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Radiomics Signature: A potential biomarker for the prediction of survival in Advanced Hepatocellular Carcinoma.

ABSTRACT: Objectives: To develop and validate radiomics nomograms for the pretreatment predictions of overall survival (OS) and time to progression (TTP) in the patients with advanced hepatocellular carcinoma (HCC) treated with apatinib plus transarterial chemoembolization (TACE), and to assess the incremental value of the clinical-radiomics nomograms for estimating individual OS and TTP. Methods: A total of 60 patients with advanced HCC (BCLC stage C) treated with apatinib plus TACE were divided into a training set (n=48) and a validation set (n=12). The predictors identified from the clinical variables and the radiomics signature constructed from the computed tomography images, such as ɑ-fetoprotein level (AFP), formfactor, the grey level co-occurrence matrix, the gray level size zone matrix, and the gray level run-length matrix, were used to build the clinical-radiomics nomograms and the radiomics nomograms for the prediction of OS and TTP. Results: Apatinib plus TACE benefited the patients with advanced HCC, with a 579-day median OS and a 270-day median TTP. The nomograms were built with the radiomics signature and AFP, and achieved favorable prediction efficacy with acceptable calibration curves. Decision curve analyses demonstrated that the clinical-radiomics nomograms outperformed the radiomics nomograms for the predictions of OS and TTP. Conclusions: Apatinib plus TACE may improve OS and prolonged TTP in the patients with advanced HCC. The clinical-radiomics nomograms, a noninvasive pretreatment prediction tool that incorporate radiomics signature and AFP, demonstrated good prediction accuracy for OS and TTP in these patients. These results indicate that the clinical-radiomics nomograms may provide novel insight for precise personalized medicine approaches in the patients with advanced HCC.

SUBMITTER: Li L

PROVIDER: S-EPMC8100633 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of this study was to investigate the prognostic value of computed tomography (CT) texture parameters in patients with HCC after hepatectomy and to develop a radiomics nomogram by combining clinicopathological factors and the radiomics signature.MethodsIn all, 544 eligible patients were enrolled in this retrospective study and were randomly divided into the training cohort (n = 381) and the validation cohort (n = 163). The tumor regions of interest (ROIs) were delineated, and the corresponding texture parameters were extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in the training cohort, and a radiomics signature was established. Then, the radiomics signature was further validated as an independent risk factor for overall survival (OS). The radiomics nomogram was established based on the Cox regression model. The concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram.ResultsThe radiomics signature was formulated based on 7 OS-related texture parameters, which were selected in the training cohort. In addition, the radiomics nomogram was developed based on the following five variables: α-fetoprotein (AFP), platelet-to-lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and radiomics score (Rad-score). The nomogram displayed good accuracy in predicting OS (C-index = 0.747) in the training cohort and was confirmed in the validation cohort (C-index = 0.777). The calibration plots also showed excellent agreement between the actual and predicted survival probabilities. The DCA indicated that the radiomics nomogram showed better clinical utility than the clinicopathologic nomogram.ConclusionThe radiomics signature is a potential prognostic biomarker of HCC after hepatectomy. The radiomics nomogram that integrated the radiomics signature can provide a more accurate estimation of OS than the clinicopathologic nomogram for HCC patients after hepatectomy.

Project description:BACKGROUND:This study aims to establish a radiomics analysis system for the diagnosis and clinical behaviour prediction of hepatocellular carcinoma (HCC) based on multi-parametric ultrasound imaging. METHODS:A total of 177 patients with focal liver lesions (FLLs) were included in the study. Every patient underwent multi-modal ultrasound examination, including B-mode ultrasound (BMUS), shear wave elastography (SWE), and shear wave viscosity (SWV) imaging. The radiomics analysis system was built on sparse representation theory (SRT) and support vector machine (SVM) for asymmetric data. Through the sparse regulation from the SRT, the proposed radiomics system can effectively avoid over-fitting issues that occur in regular radiomics analysis. The purpose of the proposed system includes differential diagnosis between benign and malignant FLLs, pathologic diagnosis of HCC, and clinical prognostic prediction. Three biomarkers, including programmed cell death protein 1 (PD-1), antigen Ki-67 (Ki-67) and microvascular invasion (MVI), were included and analysed. We calculated the accuracy (ACC), sensitivity (SENS), specificity (SPEC) and area under the receiver operating characteristic curve (AUC) to evaluate the performance of the radiomics models. RESULTS:A total of 2560 features were extracted from the multi-modal ultrasound images for each patient. Five radiomics models were built, and leave-one-out cross-validation (LOOCV) was used to evaluate the models. In LOOCV, the AUC was 0.94 for benign and malignant classification (95% confidence interval [CI]: 0.88 to 0.98), 0.97 for malignant subtyping (95% CI: 0.93 to 0.99), 0.97 for PD-1 prediction (95% CI: 0.89 to 0.98), 0.94 for Ki-67 prediction (95% CI: 0.87 to 0.97), and 0.98 for MVI prediction (95% CI: 0.93 to 0.99). The performance of each model improved when the viscosity modality was included. CONCLUSIONS:Radiomics analysis based on multi-modal ultrasound images could aid in comprehensive liver tumor evaluations, including diagnosis, differential diagnosis, and clinical prognosis.

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Radiomics Signature: A potential biomarker for the prediction of survival in Advanced Hepatocellular Carcinoma.

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