A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma

One class classification-empowered radiomics for noninvasively accurate prediction of glioma isocitrate dehydrogenase mutation using multiparametric magnetic resonance imaging

BACKGROUND

Noninvasive detection of isocitrate dehydrogenase (IDH) mutations is crucial for preoperative decision-making in patients with glioma. While radiomics has been applied, data imbalance-specifically between IDH wild-type and mutated genes-remains underexplored. We developed a one-class classification-empowered radiomics (OCCR) model, trained exclusively on IDH wild-type patients, to distinguish them from IDH mutation cases.

MATERIALS AND METHODS

This study included 495 patients from the UCSF Preoperative Diffuse Glioma MRI dataset. T1, T1ce, and FLAIR sequences were registered to T2 and resampled to a 1-mm isotropic resolution. The coregistered data were skull-stripped, and the tumor region was segmented using an ensemble model, followed by manual refinement. We extracted 386 radiomics features from the four MRI sequences and input them into an auto-encoder with 7 hidden layers for reconstruction. The OCCR model was trained on wild-type IDH patients, using the mean square error between the original and reconstructed features as guidance. During validation, reconstruction error was used to differentiate IDH mutations from the wild type.

RESULTS

The hold-out validation demonstrated that OCCR performance improved as the number of training samples increased, achieving a peak area under the receiver operating characteristic curve of 0.8018. Visualization of reconstruction errors highlighted first-order and gray-level co-occurrence matrix features in the T1ce sequence.

CONCLUSIONS

This study demonstrates the feasibility of integrating one-class classification into radiomics for the determination of preoperative IDH mutation status in patients with glioma using multiparametric MRI. This versatile model holds potential for other diseases with substantial data imbalance.

Radiomics Diagnosis of Microvascular Invasion in Hepatocellular Carcinoma Using 3D Ultrasound and Whole-Slide Image Fusion

This study aims to develop a machine learning model that accurately diagnoses microvascular invasion (MVI) in hepatocellular carcinoma by using radiomic features from MVI-positive regions of interest (ROIs). Unlike previous studies, which do not account for the location and distribution of MVI, this research focuses on correlating preoperative imaging with postoperative pathological MVI. This study involves obtaining ex vivo 3D ultrasound images of 36 hepatic specimens from nine rabbits. These images are fused with whole-slide images to localize MVI regions precisely. The identified MVI regions are segmented into MVI-positive ROIs, with a 1:3 ratio of positive to negative ROIs. Radiomic features are extracted from each ROI, and 30 features highly associated with MVI are selected for model development. The performance of several machine learning models is evaluated using metrics such as sensitivity, specificity, accuracy, the area under the curve (AUC), and F1 score. The GBDT model achieves the best results, with an AUC of 0.91, an F1 score of 0.85, a sensitivity of 0.76, a specificity of 0.92, and an accuracy of 0.86. The high diagnostic accuracy of these models highlights the potential for future clinical application in the precise diagnosis of MVI using radiomic features from MVI-positive ROIs.

Multiparametric magnetic resonance imaging-derived radiomics for the prediction of Ki67 expression in intrahepatic cholangiocarcinoma

BackgroundIntrahepatic cholangiocarcinoma (ICC) is an aggressive liver malignancy, and Ki67 is associated with prognosis in patients with ICC and is an attractive therapeutic target.PurposeTo predict Ki67 expression based on multiparametric magnetic resonance imaging (MRI) radiomics multiscale tumor region in patients with ICC.Material and MethodsA total of 191 patients (training cohort, n = 133; validation cohort, n = 58) with pathologically confirmed ICC were enrolled in this retrospective study. All patients underwent baseline abdominal MR scans in our institution. Univariate logistic analysis was conducted of the correlation between clinical and MRI characteristics and Ki67 expression. Radiomics features were extracted from the image of six MRI sequences (T1-weighted imaging, fat-suppression T2-weighted imaging, diffusion-weighted imaging, and 3-phases contrast-enhanced T1-weighted imaging sequences). Using the least absolute shrinkage and selection operator (LASSO) to select Ki67-related radiomics features in four different tumor volumes (VOItumor, VOI+8mm, VOI+10mm, VOI+12mm). The Rad-score was calculated with logistic regression, and models for prediction of Ki67 expression were constructed. The receiver operating curve was used to analyze the predictive performance of each model.ResultsClinical and regular MRI characteristics were independent of Ki67 expression. Four Rad-scores all showed favorable prediction efficiency in both the training and validation cohorts (AUC = 0.849-0.912 vs. 0.789-0.838). DeLong's test showed that there was no significant difference between the AUC of the radiomics scores, while the Rad-score (VOI+10mm) performed the most stable predictive efficiency with △AUC 0.033.ConclusionMultiparametric MRI radiomics based on multiscale tumor regions can help predict the expression status of Ki67 in ICC patients.

Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study

BACKGROUND

Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) images, we developed a novel radiomics model. It combined bi-regional features and two machine learning algorithms. The aim of this study was to validate its potential value for preoperative prediction of MVI.

METHODS

This retrospective study included 304 HCC patients (training cohort, 216 patients; testing cohort, 88 patients) from three hospitals. Intratumoral and peritumoral volumes of interest were delineated in arterial phase, portal venous phase, and hepatobiliary phase images. Conventional radiomics (CR) and deep learning radiomics (DLR) features were extracted based on FeAture Explorer software and the 3D ResNet-18 extractor, respectively. Clinical variables were selected using univariate and multivariate analyses. Clinical, CR, DLR, CR-DLR, and clinical-radiomics (Clin-R) models were built using support vector machines. The predictive capacity of the models was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULTS

The bi-regional CR-DLR model showed more gains and gave better predictive performance than the single-regional models or single-machine learning models. Its AUC, accuracy, sensitivity, and specificity were 0.844, 76.9%, 87.8%, and 69.1% in the training cohort and 0.740, 73.9%, 50%, and 84.5% in the testing cohort. Alpha-fetoprotein (odds ratio was 0.32) and maximum tumor diameter (odds ratio was 1.270) were independent predictors. The AUCs of the clinical model and the Clin-R model were 0.655 and 0.672, respectively. There was no significant difference in the AUCs between all the models (P > 0.005).

CONCLUSION

Based on Gd-EOB-DTPA-enhanced MRI images, we focused on developing a radiomics model that combines bi-regional features and two machine learning algorithms (CR and DLR). The application of the new model will provide a more accurate and non-invasive diagnostic solution for medical imaging. It will provide valuable information for clinical personalized treatment, thereby improving patient prognosis.

CLINICAL TRIAL NUMBER

Not applicable.

Multiregional Radiomics to Predict Microvascular Invasion in Hepatocellular Carcinoma Using Multisequence MRI

OBJECTIVES

This study aimed to develop a multiregional radiomics-based model using multisequence MRI to predict microvascular invasion in hepatocellular carcinoma.

METHODS

We enrolled 141 patients with hepatocellular carcinoma, including 61 with microvascular invasion, who were diagnosed between March 2017 and July 2022. Clinical data were compared using the Wilcoxon rank-sum test or χ2 test. Patients were randomly divided into training (n=112, 80%) and test (n=29, 20%) data sets. Four MRI sequences-including T2-weighted imaging, T2-weighted imaging with fat suppression, arterial phase-contrast enhancement, and portal venous phase contrast enhancement-were used to build the radiomics model. The tumor volumes of interest were manually delineated, and the expand-5 mm and expand-10 mm volumes of interest were automatically generated. A total of 1409 radiomic features were extracted from each volume of interest. Feature selection was performed using the least absolute shrinkage and selection operator and Spearman correlation analysis. Three logistic regression models (Tumor, Tumor-Expand5, and Tumor-Expand10) were established based on the radiomic features. Model performance was assessed using receiver operating characteristic analysis and Delong's test.

RESULTS

Maximum tumor diameter, hepatitis B virus DNA, and aspartate aminotransferase levels were significantly different between the groups. The Tumor-Expand5mm model exhibited the best performance among the 3 models, with areas under the curve of 0.90 and 0.84 in the training and test data sets.

CONCLUSIONS

The Tumor-Expand5 model based on multisequence MRI shows great potential for predicting microvascular invasion in patients with hepatocellular carcinoma, and may further contribute to personal clinical decision-making.

Histopathological diagnosis of microvascular invasion in hepatocellular carcinoma: Is it reliable?

BACKGROUND

Microvascular invasion (MVI) is a critical prognostic factor for postoperative hepatocellular carcinoma recurrence, but the reliability of its current pathological diagnosis remains uncertain.

