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Development and validation of an ultrasound­based radiomics nomogram to predict lymph node status in patients with high-grade serous ovarian cancer: a retrospective analysis.
Qi, Yue; Liu, Jinchi; Wang, Xinyue; Zhang, Yuqing; Li, Zhixun; Qi, Xinyu; Huang, Ying.
Afiliação
  • Qi Y; Department of Ultrasound, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, China.
  • Liu J; Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China.
  • Wang X; Department of Ultrasound, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, China.
  • Zhang Y; Department of Ultrasound, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, China.
  • Li Z; Department of Ultrasound, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, China.
  • Qi X; Department of Ultrasound, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, China.
  • Huang Y; Department of Ultrasound, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, China. huangying712@163.com.
J Ovarian Res ; 17(1): 48, 2024 Feb 22.
Article em En | MEDLINE | ID: mdl-38389075
ABSTRACT

BACKGROUND:

Despite advances in medical imaging technology, the accurate preoperative prediction of lymph node status remains challenging in ovarian cancer. This retrospective study aimed to investigate the feasibility of using ultrasound-based radiomics combined with preoperative clinical characteristics to predict lymph node metastasis (LNM) in patients with high-grade serous ovarian cancer (HGSOC).

RESULTS:

Patients with 401 HGSOC lesions from two institutions were enrolled institution 1 for the training cohort (n = 322) and institution 2 for the external test cohort (n = 79). Radiomics features were extracted from the three preoperative ultrasound images of each lesion. During feature selection, primary screening was first performed using the sample variance F-value, followed by recursive feature elimination (RFE) to filter out the 12 most significant features for predicting LNM. The radscore derived from these 12 radiomic features and three clinical characteristics were used to construct a combined model and nomogram to predict LNM, and subsequent 10-fold cross-validation was performed. In the test phase, the three models were tested with external test cohort. The radiomics model had an area under the curve (AUC) of 0.899 (95% confidence interval [CI] 0.864-0.933) in the training cohort and 0.855 (95%CI 0.774-0.935) in the test cohort. The combined model showed good calibration and discrimination in the training cohort (AUC = 0.930) and test cohort (AUC = 0.881), which were superior to those of the radiomic and clinical models alone.

CONCLUSIONS:

The nomogram consisting of the radscore and preoperative clinical characteristics showed good diagnostic performance in predicting LNM in patients with HGSOC. It may be used as a noninvasive method for assessing the lymph node status in these patients.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Nomogramas Limite: Female / Humans Idioma: En Revista: J Ovarian Res Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Nomogramas Limite: Female / Humans Idioma: En Revista: J Ovarian Res Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China