ArticleEuropean radiology2021
PET-CT radiomics by integrating primary tumor and peritumoral areas predicts E-cadherin expression and correlates with pelvic lymph node metastasis in early-stage cervical cancer.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- PET-CT radiomics and artificial intelligence for predicting lymph node metastasis in cervical cancer: a systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Pooled it
- Imaging based artificial intelligence for predicting lymph node metastasis in cervical cancer patients: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- Development and validation of anAnnals of nuclear medicine · 2025Article
- Research on the Precise Differentiation of Pathological Subtypes of Non-Small Cell Lung Cancer Based onCancers · 2025Article
- Computed tomography-based radiomics modeling to predict patient overall survival in cervical cancer with intensity-modulated radiotherapy combined with concurrent chemotherapy.The Journal of international medical research · 2025Observational
- [Diagnostics (Basel, Switzerland) · 2024Review
- More than meets the eye: 2-[European journal of nuclear medicine and molecular imaging · 2024Article
- Peri- and intra-nodular radiomic features based onFrontiers in medicine · 2024Article
- Multicentric development and evaluation ofEuropean journal of nuclear medicine and molecular imaging · 2023Article
- Article
- The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer.Cancer medicine · 2023Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Development of machine learning models integrating PET/CT radiomic and immunohistochemical pathomic features for treatment strategy choice of cervical cancer with negative pelvic lymph node by mediating COX-2 expression.Frontiers in physiology · 2022Article
- Use of radiomics containing an effective peritumoral area to predict early recurrence of solitary hepatocellular carcinoma ≤5 cm in diameter.Frontiers in oncology · 2022Article
- AI in spotting high-risk characteristics of medical imaging and molecular pathology.Precision clinical medicine · 2021Review
- Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics.Technology in cancer research & treatmentArticle
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6 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo 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).
methodsNinety-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.
resultsThe 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).
conclusionsThe 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. KEY POINTS: • By integrating the primary tumor and peritumoral area based on PET-CT, radiomics was feasible. • The rad-score was associated with E-cad expression and PLNM in patients with ESCC. • Radiomics that integrated the primary tumor and peritumoral areas based on PET-CT could predict E-cad expression in patients with ESCC.
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