ArticleJournal of ovarian research2023
CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer.
Article in Journal of ovarian research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review.Cancer reports (Hoboken, N.J.) · 2025Pooled it
- Explainable deep learning and radiomics integration for differentiating insulinomas from NF-PNETs in EUS imaging.BMC medical informatics and decision making · 2026Article
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- Survival prediction and risk stratification in R0-resected ovarian cancer: a multi-modal deep learning approach.NPJ precision oncology · 2026Article
- Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn's disease.Annals of medicine · 2025Article
- Radiomics and radiogenomics in ovarian cancer: a review with a focus on ultrasound applications.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- Integrative intratumoral and peritumoral radiomic model for predicting endometrial cancer grade.Oncology letters · 2025Article
- Supramolecular assembly of novel achatin structurally similar peptide as potential G protein-coupled receptors anticancer ligands: focus on its pseudo-macrocycle nature.Royal Society open science · 2025Article
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.Scientific reports · 2025Article
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.BMC medical imaging · 2025Article
- Homologous recombination deficiency (HRD) diagnostics: underlying mechanisms and new perspectives.Cancer metastasis reviews · 2024Review
- Predicting progression-free survival in patients with epithelial ovarian cancer using an interpretable random forest model.Heliyon · 2024Article
- CT-based radiomics predicts CD38 expression and indirectly reflects clinical prognosis in epithelial ovarian cancer.Heliyon · 2024Article
- Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics.Journal of ovarian research · 2024Article
- Article
- CT radiomics prediction of CXCL9 expression and survival in ovarian cancer.Journal of ovarian research · 2023Article
- A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility.Insights into imaging · 2023Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aimed to evaluate the prognostic value of C-C motif chemokine receptor type 5 (CCR5) expression level for patients with ovarian cancer and to establish a radiomics model that can predict CCR5 expression level using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database.
methodsA total of 343 cases of ovarian cancer from the TCGA were used for the gene-based prognostic analysis. Fifty seven cases had preoperative computed tomography (CT) images stored in TCIA with genomic data in TCGA were used for radiomics feature extraction and model construction. 89 cases with both TCGA and TCIA clinical data were used for radiomics model evaluation. After feature extraction, a radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. A prognostic scoring system incorporating radiomics signature based on CCR5 expression level and clinicopathologic risk factors was proposed for survival prediction.
resultsCCR5 was identified as a differentially expressed prognosis-related gene in tumor and normal sample, which were involved in the regulation of immune response and tumor invasion and metastasis. Four optimal radiomics features were selected to predict overall survival. The performance of the radiomics model for predicting the CCR5 expression level with 10-fold cross- validation achieved Area Under Curve (AUCs) of 0.770 and of 0.726, respectively, in the training and validation sets. A predictive nomogram was generated based on the total risk score of each patient, the AUCs of the time-dependent receiver operating characteristic (ROC) curve of the model was 0.8, 0.673 and 0.792 for 1-year, 3-year and 5-year, respectively. Along with clinical features, important imaging biomarkers could improve the overall survival accuracy of the prediction model.
conclusionThe expression levels of CCR5 can affect the prognosis of patients with ovarian cancer. CT-based radiomics could serve as a new tool for prognosis prediction.
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