scholarly journals Computed Tomography-Based Radiomics Nomogram: Potential to Predict Local Recurrence of Gastric Cancer After Radical Resection

2021 ◽  
Vol 11 ◽  
Author(s):  
Liebin Huang ◽  
Bao Feng ◽  
Yueyue Li ◽  
Yu Liu ◽  
Yehang Chen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Computed Tomography ◽  
Validation Cohort ◽  
Radical Resection ◽  
Curve Analysis ◽  
Internal Validation ◽  
Training Cohort ◽  
Clinical Model ◽  
Radiomics Signature

ObjectiveAccurate prediction of postoperative recurrence risk of gastric cancer (GC) is critical for individualized precision therapy. We aimed to investigate whether a computed tomography (CT)-based radiomics nomogram can be used as a tool for predicting the local recurrence (LR) of GC after radical resection.Materials and Methods342 patients (194 in the training cohort, 78 in the internal validation cohort, and 70 in the external validation cohort) with pathologically proven GC from two centers were included. Radiomics features were extracted from the preoperative CT imaging. The clinical model, radiomics signature, and radiomics nomogram, which incorporated the radiomics signature and independent clinical risk factors, were developed and verified. Furthermore, the performance of these three models was assessed by using the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).ResultsThe radiomics signature, which was comprised of two selected radiomics features, namely, contrast_GLCM and dissimilarity_GLCM, showed better performance than the clinical model in predicting the LR of GC, with AUC values of 0.83 in the training cohort, 0.84 in the internal validation cohort, and 0.73 in the external cohort, respectively. By integrating the independent clinical risk factors (N stage, bile acid duodenogastric reflux and nodular or irregular outer layer of the gastric wall) into the radiomics signature, the radiomics nomogram achieved the highest accuracy in predicting LR, with AUC values of 0.89, 0.89 and 0.80 in the three cohorts, respectively. DCA in the validation cohort showed that radiomics nomogram added more net benefit than the clinical model within the range of 0.01-0.98.ConclusionThe CT-based radiomics nomogram has the potential to predict the LR of GC after radical resection.

scholarly journals Prediction of 90-day mortality among sepsis patients based on a nomogram integrating diverse clinical indices

2021 ◽  
Author(s):  
Qing-Bo Zeng ◽  
Long-Ping He ◽  
Nian-Qing Zhang ◽  
Qing-Wei Lin ◽  
Lin-Cui Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Validation Cohort ◽  
International Normalized Ratio ◽  
Curve Analysis ◽  
Individual Risk ◽  
Decision Curve Analysis ◽  
Training Cohort ◽  
Receiver Operating Characteristic Curves ◽  
Individual Risk Factors

Abstract Background Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work aimed to define a nomogram for reliably predicting mortality. Methods We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots and decision curve analysis. Results Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. Conclusion This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

scholarly journals Acute Tumor Transition Angle on Computed Tomography Predicts Chromosomal Instability Status of Primary Gastric Cancer: Radiogenomics Analysis from TCGA and Independent Validation

Cancers ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 641 ◽  
Author(s):  
Ying-Chieh Lai ◽  
Ta-Sen Yeh ◽  
Ren-Chin Wu ◽  
Cheng-Kun Tsai ◽  
Lan-Yan Yang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Computed Tomography ◽  
Chromosomal Instability ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Genomic Analysis ◽  
Ct Imaging ◽  
The Cancer Genome Atlas ◽  
Tumor Diameter ◽  
Training Cohort

Chromosomal instability (CIN) of gastric cancer is correlated with distinct outcomes. This study aimed to investigate the role of computed tomography (CT) imaging traits in predicting the CIN status of gastric cancer. We screened 443 patients in the Cancer Genome Atlas gastric cancer cohort to filter 40 patients with complete CT imaging and genomic data as the training cohort. CT imaging traits were subjected to logistic regression to select independent predictors for the CIN status. For the validation cohort, we prospectively enrolled 18 gastric cancer patients for CT and tumor genomic analysis. The imaging predictors were tested in the validation cohort using receiver operating characteristic curve (ROC) analysis. Thirty patients (75%) in the training cohort and 9 patients (50%) in the validation cohort had CIN subtype gastric cancers. Smaller tumor diameter (p = 0.017) and acute tumor transition angle (p = 0.045) independently predict CIN status in the training cohort. In the validation cohort, acute tumor transition angle demonstrated the highest accuracy, sensitivity, and specificity of 88.9%, 88.9%, and 88.9%, respectively, and areas under ROC curve of 0.89. In conclusion, this pilot study showed acute tumor transition angle on CT images may predict the CIN status of gastric cancer.

