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MRI-Radiomics Prediction for Cytokeratin 19-Positive Hepatocellular Carcinoma: A Multicenter Study.

ABSTRACT: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and has poor prognosis. Cytokeratin (CK)19-positive (CK19+) HCC is especially aggressive; early identification of this subtype and timely intervention can potentially improve clinical outcomes. In the present study, we developed a preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI)-based radiomics model for noninvasive and accurate classification of CK19+ HCC. A multicenter and time-independent cohort of 257 patients were retrospectively enrolled (training cohort, n = 143; validation cohort A, n = 75; validation cohort B, n = 39). A total of 968 radiomics features were extracted from preoperative multisequence MR images. The maximum relevance minimum redundancy algorithm was applied for feature selection. Multiple logistic regression, support vector machine, random forest, and artificial neural network (ANN) algorithms were used to construct the radiomics model, and the area under the receiver operating characteristic (AUROC) curve was used to evaluate the diagnostic performance of corresponding classifiers. The incidence of CK19+ HCC was significantly higher in male patients. The ANN-derived combined classifier comprising 12 optimal radiomics features showed the best diagnostic performance, with AUROCs of 0.857, 0.726, and 0.790 in the training cohort and validation cohorts A and B, respectively. The combined model based on multisequence MRI radiomics features can be used for preoperative noninvasive and accurate classification of CK19+ HCC, so that personalized management strategies can be developed.

SUBMITTER: Yang F

PROVIDER: S-EPMC8406635 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRadiomics have been increasingly used in the clinical management of hepatocellular carcinoma (HCC), such as markers prediction. Ki-67 and cytokeratin 19 (CK-19) are important prognostic markers of HCC. Radiomics has been introduced by many researchers in the prediction of these markers expression, but its diagnostic value remains controversial. Therefore, this review aims to assess the diagnostic value of radiomics in predicting Ki-67 and CK-19 expression in HCC.MethodsOriginal studies were systematically searched in PubMed, EMBASE, Cochrane Library, and Web of Science from inception to May 2023. All included studies were evaluated by the radiomics quality score. The C-index was used as the effect size of the performance of radiomics in predicting Ki-67and CK-19 expression, and the positive cutoff values of Ki-67 label index (LI) were determined by subgroup analysis and meta-regression.ResultsWe identified 34 eligible studies for Ki-67 (18 studies) and CK-19 (16 studies). The most common radiomics source was magnetic resonance imaging (MRI; 25/34). The pooled C-index of MRI-based models in predicting Ki-67 was 0.89 (95% CI:0.86-0.92) in the training set, and 0.87 (95% CI: 0.82-0.92) in the validation set. The pooled C-index of MRI-based models in predicting CK-19 was 0.86 (95% CI:0.81-0.90) in the training set, and 0.79 (95% CI: 0.73-0.84) in the validation set. Subgroup analysis suggested Ki-67 LI cutoff was a significant source of heterogeneity (I 2 = 0.0% P>0.05), and meta-regression showed that the C-index increased as Ki-67 LI increased.ConclusionRadiomics shows promising diagnostic value in predicting positive Ki-67 or CK-19 expression. But lacks standardized guidelines, which makes the model and variables selection dependent on researcher experience, leading to study heterogeneity. Therefore, standardized guidelines are warranted for future research.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023427953.

Project description:ObjectiveTo assess the value of qualitative and quantitative MRI radiomics features for noninvasive prediction of immuno-oncologic characteristics and outcomes of hepatocellular carcinoma (HCC).MethodsThis retrospective, IRB-approved study included 48 patients with HCC (M/F 35/13, mean age 60y) who underwent hepatic resection or transplant within 4 months of abdominal MRI. Qualitative imaging traits, quantitative nontexture related and texture features were assessed in index lesions on contrast-enhanced T1-weighted and diffusion-weighted images. The association of imaging features with immunoprofiling and genomics features was assessed using binary logistic regression and correlation analyses. Binary logistic regression analysis was also employed to analyse the association of radiomics, histopathologic and genomics features with radiological early recurrence of HCC at 12 months.ResultsQualitative (r = - 0.41-0.40, p < 0.042) and quantitative (r = - 0.52-0.45, p < 0.049) radiomics features correlated with immunohistochemical cell type markers for T-cells (CD3), macrophages (CD68) and endothelial cells (CD31). Radiomics features also correlated with expression of immunotherapy targets PD-L1 at protein level (r = 0.41-0.47, p < 0.029) as well as PD1 and CTLA4 at mRNA expression level (r = - 0.48-0.47, p < 0.037). Finally, radiomics features, including tumour size, showed significant diagnostic performance for assessment of early HCC recurrence (AUC 0.76-0.80, p < 0.043), while immunoprofiling and genomic features did not (p = 0.098-0929).ConclusionsMRI radiomics features may serve as noninvasive predictors of HCC immuno-oncological characteristics and tumour recurrence and may aid in treatment stratification of HCC patients. These results need prospective validation.Key points• MRI radiomics features showed significant associations with immunophenotyping and genomics characteristics of hepatocellular carcinoma. • Radiomics features, including tumour size, showed significant associations with early hepatocellular carcinoma recurrence after resection.

