scholarly journals A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer

2021 ◽  
Author(s):  
Yumin Hu ◽  
Qiaoyou Weng ◽  
Haihong Xia ◽  
Tao Chen ◽  
Chunli Kong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ovarian Cancer ◽  
Clinical Data ◽  
Arterial Phase ◽  
Multivariable Logistic Regression Analysis ◽  
Clinical Factors ◽  
Good Discrimination ◽  
Training Cohort ◽  
Ovarian Cancers ◽  
Radiomics Signature

Abstract Purpose To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs). Methods A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated. Results Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram. Conclusion The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.

scholarly journals Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB–IV) Lung Adenocarcinoma Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Duo Hong ◽  
Lina Zhang ◽  
Ke Xu ◽  
Xiaoting Wan ◽  
Yan Guo
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Clinical Factors ◽  
Combined Model ◽  
Training Cohort ◽  
Clinical Model ◽  
Pre Treatment ◽  
Off Time ◽  
Radiomics Signature

PurposeThe purpose of this study was to investigate the prognostic value of pre-treatment CT radiomics and clinical factors for the overall survival (OS) of advanced (IIIB–IV) lung adenocarcinoma patients.MethodsThis study involved 165 patients with advanced lung adenocarcinoma. The Lasso–Cox regression model was used for feature selection and radiomics signature building. Then a clinical model was built based on clinical factors; a combined model in the form of nomogram was constructed with both clinical factors and the radiomics signature. Harrell’s concordance index (C-Index) and Receiver operating characteristic (ROC) curves at cut-off time points of 1-, 2-, and 3- year were used to estimate and compare the predictive ability of all three models. Finally, the discriminatory ability and calibration of the nomogram were analyzed.ResultsThirteen significant features were selected to build the radiomics signature whose C-indexes were 0.746 (95% CI, 0.699 to 0.792) in the training cohort and 0.677 (95% CI, 0.597 to 0.766) in the validation cohort. The C-indexes of combined model achieved 0.799 (95% CI, 0.757 to 0.84) in the training cohort and 0.733 (95% CI, 0.656 to 0.81) in the validation cohort, which outperformed the clinical model and radiomics signature. Moreover, the areas under the curve (AUCs) of the radiomic signature for 2-year prediction was superior to that of the clinical model. The combined model had the best AUCs for 2- and 3-year predictions.ConclusionsRadiomic signatures and clinical factors have prognostic value for OS in advanced (IIIB–IV) lung adenocarcinoma patients. The optimal model should be selected according to different cut-off time points in clinical application.

scholarly journals Development and validation of a radiomics nomogram for identification of severity of patients with COVID-19

2020 ◽  
Author(s):  
Jian Wang ◽  
Zhihua Xu ◽  
Guohua Cheng ◽  
Qiuxiang Hu ◽  
Linyang He ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Chest Ct ◽  
Multivariable Logistic Regression Analysis ◽  
Good Discrimination ◽  
Decision Curve Analysis ◽  
Selection Operator ◽  
Lasso Method ◽  
Development And Validation ◽  
Radiomics Signature ◽  
Good Calibration

Abstract Background The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severe COVID-19 determines the management and treatment, even prognosis. Thus, we aim to develop and validate a radiomics nomogram for identifying severe patients with COVID-19.Methods There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness.Results The radiomics signature consisting of 4 selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P < 0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts, showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19.Conclusions We present an easy-to-use radiomics nomogram to identify the severe patients with COVID-19 for better guiding a prompt management and treatment.

scholarly journals CT-based Radiomic Nomogram for Predicting the Severity of Patients With COVID-19

2021 ◽  
Author(s):  
Hengfeng Shi ◽  
Zhihua Xu ◽  
Guohua Cheng ◽  
Hongli Ji ◽  
Linyang He ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Chest Ct ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Good Discrimination ◽  
Decision Curve Analysis ◽  
Selection Operator ◽  
Lasso Method ◽  
Radiomics Signature ◽  
Good Calibration

Abstract Background: The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severe COVID-19 determines the management and treatment, even prognosis. We aim to develop and validate a radiomics nomogram for identifying severe patients with COVID-19. To develop and validate a radiomics nomogram for identifying severe patients with COVID-19.Methods: There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness.Results: The radiomics signature consisting of 4 selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P<0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts, showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19.Conclusion: We present an easy-to-use radiomics nomogram to identify the severe patients with COVID-19 for better guiding a prompt management and treatment.

scholarly journals Preoperative Nomogram for Differentiation of Histological Subtypes in Ovarian Cancer Based on Computer Tomography Radiomics

2021 ◽  
Vol 11 ◽  
Author(s):  
Haiyan Zhu ◽  
Yao Ai ◽  
Jindi Zhang ◽  
Ji Zhang ◽  
Juebin Jin ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Clinical Factors ◽  
Histological Subtypes ◽  
Net Benefit ◽  
Threshold Probability ◽  
Combined Model ◽  
Preoperative Ct ◽  
Selection Operator ◽  
Radiomics Signature

