelioma: Consideration of 2D and 3D segmentation. Lung Cancer 157:30–39

    Article  CAS  PubMed  Google Scholar 

  24. Ortiz-Ramon R, Larroza A, Ruiz-Espana S, Arana E, Moratal D (2018) Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 28:4514–4523

    Article  PubMed  Google Scholar 

  25. Yang L, Yang J, Zhou X et al (2019) Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. Eur Radiol 29:2196–2206

    Article  PubMed  Google Scholar 

  26. Fu GS, Levin-Schwartz Y, Lin QH, Zhang D (2019) Machine learning for medical imaging. J Healthc Eng 2019:9874591

    Article  PubMed  PubMed Central  Google Scholar 

  27. Qian Z, Li Y, Wang Y et al (2019) Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers. Cancer Lett 451:128–135

    Article  CAS  PubMed  Google Scholar 

  28. Liu J, Zeng P, Guo W et al (2021) Prediction of high-risk cytogenetic status in multiple myeloma based on magnetic resonance imaging: utility of radiomics and comparison of machine learning methods. J Magn Reson Imaging 54:1303–1311

    Article  PubMed  Google Scholar 

  29. Chen H, Zhang X, Wang X et al (2021) MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study. Eur Radiol 31:7913–7924

    Article  PubMed  Google Scholar 

  30. Fang J, Zhang B, Wang S et al (2020) Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer. Theranostics 10:2284–2292

    Article  PubMed  PubMed Central  Google Scholar 

  31. Shu Z, Fang S, Ding Z et al (2019) MRI-based radiomics nomogram to detect primary rectal cancer with synchronous liver metastases. Sci Rep 9:3374

    Article  PubMed  PubMed Central  Google Scholar 

  32. Gui J, Li H (2005) Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics 21:3001–3008

    Article  CAS  PubMed  Google Scholar 

  33. Yin P, Mao N, Zhao C et al (2019) Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. Eur Radiol 29:1841–1847

    Article  PubMed  Google Scholar 

  34. Lee SM, Park CM, Goo JM, Lee HJ, Wi JY, Kang CH (2013) Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as ground-glass nodules: differentiation by using CT features. Radiology 268:265–273

    Article  PubMed  Google Scholar 

  35. Aslan K, Onan MA, Yilmaz C, Bukan N, Erdem M (2020) Comparison of HE 4, CA 125, ROMA score and ultrasound score in the differential diagnosis of ovarian masses. J Gynecol Obstet Hum Reprod 49:101713

    Article  PubMed  Google Scholar 

  36. Gąsiorowska E, Michalak M, Warchoł W et al (2015) Clinical application of HE4 and CA125 in ovarian cancer type I and type II detection and differential diagnosis. Ginekol Pol 86:88–93

    Article  PubMed  Google Scholar 

  37. Kurman RJ, Carcangiu ML, Herrington CS (2014) World Health Organisation classification of tumours of the female reproductive organs. International Agency for Research on Cancer

  38. Yu XP, Wang L, Yu HY et al (2021) MDCT-based radiomics features for the differentiation of serous borderline ovarian tumors and serous malignant ovarian tumors. Cancer Manag Res 13:329–336

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This work was supported by the Chinese National Key Research and Development Project (Grant No. 2021YFC2500400 and Grant No. 2021YFC2500402), the Breeding Project of National Natural Science Foundation of China, Tianjin Medical University Cancer Institute and Hospital (Grant No. 210207), and the Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-009A).

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Author notes

  1. Jiaojiao Li and Xubin Li have contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, First Affiliated Hospital of Hebei North University, No. 12, Changqing Road, Qiaoxi District, Zhangjiakou, 075000, China

    Jiaojiao Li & Shujun Cui

  2. Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi District, Tianjin, 300060, China

    Jiaojiao Li, Xubin Li, Juanwei Ma, Fang Wang & Zhaoxiang Ye

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  1. Jiaojiao Li
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Correspondence to Shujun Cui or Zhaoxiang Ye.

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The scientific guarantor of this publication is Dr. Zhaoxiang Ye.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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Li, J., Li, X., Ma, J. et al. Computed tomography–based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers. Eur Radiol 33, 5193–5204 (2023). https://doi.org/10.1007/s00330-022-09318-w

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  • DOI: https://doi.org/10.1007/s00330-022-09318-w

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