Construction of a novel radiomics nomogram for the prediction of aggressive intrasegmental recurrence of HCC after radiofrequency ablation

Nomogram to Predict Tumor Remnant of Small Hepatocellular Carcinoma after Microwave Ablation

RATIONALE AND OBJECTIVES

This investigation sought to create a nomogram to predict the ablation effect after microwave ablation in patients with hepatocellular carcinoma, which can guide the selection of microwave ablation for small hepatocellular carcinomas.

METHODS

In this two-center retrospective study, 233 patients with hepatocellular carcinoma treated with microwave ablation (MWA) between January 2016 and December 2023 were enrolled and analyzed for their clinical baseline data, laboratory parameters, and MR imaging characteristics. Logistic regression analysis was used to screen the features, and clinical and imaging feature models were developed separately. Finally, a nomogram was established. All models were evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA).

RESULTS

Two models and a nomogram were developed to predict ablation outcomes after MWA based on a training set (n = 182, including complete ablation: 136, incomplete ablation: 46) and an external validation set (n = 51, complete ablation: 36, incomplete ablation: 15). The clinical models and nomogram performed well in the external validation cohort. The AUC of the nomogram was 0.966 (95% CI: 0.944- 0.989), with a sensitivity of 0.935, a specificity of 0.882, and an accuracy of 0.896.

CONCLUSIONS

Combining clinical data and imaging features, a nomogram was constructed that could effectively predict the postoperative ablation outcome in hepatocellular carcinoma patients undergoing MWA, which could help clinicians provide treatment options for hepatocellular carcinoma patients.

Bridging the Gap Between Imaging and Molecular Characterization: Current Understanding of Radiomics and Radiogenomics in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common malignancy worldwide and the third leading cause of cancer-related deaths. Imaging plays a crucial role in the screening, diagnosis, and monitoring of HCC; however, the potential mechanism regarding phenotypes or molecular subtyping remains underexplored. Radiomics significantly expands the selection of features available by extracting quantitative features from imaging data. Radiogenomics bridges the gap between imaging and genetic/transcriptomic information by associating imaging features with critical genes and pathways, thereby providing biological annotations to these features. Despite challenges in interpreting these connections, assessing their universality, and considering the diversity in HCC etiology and genetic information across different populations, radiomics and radiogenomics offer new perspectives for precision treatment in HCC. This article provides an up-to-date summary of the advancements in radiomics and radiogenomics throughout the HCC care continuum, focusing on the clinical applications, advantages, and limitations of current techniques and offering prospects. Future research should aim to overcome these challenges to improve the prognosis of HCC patients and leverage imaging information for patient benefit.

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.

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.

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.

Towards reproducible radiomics research: introduction of a database for radiomics studies

OBJECTIVES

To investigate the model-, code-, and data-sharing practices in the current radiomics research landscape and to introduce a radiomics research database.

METHODS

A total of 1254 articles published between January 1, 2021, and December 31, 2022, in leading radiology journals (European Radiology, European Journal of Radiology, Radiology, Radiology: Artificial Intelligence, Radiology: Cardiothoracic Imaging, Radiology: Imaging Cancer) were retrospectively screened, and 257 original research articles were included in this study. The categorical variables were compared using Fisher's exact tests or chi-square test and numerical variables using Student's t test with relation to the year of publication.

RESULTS

Half of the articles (128 of 257) shared the model by either including the final model formula or reporting the coefficients of selected radiomics features. A total of 73 (28%) models were validated on an external independent dataset. Only 16 (6%) articles shared the data or used publicly available open datasets. Similarly, only 20 (7%) of the articles shared the code. A total of 7 (3%) articles both shared code and data. All collected data in this study is presented in a radiomics research database (RadBase) and could be accessed at https://github.com/EuSoMII/RadBase .

CONCLUSION

According to the results of this study, the majority of published radiomics models were not technically reproducible since they shared neither model nor code and data. There is still room for improvement in carrying out reproducible and open research in the field of radiomics.

CLINICAL RELEVANCE STATEMENT

To date, the reproducibility of radiomics research and open science practices within the radiomics research community are still very low. Ensuring reproducible radiomics research with model-, code-, and data-sharing practices will facilitate faster clinical translation.

KEY POINTS

• There is a discrepancy between the number of published radiomics papers and the clinical implementation of these published radiomics models. • The main obstacle to clinical implementation is the lack of model-, code-, and data-sharing practices. • In order to translate radiomics research into clinical practice, the radiomics research community should adopt open science practices.