ObjectivesNon-invasive method to predict the histological subtypes preoperatively is essential for the overall management of ovarian cancer (OC). The feasibility of radiomics in the differentiating of epithelial ovarian cancer (EOC) and non-epithelial ovarian cancer (NEOC) based on computed tomography (CT) images was investigated.MethodsRadiomics features were extracted from preoperative CT for 101 patients with pathologically proven OC. Radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) logistic regression. A nomogram was developed with the combination of radiomics features and clinical factors to differentiate EOC and NEOC.ResultsEight radiomics features were selected to build a radiomics signature with an area under curve (AUC) of 0.781 (95% confidence interval (CI), 0.666 -0.897) in the discrimination between EOC and NEOC. The AUC of the combined model integrating clinical factors and radiomics features was 0.869 (95% CI, 0.783 -0.955). The nomogram demonstrated that the combined model provides a better net benefit to predict histological subtypes compared with radiomics signature and clinical factors alone when the threshold probability is within a range from 0.43 to 0.97.ConclusionsNomogram developed with CT radiomics signature and clinical factors is feasible to predict the histological subtypes preoperative for patients with OC.

scholarly journals Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Yun Bian ◽  
Shiwei Guo ◽  
Hui Jiang ◽  
Suizhi Gao ◽  
Chengwei Shao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Validation Cohort ◽  
Ductal Adenocarcinoma ◽  
Arterial Phase ◽  
Multivariable Logistic Regression Analysis ◽  
Preoperative Prediction ◽  
Multivariable Logistic Regression ◽  
Radiomics Signature

Abstract Purpose To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC). Materials and methods In this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA). Results The radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68–0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69–0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful. Conclusions The presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC.

scholarly journals A Radiomics Signature-Based Nomogram to Predict the Progression-Free Survival of Patients With Hepatocellular Carcinoma After Transcatheter Arterial Chemoembolization Plus Radiofrequency Ablation

2021 ◽  
Vol 8 ◽  
Author(s):  
Shiji Fang ◽  
Linqiang Lai ◽  
Jinyu Zhu ◽  
Liyun Zheng ◽  
Yuanyuan Xu ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Progression Free Survival ◽  
Clinical Factors ◽  
Combined Model ◽  
Free Survival ◽  
Training Cohort ◽  
Clinical Model ◽  
Cox Analysis ◽  
Radiomics Signature ◽  
Arterial Chemoembolization

Objective: The study aims to establish an magnetic resonance imaging radiomics signature-based nomogram for predicting the progression-free survival of intermediate and advanced hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE) plus radiofrequency ablationMaterials and Methods: A total of 113 intermediate and advanced HCC patients treated with TACE and RFA were eligible for this study. Patients were classified into a training cohort (n = 78 cases) and a validation cohort (n = 35 cases). Radiomics features were extracted from contrast-enhanced T1W images by analysis kit software. Dimension reduction was conducted to select optimal features using the least absolute shrinkage and selection operator (LASSO). A rad-score was calculated and used to classify the patients into high-risk and low-risk groups and further integrated into multivariate Cox analysis. Two prediction models based on radiomics signature combined with or without clinical factors and a clinical model based on clinical factors were developed. A nomogram comcined radiomics signature and clinical factors were established and the concordance index (C-index) was used for measuring discrimination ability of the model, calibration curve was used for measuring calibration ability, and decision curve and clinical impact curve are used for measuring clinical utility.Results: Eight radiomics features were selected by LASSO, and the cut-off of the Rad-score was 1.62. The C-index of the radiomics signature for PFS was 0.646 (95%: 0.582–0.71) in the training cohort and 0.669 (95% CI:0.572–0.766) in validation cohort. The median PFS of the low-risk group [30.4 (95% CI: 19.41–41.38)] months was higher than that of the high-risk group [8.1 (95% CI: 4.41–11.79)] months in the training cohort (log rank test, z = 16.58, p &lt; 0.001) and was verified in the validation cohort. Multivariate Cox analysis showed that BCLC stage [hazard ratio (HR): 2.52, 95% CI: 1.42–4.47, p = 0.002], AFP level (HR: 2.01, 95% CI: 1.01–3.99 p = 0.046), time interval (HR: 0.48, 95% CI: 0.26–0.87, p = 0.016) and radiomics signature (HR 2.98, 95% CI: 1.60–5.51, p = 0.001) were independent prognostic factors of PFS in the training cohort. The C-index of the combined model in the training cohort was higher than that of clinical model for PFS prediction [0.722 (95% CI: 0.657–0.786) vs. 0.669 (95% CI: 0.657–0.786), p＜0.001]. Similarly, The C-index of the combined model in the validation cohort, was higher than that of clinical model [0.821 (95% CI: 0.726–0.915) vs. 0.76 (95% CI: 0.667–0.851), p = 0.004]. The calibration curve, decision curve and clinical impact curve showed that the nomogram can be used to accurately predict the PFS of patients.Conclusion: The radiomics signature was a prognostic risk factor, and a nomogram combined radiomics and clinical factors acts as a new strategy for predicted the PFS of intermediate and advanced HCC treated with TACE plus RFA.

scholarly journals Plasma cells shape the mesenchymal identity of ovarian cancers through transfer of exosome-derived microRNAs