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 Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuntai Cao ◽  
Guojin Zhang ◽  
Jing Zhang ◽  
Yingjie Yang ◽  
Jialiang Ren ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Computed Tomography ◽  
Validation Cohort ◽  
Excellent Performance ◽  
Training Cohort ◽  
Enhanced Ct ◽  
Venous Phase ◽  
Phase Models ◽  
Colorectal Cancer Patients ◽  
Radiomics Signature

BackgroundThis study aimed to develop and validate a computed tomography (CT)-based radiomics model to predict microsatellite instability (MSI) status in colorectal cancer patients and to identify the radiomics signature with the most robust and high performance from one of the three phases of triphasic enhanced CT.MethodsIn total, 502 colorectal cancer patients with preoperative contrast-enhanced CT images and available MSI status (441 in the training cohort and 61 in the external validation cohort) were enrolled from two centers in our retrospective study. Radiomics features of the entire primary tumor were extracted from arterial-, delayed-, and venous-phase CT images. The least absolute shrinkage and selection operator method was used to retain the features closely associated with MSI status. Radiomics, clinical, and combined Clinical Radiomics models were built to predict MSI status. Model performance was evaluated by receiver operating characteristic curve analysis.ResultsThirty-two radiomics features showed significant correlation with MSI status. Delayed-phase models showed superior predictive performance compared to arterial- or venous-phase models. Additionally, age, location, and carcinoembryonic antigen were considered useful predictors of MSI status. The Clinical Radiomics nomogram that incorporated both clinical risk factors and radiomics parameters showed excellent performance, with an AUC, accuracy, and sensitivity of 0.898, 0.837, and 0.821 in the training cohort and 0.964, 0.918, and 1.000 in the validation cohort, respectively.ConclusionsThe proposed CT-based radiomics signature has excellent performance in predicting MSI status and could potentially guide individualized therapy.

Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

2016 ◽  
Vol 34 (18) ◽  
pp. 2157-2164 ◽  
Author(s):  
Yan-qi Huang ◽  
Chang-hong Liang ◽  
Lan He ◽  
Jie Tian ◽  
Cui-shan Liang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Lymph Node ◽  
Validation Cohort ◽  
Good Discrimination ◽  
Internal Validation ◽  
Preoperative Prediction ◽  
Individualized Prediction ◽  
Radiomics Signature ◽  
Good Calibration

Purpose To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC). Patients and Methods The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous–phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011. Results The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Conclusion This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.

scholarly journals Prediction of 90-Day Mortality among Sepsis Patients Based on a Nomogram Integrating Diverse Clinical Indices

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Qingbo Zeng ◽  
Longping He ◽  
Nianqing Zhang ◽  
Qingwei Lin ◽  
Lincui Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Validation Cohort ◽  
International Normalized Ratio ◽  
Curve Analysis ◽  
Individual Risk ◽  
Decision Curve Analysis ◽  
Training Cohort ◽  
Receiver Operating Characteristic Curves ◽  
Individual Risk Factors

Background. Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work was aimed at defining a nomogram for reliably predicting mortality. Methods. We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis. Results. Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. Conclusion. This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

scholarly journals Pretreatment Prediction of Relapse Risk in Patients with Osteosarcoma Using Radiomics Nomogram Based on CT: A Retrospective Multicenter Study

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jin Liu ◽  
Tao Lian ◽  
Haimei Chen ◽  
Xiaohong Wang ◽  
Xianyue Quan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Multicenter Study ◽  
Cox Regression ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Relapse Risk ◽  
Significant Difference ◽  
Prediction Of Relapse ◽  
Radiomics Signature

Objective. To develop and externally validate a CT-based radiomics nomogram for pretreatment prediction of relapse in osteosarcoma patients within one year. Materials and Methods. In this multicenter retrospective study, a total of 80 patients (training cohort: 63 patients from three hospitals; validation cohort: 17 patients from three other hospitals) with osteosarcoma, undergoing pretreatment CT between August 2010 and December 2018, were identified from multicenter databases. Radiomics features were extracted and selected from tumor regions on CT image, and then, the radiomics signature was constructed. The radiomics nomogram that incorporated the radiomics signature and clinical-based risk factors was developed to predict relapse risk with a multivariate Cox regression model using the training cohort and validated using the external validation cohort. The performance of the nomogram was assessed concerning discrimination, calibration, reclassification, and clinical usefulness. Results. Kaplan-Meier curves based on the radiomics signature showed a significant difference between the high-risk and the low-risk groups in both training and validation cohorts ( P < 0.001 and P = 0.015 , respectively). The radiomics nomogram achieved good discriminant results in the training cohort ( C -index: 0.779) and the validation cohort ( C -index: 0.710) as well as good calibration. Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinical-based nomogram ( P < 0.001 ). Conclusions. This multicenter study demonstrates that a radiomics nomogram incorporated the radiomics signature and clinical-based risk factors can increase the predictive value of the osteosarcoma relapse risk, which supports the clinical application in different institutions.