AIM

To evaluate the accuracy of current 7-point sampling methods and propose an optimal pathological protocol using whole-mount slide imaging (WSI) for better MVI detection.

METHODS

We utilized 40 New Zealand white rabbits to establish VX2 liver tumor models. The entire tumor-containing liver lobe was subsequently obtained, following which five different sampling protocols (A-E) were employed to evaluate the detection rate, accuracy, quantity, and distribution of MVI, with the aim of identifying the optimal sampling method.

RESULTS

VX2 liver tumor models were successfully established in 37 rabbits, with an incidence of MVI of 81.1% (30/37). The detection rates [27% (10/37), 43% (16/37), 62% (23/37), 68% (25/37), and 93% (14/15)] and quantity (15, 36, 107, 125, and 395) of MVI increased significantly from protocols A to E. The distribution of MVI showed fewer MVIs farther away from the tumor, but the percentage of MVI detected quantity gradually increased from 6.7% to 48.3% in the distant nonneoplastic liver tissue from protocols A to E. Protocol C was identified as the optimal sampling method by comparing them in sequence. The sampling protocol of three consecutive interval WSIs at the tumor center (WSI3) was further screened to determine the optimal number of WSIs. Protocol A (7-point sampling method) exhibited only 46% accuracy and a high false-negative rate of 67%. Notably, the WSI3 protocol improved the accuracy to 78% and decreased the false-negative rate to 27%.

CONCLUSION

The current 7-point sampling method has a high false-negative rate in MVI detection. In contrast, the WSI3 protocol provides a practical and effective approach to improve MVI diagnostic accuracy, which is crucial for hepatocellular carcinoma diagnosis and treatment planning.

AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease

The integration of artificial intelligence (AI) into hepatology is revolutionizing the diagnosis and management of liver diseases amidst a rising global burden of conditions like metabolic-associated steatotic liver disease (MASLD). AI harnesses vast datasets and complex algorithms to enhance clinical decision making and patient outcomes. AI's applications in hepatology span a variety of conditions, including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, MASLD, hepatitis B, and hepatocellular carcinoma. It enables early detection, predicts disease progression, and supports more precise treatment strategies. Despite its transformative potential, challenges remain, including data integration, algorithm transparency, and computational demands. This review examines the current state of AI in hepatology, exploring its applications, limitations, and the opportunities it presents to enhance liver health and care delivery.

Radiomic analysis based on magnetic resonance imaging for the prediction of VEGF expression in hepatocellular carcinoma patients

OBJECTIVE

The purpose of this study was to investigate the ability of radiomic characteristics of magnetic resonance images to predict vascular endothelial growth factor (VEGF) expression in hepatocellular carcinoma (HCC) patients.

METHODS

One hundred and twenty-four patients with HCC who underwent fat-suppressed T2-weighted imaging (FS-T2WI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) one week before surgical resection were enrolled in this retrospective study. Immunohistochemical analysis was used to evaluate the expression level of VEGF. Radiomic features were extracted from the axial FS-T2WI, DCE-MRI (arterial phase and portal venous phase) images of axial MRI. Least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses were performed to select the best radiomic features. Multivariate logistic regression models were constructed and validated using tenfold cross-validation. Receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) were employed to evaluate these models.

RESULTS

Our results show that there were 94 patients with high VEGF expression and 30 patients with low VEGF expression among the 124 HCC patients. The FS-T2WI, DCE-MRI and combined MRI radiomics models had AUCs of 0.8713, 0.7819, and 0.9191, respectively. There was no significant difference in the AUC between the FS-T2WI radiomics model and the DCE-MRI radiomics model (p > 0.05), but the AUC for the combined model was significantly greater than the AUCs for the other two models (p < 0.05) according to the DeLong test. The combined model had the greatest net benefit according to the DCA results.

CONCLUSION

The radiomic model based on multisequence MR images has the potential to predict VEGF expression in HCC patients. The combined model showed the best performance.

Intravoxel incoherent motion and enhanced T2*-weighted angiography for preoperative prediction of microvascular invasion in hepatocellular carcinoma

Objective

To investigate the value of the combined application of intravoxel incoherent motion (IVIM) and enhanced T2*-weighted angiography (ESWAN) for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Materials and methods

76 patients with pathologically confirmed HCC were retrospectively enrolled and divided into the MVI-positive group (n=26) and MVI-negative group (n=50). Conventional MRI, IVIM, and ESWAN sequences were performed. Three region of interests (ROIs) were placed on the maximum axial slice of the lesion on D, D*, and f maps derived from IVIM sequence, and R2* map derived from ESWAN sequence, and intratumoral susceptibility signal (ITSS) from the phase map derived from ESWAN sequence was also automatically measured. Receiver operating characteristic (ROC) curves were drawn to evaluate the ability for predicting MVI. Univariate and multivariate logistic regression were used to screen independent risk predictors in clinical and imaging information. The Delong's test was used to compare the differences between the area under curves (AUCs).

Results

The D and D* values of MVI-negative group were significantly higher than those of MVI-positive group (P=0.038, and P=0.023), which in MVI-negative group were 0.892×10-3 (0.760×10-3, 1.303×10-3) mm2/s and 0.055 (0.025, 0.100) mm2/s, and in MVI-positive group were 0.591×10-3 (0.372×10-3, 0.824×10-3) mm2/s and 0.028 (0.006, 0.050)mm2/s, respectively. The R2* and ITSS values of MVI-negative group were significantly lower than those of MVI-positive group (P=0.034, and P=0.005), which in MVI-negative group were 29.290 (23.117, 35.228) Hz and 0.146 (0.086, 0.236), and in MVI-positive group were 43.696 (34.914, 58.083) Hz and 0.199 (0.155, 0.245), respectively. After univariate and multivariate analyses, only AFP (odds ratio, 0.183; 95% CI, 0.041-0.823; P = 0.027) was the independent risk factor for predicting the status of MVI. The AUCs of AFP, D, D*, R2*, and ITSS for prediction of MVI were 0.652, 0.739, 0.707, 0.798, and 0.657, respectively. The AUCs of IVIM (D+D*), ESWAN (R2*+ITSS), and combination (D+D*+R2*+ITSS) for predicting MVI were 0.772, 0.800, and, 0.855, respectively. When IVIM combined with ESWAN, the performance was improved with a sensitivity of 73.1% and a specificity of 92.0% (cut-off value: 0.502) and the AUC was significantly higher than AFP (P=0.001), D (P=0.038), D* (P=0.023), R2* (P=0.034), and ITSS (P=0.005).

Conclusion

The IVIM and ESWAN parameters showed good efficacy in prediction of MVI in patients with HCC. The combination of IVIM and ESWAN may be useful for noninvasive prediction of MVI before clinical operation.

Development and validation of [18 F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy

PURPOSE

Biochemical recurrence (BCR) following radical prostatectomy (RP) is a significant concern for patients with prostate cancer. Reliable prediction models are needed to identify patients at risk for BCR and facilitate appropriate management. This study aimed to develop and validate a clinical-radiomics model based on preoperative [18 F]PSMA-1007 PET for predicting BCR-free survival (BRFS) in patients who underwent RP for prostate cancer.

MATERIALS AND METHODS

A total of 236 patients with histologically confirmed prostate cancer who underwent RP were retrospectively analyzed. All patients had a preoperative [18 F]PSMA-1007 PET/CT scan. Radiomics features were extracted from the primary tumor region on PET images. A radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The performance of the radiomics signature in predicting BRFS was assessed using Harrell's concordance index (C-index). The clinical-radiomics nomogram was constructed using the radiomics signature and clinical features. The model was externally validated in an independent cohort of 98 patients.

RESULTS

The radiomics signature comprised three features and demonstrated a C-index of 0.76 (95% CI: 0.60-0.91) in the training cohort and 0.71 (95% CI: 0.63-0.79) in the validation cohort. The radiomics signature remained an independent predictor of BRFS in multivariable analysis (HR: 2.48, 95% CI: 1.47-4.17, p < 0.001). The clinical-radiomics nomogram significantly improved the prediction performance (C-index: 0.81, 95% CI: 0.66-0.95, p = 0.007) in the training cohort and (C-index: 0.78 95% CI: 0.63-0.89, p < 0.001) in the validation cohort.

CONCLUSION

We developed and validated a novel [18 F]PSMA-1007 PET-based clinical-radiomics model that can predict BRFS following RP in prostate cancer patients. This model may be useful in identifying patients with a higher risk of BCR, thus enabling personalized risk stratification and tailored management strategies.

