Predicting hepatocellular carcinoma early recurrence after ablation based on magnetic resonance imaging radiomics nomogram

Predicting early recurrence of hepatocellular carcinoma after thermal ablation based on longitudinal MRI with a deep learning approach

BACKGROUND

Accurate prediction of early recurrence (ER) is essential to improve the prognosis of patients with hepatocellular carcinoma (HCC) underwent thermal ablation (TA). Therefore, a deep learning model system using longitudinal magnetic resonance imaging (MRI) was developed to predict ER of patients with HCC.

METHODS

From 2014, April to 2017, May, a total of 289 eligible patients with HCC underwent TA were retrospectively enrolled from 3 hospitals and assigned into one training cohort (n = 254) and one external testing cohort (n = 35). Two deep learning models (Pre and PrePost) were developed using the pre-operative MRI and longitudinal MRI (pre- and post-operative) to predict ER for the patients with HCC after TA, respectively. Then, an integrated model (DL_Clinical) incorporating PrePost model signature and clinical variables was built for post-ablation ER risk stratification for the patients with HCC.

RESULTS

In the external testing cohort, the area under the receiver operating characteristic curve (AUC) of the DL_Clinical model was better than that of the Clinical (0.740 vs 0.571), Pre (0.740 vs 0.648), and PrePost model (0.740 vs 0.689). Additionally, there was a significant difference in RFS between the high- and low-risk groups which were divided by the DL_Clinical model (P = .04).

CONCLUSIONS

The PrePost model developed using longitudinal MRI showed outstanding performance for predicting post-ablation ER of HCC. The DL_Clinical model could stratify the patients into high- and low-risk groups, which may help physicians in treatment and surveillance strategy selection in clinical practice.

The application of radiomics in cancer imaging with a focus on lung cancer, renal cell carcinoma, gastrointestinal cancer, and head and neck cancer: A systematic review

PURPOSE

To study the application of radiomics in cancer imaging with a focus on lung cancer, renal cell carcinoma, gastrointestinal cancer, and head and neck cancer.

METHODS

Different electronic databases were considered. Articles published in the last five years were analyzed (January 2019 and December 2023). Papers were selected by two investigators with over 15 years of experience in Radiomics analysis in cancer imaging. The methodological quality of each radiomics study was performed using the Radiomic Quality Score (RQS) by two different readers in consensus and then by a third operator to solve disagreements between the two readers.

RESULTS

19 articles are included in the review. Among the analyzed studies, only one study achieved an RQS of 18 reporting multivariable analyzes with also non-radiomics features and using the validation phase considering two datasets from two distinct institutes and open science and data domain.

CONCLUSION

This informative review has brought attention to the increasingly consolidated potential of Radiomics, although there are still several aspects to be evaluated before the transition to routine clinical practice. There are several challenges to address, including the need for standardization at all stages of the workflow and the potential for cross-site validation using heterogeneous real-world datasets. It will be necessary to establish and promote an imaging data acquisition protocol, conduct multicenter prospective quality control studies, add scanner differences and vendor-dependent characteristics; to collect images of individuals at additional time points, to report calibration statistics.

Development and nomogram prediction of early postoperative recurrence in hepatocellular carcinoma based on preoperative CT imaging radiomic features and serum features related to microvascular infiltration

Background

Hepatocellular carcinoma (HCC) is characterized by high postoperative recurrence rates, and predicting early recurrence is crucial for improving clinical outcomes, yet remains challenging. Both preoperative computed tomography (CT) imaging radiomic features and serum biomarkers related to microvascular infiltration are important indicators of HCC prognosis. This study aimed to develop a nomogram model incorporating both preoperative CT radiomic features and serum biomarkers associated with microvascular infiltration to predict early postoperative recurrence in HCC patients.

Methods

The study included 156 HCC patients who underwent radical surgery at the Tumor Hospital Affiliated to Nantong University between January 2021 and January 2022. Preoperative CT imaging data were obtained for each patient, and radiomic features were extracted using the 3D Slicer software. Preoperative serum biomarkers related to microvascular invasion were collected, including alpha-fetoprotein (AFP), vascular endothelial growth factor A (VEGF-A), Speckled Protein 100 (SP100), and the Fibrosis-4 (FIB-4) index levels. Postoperative follow-up was conducted for 2 years, during which recurrence data were collected. The radiomics score was generated through dimensionality reduction and least absolute shrinkage and selection operator (LASSO) regression analysis. Univariate and logistic regression analyses were used to identify independent risk factors for early postoperative recurrence of HCC. The nomogram model was constructed using R language, and its predictive performance was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis curves.

Results

Among the 156 patients, 60 experienced early recurrence, while 96 did not. Feature reduction through LASSO regression identified 10 optimal features from the venous phase and 4 optimal features from the arterial phase, leading to the development of a radiomics score formula. The early recurrence group had significantly higher radiomics scores than the non-early recurrence group [-1.35 (-2.29, 1.21) vs. 0.94 (-0.40, 1.87), P<0.001]. Logistic multivariate regression analysis identified lesion number, Edmondson grade, AFP and VEGF-A levels, and radiomics score as independent risk factors for early postoperative recurrence of HCC (P<0.05). The nomogram model demonstrated high predictive performance with area under the curve (AUC) values of 0.9265 and 0.9255 in the training and internal test sets, respectively. The model demonstrated good net benefit across a threshold range of 0.01-75%, effectively identifying high-risk patients for early postoperative recurrence.