scholarly journals Dual-Energy Computed Tomography-Based Radiomics to Predict Peritoneal Metastasis in Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yong Chen ◽  
Wenqi Xi ◽  
Weiwu Yao ◽  
Lingyun Wang ◽  
Zhihan Xu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Computed Tomography ◽  
Peritoneal Metastasis ◽  
Primary Tumor ◽  
Dual Energy ◽  
Training Cohort ◽  
Clinical Model ◽  
Dual Energy Computed Tomography ◽  
Appropriate Treatment ◽  
Conventional Ct

ObjectiveTo develop and validate a dual-energy computed tomography (DECT) derived radiomics model to predict peritoneal metastasis (PM) in patients with gastric cancer (GC).MethodsThis retrospective study recruited 239 GC (non-PM = 174, PM = 65) patients with histopathological confirmation for peritoneal status from January 2015 to December 2019. All patients were randomly divided into a training cohort (n = 160) and a testing cohort (n = 79). Standardized iodine-uptake (IU) images and 120-kV-equivalent mixed images (simulating conventional CT images) from portal-venous and delayed phases were used for analysis. Two regions of interest (ROIs) including the peritoneal area and the primary tumor were independently delineated. Subsequently, 1691 and 1226 radiomics features were extracted from the peritoneal area and the primary tumor from IU and mixed images on each phase. Boruta and Spearman correlation analysis were used for feature selection. Three radiomics models were established, including the R_IU model for IU images, the R_MIX model for mixed images and the combined radiomics model (the R_comb model). Random forest was used to tune the optimal radiomics model. The performance of the clinical model and human experts to assess PM was also recorded.ResultsFourteen and three radiomics features with low redundancy and high importance were extracted from the IU and mixed images, respectively. The R_IU model showed significantly better performance to predict PM than the R_MIX model in the training cohort (AUC, 0.981 vs. 0.917, p = 0.034). No improvement was observed in the R_comb model (AUC = 0.967). The R_IU model was the optimal radiomics model which showed no overfitting in the testing cohort (AUC = 0.967, p = 0.528). The R_IU model demonstrated significantly higher predictive value on peritoneal status than the clinical model and human experts in the testing cohort (AUC, 0.785, p = 0.005; AUC, 0.732, p &lt;0.001, respectively).ConclusionDECT derived radiomics could serve as a non-invasive and easy-to-use biomarker to preoperatively predict PM for GC, providing opportunity for those patients to tailor appropriate treatment.

scholarly journals A Computed Tomography-Derived Radiomics Approach for Predicting Uncommon EGFR Mutation in Patients With NSCLC

2021 ◽  
Vol 11 ◽  
Author(s):  
Wufei Chen ◽  
Yanqing Hua ◽  
Dingbiao Mao ◽  
Hao Wu ◽  
Mingyu Tan ◽  
...  
Keyword(s):  
Egfr Mutation ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Integrated Model ◽  
Curve Analysis ◽  
Support Vector ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Nsclc Patients ◽  
Radiomics Signature

PurposeThis study aims to develop a CT-based radiomics approach for identifying the uncommon epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer (NSCLC).MethodsThis study involved 223 NSCLC patients (107 with uncommon EGFR mutation-positive and 116 with uncommon EGFR mutation-negative). A total of 1,269 radiomics features were extracted from the non-contrast-enhanced CT images after image segmentation and preprocessing. Support vector machine algorithm was used for feature selection and model construction. Receiver operating characteristic curve analysis was applied to evaluate the performance of the radiomics signature, the clinicopathological model, and the integrated model. A nomogram was developed and evaluated by using the calibration curve and decision curve analysis.ResultsThe radiomics signature demonstrated a good performance for predicting the uncommon EGFR mutation in the training cohort (area under the curve, AUC = 0.802; 95% confidence interval, CI: 0.736–0.858) and was verified in the validation cohort (AUC = 0.791, 95% CI: 0.642–0.899). The integrated model combined radiomics signature with clinicopathological independent predictors exhibited an incremental performance compared with the radiomics signature or the clinicopathological model. A nomogram based on the integrated model was developed and showed good calibration (Hosmer–Lemeshow test, P = 0.92 in the training cohort and 0.608 in the validation cohort) and discrimination capacity (AUC of 0.816 in the training cohort and 0.795 in the validation cohort).ConclusionRadiomics signature combined with the clinicopathological features can predict uncommon EGFR mutation in NSCLC patients.

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.

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