Differentiating HCC from ICC and prediction of ICC grade based on MRI deep-radiomics: Using lesions and their extended regions

PURPOSE

This study aimed to evaluate the ability of MRI-based intratumoral and peritumoral radiomics features of liver tumors to differentiate between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) and to predict ICC differentiation.

METHODS

This study retrospectively collected 87 HCC patients and 75 ICC patients who were confirmed pathologically. The standard region of interest (ROI) of the lesion drawn by the radiologist manually shrank inward and expanded outward to form multiple ROI extended regions. A three-step feature selection method was used to select important radiomics features and convolution features from extended regions. The predictive performance of several machine learning classifiers on dominant feature sets was compared. The extended region performance was assessed by area under the curve (AUC), specificity, sensitivity, F1-score and accuracy.

RESULTS

The performance of the model is further improved by incorporating convolution features. Compared with the standard ROI, the extended region obtained better prediction performance, among which 6 mm extended region had the best prediction ability (Classification: AUC = 0.96, F1-score = 0.94, Accuracy: 0.94; Grading: AUC = 0.94, F1-score = 0.93, Accuracy = 0.89).

CONCLUSION

Larger extended region and fusion features can improve tumor predictive performance and have potential value in tumor radiology.

[Prediction of microvascular invasion in hepatocellular carcinoma based on multi-phase dynamic enhanced CT radiomics feature and multi-classifier hierarchical fusion model]

OBJECTIVE

To predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using a model based on multiphase dynamic-enhanced CT (DCE-CT) radiomics feature and hierarchical fusion of multiple classifiers.

METHODS

We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January, 2016 and April, 2020. The volume of interest was outlined in the early arterial phase, late arterial phase, portal venous phase and equilibrium phase, and radiomics features of these 4 phases were extracted. Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase. According to the hierarchical fusion strategy, a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model. The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve (AUC), accuracy, sensitivity, and specificity. The prediction model was also compared with the fusion models using a single phase or multiple phases, models based on a single phase with a single classifier, models with different base classifier diversities, and 8 classifier models based on other ensemble methods.

RESULTS

The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers, with AUC, accuracy, sensitivity, and specificity of 0.828, 0.766, 0.877, and 0.648, respectively. Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models.

CONCLUSION

The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.

The Effectiveness of an Adaptive Method to Analyse the Transition between Tumour and Peritumour for Answering Two Clinical Questions in Cancer Imaging

Based on the well-known role of peritumour characterization in cancer imaging to improve the early diagnosis and timeliness of clinical decisions, this study innovated a state-of-the-art approach for peritumour analysis, mainly relying on extending tumour segmentation by a predefined fixed size. We present a novel, adaptive method to investigate the zone of transition, bestriding tumour and peritumour, thought of as an annular-like shaped area, and detected by analysing gradient variations along tumour edges. For method validation, we applied it on two datasets (hepatocellular carcinoma and locally advanced rectal cancer) imaged by different modalities and exploited the zone of transition regions as well as the peritumour ones derived by adopting the literature approach for building predictive models. To measure the zone of transition's benefits, we compared the predictivity of models relying on both "standard" and novel peritumour regions. The main comparison metrics were informedness, specificity and sensitivity. As regards hepatocellular carcinoma, having circular and regular shape, all models showed similar performance (informedness = 0.69, sensitivity = 84%, specificity = 85%). As regards locally advanced rectal cancer, with jagged contours, the zone of transition led to the best informedness of 0.68 (sensitivity = 89%, specificity = 79%). The zone of transition advantages include detecting the peritumour adaptively, even when not visually noticeable, and minimizing the risk (higher in the literature approach) of including adjacent diverse structures, which was clearly highlighted during image gradient analysis.

Radiomics and liver: Where we are and where we are headed?

Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.

Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score

BACKGROUND

Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies.

AIM

To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS).

METHODS

A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05.

RESULTS

One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072).

CONCLUSION

Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.

A Systematic Review and Meta-Analysis of MRI Radiomics for Predicting Microvascular Invasion in Patients with Hepatocellular Carcinoma

BACKGROUND

The prediction power of MRI radiomics for microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains uncertain.

OBJECTIVE

To investigate the prediction performance of MRI radiomics for MVI in HCC.

METHODS

Original studies focusing on preoperative prediction performance of MRI radiomics for MVI in HCC, were systematically searched from databases of PubMed, Embase, Web of Science and Cochrane Library. Radiomics quality score (RQS) and risk of bias of involved studies were evaluated. Meta-analysis was carried out to demonstrate the value of MRI radiomics for MVI prediction in HCC. Influencing factors of the prediction performance of MRI radiomics were identified by subgroup analyses.

RESULTS

13 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement were eligible for this systematic review and meta-analysis. The studies achieved an average RQS of 14 (ranging from 11 to 17), accounting for 38.9% of the total points. MRI radiomics achieved a pooled sensitivity of 0.82 (95%CI: 0.78 - 0.86), specificity of 0.79 (95%CI: 0.76 - 0.83) and area under the summary receiver operator characteristic curve (AUC) of 0.88 (95%CI: 0.84 - 0.91) to predict MVI in HCC. Radiomics models combined with clinical features achieved superior performances compared to models without the combination (AUC: 0.90 vs 0.85, P < 0.05).

CONCLUSION

MRI radiomics has the potential for preoperative prediction of MVI in HCC. Further studies with high methodological quality should be designed to improve the reliability and reproducibility of the radiomics models for clinical application. The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42022333822).

Clinical application of dual-layer spectral CT multi-parameter feature to predict microvascular invasion in hepatocellular carcinoma

OBJECTIVE

This study aimed to investigate the feasibility of using dual-layer spectral CT multi-parameter feature to predict microvascular invasion of hepatocellular carcinoma.

METHODS

This retrospective study enrolled 50 HCC patients who underwent multiphase contrast-enhanced spectral CT studies preoperatively. Combined clinical data, radiological features with spectral CT quantitative parameter were constructed to predict MVI. ROC was applied to identify potential predictors of MVI. The CT values obtained by simulating the conventional CT scans with 70 keV images were compared with those obtained with 40 keV images.

RESULTS

50 hepatocellular carcinomas were detected with 30 lesions (Group A) with microvascular invasion and 20 (Group B) without. There were significant differences in AFP,tumer size, IC, NIC,slope and effective atomic number in AP and ICrr in VP between Group A ((1000(10.875,1000),4.360±0.3105, 1.7750 (1.5350,1.8825) mg/ml, 0.1785 (0.1621,0.2124), 2.0362±0.2108,8.0960±0.1043,0.2830±0.0777) and Group B (4.750(3.325,20.425),3.190±0.2979,1.4700 (1.4500,1.5775) mg/ml, 0.1441 (0.1373,0.1490),1.8601±0.1595, 7.8105±0.7830 and 0.2228±0.0612) (all p < 0.05). Using 0.1586 as the threshold for NIC, one could obtain an area-under-curve (AUC) of 0.875 in ROC to differentiate between tumours with and without microvascular invasion. AUC was 0.625 with CT value at 70 keV and improved to 0.843 at 40 keV.

CONCLUSION

Dual-layer spectral CT provides additional quantitative parameters than conventional CT to enhance the differentiation between hepatocellular carcinoma with and without microvascular invasion. Especially, the normalized iodine concentration (NIC) in arterial phase has the greatest potential application value in determining whether microvascular invasion exists, and can offer an important reference for clinical treatment plan and prognosis assessment.

A Nomogram of Magnetic Resonance Imaging for Preoperative Assessment of Microvascular Invasion and Prognosis of Hepatocellular Carcinoma

BACKGROUND

Microvascular invasion (MVI) is a predictor of recurrence and overall survival in hepatocellular carcinoma (HCC), the preoperative diagnosis of MVI through noninvasive methods play an important role in clinical treatment.

AIMS

To investigate the effectiveness of radiomics features in evaluating MVI in HCC before surgery.

METHODS

We included 190 patients who had undergone contrast-enhanced MRI and curative resection for HCC between September 2015 and November 2021 from two independent institutions. In the training cohort of 117 patients, MVI-related radiomics models based on multiple sequences and multiple regions from MRI were constructed. An independent cohort of 73 patients was used to validate the proposed models. A final Clinical-Imaging-Radiomics nomogram for preoperatively predicting MVI in HCC patients was generated. Recurrence-free survival was analyzed using the log-rank test.

RESULTS

For tumor-extracted features, the performance of signatures in fat-suppressed T1-weighted images and hepatobiliary phase was superior to that of other sequences in a single-sequence model. The radiomics signatures demonstrated better discriminatory ability than that of the Clinical-Imaging model for MVI. The nomogram incorporating clinical, imaging and radiomics signature showed excellent predictive ability and achieved well-fitted calibration curves, outperforming both the Radiomics and Clinical-Radiomics models in the training and validation cohorts.

