Radiomics for the Preoperative Evaluation of Microvascular Invasion in Hepatocellular Carcinoma: A Meta-Analysis

Li, Liujun; Wu, Chaoqun; Huang, Yongquan; Chen, Jiaxin; Ye, Dalin; Su, Zhongzhen

doi:10.3389/fonc.2022.831996

Cited by 22 publications

(16 citation statements)

References 46 publications

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“…In addition, we found significant heterogeneity in the pooled sensitivity and specificity. This observation is similar to that in published meta-analyses of diagnostic accuracy using radiomics (49)(50)(51)(52). Although radiomic models help predict the diagnosis of LNM in breast cancer, they involve numerous analysis methods.…”

Section: Discussionsupporting

confidence: 85%

Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis

et al. 2022

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BackgroundThis study aimed to perform a meta‐analysis to evaluate the diagnostic performance of radiomics in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM) in breast cancer.Materials and methodsMultiple electronic databases were systematically searched to identify relevant studies published before April 29, 2022: PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The overall diagnostic odds ratio (DOR), sensitivity, specificity, and area under the curve (AUC) were calculated to evaluate the diagnostic performance of radiomic features for lymph node metastasis (LNM) in patients with breast cancer. Spearman’s correlation coefficient was determined to assess the threshold effect, and meta-regression and subgroup analyses were performed to explore the possible causes of heterogeneity.ResultsA total of 30 studies with 5611 patients were included in the meta-analysis. Pooled estimates suggesting overall diagnostic accuracy of radiomics in detecting LNM were determined: DOR, 23 (95% CI, 16-33); sensitivity, 0.86 (95% CI, 0.82-0.88); specificity, 0.79 (95% CI, 0.73-0.84); and AUC, 0.90 (95% CI, 0.87-0.92). The meta-analysis showed significant heterogeneity between sensitivity and specificity across the included studies, with no evidence for a threshold effect. Meta-regression and subgroup analyses showed that combined clinical factors, modeling method, region, and imaging modality (magnetic resonance imaging [MRI], ultrasound, computed tomography [CT], and X-ray mammography [MMG]) contributed to the heterogeneity in the sensitivity analysis (P < 0.05). Furthermore, modeling methods, MRI, and MMG contributed to the heterogeneity in the specificity analysis (P < 0.05).ConclusionOur results show that radiomics has good diagnostic performance in predicting ALNM and SLNM in breast cancer. Thus, we propose this approach as a clinical method for the preoperative identification of LNM.

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Section: Discussionsupporting

confidence: 85%

Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis

et al. 2022

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“…The quantitative radiomics features can reflect the microscopic pathological changes of HCC by extracting features from the overall level of the tumor on the basis of conventional imaging images and evaluating the internal heterogeneity of the tumor (34,35). Several previous similar studies have demonstrated that radiomics has high accuracy in evaluating the MVI in HCC; however, all of these studies analyzed CT-, MRI-, and ultrasound-based radiomics (13)(14)(15). This meta-analysis demonstrates that radiomics-based MRI has high diagnostic performance for predicting the MVI of HCC and can be used as a reliable and quantitative method for the non-invasive diagnosis of MVI in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

“…MRI can also provide better soft-tissue resolution, multiparameters, and more stable features for assessing tumor heterogeneity. Previous similar studies have included CT-, MRI-, and US-combined radiomics original studies (13)(14)(15). Although they made a subgroup analysis of different imaging modalities, the number of MRI-based radiomics studies included was small.…”

Section: Introductionmentioning

confidence: 99%

“…As a new and non-invasive technology, radiomics can high-throughput-extract features from large quantities of images to improve diagnostic or prognostic accuracy, which is also effective to preoperatively predict MVI ( 13 ). As imaging markers, the extracted radiomics feature can reflect the microscopic pathological changes of the tumor ( Supplementary Figure S1 ), which is promising in the diagnosis of carcinomas ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

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The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis

Gao

et al. 2023

Front. Oncol.

