Assessment of Ablative Margin After Microwave Ablation for Hepatocellular Carcinoma Using Deep Learning-Based Deformable Image Registration

Artificial intelligence in interventional radiology: state of the art

Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.

Intraprocedural assessment of ablation margins using computed tomography co-registration in hepatocellular carcinoma treatment with percutaneous ablation: IAMCOMPLETE study

PURPOSE

The primary objective of this study was to determine the feasibility of ablation margin quantification using a standardized scanning protocol during thermal ablation (TA) of hepatocellular carcinoma (HCC), and a rigid registration algorithm. Secondary objectives were to determine the inter- and intra-observer variability of tumor segmentation and quantification of the minimal ablation margin (MAM).

MATERIALS AND METHODS

Twenty patients who underwent thermal ablation for HCC were included. There were thirteen men and seven women with a mean age of 67.1 ± 10.8 (standard deviation [SD]) years (age range: 49.1-81.1 years). All patients underwent contrast-enhanced computed tomography examination under general anesthesia directly before and after TA, with preoxygenated breath hold. Contrast-enhanced computed tomography examinations were analyzed by radiologists using rigid registration software. Registration was deemed feasible when accurate rigid co-registration could be obtained. Inter- and intra-observer rates of tumor segmentation and MAM quantification were calculated. MAM values were correlated with local tumor progression (LTP) after one year of follow-up.

RESULTS

Co-registration of pre- and post-ablation images was feasible in 16 out of 20 patients (80%) and 26 out of 31 tumors (84%). Mean Dice similarity coefficient for inter- and intra-observer variability of tumor segmentation were 0.815 and 0.830, respectively. Mean MAM was 0.63 ± 3.589 (SD) mm (range: -6.26-6.65 mm). LTP occurred in four out of 20 patients (20%). The mean MAM value for patients who developed LTP was -4.00 mm, as compared to 0.727 mm for patients who did not develop LTP.

CONCLUSION

Ablation margin quantification is feasible using a standardized contrast-enhanced computed tomography protocol. Interpretation of MAM was hampered by the occurrence of tissue shrinkage during TA. Further validation in a larger cohort should lead to meaningful cut-off values for technical success of TA.

Ablation margin quantification after thermal ablation of malignant liver tumors: How to optimize the procedure? A systematic review of the available evidence

Introduction

To minimize the risk of local tumor progression after thermal ablation of liver malignancies, complete tumor ablation with sufficient ablation margins is a prerequisite. This has resulted in ablation margin quantification to become a rapidly evolving field. The aim of this systematic review is to give an overview of the available literature with respect to clinical studies and technical aspects potentially influencing the interpretation and evaluation of ablation margins.

Methods

The Medline database was reviewed for studies on radiofrequency and microwave ablation of liver cancer, ablation margins, image processing and tissue shrinkage. Studies included in this systematic review were analyzed for qualitative and quantitative assessment methods of ablation margins, segmentation and co-registration methods, and the potential influence of tissue shrinkage occurring during thermal ablation.

Results

75 articles were included of which 58 were clinical studies. In most clinical studies the aimed minimal ablation margin (MAM) was ≥ 5 mm. In 10/31 studies, MAM quantification was performed in 3D rather than in three orthogonal image planes. Segmentations were performed either semi-automatically or manually. Rigid and non-rigid co-registration algorithms were used about as often. Tissue shrinkage rates ranged from 7% to 74%.

Conclusions

There is a high variability in ablation margin quantification methods. Prospectively obtained data and a validated robust workflow are needed to better understand the clinical value. Interpretation of quantified ablation margins may be influenced by tissue shrinkage, as this may cause underestimation.

A Two-stage Diagnostic Framework for Post-ablation Treatment Response Assessment in Patients with Hepatocellular Carcinoma

Contrast-enhanced ultrasound (CEUS) video plays an important role in post-ablation treatment response assessment in patients with hepatocellular carcinoma (HCC). However, the assessment of treatment response using CEUS video is challenging due to issues such as high inter-frame data repeatability, small ablation area and poor imaging quality of CEUS video. To address these issues, we propose a two-stage diagnostic framework for post-ablation treatment response assessment in patients with HCC using CEUS video. The first stage is a location stage, which is used to locate the ablation area. At this stage, we propose a Yolov5-SFT to improve the location results of the ablation area and a similarity comparison module (SCM) to reduce data repeatability. The second stage is an assessment stage, which is used for the evaluation of postoperative efficacy. At this stage, we design an EfficientNet-SK to improve assessment accuracy. The Experimental results on the self-collected data show that the proposed framework outperforms other selected algorithms, and can effectively assist doctors in the assessment of post-ablation treatment response.

Ablative margins in percutaneous thermal ablation of hepatic tumors: a systematic review

INTRODUCTION

This study aims to systematically review current evidence on ablative margins and correlation to local tumor progression (LTP) after thermal ablation of hepatocellular carcinoma (HCC) and colorectal liver metastases (CRLM).

METHODS

A systematic search was performed in PubMed (MEDLINE) and Web of Science to identify all studies that reported on ablative margins (AM) and related LTP rates. Studies were assessed for risk of bias and synthesized separately per tumor type. Where possible, results were pooled to calculate risk differences (RD) as function of AM.

RESULTS

In total, 2910 articles were identified of which 43 articles were eligible for final analysis. There was high variability in AM measurement methodology across studies in terms of measurement technique, imaging modalities, and timing. Most common margin stratification was < 5 mm and > 5 mm, for which data were available in 25/43 studies (58%). Of these, all studies favored AM > 5 mm to reduce the risk of LTP, with absolute RD of 16% points for HCC and 47% points for CRLM as compared to AM < 5 mm.