CONCLUSIONS

The Clinical-Imaging-Radiomics nomogram model of multiple regions and multiple sequences based on serum alpha-fetoprotein, three MRI characteristics, and 12 radiomics signatures achieved good performance for predicting MVI in HCC patients, which may help clinicians select optimal treatment strategies to improve subsequent clinical outcomes.

Radiomics nomogram for prediction of glypican-3 positive hepatocellular carcinoma based on hepatobiliary phase imaging

Introduction

The hepatobiliary-specific phase can help in early detection of changes in lesion tissue density, internal structure, and microcirculatory perfusion at the microscopic level and has important clinical value in hepatocellular carcinoma (HCC). Therefore, this study aimed to construct a preoperative nomogram for predicting the positive expression of glypican-3 (GPC3) based on gadoxetic acid-enhanced (Gd-EOB-DTPA) MRI hepatobiliary phase (HBP) radiomics, imaging and clinical feature.

Methods

We retrospectively included 137 patients with HCC who underwent Gd-EOB-DTPA-enhanced MRI and subsequent liver resection or puncture biopsy at our hospital from January 2017 to December 2021 as training cohort. Subsequently collected from January 2022 to June 2023 as a validation cohort of 49 patients, Radiomic features were extracted from the entire tumor region during the HBP using 3D Slicer software and screened using a t-test and least absolute shrinkage selection operator algorithm (LASSO). Then, these features were used to construct a radiomics score (Radscore) for each patient, which was combined with clinical factors and imaging features of the HBP to construct a logistic regression model and subsequent nomogram model. The clinicoradiologic, radiomics and nomogram models performance was assessed by the area under the curve (AUC), calibration, and decision curve analysis (DCA). In the validation cohort,the nomogram performance was assessed by the area under the curve (AUC).

Results

In the training cohort, a total of 1688 radiomics features were extracted from each patient. Next, radiomics with ICCs<0.75 were excluded, 1587 features were judged as stable using intra- and inter-class correlation coefficients (ICCs), 26 features were subsequently screened using the t-test, and 11 radiomics features were finally screened using LASSO. The nomogram combining Radscore, age, serum alpha-fetoprotein (AFP) >400ng/mL, and non-smooth tumor margin (AUC=0.888, sensitivity 77.7%, specificity 91.2%) was superior to the radiomics (AUC=0.822, sensitivity 81.6%, specificity 70.6%) and clinicoradiologic (AUC=0.746, sensitivity 76.7%, specificity 64.7%) models, with good consistency in calibration curves. DCA also showed that the nomogram had the highest net clinical benefit for predicting GPC3 expression.In the validation cohort, the ROC curve results showed predicted GPC3-positive expression nomogram model AUC, sensitivity, and specificity of 0.800, 58.5%, and 100.0%, respectively.

Conclusion

HBP radiomics features are closely associated with GPC3-positive expression, and combined clinicoradiologic factors and radiomics features nomogram may provide an effective way to non-invasively and individually screen patients with GPC3-positive HCC.

Prediction of Microvascular Invasion in Solitary AFP-Negative Hepatocellular Carcinoma ≤ 5 cm Using a Combination of Imaging Features and Quantitative Dual-Layer Spectral-Detector CT Parameters

RATIONALE AND OBJECTIVES

AFP-negative hepatocellular carcinoma (AFPN-HCC) within 5 cm is a special subgroup of HCC. This study aimed to investigate the value of dual-layer spectral-detector CT (DLCT) and construct a scoring model based on imaging features as well as DLCT for predicting microvascular invasion (MVI) in AFPN-HCC within 5 cm.

METHODS

This retrospective study enrolled 104 HCC patients who underwent multiphase contrast-enhanced DLCT studies preoperatively. Combined radiological features (CR) and combined DLCT quantitative parameter (CDLCT) were constructed to predict MVI. Multivariable logistic regression was applied to identify potential predictors of MVI. Based on the coefficient of the regression model, a scoring model was developed. The predictive efficacy was assessed through ROC analysis.

RESULTS

Microvascular invasion (MVI) was found in 28 (26.9%) AFPN-HCC patients. Among single parameters, the effective atomic number in arterial phase demonstrated the best predictive efficiency for MVI with an area under the curve (AUC) of 0.792. CR and CDLCT showed predictive performance with AUCs of 0.848 and 0.849, respectively. A risk score (RS) was calculated using the independent predictors of MVI as follows: RS = 2 × (mosaic architecture) + 2 × (corona enhancement) + 2 × (incomplete tumor capsule) + 2 × (2-trait predictor of venous invasion [TTPVI]) + 3 × (CDLCT > -1.229). Delong's test demonstrated this scoring system could significantly improve the AUC to 0.929 compared with CR (p = 0.016) and CDLCT (p = 0.034).

CONCLUSION

The scoring model combining radiological features with DLCT provides a promising tool for predicting MVI in solitary AFPN-HCC within 5 cm preoperatively.

Noninvasive imaging-based machine learning algorithm to identify progressive disease in advanced hepatocellular carcinoma receiving second-line systemic therapy

The aim of this study was to predict tyrosine kinase inhibitors (TKI) plus anti-PD-1 antibodies (TKI-PD-1) efficacy as second-line treatment in advanced hepatocellular carcinoma (HCC) using radiomics analysis. From November 2018 to November 2019, a total of 55 patients were included. Radiomic features were obtained from the CT images before treatment and filtered using intraclass correlation coefficients (ICCs) and least absolute shrinkage and selection operator (LASSO) methods. Subsequently, ten prediction algorithms were developed and validated based on radiomic characteristics. The accuracy of the constructed model was measured through area under the receiver operating characteristic curve (AUC) analysis; survival analysis was performed via Kaplan-Meier and Cox regression analyses. Overall, 18 (32.7%) out of 55 patients had progressive disease. Through ICCs and LASSO, ten radiomic features were entered into the algorithm construction and validation. Ten machine learning algorithms showed different accuracies, with the support vector machine (SVM) model having the highest AUC value of 0.933 in the training cohort and 0.792 in the testing cohort. The radiomic features were associated with overall survival. In conclsion, the SVM algorithm is a useful method to predict TKI-PD-1 efficacy in patients with advanced HCC using images taken prior to treatment.

CT-based radiomics models may predict the early efficacy of microwave ablation in malignant lung tumors

PURPOSE

To establish and validate radiomics models for predicting the early efficacy (less than 3 months) of microwave ablation (MWA) in malignant lung tumors.

METHODS

The study enrolled 130 malignant lung tumor patients (72 in the training cohort, 32 in the testing cohort, and 26 in the validation cohort) treated with MWA. Post-operation CT images were analyzed. To evaluate the therapeutic effect of ablation, three models were constructed by least absolute shrinkage and selection operator and logistic regression: the tumoral radiomics (T-RO), peritumoral radiomics (P-RO), and tumoral-peritumoral radiomics (TP-RO) models. Univariate and multivariate analyses were performed to identify clinical variables and radiomics features associated with early efficacy, which were incorporated into the combined radiomics (C-RO) model. The performance of the C-RO model was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). The C-RO model was used to derive the best cutoff value of ROC and to distinguish the high-risk group (Nomo-score of C-RO model below than cutoff value) from the low-risk group (Nomo-score of C-RO model higher than cutoff value) for survival analysis of patients.

RESULTS

Four radiomics features were selected from the region of interest of tumoral and peritumoral CT images, which showed good performance for evaluating prognosis and early efficacy in three cohorts. The C-RO model had the highest AUC value in all models, and the C-RO model was better than the P-RO model (AUC in training, 0.896 vs. 0.740; p = 0.036). The DCA confirmed the clinical benefit of the C-RO model. Survival analysis revealed that in the C-RO model, the low-risk group defined by best cutoff value had significantly better progression-free survival than the high-risk group (p<0.05).

CONCLUSIONS

CT-based radiomics models in malignant lung tumor patients after MWA could be useful for individualized risk classification and treatment.

Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography

Background

Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases.

Patients and Methods

620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA).

Results

The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959-0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 -0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (P < 0.001, P = 0.002, respectively) and validation sets (P < 0.001, P = 0.034, respectively).

Conclusion

Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses.

