Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

A novel hybrid model for predicting tertiary lymphoid structures and targeted immunotherapy outcomes in hepatocellular carcinoma: a multicenter retrospective study

OBJECTIVE

To develop a novel hybrid model for preoperative prediction of tertiary lymphoid structures (TLSs) of hepatocellular carcinoma (HCC), and to identify patients who may benefit from postoperative targeted immunotherapy.

METHODS

Retrospective data were gathered from 332 patients with HCC who underwent surgical resection and gadoxetate disodium (Gd-EOB-DTPA) enhanced MRI at two tertiary hospitals (training cohort, n = 205; internal validation cohort, n = 90; and external validation cohort, n = 37) between March 2020 and January 2023. Radiomic features were extracted from Gd-EOB-DTPA-enhanced MRI sequences. These signatures were integrated with clinical-radiologic (CR) factors into a hybrid model and nomogram for clinical application. The performance of the model was assessed using the area under the curve (AUC) and 95% confidence intervals (CI).

RESULTS

The hybrid model outperformed the radiomics and CR models in the training cohort (AUC = 0.860 [95% CI: 0.805, 0.904], 0.784 [95% CI: 0.721, 0.838], and 0.809 [95% CI: 0.748, 0.860]). The hybrid model showed optimal performance, with AUCs of 0.823 (95% CI: 0.728, 0.895) and 0.875 (95% CI: 0.725, 0.960) in the internal and external validation cohorts, respectively. The calibration curve demonstrated that the nomogram had good diagnostic ability, and decision curve analysis indicated good clinical utility across all cohorts. Importantly, patients with a predicted high risk of TLSs from the hybrid model gained a survival benefit from targeted immunotherapy.

CONCLUSION

The hybrid model showed satisfactory performance in predicting intra-tumoral TLS positivity and targeted immunotherapy benefit in patients with HCC, potentially assisting clinicians in selecting precise individualized therapies.

KEY POINTS

Question How can accurate preoperative risk stratification of tertiary lymphoid structures positivity HCC be achieved to support targeted immunotherapy decision-making? Findings A hybrid model combining radiomics model and clinical-radiological model may be a reliable marker for predicting tertiary lymphoid structures positivity HCC. Clinical relevance Using this hybrid model may be useful in predicting tertiary lymphoid structures and screening candidate patients for targeted immunotherapy based on multiparametric MRI, which has potential clinical value in guiding clinical decision-making and improving patient outcomes.

Pretreatment radiomic imaging features combined with immunological indicators to predict targeted combination immunotherapy response in advanced hepatocellular carcinoma

BACKGROUND

Early symptoms of hepatocellular carcinoma (HCC) are not obvious, and more than 70% of which does not receive radical hepatectomy, when first diagnosed. In recent years, molecular-targeted drugs combined with immunotherapy and other therapeutic methods have provided new treatment options for middle and advanced HCC (aHCC). Predicting the effect of targeted combined immunotherapy has become a hot topic in current research.

AIM

To explore the relationship between nodule enhancement in hepatobiliary phase and the efficacy of combined targeted immunotherapy for aHCC.

METHODS

Data from 56 patients with aHCC for magnetic resonance imaging with gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid were retrospectively collected. Signal intensity of intrahepatic nodules was measured, and the hepatobiliary relative enhancement ratio (RER) was calculated. Progression-free survival (PFS) of patients with high and low reinforcement of HCC nodules was compared. The model was validated using receiver operating characteristic curves. Univariate and multivariate logistic regression and Kaplan-Meier analysis were performed to explore factors influencing the efficacy of targeted immunization and PFS.

RESULTS

Univariate and multivariate analyses revealed that the RER, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and prognostic nutritional index were significantly associated with the efficacy of tyrosine kinase inhibitors combined with immunotherapy (P < 0.05). The area under the curve of the RER for predicting the efficacy of tyrosine kinase inhibitors combined with anti-programmed death 1 antibody in patients with aHCC was 0.876 (95% confidence interval: 0.781-0.971, P < 0.05), the optimal cutoff value was 0.904, diagnostic sensitivity was 87.5%, and specificity was 79.2%. Kaplan-Meier analysis showed that neutrophil-to-lymphocyte ratio < 5, platelet-to-lymphocyte ratio < 300, prognostic nutritional index < 45, and RER < 0.9 significantly improved PFS.

CONCLUSION

AHCC nodules enhancement in the hepatobiliary stage was significantly correlated with PFS. Imaging information and immunological indicators had high predictive efficacy for targeted combined immunotherapy and were associated with PFS.

The evolving landscape of biomarkers for systemic therapy in advanced hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains one of the most prevalent and deadliest cancers. With the approval of multiple first- and second-line agents, especially the combination therapies based on immune checkpoint inhibitor (ICI) regimens, the landscape of systemic therapy for advanced HCC (aHCC) is more diverse than ever before. The efficacy of current systemic therapies shows great heterogeneity in patients with aHCC, thereby identifying biomarkers for response prediction and patient stratification has become an urgent need. The main biomarkers for systemic therapy in hepatocellular carcinoma are derived from peripheral blood, tissues, and imaging. Currently, the understanding of the clinical response to systemic therapy indicates unequivocally that a single biomarker cannot be used to identify patients who are likely to benefit from these treatments. In this review, we provide an integrated landscape of the recent development in molecular targeted therapies and ICIs-based therapies, especially focusing on the role of clinically applicable predictive biomarkers. Additionally, we further highlight the latest advancements in biomarker-driven therapies, including targeted treatments, adoptive cell therapies, and bispecific antibodies.

Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma

The tumor microenvironment (TME) of hepatocellular carcinoma (HCC) has garnered significant attention, especially with the rise of immunotherapy as a treatment strategy. Radiomics, an innovative technique, offers valuable insights into the intricate structure of the TME. This review highlights recent advancements in radiomics for analyzing the HCC TME, identifies key areas that warrant further research, and explores comprehensive multi-omics approaches that extend the potential of radiomics to new frontiers.

Predicting treatment response and prognosis of immune checkpoint inhibitors-based combination therapy in advanced hepatocellular carcinoma using a longitudinal CT-based radiomics model: a multicenter study

BACKGROUND

Identifying effective predictive strategies to assess the response of immune checkpoint inhibitors (ICIs)-based combination therapy in advanced hepatocellular carcinoma (HCC) is crucial. This study presents a new longitudinal CT-based radiomics model to predict treatment response and prognosis in advanced HCC patients undergoing ICIs-based combination therapy.

METHODS

Longitudinal CT images were collected before and during the treatment for HCC patients across three institutions from January 2019 to April 2022. A total of 1316 radiomic features were extracted from arterial and portal venous phase abdominal CT images for each patient. A model called Longitudinal Whole-liver CT-based Radiomics (LWCTR) was developed to categorize patients into responders or non-responders using radiomic features and clinical information through support vector machine (SVM) classifiers. The area under the curve (AUC) was used as the performance metric and subsequently applied for risk stratification and prognostic assessment. The Shapley Additive explanations (SHAP) method was used to calculate the Shapley value, which explains the contribution of each feature in the SVM model to the prediction.