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ObjectiveThe aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodThe databases of PubMed, Cochrane library, Embase, Web of Science, Ovid MEDLINE, Springer, and Science Direct were searched for original studies from their inception to 20 August 2022. The quality of each study included was assessed according to the Quality Assessment of Diagnostic Accuracy Studies 2 and the radiomics quality score. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was plotted and the area under the curve (AUC) was calculated to evaluate the diagnostic accuracy. Sensitivity analysis and subgroup analysis were performed to explore the source of the heterogeneity. Deeks’ test was used to assess publication bias. ResultsA total of 15 studies involving 981 patients were included. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.79 (95%CI: 0.72–0.85), 0.81 (95%CI: 0.73–0.87), 4.1 (95%CI:2.9–5.9), 0.26 (95%CI: 0.19–0.35), 16 (95%CI: 9–28), and 0.87 (95%CI: 0.84–0.89), respectively. The results showed great heterogeneity among the included studies. Sensitivity analysis indicated that the results of this study were statistically reliable. The results of subgroup analysis showed that hepatocyte-specific contrast media (HSCM) had equivalent sensitivity and equivalent specificity compared to the other set. The least absolute shrinkage and selection operator method had high sensitivity and specificity than other methods, respectively. The investigated area of the region of interest had high specificity compared to the volume of interest. The imaging-to-surgery interval of 15 days had higher sensitivity and slightly low specificity than the others. Deeks’ test indicates that there was no publication bias (P=0.71).ConclusionRadiomics-based MRI has high accuracy in predicting MVI in HCC, and it can be considered as a non-invasive method for assessing MVI in HCC.

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“…Doing so can describe tumor heterogeneity and reflect the histopathologic grading and prognosis ( 10 , 11 ). Therefore, radiomics can potentially be effective in the preoperative prediction of MVI ( 12 , 13 ). Some studies suggest that liver radiomics is insufficiently mature to routinely integrate into clinical practice ( 14 , 15 ).…”

Section: Introductionmentioning

confidence: 99%

Development of a magnetic resonance imaging-derived radiomics model to predict microvascular invasion in patients with hepatocellular carcinoma

Liu¹,

Cheng²,

Liao³

et al. 2023

Quant Imaging Med Surg

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Background Hepatocellular carcinoma (HCC) with microvascular invasion (MVI) has a poor prognosis, is prone to recurrence and metastasis, and requires more complex surgical techniques. Radiomics is expected to enhance the discriminative performance for identifying HCC, but the current radiomics models are becoming increasingly complex, tedious, and difficult to integrate into clinical practice. The purpose of this study was to investigate whether a simple prediction model using noncontrast-enhanced T2-weighted magnetic resonance imaging (MRI) could preoperatively predict MVI in HCC. Methods A total of 104 patients with pathologically confirmed HCC (training cohort, n=72; test cohort, n=32; ratio, about 7:3) who underwent liver MRI within 2 months prior to surgery were retrospectively included. A total of 851 tumor-specific radiomic features were extracted on T2-weighted imaging (T2WI) for each patient using AK software (Artificial Intelligence Kit Version; V. 3.2.0R, GE Healthcare). Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used in the training cohort for feature selection. The selected features were incorporated into a multivariate logistic regression model to predict MVI, which was validated in the test cohort. The model’s effectiveness was evaluated using the receiver operating characteristic and calibration curves in the test cohort. Results Eight radiomic features were identified to establish a prediction model. In the training cohort, the area under the curve, accuracy, specificity, sensitivity, and positive and negative predictive values of the model for predicting MVI were 0.867, 72.7%, 84.2%, 64.7%, 72.7%, and 78.6%, respectively; while in the test cohort, they were 0.820, 75%, 70.6%, 73.3%, 75%, and 68.8%, respectively. The calibration curves displayed good consistency between the prediction of MVI by the model and actual pathological results in both the training and validation cohorts. Conclusions A prediction model using radiomic features from single T2WI can predict MVI in HCC. This model has the potential to be a simple and fast method to provide objective information for decision-making during clinical treatment.

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Radiomics for the Preoperative Evaluation of Microvascular Invasion in Hepatocellular Carcinoma: A Meta-Analysis

Cited by 22 publications

References 46 publications

Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis

Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis

The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis

Development of a magnetic resonance imaging-derived radiomics model to predict microvascular invasion in patients with hepatocellular carcinoma

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