Radiomics models based on multisequence MRI for predicting PD-1/PD-L1 expression in hepatocellular carcinoma

The purpose of this study was to explore the effectiveness of radiomics based on multisequence MRI in predicting the expression of PD-1/PD-L1 in hepatocellular carcinoma (HCC). One hundred and eight patients with HCC who underwent contrast-enhanced MRI 2 weeks before surgical resection were enrolled in this retrospective study. Corresponding paraffin sections were collected for immunohistochemistry to detect the expression of PD-1 and PD-L1. All patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Univariate and multivariate analyses were used to select potential clinical characteristics related to PD-1 and PD-L1 expression. Radiomics features were extracted from the axial fat-suppression T2-weighted imaging (FS-T2WI) images and the arterial phase and portal venous phase images from the axial dynamic contrast-enhanced MRI, and the corresponding feature sets were generated. The least absolute shrinkage and selection operator (LASSO) was used to select the optimal radiomics features for analysis. Logistic regression analysis was performed to construct single-sequence and multisequence radiomics and radiomic-clinical models. The predictive performance was judged by the area under the receiver operating characteristic curve (AUC) in the training and validation cohorts. In the whole cohort, PD-1 expression was positive in 43 patients, and PD-L1 expression was positive in 34 patients. The presence of satellite nodules served as an independent predictor of PD-L1 expression. The AUC values of the FS-T2WI, arterial phase, portal venous phase and multisequence models in predicting the expression of PD-1 were 0.696, 0.843, 0.863, and 0.946 in the training group and 0.669, 0.792, 0.800 and 0.815 in the validation group, respectively. The AUC values of the FS-T2WI, arterial phase, portal venous phase, multisequence and radiomic-clinical models in predicting PD-L1 expression were 0.731, 0.800, 0.800, 0.831 and 0.898 in the training group and 0.621, 0.743, 0.771, 0.810 and 0.779 in the validation group, respectively. The combined models showed better predictive performance. The results of this study suggest that a radiomics model based on multisequence MRI has the potential to predict the preoperative expression of PD-1 and PD-L1 in HCC, which could become an imaging biomarker for immune checkpoint inhibitor (ICI)-based treatment.

Quality of radiomics for predicting microvascular invasion in hepatocellular carcinoma: a systematic review

OBJECTIVES

To comprehensively evaluate the reporting quality, risk of bias, and radiomics methodology quality of radiomics models for predicting microvascular invasion in hepatocellular carcinoma.

METHODS

A systematic search of available literature was performed in PubMed, Embase, Web of Science, Scopus, and the Cochrane Library up to January 21, 2022. Studies that developed and/or validated machine learning models based on radiomics data to predict microvascular invasion in hepatocellular carcinoma were included. These studies were reviewed by two investigators and the consensus data were used for analyzing. The reporting quality, risk of bias, and radiomics methodological quality were evaluated by Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD), Prediction model Risk of Bias Assessment Tool, and Radiomics Quality Score (RQS), respectively.

RESULTS

A total of 30 studies met eligibility criteria with 24 model developing studies and 6 model developing and external validation studies. The median overall TRIPOD adherence was 75.4% (range 56.7-94.3%). All studies were at high risk of bias with at least 2 of 20 sources of bias. Furthermore, 28 studies showed unclear risks of bias in up to 5 signaling questions because of the lack of specified reports. The median RQS score was 37.5% (range 25-61.1%).

CONCLUSION

Current radiomic models for MVI-status prediction have moderate to good reporting quality, moderate radiomics methodology quality, and high risk of bias in model development and validation.

KEY POINTS

• Current microvascular invasion prediction radiomics studies have moderate to good reporting quality, moderate radiomics methodology quality, and high risk of bias in model development and validation. • Data representativeness, feature robustness, events-per-variable ratio, evaluation metrics, and appropriate validation are five main aspects futures studies should focus more on to improve the quality of radiomics. • Both Radiomics Quality Score and Prediction model Risk of Bias Assessment Tool are needed to comprehensively evaluate a radiomics study.

Multiparametric MRI-based radiomics for the prediction of microvascular invasion in hepatocellular carcinoma

BACKGROUND

Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is essential in obtaining a successful surgical treatment, in decreasing recurrence, and in improving survival.

PURPOSE

To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics in the prediction of peritumoral MVI in HCC.

MATERIAL AND METHODS

A total of 102 patient with pathologically proven HCC after surgical resection from June 2014 to March 2018 were enrolled in this retrospective study. Histological analysis of resected specimens confirmed positive MVI in 48 patients and negative MVI in 54 patients. Radiomics features were extracted from four MRI sequences and selected with the least absolute shrinkage and selection operator (LASSO) regression and used to analyze the tumoral and peritumoral regions for MVI. Univariate logistic regression was employed to identify the most important clinical factors, which were integrated with the radiomics signature to develop a nomogram.

RESULTS

In total, 11 radiomics features were selected and used to build the radiomics signature. The serum level of alpha-fetoprotein was identified as the clinical factor with the highest predictive value. The developed nomogram achieved the highest AUC in predicting MVI status. The decision curve analysis confirmed the potential clinical utility of the proposed nomogram.

CONCLUSION

The multiparametric MRI-based radiomics nomogram is a promising tool for the preoperative diagnosis of peritumoral MVI in HCCs and helps determine the appropriate medical or surgical therapy.

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

Objective

The aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Method

The 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.

Results

A 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).

Conclusion

Radiomics-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.

Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma

The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.

Radiomic Analysis Based on Magnetic Resonance Imaging for Predicting PD-L2 Expression in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumour and the third leading cause of cancer death in the world. The emerging field of radiomics involves extracting many clinical image features that cannot be recognized by the human eye to provide information for precise treatment decision making. Radiomics has shown its importance in HCC identification, histological grading, microvascular invasion (MVI) status, treatment response, and prognosis, but there is no report on the preoperative prediction of programmed death ligand-2 (PD-L2) expression in HCC. The purpose of this study was to investigate the value of MRI radiomic features for the non-invasive prediction of immunotherapy target PD-L2 expression in hepatocellular carcinoma (HCC). A total of 108 patients with HCC confirmed by pathology were retrospectively analysed. Immunohistochemical analysis was used to evaluate the expression level of PD-L2. 3D-Slicer software was used to manually delineate volumes of interest (VOIs) and extract radiomic features on preoperative T2-weighted, arterial-phase, and portal venous-phase MR images. Least absolute shrinkage and selection operator (LASSO) was performed to find the best radiomic features. Multivariable logistic regression models were constructed and validated using fivefold cross-validation. The area under the receiver characteristic curve (AUC) was used to evaluate the predictive performance of each model. The results show that among the 108 cases of HCC, 50 cases had high PD-L2 expression, and 58 cases had low PD-L2 expression. Radiomic features correlated with PD-L2 expression. The T2-weighted, arterial-phase, and portal venous-phase and combined MRI radiomics models showed AUCs of 0.789 (95% CI: 0.702-0.875), 0.727 (95% CI: 0.632-0.823), 0.770 (95% CI: 0.682-0.875), and 0.871 (95% CI: 0.803-0.939), respectively. The combined model showed the best performance. The results of this study suggest that prediction based on the radiomic characteristics of MRI could noninvasively predict the expression of PD-L2 in HCC before surgery and provide a reference for the selection of immune checkpoint blockade therapy.

CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy

PURPOSE

To establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features.

METHODS

This study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models.

RESULTS

A total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1-3 years AUC:0.774-0.837; validation cohort 1-3 years AUC:0.754-0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups.

CONCLUSIONS

The CR model could predict the OS of the HCC patients after hepatectomy.

Radiomics nomogram for prediction of microvascular invasion in hepatocellular carcinoma based on MR imaging with Gd-EOB-DTPA

OBJECTIVE

To develop a radiomics nomogram for predicting microvascular invasion (MVI) before surgery in hepatocellular carcinoma (HCC) patients.

MATERIALS AND METHODS

The data from a total of 189 HCC patients (training cohort: n = 141; validation cohort: n = 48) were collected, involving the clinical data and imaging characteristics. Radiomics features of all patients were extracted from hepatobiliary phase (HBP) in 15 min. Least absolute shrinkage selection operator (LASSO) regression and logistic regression were utilized to reduce data dimensions, feature selection, and to construct a radiomics signature. Clinicoradiological factors were identified according to the univariate and multivariate analyses, which were incorporated into the final predicted nomogram. A nomogram was developed to predict MVI of HCC by combining radiomics signatures and clinicoradiological factors. Radiomics nomograms were evaluated for their discrimination capability, calibration, and clinical usefulness.