Conclusions

The nomogram model based on preoperative serum biomarkers related to microvascular infiltration and CT radiomic features demonstrated high predictive performance for early postoperative recurrence of HCC. However, further studies, including external validation, are needed to establish the model's generalizability and clinical applicability.

Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis

BACKGROUND

The aim of this study was to conduct a systematic review and meta-analysis to comprehensively evaluate the performance and methodological quality of artificial intelligence (AI) in predicting recurrence after single first-line treatment for liver cancer.

METHODS

A rigorous and systematic evaluation was conducted on the AI studies related to recurrence after single first-line treatment for liver cancer, retrieved from the PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases. The area under the curve (AUC), sensitivity (SENC), and specificity (SPEC) of each study were extracted for meta-analysis.

RESULTS

Six percutaneous ablation (PA) studies, 16 surgical resection (SR) studies, and 5 transarterial chemoembolization (TACE) studies were included in the meta-analysis for predicting recurrence after hepatocellular carcinoma (HCC) treatment, respectively. Four SR studies and 2 PA studies were included in the meta-analysis for recurrence after intrahepatic cholangiocarcinoma (ICC) and colorectal cancer liver metastasis (CRLM) treatment. The pooled SENC, SEPC, and AUC of AI in predicting recurrence after primary HCC treatment via PA, SR, and TACE were 0.78, 0.90, and 0.92; 0.81, 0.77, and 0.86; and 0.73, 0.79, and 0.79, respectively. The values for ICC treated with SR and CRLM treated with PA were 0.85, 0.71, 0.86 and 0.69, 0.63,0.74, respectively.

CONCLUSION

This systematic review and meta-analysis demonstrates the comprehensive application value of AI in predicting recurrence after a single first-line treatment of liver cancer, with satisfactory results, indicating the clinical translation potential of AI in predicting recurrence after liver cancer treatment.

Radiomics-based Machine Learning to Predict the Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

Recurrence of hepatocellular carcinoma (HCC) is a major concern in its management. Accurately predicting the risk of recurrence is crucial for determining appropriate treatment strategies and improving patient outcomes. A certain amount of radiomics models for HCC recurrence prediction have been proposed. This study aimed to assess the role of radiomics models in the prediction of HCC recurrence and to evaluate their methodological quality.

MATERIALS AND METHODS

Databases Cochrane Library, Web of Science, PubMed, and Embase were searched until July 11, 2023 for studies eligible for the meta-analysis. Their methodological quality was evaluated using the Radiomics Quality Score (RQS). The predictive ability of the radiomics model, clinical model, and the combined model integrating the clinical characteristics with radiomics signatures was measured using the concordance index (C-index), sensitivity, and specificity. Radiomics models in included studies were compared based on different imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound/sonography (US), contrast-enhanced ultrasound (CEUS).

RESULTS

A total of 49 studies were included. On the validation cohort, radiomics model performed better (CT: C-index = 0.747, 95% CI: 0.70-0.79; MRI: C-index = 0.788, 95% CI: 0.75-0.83; CEUS: C-index = 0.763, 95% CI: 0.60-0.93) compared to the clinical model (C-index = 0.671, 95% CI: 0.65-0.70), except for ultrasound-based models (C-index = 0.560, 95% CI: 0.53-0.59). The combined model outperformed other models (CT: C-index = 0.790, 95% CI: 0.76-0.82; MRI: C-index = 0.826, 95% CI: 0.79-0.86; US: C-index = 0.760, 95% CI: 0.65-0.87), except for CEUS-based combined models (C-index = 0.707, 95% CI: 0.44-0.97).

CONCLUSION

Radiomics holds the potential to predict HCC recurrence and demonstrates enhanced predictive value across various imaging modalities when integrated with clinical features. Nevertheless, further studies are needed to optimize the radiomics approach and validate the results in larger, multi-center cohorts.

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.

An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies

The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.

MRI-based radiomics model and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma

BACKGROUND

Prediction of locoregional treatment response is important for further therapeutic strategy in patients with hepatocellular carcinoma. This study aimed to investigate the role of MRI-based radiomics and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma.

METHODS

The initial postoperative MRI after locoregional treatment in 100 patients with hepatocellular carcinoma was retrospectively analysed. The outcome was evaluated according to mRECIST at 6 months. We delineated the tumour volume of interest on arterial phase, portal venous phase and T2WI. The radiomics features were selected by using the independent sample t test or nonparametric Mann‒Whitney U test and the least absolute shrinkage and selection operator. The clinical variables were selected by using univariate analysis and multivariate analysis. The radiomics model and combined model were constructed via multivariate logistic regression analysis. A nomogram was constructed that incorporated the Rad score and selected clinical variables.

RESULTS

Fifty patients had an objective response, and fifty patients had a nonresponse. Nine radiomics features in the arterial phase were selected, but none of the portal venous phase or T2WI radiomics features were predictive of the treatment response. The best radiomics model showed an AUC of 0.833. Two clinical variables (hCRP and therapy method) were selected. The AUC of the combined model was 0.867. There was no significant difference in the AUC between the combined model and the best radiomics model (P = 0.573). Decision curve analysis demonstrated the nomogram has satisfactory predictive value.

CONCLUSIONS

MRI-based radiomics analysis may serve as a promising and noninvasive tool to predict outcome of locoregional treatment in HCC patients, which will facilitate the individualized follow-up and further therapeutic strategies guidance.