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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.
Ren, Jing; Mao, Li; Zhao, Jia; Li, Xiu-Li; Wang, Chen; Liu, Xin-Yu; Jin, Zheng-Yu; He, Yong-Lan; Li, Yuan; Xue, Hua-Dan.
Afiliación
  • Ren J; 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.
  • Mao L; AI Lab, Deepwise Healthcare, Beijing, People's Republic of China.
  • Zhao J; 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.
  • Li XL; AI Lab, Deepwise Healthcare, Beijing, People's Republic of China.
  • Wang C; 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.
  • Liu XY; 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.
  • Jin ZY; 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.
  • He YL; 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. heyonglan@pumch.cn.
  • Li Y; 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. liyuan10833@pumch.cn.
  • Xue HD; 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. bjdanna95@hotmail.com.
Radiol Med ; 128(8): 900-911, 2023 Aug.
Article en En | MEDLINE | ID: mdl-37368228
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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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias Ováricas / Endometriosis Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: Radiol Med Año: 2023 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias Ováricas / Endometriosis Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: Radiol Med Año: 2023 Tipo del documento: Article