RESULTS

In the clinicoradiological factors, gender, alpha-fetoprotein (AFP) level, tumor shape and halo sign served as the independent risk factors of MVI, with which the area under the curve (AUC) is 0.802. Radiomics signatures covering 14 features at HBP 15 min can effectively predict MVI in HCC, to construct radiomics signature model, with the AUC of 0.732. In the final nomogram model the clinicoradiological factors and radiomics signatures were integrated, outperforming the clinicoradiological model (AUC 0.884 vs. 0.802; p <0.001) and radiomics signatures model (AUC 0.884 vs. 0.732; p < 0.001) according to Delong test results. A robust calibration and discrimination were demonstrated in the nomogram model. The results of decision curve analysis (DCA) showed more significantly clinical efficiency of the nomogram model in comparison to the clinicoradiological model and the radiomic signature model.

CONCLUSIONS

Depending on the clinicoradiological factors and radiological features on HBP 15 min images, nomograms can effectively predict MVI status in HCC patients.

Preoperative Evaluation of Gd-EOB-DTPA-Enhanced MRI Radiomics-Based Nomogram in Small Solitary Hepatocellular Carcinoma (≤3 cm) With Microvascular Invasion: A Two-Center Study

BACKGROUND

Preoperative evaluation of microvascular invasion (MVI) in small solitary hepatocellular carcinoma (HCC; maximum lesion diameter ≤ 3 cm) is important for treatment decisions.

PURPOSE

To apply gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI to develop and validate a nomogram for preoperative evaluation of MVI in small solitary HCC and to compare the effectiveness of radiomics evaluation models based on different volumes of interest (VOIs).

STUDY TYPE

Retrospective.

POPULATION

A total of 196 patients include 62 MVI-positive and 134 MVI-negative patients were enrolled (training cohort, n = 105; testing cohort, n = 45; external validation cohort, n = 46).

FIELD STRENGTH/SEQUENCE

3.0 T, fat suppressed fast-spin-echo T2-weighted and Gd-EOB-DTPA-enhanced T1-weighted magnetization-prepared rapid gradient-echo sequences.

ASSESSMENT

Radiomics features were extracted on T2-weighted, arterial phase (AP), and hepatobiliary phase (HBP) images from different VOIs (VOI_intratumor and VOI_{intratumor+peritumor} ) and filtered by the least absolute shrinkage selection operator (LASSO) regression. From VOI_intratumor and VOI_{intratumor+peritumor} , eight radiomics models were constructed based on three MRI sequences (T2-weighted, AP, and HBP) and fused sequences (combined of three sequences). Nomograms were constructed of a clinical-radiological (CR) model and a clinical-radiological-radiomics (CRR) model.

STATISTICAL TESTS

One-way analysis of variance, independent t-test, Chi-square test or Fisher's exact test, Wilcoxon rank-sum test, LASSO, logistic regression analysis, area under the curve (AUC), nomograms, decision curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI) analyses, and DeLong test.

RESULTS

Among eight radiomics models, the fused sequences-based VOI_{intratumor+peritumor} radiomics model showed the best performance. The CRR model containing the best performance radiomics model and CR model with the AUC values were 0.934, 0.889, and 0.875, respectively. NRI and IDI analyses showed that the CRR model improved evaluation efficacy over the CR model for all three cohorts (all P-value <0.05).

DATA CONCLUSION

The CRR model nomogram could preoperatively evaluate MVI in small solitary HCC. The radiomics model based on VOI_{intratumor+peritumor} might achieve better evaluation results.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY STAGE: 2.

Role of MRI-Derived Radiomics Features in Determining Degree of Tumor Differentiation of Hepatocellular Carcinoma

Background: To investigate radiomics ability in predicting hepatocellular carcinoma histological degree of differentiation by using volumetric MR imaging parameters. Methods: Volumetric venous enhancement and apparent diffusion coefficient were calculated on baseline MRI of 171 lesions. Ninety-five radiomics features were extracted, then random forest classification identified the performance of the texture features in classifying tumor degree of differentiation based on their histopathological features. The Gini index was used for split criterion, and the random forest was optimized to have a minimum of nine participants per leaf node. Predictor importance was estimated based on the minimal depth of the maximal subtree. Results: Out of 95 radiomics features, four top performers were apparent diffusion coefficient (ADC) features. The mean ADC and venous enhancement map alone had an overall error rate of 39.8%. The error decreased to 32.8% with the addition of the radiomics features in the multi-class model. The area under the receiver-operator curve (AUC) improved from 75.2% to 83.2% with the addition of the radiomics features for distinguishing well- from moderately/poorly differentiated HCCs in the multi-class model. Conclusions: The addition of radiomics-based texture analysis improved classification over that of ADC or venous enhancement values alone. Radiomics help us move closer to non-invasive histologic tumor grading of HCC.

Using pre-operative radiomics to predict microvascular invasion of hepatocellular carcinoma based on Gd-EOB-DTPA enhanced MRI

Objectives

We aimed to investigate the value of performing gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) radiomics for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on multiple sequences.

Methods

We randomly allocated 165 patients with HCC who underwent partial hepatectomy to training and validation sets. Stepwise regression and the least absolute shrinkage and selection operator algorithm were used to select significant variables. A clinicoradiological model, radiomics model, and combined model were constructed using multivariate logistic regression. The performance of the models was evaluated, and a nomogram risk-prediction model was built based on the combined model. A concordance index and calibration curve were used to evaluate the discrimination and calibration of the nomogram model.

Results

The tumour margin, peritumoural hypointensity, and seven radiomics features were selected to build the combined model. The combined model outperformed the radiomics model and the clinicoradiological model and had the highest sensitivity (90.89%) in the validation set. The areas under the receiver operating characteristic curve were 0.826, 0.755, and 0.708 for the combined, radiomics, and clinicoradiological models, respectively. The nomogram model based on the combined model exhibited good discrimination (concordance index = 0.79) and calibration.

Conclusions

The combined model based on radiomics features of Gd-EOB-DTPA enhanced MRI, tumour margin, and peritumoural hypointensity was valuable for predicting HCC microvascular invasion. The nomogram based on the combined model can intuitively show the probabilities of MVI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00855-w.

Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review

OBJECTIVE

Artificial intelligence (AI) has begun to play a pivotal role in hepatic imaging. This systematic scoping review summarizes the latest progress of AI in evaluating hepatic diseases based on computed tomography (CT) and magnetic resonance (MR) imaging.

METHODS

We searched PubMed and Web of Science for publications, using terms related to deep learning, radiomics, imaging methods (CT or MR), and the liver. Two reviewers independently selected articles and extracted data from each eligible article. The Quality Assessment of Diagnostic Accuracy Studies-AI (QUADAS-AI) tool was used to assess the risk of bias and concerns regarding applicability.

RESULTS

The screening identified 45 high-quality publications from 235 candidates, including 8 on diffuse liver diseases and 37 on focal liver lesions. Nine studies used deep learning and 36 studies used radiomics. All 45 studies were rated as low risk of bias in patient selection and workflow, but 36 (80%) were rated as high risk of bias in the index test because they lacked external validation. In terms of concerns regarding applicability, all 45 studies were rated as low concerns. These studies demonstrated that deep learning and radiomics can evaluate liver fibrosis, cirrhosis, portal hypertension, and a series of complications caused by cirrhosis, predict the prognosis of malignant hepatic tumors, and differentiate focal hepatic lesions.

CONCLUSIONS

The latest studies have shown that deep learning and radiomics based on hepatic CT and MR imaging have potential application value in the diagnosis, treatment evaluation, and prognosis prediction of common liver diseases. The AI methods may become useful tools to support clinical decision-making in the future.

ADVANCES IN KNOWLEDGE

Deep learning and radiomics have shown their potential in the diagnosis, treatment evaluation, and prognosis prediction of a series of common diffuse liver diseases and focal liver lesions.

Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis

PURPOSE

To assess the methodological quality and to evaluate the predictive performance of radiomics studies for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

METHODS

Publications between 2017 and 2021 on radiomic MVI prediction in HCC based on CT, MR, ultrasound, and PET/CT were included. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Methodological quality was assessed through the radiomics quality score (RQS). Fourteen studies classified as TRIPOD Type 2a or above were used for meta-analysis using random-effects model. Further analyses were performed to investigate the technical factors influencing the predictive performance of radiomics models.

