Journal
EUROPEAN RADIOLOGY
Volume 31, Issue 8, Pages 5967-5979Publisher
SPRINGER
DOI: 10.1007/s00330-021-07690-7
Keywords
Cervical cancer; Radiomics; PET-CT; E-cadherin; Lymph nodes
Funding
- LIAONING Science & Technology Project [2017225012]
- LIAONING Science Natural Science Foundation [2019-MS-373]
- 345 Talent Project
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This study found that radiomics based on PET-CT scans can predict E-cad expression levels in early-stage cervical cancer patients and is correlated with pelvic lymph node metastasis. The radiomics score can help evaluate patients' prognosis and achieve more individualized medical treatments.
Objectives To explore the role of radiomics in integrating primary tumor and peritumoral areas based on PET-CT scans for predicting E-cadherin (E-cad) expression in early-stage cervical cancer (ESCC) and its correlation with pelvic lymph node metastasis (PLNM). Methods Ninety-seven ESCC patients who had undergone PET-CT scans were retrospectively analyzed. The ROI of primary tumors, peritumoral areas, and plus tumors were semi-automatically segmented on PET-CT images. A total of 1188 radiomics features were extracted, selected, and eventually integrated into radiomics score (rad-score). The rad-score difference between patients with E-cad expression of high and low was analyzed using Mann-Whitney tests. Characteristic correlation was tested using a Spearman analysis. Four models were established using logistic regression algorithms and evaluated using ROC and calibration curves. A DeLong test was used to perform pairwise comparisons of AUCs. Results The rad-score of patients with low E-cad expression was higher than that of patients with high E-cad expression in both training and testing cohorts (p < 0.001 and p = 0.027, respectively). A significant correlation was observed between the rad-score and E-cad (p < 0.001). PLNM correlated slightly with rad-score and E-cad values (p = 0.01 and p < 0.001, respectively). The ROC curve and calibration curve of the rad-score model performed best in both training and testing cohorts (AUC = 0.915, 0.844, p < 0.001, respectively). Conclusions The radiomics of integrating primary tumor and peritumoral areas based on PET-CT showed correlations with PLNM. It was also able to predict E-cad expression in ESCC patients, allowing for evaluation of those patients' prognosis and more individualized medical treatment.
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