RESULTS

This study included 395 eligible participants, with a median age of 57 years (IQR 51-66), comprising 344 males and 51 females. The LWCTR model performed well in predicting treatment response, achieving an AUC of 0.883 (95% confidence interval [CI] 0.881-0.888) in the training cohort, 0.876 (0.858-0.895) in the internal validation cohort, and 0.875 (0.860-0.887) in the external test cohort. The Rad-Nomo model, integrating the LWCTR model's prediction score (Rad-score) with the modified Response Evaluation Criteria in Solid Tumors (mRECIST), demonstrated strong prognostic performance. It achieved time-dependent AUC values of 0.902, 0.823, and 0.850 at 1, 2, and 3 years in the internal validation cohort and 0.893, 0.848, and 0.762 at the same intervals in the external test cohort.

CONCLUSION

The proposed LWCTR model performs well in predicting treatment response and prognosis in patients with HCC receiving ICIs-based combination therapy, potentially contributing to personalized and timely treatment decisions.

Alpha-fetoprotein combined with initial tumor shape irregularity in predicting the survival of patients with advanced hepatocellular carcinoma treated with immune-checkpoint inhibitors: a retrospective multi-center cohort study

BACKGROUND

Immune checkpoint inhibitors (ICIs) are playing a significant role in the treatment of hepatocellular carcinoma (HCC). This study aims to explore the prognostic value of alpha-fetoprotein (AFP) and initial tumor shape irregularity in patients treated with ICIs.

METHODS

In this retrospective, multi-center study, 296 HCC patients were randomly divided into the training set and the validation set in a 3:2 ratio. The training set was used to evaluate prognostic factors and to develop an easily applicable ATSI (AFP and Tumor Shape Irregularity) score, which was verified in the validation set.

RESULTS

The ATSI score was developed from two independent prognostic risk factors: baseline AFP ≥ 400 ng/ml (HR 1.73, 95% CI 1.01-2.96, P = 0.046) and initial tumor shape irregularity (HR 1.94, 95% CI 1.03-3.65, P = 0.041). The median overall survival (OS) was not reached (95% CI 28.20-NA) in patients who met no criteria (0 points), 25.8 months (95% CI 14.17-NA) in patients who met one criterion (1 point), and 17.03 months (95% CI 11.73-23.83) in patients who met two criteria (2 points) (P = 0.001). The median progression-free survival (PFS) was 10.83 months (95% CI 9.27-14.33) for 0 points, 8.03 months (95% CI 6.77-10.57) for 1 point, and 5.03 months (95% CI 3.83-9.67) for 2 points (P < 0.001). The validation set effectively verified these results (median OS, 37.43/24.27/14.03 months for 0/1/2 points, P = 0.028; median PFS, 13.93/8.30/4.90 months for 0/1/2 points, P < 0.001).

CONCLUSIONS

The ATSI score can effectively predict prognosis in HCC patients receiving ICIs.

A nomogram based on quantitative MR signal intensity predicts early response to combined systemic treatment in patients with hepatocellular carcinoma

Objective

This study aimed to develop and evaluate the value of a nomogram based on quantitative MR signal intensity to predict response to combined systemic therapy of anti-angiogenesis and immune checkpoint inhibitor (ICI) in hepatocellular carcinoma (HCC) patients.

Methods

117 HCC patients who underwent the combined systemic treatment at a tertiary hospital between September 2020 and May 2024 were enrolled and divided into a development cohort (n = 82) and a validation cohort (n = 35). The predictive value of the relative signal intensity attenuation index (rSIAI) based on enhanced MR parameters and laboratory parameters on disease control was evaluated using receiver operating characteristic (ROC) curves, with the determination of optimal cut-off values (COVs) accomplished via Youden's index. Univariate and multivariable analyses were conducted to evaluate the association between COVs and disease control. The validity of the COVs was further confirmed through chi-square testing and calculation of Cramer's V coefficient (V). A nomogram was constructed based on the multivariable logistic regression model and evaluated for clinical applicability.

Results

rSIAI from arterial to portal phase (rSI_ap) in combination with peripheral T-cell subset (CD4+) achieved the most accurate predictive performance for outcome compared to rSI_ap or CD4+ alone, with an area under the curve (AUC) of the ROC of 0.845 (95% CI, 0.748-0.915). A nomogram based on rSI_ap and CD4+ was constructed. Calibration and decision curve analyses confirmed the clinical relevance and value of the nomogram.

Conclusion

The nomogram based on rSI_ap has the potential to be a non-invasive tool for predicting disease control in advanced HCC patients who have received combined anti-angiogenesis and ICI therapies.

Radiomics-based biomarker for PD-1 status and prognosis analysis in patients with HCC

Purpose

To investigate the impact of preoperative contrast-enhanced CT-based radiomics model on PD-1 prediction in hepatocellular carcinoma (HCC) patients.

Methods

The study included 105 HCC patients (training cohort: 72; validation cohort: 33) who underwent preoperative contrast-enhanced CT and received systemic sorafenib treatment after surgery. Radiomics score was built for each patient and was integrated with independent clinic radiologic predictors into the radiomics model using multivariable logistic regression analysis.

Results

Seventeen radiomics features were finally selected to construct the radiomics score. In multivariate analysis, serum creatine and peritumoral enhancement were significant independent factors for PD-1 prediction. The radiomics model integrated radiomics signature with serum creatine and peritumoral enhancement showed good discriminative performance (AUC of 0.897 and 0.794 in the training and validation cohort). Overall survival (OS) was significantly different between the radiomics-predicted PD-1-positive and PD-1-negative groups (OS: 29.66 months, CI:16.03-44.40 vs. 31.04 months, CI: 17.10-44.07, P<0.001). Radiomics-predicted PD-1 was an independent predictor of OS of patients treated with sorafenib after surgery. (Hazard ratio [HR]: 1.61 [1.23-2.1], P<0.001).

Conclusion

The proposed model based on radiomic signature helps to evaluate PD-1 status of HCC patients and may be used for evaluating patients most likely to benefit from sorafenib as a potentially combination therapy regimen with immune checkpoint therapies.

Cohort-based nomogram for forensic prediction of SCD: a single-center pilot study

Forensic diagnosis of sudden cardiac death (SCD) is an extremely important part of routine forensic practice. The present study aimed to develop and validate nomograms for predicting the probability of SCD with special regards to ischemic heart disease-induced SCD (IHD-induced SCD) based on multiple autopsy variables. A total of 3322 cases, were enrolled and randomly assigned into a training cohort (n = 2325) and a validation cohort (n = 997), respectively. Prediction models of SCD and IHD-induced SCD were developed through multivariable logistic regression based on variables selected by LASSO regression or ridge regression, and prediction model with higher area under the curve (AUC) of the receiver operating characteristic (ROC) curve in the validation cohort was used to establish nomograms. For SCD prediction, discrimination of the nomogram was determined based on the ROC with AUC of 0.751 (95% CI, 0.726-0.775) and 0.735 (95% CI, 0.696-0.774) in the training cohort and validation cohort respectively. The AUC of IHD-induced SCD prediction nomogram in the training cohort and validation cohort were 0.742 (95% CI, 0.716-0.768) and 0.738 (95% CI, 0.698-0.777). To facilitate the use of nomograms in routine casework in forensic practice, web calculators ( https://forensic.shinyapps.io/Forensic_SCD/ , https://forensic.shinyapps.io/Forensic_IHDinducedSCD/ ) were constructed. In conclusion, the present study developed and validated simple and practical nomograms for predicting the probability of SCD and IHD-induced SCD based on multiple autopsy variables. The nomograms have certain efficiency for discrimination and calibration to provide a novel approach to diagnose cause of death, and may become a valuable tool in forensic practice.