RESULTS

Twenty-three studies including 4947 patients were included. The risk of bias was mainly related to analysis domain. The RQS reached an average of (37.7 ± 11.4)% with main methodological insufficiencies of scientific study design, external validation, and open science. The pooled areas under the receiver operating curve (AUC) were 0.85 (95% CI 0.82-0.89), 0.87 (95% CI 0.83-0.92), and 0.74 (95% CI 0.67-0.80), respectively, for CT, MR, and ultrasound radiomics models. The pooled AUC of ultrasound radiomics model was significantly lower than that of CT (p = 0.002) and MR (p < 0.001). Portal venous phase for CT and hepatobiliary phase for MR were superior to other imaging sequences for radiomic MVI prediction. Segmentation of both tumor and peritumor regions showed better performance than tumor region.

CONCLUSION

Radiomics models show promising prediction performance for predicting MVI in HCC. However, improvements in standardization of methodology are required for feasibility confirmation and clinical translation.

A Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma ≤ 5 cm

Aim

The aim of this study is to establish and validate a radiomics-based model using preoperative Gd-EOB-DTPA-enhanced MRI to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma ≤ 5 cm.

Methods

Clinicopathologic and MRI data of 178 patients with solitary hepatocellular carcinoma (HCC) (≤5 cm) were retrospectively collected from a single medical center between May 2017 and November 2020. Patients were randomly assigned into training and test subsets by a ratio of 7:3. Imaging features were extracted from the segmented tumor volume of interest with 1-cm expansion on arterial phase (AP) and hepatobiliary phase (HBP) images. Different models based on the significant clinical risk factors and/or selected imaging features were established and the predictive performance of the models was evaluated.

Results

Three radiomics models, the AP_model, the HBP_model, and the AP+HBP_model, were constructed for MVI prediction. Among them, the AP+HBP_model outperformed the other two. When it was combined with a clinical model, consisting of tumor size and alpha-fetoprotein (AFP), the combined model (AP+HBP+Clin_model) showed an area under the curve of 0.90 and 0.70 in the training and test subsets, respectively. Its sensitivity and specificity were 0.91 and 0.76 in the training subset and 0.60 and 0.79 in the test subset, respectively. The calibration curve illustrated that the combined model possessed a good agreement between the predicted and the actual probabilities.

Conclusions

The radiomics-based model combining imaging features from the arterial and hepatobiliary phases of Gd-EOB-DTPA-enhanced MRI and clinical risk factors provides an effective and reliable tool for the preoperative prediction of MVI in patients with HCC ≤ 5 cm.

MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018

Differentiating hepatocellular carcinoma (HCC) from other primary liver malignancies in the Liver Imaging Reporting and Data System (LI-RADS) M (LR-M) tumours noninvasively is critical for patient treatment options, but visual evaluation based on medical images is a very challenging task. This study aimed to evaluate whether magnetic resonance imaging (MRI) models based on radiomics features could further improve the ability to classify LR-M tumour subtypes. A total of 102 liver tumours were defined as LR-M by two radiologists based on LI-RADS and were confirmed to be HCC (n = 31) and non-HCC (n = 71) by surgery. A radiomics signature was constructed based on reproducible features using the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression algorithms with tenfold cross-validation. Logistic regression modelling was applied to establish different models based on T2-weighted imaging (T2WI), arterial phase (AP), portal vein phase (PVP), and combined models. These models were verified independently in the validation cohort. The area under the curve (AUC) of the models based on T2WI, AP, PVP, T2WI + AP, T2WI + PVP, AP + PVP, and T2WI + AP + PVP were 0.768, 0.838, 0.778, 0.880, 0.818, 0.832, and 0.884, respectively. The combined model based on T2WI + AP + PVP showed the best performance in the training cohort and validation cohort. The discrimination efficiency of each radiomics model was significantly better than that of junior radiologists’ visual assessment (p < 0.05; Delong). Therefore, the MRI-based radiomics models had a good ability to discriminate between HCC and non-HCC in LR-M tumours, providing more options to improve the accuracy of LI-RADS classification.

Computed Tomography Radiomics-Based Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Background

Due to the high recurrence rate in hepatocellular carcinoma (HCC) after resection, preoperative prognostic prediction of HCC is important for appropriate patient management. Exploring and developing preoperative diagnostic methods has great clinical value in treating patients with HCC. This study sought to develop and evaluate a novel combined clinical predictive model based on standard triphasic computed tomography (CT) to discriminate microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Methods

The preoperative findings of 82 patients with HCC, including conventional clinical factors, CT imaging findings, and CT texture analysis (TA), were analyzed retrospectively. All included cases were divided into MVI-negative (n = 33; no MVI) and MVI-positive (n = 49; low or high risk of MVI) groups. TA parameters were extracted from non-enhanced, arterial, portal venous, and equilibrium phase images and subsequently calculated using the Artificial Intelligence Kit. After statistical analyses, a clinical model comprising conventional clinical and CT image risk factors, radiomics signature models, and a novel combined model (fused radiomic signature) was constructed. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used to assess the performance of the various models in discriminating MVI.

Results

We found that tumor diameter and pathological grade were effective clinical predictors in clinical model and 12 radiomics features were effective for MVI prediction of each CT phase. The AUCs of the clinical, plain, artery, venous, and delay models were 0.77 (95% CI: 0.67–0.88), 0.75 (95% CI: 0.64–0.87), 0.79 (95% CI: 0.69–0.89), 0.73 (95% CI: 0.61–0.85), and 0.80 (95% CI: 0.70–0.91), respectively. The novel combined model exhibited the best performance, with an AUC of 0.83 (95% CI: 0.74–0.93).

Conclusions

Models derived from triphasic CT can preoperatively predict MVI in patients with HCC. Of the models tested here, the novel combined model was most predictive and could become a useful tool to guide subsequent personalized treatment of HCC.

Preoperative Prediction of Microvascular Invasion Risk Grades in Hepatocellular Carcinoma Based on Tumor and Peritumor Dual-Region Radiomics Signatures

Objective

To predict preoperative microvascular invasion (MVI) risk grade by analyzing the radiomics signatures of tumors and peritumors on enhanced magnetic resonance imaging (MRI) images of hepatocellular carcinoma (HCC).

Methods

A total of 501 HCC patients (training cohort n = 402, testing cohort n = 99) who underwent preoperative Gd-EOB-DTPA-enhanced MRI and curative liver resection within a month were studied retrospectively. Radiomics signatures were selected using the least absolute shrinkage and selection operator (Lasso) algorithm. Unimodal radiomics models based on tumors and peritumors (10mm or 20mm) were established using the Logistic algorithm, using plain T1WI, arterial phase (AP), portal venous phase (PVP), and hepatobiliary phase (HBP) images. Multimodal radiomics models based on different regions of interest (ROIs) were established using a combinatorial modeling approach. Moreover, we merged radiomics signatures and clinico-radiological features to build unimodal and multimodal clinical radiomics models.

Results

In the testing cohort, the AUC of the dual-region (tumor & peritumor 20 mm)radiomics model and single-region (tumor) radiomics model were 0.741 vs 0.694, 0.733 vs 0.725, 0.667 vs 0.710, and 0.559 vs 0.677, respectively, according to AP, PVP, T1WI, and HBP images. The AUC of the final clinical radiomics model based on tumor and peritumoral 20mm incorporating radiomics features in AP&PVP&T1WI images for predicting MVI classification in the training and testing cohorts were 0.962 and 0.852, respectively.

Conclusion

The radiomics signatures of the dual regions for tumor and peritumor on AP and PVP images are of significance to predict MVI.

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

Background

Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). To perform a meta-analysis to investigate the diagnostic performance of radiomics for the preoperative evaluation of MVI in HCC and the effect of potential factors.

Materials and Methods

A systematic literature search was performed in PubMed, Embase, and the Cochrane Library for studies focusing on the preoperative evaluation of MVI in HCC with radiomics methods. Data extraction and quality assessment of the retrieved studies were performed. Statistical analysis included data pooling, heterogeneity testing and forest plot construction. Meta-regression and subgroup analyses were performed to reveal the effect of potential explanatory factors [design, combination of clinical factors, imaging modality, number of participants, and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) applicability risk] on the diagnostic performance.

Results

Twenty-two studies with 4,129 patients focusing on radiomics for the preoperative prediction of MVI in HCC were included. The pooled sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were 84% (95% CI: 81, 87), 83% (95% CI: 78, 87) and 0.90 (95% CI: 0.87, 0.92). Substantial heterogeneity was observed among the studies (I²=94%, 95% CI: 88, 99). Meta-regression showed that all investigative covariates contributed to the heterogeneity in the sensitivity analysis (P < 0.05). Combined clinical factors, MRI, CT and number of participants contributed to the heterogeneity in the specificity analysis (P < 0.05). Subgroup analysis showed that the pooled sensitivity, specificity and AUC estimates were similar among studies with CT or MRI.

