Objective We 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. Methods A 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. Results CCR5 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. Conclusion The 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.
Objective We 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 radiomic model that can predict CCR5 expression level using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database. Methods A total of 343 cases of ovarian cancer from the TCGA were used for the gene-based prognostic analysis. 57 cases had preoperative computed tomography (CT) images stored in TCIA with genomic data in TCGA were used for radiomic feature extraction and model construction. 89 cases with both TCGA and TCIA clinical data were used for radiomic model evaluation. Results CCR5 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 radiomic features were selected to predict overall survival. The performance of the radiomic 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. Conclusion The 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.
Background Many ovarian cancer (OC) residual-disease prediction models were not externally validated after being constructed, the clinical applicability needs to be evaluated. Purpose To compare computed tomography urography (CTU) with PET/CT in validating models for predicting residual disease in OC. Material and Methods A total of 250 patients were included during 2018–2021. The CTU and PET/CT scans were analyzed, generating CT-Suidan, PET-Suidan, CT-Peking Union Medical College Hospital (PUMC), and PET-PUMC models. All imagings were evaluated by two readers independently, then compared to pathology. According to surgical outcomes, all patients were divided into the R0 group, with no visible residual disease, and the R1 group, with any visible residual disease. Logistic regression was used to assess the discrimination and calibration abilities of each model. Results CTU and PET/CT showed good diagnostic performance in predicting OC peritoneal metastases based on the Suidan and PUMC model (all the accuracies >0.8). As for model evaluation, the value of correct classification of the CT-Suidan, PET-Suidan, CT-PUMC, and PET-PUMC models was 0.89, 0.84, 0.88, and 0.83, respectively, representing stable calibration. The areas under the curve (AUC) of these models were 0.95, 0.90, 0.91, and 0.90, respectively. Furthermore, the accuracy of these models at the optimal threshold value (score 3) was 0.75, 0.78, 0.80, and 0.80, respectively. All two-paired comparisons of the AUCs and accuracies did not show a significant difference (all P > 0.05). Conclusion CT-Suidan, CT-PUMC, PET-Suidan, and PET-PUMC models had equal abilities in predicting the residual disease of OC. The CT-PUMC model was recommended for its economic and user-friendly characteristics.
Background Understanding the blood supply pattern of cesarean scar pregnancy (CSP) can effectively help to determine the best choice of treatment. The aim of this study was to investigate the blood supply pattern and outcomes of patients with CSP through digital subtraction angiography (DSA) imaging. Material/Methods This was a retrospective cohort study. Patients were divided into 2 groups according to the type of CSP. The DSA images of these patients were reviewed, including the type of blood supply, dominant vessel, and collateral blood supply to the gestational sac. The clinical outcomes were analyzed between the 2 groups. Results Thirty-seven patients with type I and 29 patients with type II CSP were enrolled in this study. Type II CSP showed a higher proportion of rich blood supply than type I (44.83% vs 29.72%, P >0.05). Compared with type II CSP, type I CSP tended to have bilateral dominant blood supply predominance (67.57% vs 41.38%, P <0.05). The incidence of collateral blood supply was 5.41% in the type I CSP group and 31.03% in the type II CSP group ( P <0.05). In the type II CSP group, multiple collateral blood vessels were found in 4 patients. The superior vesicle artery was the most common source of collateral blood supply in both groups. Two patients with type II CSP suffered massive bleeding during surgery after uterine artery embolization (UAE). None of the patients received a hysterectomy. Conclusions UAE is safe and effective for both types of CSP. The blood supply pattern is more complex and abnormal in type II CSP. More attention should be paid to the collateral blood supply to achieve complete embolization during the UAE procedure in the case of type II CSP.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.