2021 ◽  
Vol 7 (9) ◽  
pp. eabb0737
Author(s):  
Zhengnan Yang ◽  
Wei Wang ◽  
Linjie Zhao ◽  
Xin Wang ◽  
Ryan C. Gimple ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Cells ◽  
Plasma Cell ◽  
Plasma Cells ◽  
Ovarian Cancer Cells ◽  
Mesenchymal Phenotype ◽  
Ovarian Cancers ◽  
Novel Approach

Ovarian cancer represents a highly lethal disease that poses a substantial burden for females, with four main molecular subtypes carrying distinct clinical outcomes. Here, we demonstrated that plasma cells, a subset of antibody-producing B cells, were enriched in the mesenchymal subtype of high-grade serous ovarian cancers (HGSCs). Plasma cell abundance correlated with the density of mesenchymal cells in clinical specimens of HGSCs. Coculture of nonmesenchymal ovarian cancer cells and plasma cells induced a mesenchymal phenotype of tumor cells in vitro and in vivo. Phenotypic switch was mediated by the transfer of plasma cell–derived exosomes containing miR-330-3p into nonmesenchymal ovarian cancer cells. Exosome-derived miR-330-3p increased expression of junctional adhesion molecule B in a noncanonical fashion. Depletion of plasma cells by bortezomib reversed the mesenchymal characteristics of ovarian cancer and inhibited in vivo tumor growth. Collectively, our work suggests targeting plasma cells may be a novel approach for ovarian cancer therapy.

scholarly journals Colorectal Cancer Risk in Women with Gynecologic Cancers—A Population Retrospective Cohort Study

2021 ◽  
Vol 10 (14) ◽  
pp. 3127
Author(s):  
Szu-Chia Liao ◽  
Hong-Zen Yeh ◽  
Chi-Sen Chang ◽  
Wei-Chih Chen ◽  
Chih-Hsin Muo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Ovarian Cancer ◽  
Cervical Cancer ◽  
Cohort Study ◽  
Cancer Patients ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Gynecologic Cancer ◽  
Ovarian Cancers ◽  
Increased Risk

We conducted a retrospective cohort study to evaluate the subsequent colorectal cancer (CRC) risk for women with gynecologic malignancy using insurance claims data of Taiwan. We identified patients who survived cervical cancer (N = 25,370), endometrial cancer (N = 8149) and ovarian cancer (N = 7933) newly diagnosed from 1998 to 2010, and randomly selected comparisons (N = 165,808) without cancer, matched by age and diagnosis date. By the end of 2011, the incidence and hazard ratio (HR) of CRC were estimated. We found that CRC incidence rates were 1.26-, 2.20-, and 1.61-fold higher in women with cervical, endometrial and ovarian cancers, respectively, than in comparisons (1.09/1000 person–years). The CRC incidence increased with age. Higher adjusted HRs of CRC appeared within 3 years for women with endometrial and ovarian cancers, but not until the 4th to 7th years of follow up for cervical cancer survivals. Cancer treatments could reduce CRC risks, but not significantly. However, ovarian cancer patients receiving surgery alone had an incidence of 3.33/1000 person–years for CRC with an adjusted HR of 3.79 (95% CI 1.11–12.9) compared to patients without any treatment. In conclusion, gynecologic cancer patients are at an increased risk of developing CRC, sooner for those with endometrial or ovarian cancer than those with cervical cancer.

scholarly journals Management of hereditary ovarian cancer

2011 ◽  
Vol 152 (40) ◽  
pp. 1596-1608 ◽  
Author(s):  
József Gábor Joó ◽  
Szabolcs Ládi ◽  
B. Zsolt Nagy ◽  
Zoltán Langmár
Keyword(s):  
Ovarian Cancer ◽  
Hereditary Ovarian Cancer ◽  
Brca1 And Brca2 ◽  
Ovarian Cancers ◽  
Brca2 Mutations ◽  
Histological Phenotype ◽  
Brca1 Brca2 ◽  
Brca1 And Brca2 Mutations ◽  
High Level ◽  
Associated Tumors

Mutations in BRCA1 and BRCA2 genes account for the majority of hereditary breast and ovarian cancers. Approximately 10% of cases of ovarian cancer are due to germline mutations in BRCA1 and BRCA2. Ovarian cancer associated with BRCA1 and BRCA2 mutations has a distinct histological phenotype. This type of cancer is predominantly of serous or endometrioid histology and is high grade. Patients with BRCA1 or BRCA2 mutations should be offered risk-reducing salpingo-oophorectomy by age 40 years, or when childbearing is complete. Nowadays there are no differences between the treatments provided for sporadic and hereditary ovarian cancer, although there are indications that targeted therapy is effective in women with BRCA1/BRCA2-associated tumors. Retrospective studies reveal a high level of sensitivity to platinum agents in BRCA-associated tumors and initial trials show good efficacy and tolerability for polyADP-ribose polymerase inhibitors in mutation carriers with advanced ovarian cancers. These agents might also potentially be used in chemoprevention. Authors review the current management of hereditary ovarian cancer. Orv. Hetil., 2011, 152, 1596–1608.

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