Project description:PurposeTo investigate the role of contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics for pretherapeutic prediction of the response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC).MethodsOne hundred and twenty-two HCC patients (objective response, n = 63; non-response, n = 59) who received CE-MRI examination before initial TACE were retrospectively recruited and randomly divided into a training cohort (n = 85) and a validation cohort (n = 37). All HCCs were manually segmented on arterial, venous and delayed phases of CE-MRI, and total 2367 radiomics features were extracted. Radiomics models were constructed based on each phase and their combination using logistic regression algorithm. A clinical-radiological model was built based on independent risk factors identified by univariate and multivariate logistic regression analyses. A combined model incorporating the radiomics score and selected clinical-radiological predictors was constructed, and the combined model was presented as a nomogram. Prediction models were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.ResultsAmong all radiomics models, the three-phase radiomics model exhibited better performance in the training cohort with an area under the curve (AUC) of 0.838 (95% confidence interval (CI), 0.753 - 0.922), which was verified in the validation cohort (AUC, 0.833; 95% CI, 0.691 - 0.975). The combined model that integrated the three-phase radiomics score and clinical-radiological risk factors (total bilirubin, tumor shape, and tumor encapsulation) showed excellent calibration and predictive capability in the training and validation cohorts with AUCs of 0.878 (95% CI, 0.806 - 0.950) and 0.833 (95% CI, 0.687 - 0.979), respectively, and showed better predictive ability (P = 0.003) compared with the clinical-radiological model (AUC, 0.744; 95% CI, 0.642 - 0.846) in the training cohort. A nomogram based on the combined model achieved good clinical utility in predicting the treatment efficacy of TACE.ConclusionCE-MRI radiomics analysis may serve as a promising and noninvasive tool to predict therapeutic response to TACE in HCC, which will facilitate the individualized follow-up and further therapeutic strategies guidance in HCC patients.

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.

Project description:ObjectiveThis study aims to develop and externally validate a contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics-based model for preoperative differentiation between fat-poor angiomyolipoma (fp-AML) and hepatocellular carcinoma (HCC) in patients with noncirrhotic livers and to compare the diagnostic performance with that of two radiologists.MethodsThis retrospective study was performed with 165 patients with noncirrhotic livers from three medical centers. The dataset was divided into a training cohort (n = 99), a time-independent internal validation cohort (n = 24) from one center, and an external validation cohort (n = 42) from the remaining two centers. The volumes of interest were contoured on the arterial phase (AP) images and then registered to the venous phase (VP) and delayed phase (DP), and a total of 3,396 radiomics features were extracted from the three phases. After the joint mutual information maximization feature selection procedure, four radiomics logistic regression classifiers, including the AP model, VP model, DP model, and combined model, were built. The area under the receiver operating characteristic curve (AUC), diagnostic accuracy, sensitivity, and specificity of each radiomics model and those of two radiologists were evaluated and compared.ResultsThe AUCs of the combined model reached 0.789 (95%CI, 0.579-0.999) in the internal validation cohort and 0.730 (95%CI, 0.563-0.896) in the external validation cohort, higher than the AP model (AUCs, 0.711 and 0.638) and significantly higher than the VP model (AUCs, 0.594 and 0.610) and the DP model (AUCs, 0.547 and 0.538). The diagnostic accuracy, sensitivity, and specificity of the combined model were 0.708, 0.625, and 0.750 in the internal validation cohort and 0.619, 0.786, and 0.536 in the external validation cohort, respectively. The AUCs for the two radiologists were 0.656 and 0.594 in the internal validation cohort and 0.643 and 0.500 in the external validation cohort. The AUCs of the combined model surpassed those of the two radiologists and were significantly higher than that of the junior one in both validation cohorts.ConclusionsThe proposed radiomics model based on triple-phase CE-MRI images was proven to be useful for differentiating between fp-AML and HCC and yielded comparable or better performance than two radiologists in different centers, with different scanners and different scanning parameters.

Project description:BackgroundRadiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsTwo hundred and eight patients with pathologically confirmed HCC (training cohort: n = 146; validation cohort: n = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility.ResultsHigher α-fetoprotein level (p = 0.046), nonsmooth tumor margin (p = 0.003), arterial peritumoral enhancement (p < 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (p < 0.001) and HBP T1 maps (p < 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; p = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; p = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895-0.976) and 0.864 (95% CI 0.761-0.967) in the training and validation cohort, fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.ConclusionsThe nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

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MRI-Radiomics Prediction for Cytokeratin 19-Positive Hepatocellular Carcinoma: A Multicenter Study.

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