Conclusion

Radiomics is a promising noninvasive method that has high preoperative diagnostic performance for MVI status. Radiomics based on CT and MRI had a comparable predictive performance for MVI in HCC. Prospective, large-scale and multicenter studies with radiomics methods will improve the diagnostic power for MVI in the future.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259363, identifier CRD42021259363.

Using Systemic Inflammatory Markers to Predict Microvascular Invasion Before Surgery in Patients With Hepatocellular Carcinoma

Background

Mounting studies reveal the relationship between inflammatory markers and post-therapy prognosis. Yet, the role of the systemic inflammatory indices in preoperative microvascular invasion (MVI) prediction for hepatocellular carcinoma (HCC) remains unclear.

Patients and Methods

In this study, data of 1,058 cases of patients with HCC treated in the First Affiliated Hospital of Sun Yat-sen University from February 2002 to May 2018 were collected. Inflammatory factors related to MVI diagnosis in patients with HCC were selected by least absolute shrinkage and selection operator (LASSO) regression analysis and were integrated into an “Inflammatory Score.” A prognostic nomogram model was established by combining the inflammatory score and other independent factors determined by multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the predictive efficacy of the model.

Results

Sixteen inflammatory factors, including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, etc., were selected by LASSO regression analysis to establish an inflammatory score. Multivariate logistic regression analysis showed that inflammatory score (OR = 2.14, 95% CI: 1.63–2.88, p < 0.001), alpha fetoprotein (OR = 2.02, 95% CI: 1.46–2.82, p < 0.001), and tumor size (OR = 2.37, 95% CI: 1.70–3.30, p < 0.001) were independent factors for MVI. These three factors were then used to establish a nomogram for MVI prediction. The AUC for the training and validation group was 0.72 (95% CI: 0.68–0.76) and 0.72 (95% CI: 0.66–0.78), respectively.

Conclusion

These findings indicated that the model drawn in this study has a high predictive value which is capable to assist the diagnosis of MVI in patients with HCC.

Contrast-enhanced ultrasound-based ultrasomics score: a potential biomarker for predicting early recurrence of hepatocellular carcinoma after resection or ablation

OBJECTIVES

This study aimed to construct a prediction model based on contrast-enhanced ultrasound (CEUS) ultrasomics features and investigate its efficacy in predicting early recurrence (ER) of primary hepatocellular carcinoma (HCC) after resection or ablation.

METHODS

This study retrospectively included 215 patients with primary HCC, who were divided into a developmental cohort (n = 139) and a test cohort (n = 76). Four representative images-grayscale ultrasound, arterial phase, portal venous phase and delayed phase-were extracted from each CEUS video. Ultrasomics features were extracted from tumoral and peritumoral area inside the region of interest. Logistic regression was used to establish models, including a tumoral model, a peritumoral model and a combined model with additional clinical risk factors. The performance of the three models in predicting recurrence within 2 years was verified.

RESULTS

The combined model performed best in predicting recurrence within 2 years, with an area under the curve (AUC) of 0.845, while the tumoral model had an AUC of 0.810 and the peritumoral model one of 0.808. For prediction of recurrence-free survival, the 2-year cumulative recurrence rate was significant higher in the high-risk group (76.5%) than in the low-risk group (9.5%; p < 0.0001).

CONCLUSION

These CEUS ultrasomics models, especially the combined model, had good efficacy in predicting early recurrence of HCC. The combined model has potential for individual survival assessment for HCC patients undergoing resection or ablation.

ADVANCES IN KNOWLEDGE

CEUS ultrasomics had high sensitivity, specificity and PPV in diagnosing early recurrence of HCC, and high efficacy in predicting early recurrence of HCC (AUC > 0.8). The combined model performed better than the tumoral ultrasomics model and peritumoral ultrasomics model in predicting recurrence within 2 years. Recurrence was more likely to occur in the high-risk group than in the low-risk group, with 2-year cumulative recurrence rates, respectively, 76.5% and 9.5% (p < 0.0001).

Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment

Preoperative prediction of microvascular invasion (MVI) is of importance in hepatocellular carcinoma (HCC) patient treatment management. Plenty of radiomics models for MVI prediction have been proposed. This study aimed to elucidate the role of radiomics models in the prediction of MVI and to evaluate their methodological quality. The methodological quality was assessed by the Radiomics Quality Score (RQS), and the risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Twenty-two studies using CT, MRI, or PET/CT for MVI prediction were included. All were retrospective studies, and only two had an external validation cohort. The AUC values of the prediction models ranged from 0.69 to 0.94 in the test cohort. Substantial methodological heterogeneity existed, and the methodological quality was low, with an average RQS score of 10 (28% of the total). Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. In conclusion, a radiomics model could be an accurate and effective tool for MVI prediction in HCC patients, although the methodological quality has so far been insufficient. Future prospective studies with an external validation cohort in accordance with a standardized radiomics workflow are expected to supply a reliable model that translates into clinical utilization.

Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment

Preoperative prediction of microvascular invasion (MVI) is of importance in hepatocellular carcinoma (HCC) patient treatment management. Plenty of radiomics models for MVI prediction have been proposed. This study aimed to elucidate the role of radiomics models in the prediction of MVI and to evaluate their methodological quality. The methodological quality was assessed by the Radiomics Quality Score (RQS), and the risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Twenty-two studies using CT, MRI, or PET/CT for MVI prediction were included. All were retrospective studies, and only two had an external validation cohort. The AUC values of the prediction models ranged from 0.69 to 0.94 in the test cohort. Substantial methodological heterogeneity existed, and the methodological quality was low, with an average RQS score of 10 (28% of the total). Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. In conclusion, a radiomics model could be an accurate and effective tool for MVI prediction in HCC patients, although the methodological quality has so far been insufficient. Future prospective studies with an external validation cohort in accordance with a standardized radiomics workflow are expected to supply a reliable model that translates into clinical utilization.

Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma

BACKGROUND

Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology.

AIMS

To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation.

METHODS

We performed a systematic review of all full-text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies.

RESULTS

A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c-statistics 0.66-0.95), and to predict microvascular invasion (c-statistic 0.76-0.92), early recurrence after hepatectomy (c-statistics 0.71-0.86), and prognosis after locoregional or systemic therapies (c-statistics 0.74-0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard.

CONCLUSIONS

Quantitative image analysis demonstrates promise as a non-invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.

Progress of MRI Radiomics in Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively.

OBJECTIVE

This study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC.

METHODS

A literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis.

RESULTS

Radiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients.

CONCLUSION

Radiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.

Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives

Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor in China. Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions. Artificial intelligence (AI), such as machine learning (ML) and deep learning, has recently gained attention for its capability to reveal quantitative information on images. Currently, AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine. This review summarizes the applications of AI in various aspects of preoperative imaging of HCC, including segmentation, differential diagnosis, prediction of histopathology, early detection of recurrence after curative treatment, and evaluation of treatment response. We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.

Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs

OBJECTIVES

We aimed to develop and validate a deep convolutional neural network (DCNN) model for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and its clinical outcomes using contrast-enhanced computed tomography (CECT) in a large population of candidates for surgery.

METHODS

This retrospective study included 1116 patients with HCC who had undergone preoperative CECT and curative hepatectomy. Radiological (R), DCNN, and combined nomograms were constructed in a training cohort (n = 892) respectively based on clinicoradiological factors, DCNN probabilities, and all factors; the performance of each model was confirmed in a validation cohort (n = 244). Accuracy and the AUC to predict MVI were calculated. Disease-free survival (DFS) and overall survival (OS) after surgery were recorded.

RESULTS

The proportion of MVI-positive patients was respectively 38.8% (346/892) and 35.7 % (87/244) in the training and validation cohorts. The AUCs of the R, DCNN, and combined nomograms were respectively 0.809, 0.929, and 0.940 in the training cohorts and 0.837, 0.865, and 0.897 in the validation cohort. The combined nomogram outperformed the R nomogram in the training (p < 0.001) and validation (p = 0.009) cohorts. There was a significant difference in DFS and OS between the R, DCNN, and combined nomogram-predicted groups with and without MVI (p < 0.001).

CONCLUSIONS

The combined nomogram based on preoperative CECT performs well for preoperative prediction of MVI and outcome.

KEY POINTS

• A combined nomogram based on clinical information, preoperative CECT, and DCNN can predict MVI and clinical outcomes of patients with HCC. • DCNN provides added diagnostic ability to predict MVI. • The AUCs of the combined nomogram are 0.940 and 0.897 in the training and validation cohorts, respectively.