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Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer

  • Abdominal Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Objective

To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data.

Material and methods

We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model’s output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated.

Results

The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762–0.964]) than the clinical model (AUC = 0.792 [0.630–0.953], p = 0.295) and the radiomic signature alone (AUC = 0.781 [0.636–0.926], p = 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (p = 0.023–0.041), while the specificities and accuracies were maintained (p = 0.074–1.000).

Conclusion

Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management.

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Abbreviations

AUC:

Area under the curve

CT:

Computed tomography

CA-125:

Cancer antigen-125

CA-199:

Cancer antigen-199

CI:

Confidence interval

EOC:

Epithelial ovarian cancer

FIGO:

International federation of gynecology and obstetrics

HGSC:

High-grade serous carcinoma

NCCN:

National comprehensive cancer network

OCCC:

Ovarian clear cell carcinoma

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Acknowledgements

This work was supported by grants from Natural Science Foundation of China (grant No. 81901829), National High Level Hospital Clinical Research Funding (grant No. 2022-PUMCH-A-004) and Natural Science Foundation of China (grant No. 82271886)

Funding

This work was supported by grants from Natural Science Foundation of China (grant No. 81901829), National High Level Hospital Clinical Research Funding (grant No. 2022-PUMCH-A-004) and Natural Science Foundation of China (grant No. 82271886).

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Authors and Affiliations

  1. Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People’s Republic of China

    Jing Ren, Jia Zhao, Chen Wang, Xin-Yu Liu, Zheng-Yu Jin, Yong-Lan He & Hua-Dan Xue

  2. AI Lab, Deepwise Healthcare, Beijing, People’s Republic of China

    Li Mao & Xiu-Li Li

  3. Department of Obstetrics and Gynecology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Beijing, People’s Republic of China

    Yuan Li

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Contributions

All authors contributed to the study conception and design. YLH, ZYJ, and HDX contributed to the conception and design of the study. XYL, JZ and CW contributed to the acquisition of clinical data. JR, LM and XLL contributed to data analysis and interpretation. JR and LM contributed to statistical analyses. JR, YL, and YLH participated in manuscript preparation, edition and revision. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Yong-Lan He, Yuan Li or Hua-Dan Xue.

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Conflict of interest

Co-authors Li Mao and Xiu-Li Li are employees of AI Lab, Deepwise Healthcare, China. The other authors have no conflicts of interest to disclose. The authors not employed by AI Lab, Deepwise Healthcare were in control of this study.

Ethical approval

This retrospective study was approved by the Institutional Review Board of the Peking Union Medical College Hospital (I-22PJ945), and the consents from patients were waived.

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Ren, J., Mao, L., Zhao, J. et al. Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer. Radiol med 128, 900–911 (2023). https://doi.org/10.1007/s11547-023-01666-x

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