The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review

Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions. Furthermore, the review addresses the challenges associated with the implementation of AI and texture analysis in clinical practice, including data acquisition, standardization of imaging protocols, and model validation. Future directions and potential advancements in this field are discussed. Integration of multi-modal imaging data, incorporation of genomics and clinical data, and the development of predictive models with enhanced interpretability are proposed as potential avenues for further research. In conclusion, the application of AI and texture analysis in predicting outcomes of interventional radiological treatment for liver lesions shows great promise in augmenting clinical decision-making and improving patient care. By leveraging these technologies, clinicians can potentially enhance treatment planning, optimize intervention strategies, and ultimately improve patient outcomes in the management of liver lesions.

Magnetic resonance imaging for treatment response evaluation and prognostication of hepatocellular carcinoma after thermal ablation

Hepatocellular carcinoma (HCC) accounts for the vast majority of primary liver cancer and constitutes a major global health challenge. Tumor ablation with either radiofrequency ablation (RFA) or microwave ablation (MWA) is recommended as a curative-intent treatment for early-stage HCC. Given the widespread use of thermal ablation in routine clinical practice, accurate evaluation of treatment response and patient outcomes has become crucial in optimizing individualized management strategies. Noninvasive imaging occupies the central role in the routine management of patients with HCC. Magnetic resonance imaging (MRI) could provide full wealth of information with respect to tumor morphology, hemodynamics, function and metabolism. With accumulation of liver MR imaging data, radiomics analysis has been increasingly applied to capture tumor heterogeneity and provide prognostication by extracting high-throughput quantitative imaging features from digital medical images. Emerging evidence suggests the potential role of several qualitative, quantitative and radiomic MRI features in prediction of treatment response and patient prognosis after ablation of HCC. Understanding the advancements of MRI in the evaluation of ablated HCCs may facilitate optimal patient care and improved outcomes. This review provides an overview of the emerging role of MRI in treatment response evaluation and prognostication of HCC patients undergoing ablation. CLINICAL RELEVANCE STATEMENT: MRI-based parameters can help predict treatment response and patient prognosis after HCC ablation and thus guide treatment planning. KEY POINTS: 1. ECA-MRI provides morphological and hemodynamic assessment of ablated HCC. 2. EOB-MRI provides more information for tumor response prediction after ablation. 3. DWI improve the characterization of HCC and optimize treatment decision. 4. Radiomics analysis enables characterization of tumor heterogeneity guidance of clinical decision-making. 5. Further studies with multiple radiologists and sufficient follow-up period are needed.

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

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

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.

Multiparametric liver MRI for predicting early recurrence of hepatocellular carcinoma after microwave ablation

Background

High early recurrence (ER) of hepatocellular carcinoma (HCC) after microwave ablation (MWA) represents a sign of aggressive behavior and severely worsens prognosis. The aim of this study was to estimate the outcome of HCC following MWA and develop a response algorithmic strategy based on multiparametric MRI and clinical variables.

Methods

In this retrospective study, we reviewed the records of 339 patients (mean age, 62 ± 12 years; 106 men) treated with percutaneous MWA for HCC between January 2014 and December 2017 that were evaluated by multiparametric MRI. These patients were randomly split into a development and an internal validation group (3:1). Logistic regression analysis was used to screen imaging features. Multivariate Cox regression analysis was then performed to determine predictors of ER (within 2 years) of MWA. The response algorithmic strategy to predict ER was developed and validated using these data sets. ER rates were also evaluated by Kaplan–Meier analysis.

Results

Based on logistic regression analyses, we established an image response algorithm integrating ill-defined margins, lack of capsule enhancement, pre-ablative ADC, ΔADC, and EADC to calculate recurrence scores and define the risk of ER. In a multivariate Cox regression model, the independent risk factors of ER (p < 0.05) were minimal ablative margin (MAM) (HR 0.57; 95% CI 0.35 – 0.95; p < 0.001), the recurrence score (HR: 9.25; 95% CI 4.25 – 16.56; p = 0.021), and tumor size (HR 6.21; 95% CI 1.25 – 10.82; p = 0.014). Combining MAM and tumor size, the recurrence score calculated by the response algorithmic strategy provided predictive accuracy of 93.5%, with sensitivity of 92.3% and specificity of 83.1%. Kaplan–Meier estimates of the rates of ER in the low-risk and high-risk groups were 6.8% (95% CI 4.0 – 9.6) and 30.5% (95% CI 23.6 – 37.4), respectively.

Conclusion

A response algorithmic strategy based on multiparametric MRI and clinical variables was useful for predicting the ER of HCC after MWA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00471-5.