Myeloid response evaluated by noninvasive CT imaging predicts post-surgical survival and immune checkpoint therapy benefits in patients with hepatocellular carcinoma

Background

The potential of preoperative CT in the assessment of myeloid immune response and its application in predicting prognosis and immune-checkpoint therapy outcomes in hepatocellular carcinoma (HCC) has not been explored.

Methods

A total of 165 patients with pathological slides and multi-phase CT images were included to develop a radiomics signature for predicting the imaging-based myeloid response score (iMRS). Overall survival (OS) and recurrence-free survival (RFS) were assessed according to the iMRS risk group and validated in a surgical resection cohort (n = 98). The complementary advantage of iMRS incorporating significant clinicopathologic factors was investigated by the Cox proportional hazards analysis. Additionally, the iMRS in inferring the benefits of immune checkpoint therapy was explored in an immunotherapy cohort (n = 36).

Results

We showed that AUCs of the optimal radiomics signature for iMRS were 0.941 [95% confidence interval (CI), 0.909-0.973] and 0.833 (0.798-0.868) in the training and test cohorts, respectively. High iMRS was associated with poor RFS and OS. The prognostic performance of the Clinical-iMRS nomogram was better than that of a single parameter (p < 0.05), with a 1-, 3-, and 5-year C-index for RFS of 0.729, 0.709, and 0.713 in the training, test, and surgical resection cohorts, respectively. A high iMRS score predicted a higher proportion of objective response (vs. progressive disease or stable disease; odds ratio, 2.311; 95% CI, 1.144-4.672; p = 0.020; AUC, 0.718) in patients treated with anti-PD-1 and PD-L1.

Conclusions

iMRS may provide a promising method for predicting local myeloid immune responses in HCC patients, inferring postsurgical prognosis, and evaluating benefits of immune checkpoint therapy.

Machine learning-based ultrasomics for predicting response to tyrosine kinase inhibitor in combination with anti-PD-1 antibody immunotherapy in hepatocellular carcinoma: a two-center study

Objective

The objective of this study is to build and verify the performance of machine learning-based ultrasomics in predicting the objective response to combination therapy involving a tyrosine kinase inhibitor (TKI) and anti-PD-1 antibody for individuals with unresectable hepatocellular carcinoma (HCC). Radiomic features can reflect the internal heterogeneity of the tumor and changes in its microenvironment. These features are closely related to pathological changes observed in histology, such as cellular necrosis and fibrosis, providing crucial non-invasive biomarkers to predict patient treatment response and prognosis.

Methods

Clinical, pathological, and pre-treatment ultrasound image data of 134 patients with recurrent unresectable or advanced HCC who treated with a combination of TKI and anti-PD-1 antibody therapy at Henan Provincial People's Hospital and the First Affiliated Hospital of Zhengzhou University between December 2019 and November 2023 were collected and retrospectively analyzed. Using stratified random sampling, patients from the two hospitals were assigned to training cohort (n = 93) and validation cohort (n = 41) at a 7:3 ratio. After preprocessing the ultrasound images, regions of interest (ROIs) were delineated. Ultrasomic features were extracted from the images for dimensionality reduction and feature selection. By utilizing the extreme gradient boosting (XGBoost) algorithm, three models were developed: a clinical model, an ultrasomic model, and a combined model. By analyzing the area under the receiver operating characteristic (ROC) curve (AUC), specificity, sensitivity, and accuracy, the predicted performance of the models was evaluated. In addition, we identified the optimal cutoff for the radiomic score using the Youden index and applied it to stratify patients. The Kaplan-Meier (KM) survival curves were used to examine differences in progression-free survival (PFS) between the two groups.

Results

Twenty ultrasomic features were selected for the construction of the ultrasomic model. The AUC of the ultrasomic model for the training cohort and validation cohort were 0.999 (95%CI: 0.997-1.000) and 0.828 (95%CI: 0.690-0.966), which compared significant favorably to those of the clinical model [AUC = 0.876 (95%CI: 0.815-0.936) for the training cohort, 0.766 (95%CI: 0.597-0.935) for the validation cohort]. Compared to the ultrasomic model, the combined model demonstrated comparable performance within the training cohort (AUC = 0.977, 95%CI: 0.957-0.998) but higher performance in the validation cohort (AUC = 0.881, 95%CI: 0.758-1.000). However, there was no statistically significant difference (p > 0.05). Furthermore, ultrasomic features were associated with PFS, which was significantly different between patients with radiomic scores (Rad-score) greater than 0.057 and those with Rad-score less than 0.057 in both the training (HR = 0.488, 95% CI: 0.299-0.796, p = 0.003) and validation cohorts (HR = 0.451, 95% CI: 0.229-0.887, p = 0.02).

Conclusion

The ultrasomic features demonstrates excellent performance in accurately predicting the objective response to TKI in combination with anti-PD-1 antibody immunotherapy among patients with unresectable or advanced HCC.

Unveiling Tim-3 immune checkpoint expression in hepatocellular carcinoma through abdominal contrast-enhanced CT habitat radiomics

Introduction

Immune checkpoint inhibitors (ICIs) are important systemic therapeutic agents for hepatocellular carcinoma (HCC), among which T-cell immunoglobulin and mucin-domain containing protein 3 (Tim-3) is considered an emerging target for ICI therapy. This study aims to evaluate the prognostic value of Tim-3 expression and develop a predictive model for Tim-3 infiltration in HCC.

Methods

We collected data from 424 HCC patients in The Cancer Genome Atlas (TCGA) and data from 102 pathologically confirmed HCC patients from our center for prognostic analysis. Multivariate Cox regression analyses were performed on both datasets to determine the prognostic significance of Tim-3 expression. In radiomics analysis, we used the K-means algorithm to cluster regions of interest in arterial phase enhancement and venous phase enhancement images from patients at our center. Radiomic features were extracted from three subregions as well as the entire tumor using pyradiomics. Five machine learning methods were employed to construct Habitat models based on habitat features and Rad models based on traditional radiomic features. The predictive performance of the models was compared using ROC curves, DCA curves, and calibration curves.

Results

Multivariate Cox analyses from both our center and the TCGA database indicated that high Tim-3 expression is an independent risk factor for poor prognosis in HCC patients. Higher levels of Tim-3 expression were significantly associated with worse prognosis. Among the ten models evaluated, the Habitat model constructed using the LightGBM algorithm showed the best performance in predicting Tim-3 expression status (training set vs. test set AUC 0.866 vs. 0.824).

Discussion

This study confirmed the importance of Tim-3 as a prognostic marker in HCC. The habitat radiomics model we developed effectively predicted intratumoral Tim-3 infiltration, providing valuable insights for the evaluation of ICI therapy in HCC patients.

Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and is associated with high mortality rates due to late detection and aggressive progression. Peritumoral radiomics, an emerging technique that quantitatively analyzes the tissue surrounding the tumor, has shown significant potential in enhancing the management of HCC. This paper examines the role of peritumoral radiomics in improving diagnostic accuracy, guiding personalized treatment strategies, and refining prognostic assessments. By offering unique insights into the tumor microenvironment, peritumoral radiomics enables more precise patient stratification and informs clinical decision-making. However, the integration of peritumoral radiomics into routine clinical practice faces several challenges. Addressing these challenges through continued research and innovation is crucial for the successful implementation of peritumoral radiomics in HCC management, ultimately leading to improved patient outcomes.

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.

CT radiomics-based biomarkers can predict response to immunotherapy in hepatocellular carcinoma

Hepatocellular Carcinoma (HCC) remains a leading cause of cancer deaths. Despite the rise of immunotherapies, many HCC patients don't benefit. There's a clear need for biomarkers to guide treatment decisions. This research aims to identify such biomarkers by combining radiological data and machine learning. We analyzed clinical and CT imaging data of 54 HCC patients undergoing immunotherapy. Radiologic features were examined to develop a model predicting short-term immunotherapy effects. We utilized 9 machine learning and 2 ensemble learning techniques using RapidMiner for model construction. We conducted the validation of the above feature combination using 29 HCC patients who received immunotherapy from another hospital, and tested and validated it using XGBoost combined with a genetic algorithm. In 54 HCC patients, radiomics features varied significantly between those with partial response (PR) and stable disease (SD). Key features in Gray Level Run Length Matrix (GLRLM) and in adjacent tissues' Intensity Direct, Neighborhood Gray Tone Difference Matrix (NGTDM), and Shape correlated with short-term immunotherapy efficacy. Selected feature combinations of 15, 19, and 8/15 features yielded better outcomes. Deep learning, random forest, and naive bayes outperformed other methods, with Bagging being more reliable than Adaboost. In the validation set of 29 HCC patients, the mentioned feature combination also demonstrated favorable performance. Furthermore, we achieved improved results when employing XGBoost in conjunction with a genetic algorithm for testing and validation. The machine learning model built with CT image features derived from GLCM, GLRLM, IntensityDirect, NGTDM, and Shape can accurately forecast the short-term efficacy of immunotherapy for HCC.

Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy

BACKGROUND

Lenvatinib plus PD-1 inhibitors and interventional (LPI) therapy have demonstrated promising treatment effects in unresectable hepatocellular carcinoma (HCC). However, biomarkers for predicting the response to LPI therapy remain to be further explored. We aimed to develop a radiomics model to noninvasively predict the efficacy of LPI therapy.

METHODS

Clinical data of patients with HCC receiving LPI therapy were collected in our institution. The clinical model was built with clinical information. Nine machine learning classifiers were tested and the multilayer perceptron classifier with optimal performance was used as the radiomics model. The clinical-radiomics model was constructed by integrating clinical and radiomics scores through logistic regression analysis.

RESULTS

151 patients were enrolled in this study (2:1 randomization, 101 and 50 in the training and validation cohorts), of which three achieved complete response, 69 showed partial response, 46 showed stable disease, and 33 showed progressive disease. The objective response rate, disease control rate, and conversion resection rates were 47.7, 78.1 and 23.2%. 14 features were selected from the initially extracted 1223 for radiomics model construction. The area under the curves of the radiomics model (0.900 for training and 0.893 for validation) were comparable to that of the clinical-radiomics model (0.912 for training and 0.892 for validation), and both were superior to the clinical model (0.669 for training and 0.585 for validation). Meanwhile, the radiomics model can categorize participants into high-risk and low-risk groups for progression-free survival (PFS) and overall survival (OS) in the training (HR 1.913, 95% CI 1.121 to 3.265, p=0.016 for PFS; HR 4.252, 95% CI 2.051 to 8.816, p=0.001 for OS) and validation sets (HR 2.347, 95% CI 1.095 to 5.031, p=0.012 for PFS; HR 2.592, 95% CI 1.050 to 6.394, p=0.019 for OS).

CONCLUSION

The promising machine learning radiomics model was developed and validated to predict the efficacy of LPI therapy for patients with HCC and perform risk stratification, with comparable performance to clinical-radiomics model.

CT-based radiomics for predicting pathological grade in hepatocellular carcinoma

Objective

To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade predictions based on contrast-enhanced CT (CECT).

Methods

Patients with pathologically confirmed HCC after surgery and underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation datasets. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

In total 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation datasets, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model were evaluated as the best for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training dataset; AUC = 0.842 in the validation dataset) and was used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency.

Conclusions

Low- and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model.

Prognostic nomogram model for selecting between transarterial chemoembolization plus lenvatinib, with and without PD-1 inhibitor in unresectable hepatocellular carcinoma

OBJECTIVES

To establish and verify a prognostic nomogram model for selecting in unresectable hepatocellular carcinoma (uHCC) treated by transarterial chemoembolization plus lenvatinib (TACE-L) with or without PD-1 inhibitor.

METHODS

Data of 241 uHCC patients who underwent TACE-L (n = 128) and TACE-L plus PD-1 inhibitor (TACE-L-P, n = 113) were retrospectively reviewed. The differences in tumour responses, progression-free survival (PFS), overall survival (OS), and adverse events (AEs) between two groups were compared, and a prognostic nomogram model was established based on independent clinical-radiologic factors and confirmed by Cox regression analysis for predicting PFS and OS. The treatment selection for uHCC patients was stratified by the nomogram score.

RESULTS

Compared to TACE-L, TACE-L-P presented prolonged PFS (14.0 vs. 9.0 months, P < .001), longer OS (24.0 vs. 15.0 months, P < .001), and a better overall objective response rate (54.0% vs. 32.8%, P = .001). There was no significant difference between the rate of AEs in the TACE-L-P and the TACE-L (56.64% vs. 46.09%, P = .102) and the rate of grade ≥ 3 AEs (11.50% vs. 9.38%, P = .588), respectively. The nomogram model presented good discrimination, with a C-index of 0.790 for predicting PFS and 0.749 for predicting OS. Patients who underwent TACE-L and obtained a nomogram score >9 demonstrated improved 2-year PFS when transferred to TACE-L-P, and those with a nomogram ≤25 had better 2-year OS when transferred to TACE-L-P.

CONCLUSIONS

TACE-L-P showed significant improvements in efficiency and safety for uHCC patients compared with TACE-L. The nomogram was useful for stratifying treatment decisions and selecting a suitable population for uHCC patients.

ADVANCES IN KNOWLEDGE

Prognostic nomogram model is of great value in predicting individualized survival benefits for uHCC patients after TACE-L or/and TACE-L-P. And the nomogram was helpful for selection between TACE-L-P and TACE-L among uHCC patients.

A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.

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.

Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application

Radiomics allows the extraction of quantitative imaging features from clinical magnetic resonance imaging (MRI) and computerized tomography (CT) studies. The advantages of radiomics have primarily been exploited in oncological applications, including better characterization and staging of oncological lesions and prediction of patient outcomes and treatment response. The potential introduction of radiomics in the clinical setting requires the establishment of a standardized radiomics pipeline and a quality assurance program. Radiomics and texture analysis of the liver have improved the differentiation of hypervascular lesions such as adenomas, focal nodular hyperplasia, and hepatocellular carcinoma (HCC) during the arterial phase, and in the pretreatment determination of HCC prognostic factors (e.g., tumor grade, microvascular invasion, Ki-67 proliferation index). Radiomics of pancreatic CT and MR images has enhanced pancreatic ductal adenocarcinoma detection and its differentiation from pancreatic neuroendocrine tumors, mass-forming chronic pancreatitis, or autoimmune pancreatitis. Radiomics can further help to better characterize incidental pancreatic cystic lesions, accurately discriminating benign from malignant intrapancreatic mucinous neoplasms. Nonetheless, despite their encouraging results and exciting potential, these tools have yet to be implemented in the clinical setting. This non-systematic review will describe the essential steps in the implementation of the radiomics and feature extraction workflow from liver and pancreas CT and MRI studies for their potential clinical application. A succinct overview of reported radiomics applications in the liver and pancreas and the challenges and limitations of their implementation in the clinical setting is also discussed, concluding with a brief exploration of the future perspectives of radiomics in the gastroenterology field.

A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma

It is widely recognized that tumor immune microenvironment (TIME) plays a crucial role in tumor progression, metastasis, and therapeutic response. Despite several noninvasive strategies have emerged for cancer diagnosis and prognosis, there are still lack of effective radiomic-based model to evaluate TIME status, let alone predict clinical outcome and immune checkpoint inhibitor (ICIs) response for hepatocellular carcinoma (HCC). In this study, we developed a radiomic model to evaluate TIME status within the tumor and predict prognosis and immunotherapy response. A total of 301 patients who underwent magnetic resonance imaging (MRI) examinations were enrolled in our study. The intra-tumoral expression of 17 immune-related molecules were evaluated using co-detection by indexing (CODEX) technology, and we construct Immunoscore (IS) with the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression method to evaluate TIME. Of 6115 features extracted from MRI, five core features were filtered out, and the Radiomic Immunoscore (RIS) showed high accuracy in predicting TIME status in testing cohort (area under the curve = 0.753). More importantly, RIS model showed the capability of predicting therapeutic response to anti-programmed cell death 1 (PD-1) immunotherapy in an independent cohort with advanced HCC patients (area under the curve = 0.731). In comparison with previously radiomic-based models, our integrated RIS model exhibits not only higher accuracy in predicting prognosis but also the potential guiding significance to HCC immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00136-8.

Hepatocellular carcinoma immune prognosis score predicts the clinical outcomes of hepatocellular carcinoma patients receiving immune checkpoint inhibitors

OBJECTIVE

The predictive biomarkers of immune checkpoint inhibitors (ICIs) in hepatocellular carcinoma (HCC) still need to be further explored. This study aims to establish a new immune prognosis biomarker to predict the clinical outcomes of hepatocellular carcinoma patients receiving immune checkpoint inhibitors.

METHODS

The subjects of this study were 151 HCC patients receiving ICIs at Harbin Medical University Cancer Hospital from January 2018 to December 2021. This study collected a wide range of blood parameters from patients before treatment and used Cox's regression analysis to identify independent prognostic factors in blood parameters, as well as their β coefficient. The hepatocellular carcinoma immune prognosis score (HCIPS) was established through Lasso regression analysis and COX multivariate analysis. The cut-off value of HCIPS was calculated from the receiver operating characteristic (ROC) curve. Finally, the prognostic value of HCIPS was validated through survival analysis, stratified analyses, and nomograms.

RESULTS

HCIPS was composed of albumin (ALB) and thrombin time (TT), with a cut-off value of 0.64. There were 56 patients with HCIPS < 0.64 and 95 patients with HCIPS ≥ 0.64, patients with low HCIPS were significantly related to shorter progression-free survival (PFS) (13.10 months vs. 1.63 months, P < 0.001) and overall survival (OS) (14.83 months vs. 25.43 months, P < 0.001). HCIPS has also been found to be an independent prognostic factor in this study. In addition, the stratified analysis found a significant correlation between low HCIPS and shorter OS in patients with tumor size ≥ 5 cm (P of interaction = 0.032). The C-index and 95% CI of the nomograms for PFS and OS were 0.730 (0.680-0.779) and 0.758 (0.711-0.804), respectively.

CONCLUSIONS

As a new score established based on HCC patients receiving ICIs, HCIPS was significantly correlated with clinical outcomes in patients with ICIs and might serve as a new biomarker to predict HCC patients who cloud benefit from ICIs.

Radiomics Based on Contrast-Enhanced CT for Recognizing c-Met-Positive Hepatocellular Carcinoma: a Noninvasive Approach to Predict the Outcome of Sorafenib Resistance

OBJECTIVES

The purpose of our project was to investigate the effectiveness of radiomic features based on contrast-enhanced computed tomography (CT) that can detect the expression of c-Met in hepatocellular carcinoma (HCC) and to validate its efficacy in predicting the outcome of sorafenib resistance.

MATERIALS AND METHODS

In total, 130 patients (median age, 60 years) with pathologically confirmed HCC who underwent contrast material-enhanced CT from October 2012 to July 2020 were randomly divided into a training set (n = 91) and a test set (n = 39). Radiomic features were extracted from arterial phase (AP), portal venous phase (VP) and delayed phase (DP) images of every participant's enhanced CT images.

RESULTS

The entire group comprised 39 Met-positive and 91 Met-negative patients. The combined model, which included the clinical factors and the radiomic features, performed well in the training (area under the curve [AUC] = 0.878) and validation (AUC = 0.851) cohorts. The nomogram, which relied on the combined model, fits well in the calibration curves. Decision curve analysis (DCA) further confirmed that the clinical valuation of the nomogram achieved comparable accuracy in c-Met prediction. Among another 20 patients with HCC who had received sorafenib, the predicted high-risk group had shorter overall survival (OS) than the predicted low-risk group (p < 0.05).

CONCLUSION

A multivariate model acquired from three phases (AP, VP and DP) of enhanced CT, HBV-DNA and γ glutamyl transpeptidase isoenzyme II (GGT-II) could be considered a satisfactory preoperative marker of the expression of c-Met in patients with HCC. This approach may help in overcoming sorafenib resistance in advanced HCC.

Construction of a Nomogram to Predict Overall Survival in Patients with Early-Onset Hepatocellular Carcinoma: A Retrospective Cohort Study

Hepatocellular carcinoma (HCC) is a widespread and impactful cancer which has pertinent implications worldwide. Although most cases of HCC are typically diagnosed in individuals aged ≥60 years, there has been a notable rise in the occurrence of HCC among younger patients. However, there is a scarcity of precise prognostic models available for predicting outcomes in these younger patients. A retrospective analysis was conducted to investigate early-onset hepatocellular carcinoma (EO-LIHC) using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2018. The analysis included 1392 patients from the SEER database and our hospital. Among them, 1287 patients from the SEER database were assigned to the training cohort (n = 899) and validation cohort 1 (n = 388), while 105 patients from our hospital were assigned to validation cohort 2. A Cox regression analysis showed that age, sex, AFP, grade, stage, tumor size, surgery, and chemotherapy were independent risk factors. The nomogram developed in this study demonstrated its discriminatory ability to predict the 1-, 3-, and 5-year overall survival (OS) rates in EO-LIHC patients based on individual characteristics. Additionally, a web-based OS prediction model specifically tailored for EO-LIHC patients was created and validated. Overall, these advancements contribute to improved decision-making and personalized care for individuals with EO-LIHC.

Biomarkers for immunotherapy of hepatocellular carcinoma

Immune-checkpoint inhibitors (ICIs) are now widely used for the treatment of patients with advanced-stage hepatocellular carcinoma (HCC). Two different ICI-containing regimens, atezolizumab plus bevacizumab and tremelimumab plus durvalumab, are now approved standard-of-care first-line therapies in this setting. However, and despite substantial improvements in survival outcomes relative to sorafenib, most patients with advanced-stage HCC do not derive durable benefit from these regimens. Advances in genome sequencing including the use of single-cell RNA sequencing (both of tumour material and blood samples), as well as immune cell identification strategies and other techniques such as radiomics and analysis of the microbiota, have created considerable potential for the identification of novel predictive biomarkers enabling the accurate selection of patients who are most likely to derive benefit from ICIs. In this Review, we summarize data on the immunology of HCC and the outcomes in patients receiving ICIs for the treatment of this disease. We then provide an overview of current biomarker use and developments in the past 5 years, including gene signatures, circulating tumour cells, high-dimensional flow cytometry, single-cell RNA sequencing as well as approaches involving the microbiome, radiomics and clinical markers. Novel concepts for further biomarker development in HCC are then discussed including biomarker-driven trials, spatial transcriptomics and integrated 'big data' analysis approaches. These concepts all have the potential to better identify patients who are most likely to benefit from ICIs and to promote the development of new treatment approaches.

Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma

BACKGROUND

Noninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients.

METHODS

A total of 138 patients with HCC who received TACE were retrospectively included and randomly divided into training and validation cohorts at a ratio of 7:3. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. The inter- and intraclass correlation coefficients, the spearman's rank correlation test, and the gradient boosting decision tree algorithm were used for radiomics feature selection. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively.

RESULTS

The T-PTR radiomics models performed better than the TR and PTR models, and the T-PTR (3 mm) radiomics model demonstrated preferable performance with the AUCs of 0.884 (95%CI, 0.821-0.936) and 0.911 (95%CI, 0.825-0.975) in both training and validation cohorts. The T-PTR (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were fused to construct a combined nomogram. The combined nomogram [AUC: 0.910 (95%CI, 0.854-0.958) and 0.918 (95%CI, 0.831-0.986)] outperformed the clinical-radiological model [AUC: 0.789 (95%CI, 0.709-0.863) and 0.782 (95%CI, 0.660-0.902)] in the both cohorts and achieved good calibration capability and clinical utility.

CONCLUSIONS

CE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE.

Elevated levels of neutrophil related chemokine citrullinated histone H3, interleukin-8 and C-reaction protein in patients with immune checkpoint inhibitor therapy: predictive biomarkers for response to treatment

BACKGROUND

Immune checkpoint inhibitor (ICI) therapy has been used in various tumors. The biomarkers predictive of a response to ICI treatment remain unclear, and additional and combined biomarkers are urgently needed. Secreted factors related to the tumor microenvironment (TME) have been evaluated to identify novel noninvasive predictive biomarkers.

METHODS

We analyzed 85 patients undergoing ICI therapy as the primary cohort. The associations between ICI response and all biomarkers were evaluated. A prediction model and a nomogram were developed and validated based on the above factors.

RESULTS

Seventy-seven patients were enrolled in the validation cohort. In the primary cohort, the baseline serum levels of H3Cit, IL-8 and CRP were significantly higher in nonresponder patients. A model based on these three factors was developed, and the "risk score" of an ICI response was calculated with the formula: "risk score" = 3.4591×H3Cit + 2.5808×IL8 + 2.0045 ×CRP- 11.3844. The cutoff point of the "risk score" was 0.528, and patients with a "risk score" lower than 0.528 were more likely to benefit from ICI treatment (AUC: 0.937, 95% CI: 0.886-0.988, with sensitivity 80.60%, specificity 91.40%). The AUC was 0.719 (95% CI: 0.600-0.837, P = 0.001), with a sensitivity of 70.00% and specificity of 65.20% in the validation cohort.

CONCLUSIONS

A model incorporating H3Cit, IL-8 and CRP has an excellent prediction ability for ICI response; thus, patients with a lower "risk score" selectively benefit from ICI treatment, which may have significant clinical implications for the early detection of an ICI response.

Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study

Introduction

Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients.

Methods

Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed.

Results

The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656-0.840) and 0.702 (95% CI: 0.547-0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815-0.957) and 0.820 (95% CI: 0.648-0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not.

Conclusion

Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.

A contrast-enhanced ultrasound-based nomogram for the prediction of therapeutic efficiency of anti-PD-1 plus anti-VEGF agents in advanced hepatocellular carcinoma patients

Background

There is no study focusing on noninvasive predictors for the efficacy of sintilimab (anti-PD-1) plus IBI305 (a bevacizumab biosimilar) treatment in advanced hepatocellular carcinoma (HCC).

Method

A total of 33 patients with advanced HCC were prospectively enrolled and received sintilimab plus IBI305 treatment from November 2018 to October 2019. Baseline characteristics including clinical data, laboratory data, and tumor features based on pretreatment CT/MR were collected. Meanwhile, pretreatment contrast-enhanced ultrasound (CEUS) for target tumor was performed and quantitative parameters were derived from time-intensity curves (TICs). A nomogram was developed based on the variables identified by the univariable and multivariable logistic regression analysis. The discrimination, calibration, and clinical utility of the nomogram were evaluated.

Results

Tumor embolus and grad ratio were significant variables related to the efficacy of sintilimab plus IBI305 strategy. The nomogram based on these two variables achieved an excellent predictive performance with an area under curve (AUC) of 0.909 (95% CI, 0.813-1). A bootstrapping for 500 repetitions was performed to validate this model and the AUC of the bootstrap model was 0.91 (95% CI, 0.8-0.98). The calibration curve and decision curve analysis (DCA) showed that the nomogram had a good consistency and clinical utility.

Conclusions

This study has established and validated a nomogram by incorporating the quantitative parameters of pretreatment CEUS and baseline clinical characteristics to predict the anti-PD-1 plus anti-VEGF treatment efficacy in advanced HCC patients.

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.

Contrast-enhanced CT parameters predict short-term tumor response in patients with hepatocellular carcinoma who received sequential combined anti-angiogenesis and immune checkpoint inhibitor treatment

PURPOSE

To evaluate whether relative Hounsfield unit attenuation index (rHUAI) on contrast-enhanced computed tomography (CECT) can predict tumor response in advanced hepatocellular carcinoma (HCC) patients who received sequential combined treatment of immune checkpoint inhibitor (ICI) and anti-angiogenesis therapy.

METHOD

One hundred seventeen advanced HCC patients who underwent the sequential combined treatment in a tertiary hospital between March 2020 and December 2021 were allocated to prediction and validation cohorts (with a ratio of 2:1) based on the time of initial ICI treatment. rHUAI from the arterial to the portal-venous phase (rHU_ap) and from the portal-venous to the delayed phase (rHU_pd) was calculated. The optimal cut-off values (COVs) of rHU_ap and rHU_pd for predicting tumor response were identified using Youden's index. Univariate and multivariable analyses were performed to assess the relationship between the COVs and tumor response. The validity of COVs was verified in the validation cohort using the chi-square test and Cramer's V coefficient (V).

RESULTS

The optimal COVs of the two observers were 0.5316 and 0.3265 for rHU_ap, and -0.0208 and -0.0048 for rHU_pd, respectively. Multivariable analysis suggested that the COVs were independently associated with tumor response in the prediction cohort (rHU_ap, Odds ratio: 7.727 and 7.808, 95 % CI: 2.516-23.728 and 2.399-25.410, p value < 0.001 and 0.001; rHU_pd, Odds ratio: 0.034 and 0.011, 95 % CI: 0.002-0.600 and 0.001-0.209, p value of 0.021 and 0.003). In the validation cohort, the optimal COVs of rHU_ap had a moderate to a strong association with tumor response (V = 0.362-0.545, p < 0.05). The association between COVs of rHU_pd and tumor response was slight to strong (V = 0.24-0.545, p = 0.001 to 0.134).

CONCLUSION

rHUAI obtained from CECT has the potential as a non-invasive tool for predicting tumor response in advanced HCC patients who have received combined ICI and anti-angiogenesis treatment.

Novel Computed-Tomography-Based Transformer Models for the Noninvasive Prediction of PD-1 in Pre-Operative Settings

The expression status of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC) is associated with the checkpoint blockade treatment responses of PD-1/PD-L1. Thus, accurately and preoperatively identifying the status of PD-1 has great clinical implications for constructing personalized treatment strategies. To investigate the preoperative predictive value of the transformer-based model for identifying the status of PD-1 expression, 93 HCC patients with 75 training cohorts (2859 images) and 18 testing cohorts (670 images) were included. We propose a transformer-based network architecture, ResTransNet, that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to automatically acquire a persuasive feature to obtain a prediction score using a nonlinear classifier. The area under the curve, receiver operating characteristic curve, and decision curves were applied to evaluate the prediction model's performance. Then, Kaplan-Meier survival analyses were applied to evaluate the overall survival (OS) and recurrence-free survival (RFS) in PD-1-positive and PD-1-negative patients. The proposed transformer-based model obtained an accuracy of 88.2% with a sensitivity of 88.5%, a specificity of 88.9%, and an area under the curve of 91.1% in the testing cohort.

Current status and future perspectives of radiomics in hepatocellular carcinoma

Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.

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.

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.

Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid

OBJECTIVES

The differentiation of Warthin tumor and pleomorphic adenoma before treatment is crucial for clinical strategies. The aim of this study was to develop and test a T2-weighted-based radiomics model for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

METHODS

A total of 117 patients, including 61 cases of Warthin tumor and 56 cases of pleomorphic adenoma, were retrospectively enrolled from two centers between January 2010 and June 2022. The training set included 82 cases, and the validation set included 35 cases. From T2-weighted images, 971 radiomics features were extracted. Seven radiomics features remained after a two-step selection process. We used the seven radiomics features and clinical factors through multivariable logistic regression to build radiomics and clinical models, respectively. A radiomics-clinical model was also built that combined the independent clinical predictors with the radiomics features. Through ROC curves, the three models were evaluated and compared.

RESULTS

In the radiomics model, AUCs were 0.826 and 0.796 in training and validation sets, respectively. In the clinical model, the AUCs were 0.923 and 0.926 in the training and validation sets, respectively. Decision curve analysis revealed that the radiomics-clinical model had the best diagnostic performance for distinguishing Warthin tumor from pleomorphic adenoma of the parotid gland (AUC = 0.962 and 0.934 for the training and validation sets, respectively).

CONCLUSION

The radiomics-clinical model performed well in differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

KEY POINTS

• The clinical model outperformed the radiomics model in distinguishing pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics features extracted from T2-weighted images could help differentiate pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics-clinical model was superior to the radiomics and the clinical models for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

Prognostic model of immune checkpoint inhibitors combined with anti-angiogenic agents in unresectable hepatocellular carcinoma

Background

The combination of immune checkpoint inhibitors (ICIs) and anti-angiogenic agents has shown promising efficacy in unresectable hepatocellular carcinoma (HCC), but until now no clinical prognostic models or predictive biomarkers have been established.

Methods

From 2016 to 2021, a total of 258 HCCs treated with ICIs and tyrosine kinase inhibitors (TKIs) were retrospectively enrolled, as the study cohort. Patients' baseline data was extracted by least absolute and shrinkage selection operator (LASSO) and Cox regression. Finally, a prognostic model in the form of nomogram was developed. Model performance was assessed in terms of discrimination, calibration, and clinical utility. A 5-fold cross-validation was used to evaluate the internal repeatability of the model. In addition, the patient cohort was divided into three subgroups according to nomogram scores. Their survivals were estimated by Kaplan-Meier methods and the differences were analyzed using log-rank tests.

Results

Seven clinical parameters were selected: Eastern Cooperative Oncology Group performance status (ECOG PS), combination of transarterial chemoembolization (TACE), extrahepatic metastasis (EHM), platelet to lymphocyte ratio (PLR), alanine aminotransferase (ALT), alpha-fetoprotein (AFP), and Child-Pugh score. The model had an area under the curve (AUC) of 0.777 at 1 year and 0.772 at 2 years. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) showed that the discrimination, consistency and applicability of the model were good. In addition, cross-validation validated the discrimination of the model, and the C index value of the model is 0.7405. The median overall survival (OS) of the high-, medium- and low-risk subgroups was 7.58, 17.50 and 53.17 months, respectively, with a significant difference between the groups (P < 0.0001).

Conclusion

We developed a comprehensive and simple prognostic model for the combination of ICIs plus TKIs. And it may predict the efficacy of the combination regimen for unresectable HCC.

Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review

BACKGROUND

Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types.

METHODS

PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted.

RESULTS

In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investigated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain metastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parameters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imaging showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist.

CONCLUSIONS

There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose.

Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced CT radiomics features

Objective

To investigate the value of contrast-enhanced computed tomography (CECT) radiomics features in predicting the efficacy of epirubicin combined with ifosfamide in patients with pulmonary metastases from soft tissue sarcoma.

Methods

A retrospective analysis of 51 patients with pulmonary metastases from soft tissue sarcoma who received the chemotherapy regimen of epirubicin combined with ifosfamide was performed, and efficacy was evaluated by Recist1.1. ROIs (1 or 2) were selected for each patient. Lung metastases were used as target lesions (86 target lesions total), and the patients were divided into a progression group (n = 29) and a non-progressive group (n = 57); the latter included a stable group (n = 34) and a partial response group (n = 23). Information on lung metastases was extracted from CECT images before chemotherapy, and all lesions were delineated by ITK-SNAP software manually or semiautomatically. The decision tree classifier had a better performance in all radiomics models. A receiver operating characteristic curve was plotted to evaluate the predictive performance of the radiomics model.

Results

In total, 851 CECT radiomics features were extracted for each target lesion and finally reduced to 2 radiomics features, which were then used to construct a radiomics model. Areas under the curves of the model for predicting lesion progression were 0.917 and 0.856 in training and testing groups, respectively.

Conclusion

The model established based on the radiomics features of CECT before treatment has certain predictive value for assessing the efficacy of chemotherapy for patients with soft tissue sarcoma lung metastases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00859-6.

An M0 macrophage-related prognostic model for hepatocellular carcinoma

BACKGROUND

The role of M0 macrophages and their related genes in the prognosis of hepatocellular carcinoma (HCC) remains poorly characterized.

METHODS

Multidimensional bioinformatic methods were used to construct a risk score model using M0 macrophage-related genes (M0RGs).

RESULTS

Infiltration of M0 macrophages was significantly higher in HCC tissues than in normal liver tissues (P = 2.299e-07). Further analysis revealed 35 M0RGs that were associated with HCC prognosis; two M0RGs (OLA1 and ATIC) were constructed and validated as a prognostic signature for overall survival of patients with HCC. Survival analysis revealed the positive relationship between the M0RG signature and unfavorable prognosis. Correlation analysis showed that this risk model had positive associations with clinicopathological characteristics, somatic gene mutations, immune cell infiltration, immune checkpoint inhibitor targets, and efficacy of common drugs.

CONCLUSIONS

The constructed M0RG-based risk model may be promising for the clinical prediction of prognoses and therapeutic responses in patients with HCC.

Hepatic Tumor Stiffness Measured by Shear Wave Elastography Is Prognostic for HCC Progression Following Treatment With Anti-PD-1 Antibodies Plus Lenvatinib: A Retrospective Analysis of Two Independent Cohorts

Background

The clinical significance of liver stiffness (LS) measured by shear wave elastography (SWE) in programmed cell death protein-1 (PD-1) inhibitors treated advanced hepatocellular carcinoma (HCC) patients remains unknown. This study aimed to explore the prognostic value of baseline LS by SWE prior to PD-1 inhibitor treatment in combination with lenvatinib.

Methods

We retrospectively evaluated patients (n=133) with HCC who received anti-PD-1 antibodies plus lenvatinib at two high-volume medical centres, between January 2020 and June 2021. Univariate and multivariate logistic regression analysis were used to develop a novel nomogram. RNA sequencing and immunohistochemical staining were used to assess the heterogeneity of biological and immune characteristics associated with tumor stiffness.

Results

The objective response rate (ORR) and disease control rate (DCR) of the whole population were 23.4% and 72.2%, respectively. A LS value of the baseline tumorous foci of 19.53 kPa had the maximum sum of sensitivity and specificity, making it the optimal cut-off value for predicting PD-1 inhibitor efficacy. The nomogram comprised baseline tumor LS and albumin-bilirubin grade (ALBI), which provided favorable calibration and discrimination in the training dataset with an AUC of 0.840 (95%CI: 0.750-0.931) and a C-index of 0.828. Further, it showed acceptable discrimination in the validation cohort, with an AUC of 0.827 (95%CI: 0.673-0.980) and C-index of 0.803. The differentially expressed genes enriched in high stiffness tumors were predominantly associated with metabolic pathways, while those enriched in low stiffness tumors were related to DNA damage repair. Furthermore, patients with high stiffness tumors had a relatively lower infiltration of immune cells and histone deacetylase pathway inhibitors were identified as candidate drugs to promote the efficacy of immunotherapy.

Conclusions

Baseline LS value of tumorous foci by SWE—that is, before administration of a PD-1 inhibitor in combination with lenvatinib—is a convenient predictor of PD-1 inhibitor efficacy in patients with advanced HCC, which has potential to be used for pretreatment stratification to optimize treatment of advanced HCC.

Development and Validation of a Prognostic Model to Predict Recurrence-Free Survival After Curative Resection for Perihilar Cholangiocarcinoma: A Multicenter Study

Background

Recurrence is the main cause of death in perihilar cholangiocarcinoma (pCCA) patients after surgery. Identifying patients with a high risk of recurrence is important for decision-making regarding neoadjuvant therapy to improve long-term outcomes.

Aim

The objective of this study was to develop and validate a prognostic model to predict recurrence-free survival (RFS) after curative resection of pCCA.

Methods

Patients following curative resection for pCCA from January 2008 to January 2016 were identified from a multicenter database. Using random assignment, 70% of patients were assigned to the training cohort, and the remaining 30% were assigned to the validation cohort. Independent predictors of RFS after curative resection for pCCA were identified and used to construct a prognostic model. The predictive performance of the model was assessed using calibration curves and the C-index.

Results

A total of 341 patients were included. The median overall survival (OS) was 22 months, and the median RFS was 14 months. Independent predictors associated with RFS included lymph node involvement, macrovascular invasion, microvascular invasion, maximum tumor size, tumor differentiation, and carbohydrate antigen 19-9. The model incorporating these factors to predict 1-year RFS demonstrated better calibration and better performance than the 8th American Joint Committee on Cancer (AJCC) staging system in both the training and validation cohorts (C-indexes: 0.723 vs. 0.641; 0.743 vs. 0.607).

Conclusions

The prognostic model could identify patients at high risk of recurrence for pCCA to inform patients and surgeons, help guide decision-making for postoperative adjuvant therapy, and improve survival.

Effects of Stereotactic Body Radiation Therapy Plus PD-1 Inhibitors for Patients With Transarterial Chemoembolization Refractory

Background and Aims

Patients with intermediate-stage hepatocellular carcinoma (HCC) who are refractory to transarterial chemoembolization (TACE) have a poor prognosis. This study aimed to explore whether stereotactic body radiation therapy (SBRT) combined with PD-1 inhibitors could improve the clinical outcomes of such patients.

Methods

This retrospective cohort study included patients with intermediate-stage HCC who were diagnosed with TACE refractoriness between January 2019 and December 2020 in the Eastern Hepatobiliary Surgery Hospital and the First Affiliated Hospital of Wenzhou Medical University. The patients were divided into two groups: (1) those who switched from TACE to receive stereotactic body radiotherapy (SBRT) combined with PD-1 inhibitors; (2) those who continued TACE treatment and added PD-1 inhibitors. Progression-free survival (PFS), overall survival (OS), and tumour response were assessed in both groups after becoming refractory to TACE treatment.

Results

Of the seventy-six patients included in this study, the median PFS was 19.6 months in the SBRT-IO group (n=31) and 10.1 months in the TACE-IO group (n=45, p<0.05). The SBRT-IO group also had a significantly higher OS than the TACE-IO group (p<0.05). The objective response rate (ORR) and disease control rate (DCR) were also better in the SBRT-IO group (ORR, 71.0% vs. 15.6%, OR=8.483, 95% CI 3.319-21.680, P < 0.001; DCR, 80.6% vs. 31.1%, OR=9.226, 95% CI 3.096-27.493, P < 0.001).

Conclusions

SBRT combined with a PD-1 inhibitor improves PFS and OS in TACE-refractory patients with intermediate-stage HCC. Therefore, this therapy is a suitable option in cases of TACE treatment failure.

An update on radiomics techniques in primary liver cancers

BACKGROUND

Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting.

METHODS

This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed.

RESULTS

In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring.

CONCLUSIONS

Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model.

TRIAL REGISTRATION

Not applicable.

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.

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.