CONCLUSIONS

Current evidence supports AM > 5 mm to reduce the risk of LTP after thermal ablation of HCC and CRLM. However, standardization of AM measurement and reporting is critical to allow future meta-analyses and improved identification of optimal threshold value for clinical use.

An X-Ray C-Arm Guided Automatic Targeting System for Histotripsy

OBJECTIVE

Histotripsy is an emerging noninvasive, nonionizing and nonthermal focal cancer therapy that is highly precise and can create a treatment zone of virtually any size and shape. Current histotripsy systems rely on ultrasound imaging to target lesions. However, deep or isoechoic targets obstructed by bowel gas or bone can often not be treated safely using ultrasound imaging alone. This work presents an alternative x-ray C-arm based targeting approach and a fully automated robotic targeting system.

METHODS

The approach uses conventional cone beam CT (CBCT) images to localize the target lesion and 2D fluoroscopy to determine the 3D position and orientation of the histotripsy transducer relative to the C-arm. The proposed pose estimation uses a digital model and deep learning-based feature segmentation to estimate the transducer focal point relative to the CBCT coordinate system. Additionally, the integrated robotic arm was calibrated to the C-arm by estimating the transducer pose for four preprogrammed transducer orientations and positions. The calibrated system can then automatically position the transducer such that the focal point aligns with any target selected in a CBCT image.

RESULTS

The accuracy of the proposed targeting approach was evaluated in phantom studies, where the selected target location was compared to the center of the spherical ablation zones in post-treatment CBCTs. The mean and standard deviation of the Euclidean distance was 1.4 ±0.5 mm. The mean absolute error of the predicted treatment radius was 0.5 ±0.5 mm.

CONCLUSION

CBCT-based histotripsy targeting enables accurate and fully automated treatment without ultrasound guidance.

SIGNIFICANCE

The proposed approach could considerably decrease operator dependency and enable treatment of tumors not visible under ultrasound.

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.

Virtual Treatment Zone From Cone Beam CT Commonly Alters Treatment Plan and Identifies Tumor at Risk for Under-Treatment in US or US Fusion-Guided Microwave Ablation of Liver Tumors

Tumor ablation is included in several major cancer therapy guidelines. One technical challenge of percutaneous ablation is targeting and verification of complete treatment, which is prone to operator variabilities and human imperfections and are directly related to successful outcomes, risk for residual unablated tumor and local progression. The use of "Prediction Ablation Volume Software" may help the operating Interventional Radiologist to better plan, deliver, and verify before the ablation, via virtual treatment zones fused to target tumor. Fused and superimposed images provide 3-dimensional information from different timepoints, just when that information is most useful. The aim of this study is to evaluate the technical success and efficacy of an ablation treatment flowchart provided by a cone beam computed tomography (CBCT) "Prediction Ablation Volume Software." This is a single-center retrospective study. From April 2021 to January 2022, 29 nonconsecutive evaluable patients with 32 lesions underwent liver ablation with Prediction Ablation Volume Software. Each patient was discussed in a multidisciplinary tumor board and underwent an enhanced computed tomography or magnetic resonance imaging approximately 1 month before the procedure, as well as ∼1 month after. Technical success was defined as treatment of the tumor according to the protocol, covered completely by the Prediction Ablation Volume. Technical efficacy was defined as assessment of complete ablation of the target tumor at imaging follow up (∼1 month). Technical success, technical efficacy, and procedural factors were studied. Technical success was achieved in 30 of 32 liver lesions (94%), measuring 20 mm mean maximum diameter. The antenna was repositioned in 16 of 30 (53%) evaluable target lesions. Residual tumor was detected at 1 month imaging follow up in only 4 of 30 (13%) of the treated lesion. Technical efficacy was of 87% in this retrospective description of our process. The implementation of a CBCT Prediction Ablation Volume Software and flowchart for the treatment of liver malignancies altered the procedure, and demonstrated high technical success and efficacy. Such tools are potentially useful for procedural prediction and verification of ablation.

Artificial intelligence: A review of current applications in hepatocellular carcinoma imaging

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and currently the third-leading cause of cancer-related death worldwide. Recently, artificial intelligence (AI) has emerged as an important tool to improve clinical management of HCC, including for diagnosis, prognostication and evaluation of treatment response. Different AI approaches, such as machine learning and deep learning, are both based on the concept of developing prediction algorithms from large amounts of data, or big data. The era of digital medicine has led to a rapidly expanding amount of routinely collected health data which can be leveraged for the development of AI models. Various studies have constructed AI models by using features extracted from ultrasound imaging, computed tomography imaging and magnetic resonance imaging. Most of these models have used convolutional neural networks. These tools have shown promising results for HCC detection, characterization of liver lesions and liver/tumor segmentation. Regarding treatment, studies have outlined a role for AI in evaluation of treatment response and improvement of pre-treatment planning. Several challenges remain to fully integrate AI models in clinical practice. Future research is still needed to robustly evaluate AI algorithms in prospective trials, and improve interpretability, generalizability and transparency. If such challenges can be overcome, AI has the potential to profoundly change the management of patients with HCC. The purpose of this review was to sum up current evidence on AI approaches using imaging for the clinical management of HCC.

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.

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

Software-based assessment of tumor margins after percutaneous thermal ablation of liver tumors: A systematic review

PURPOSE

The purpose of this study was to make a systematic review of clinical studies evaluating software-based tumor margin assessment after percutaneous thermoablation (PTA) of liver tumors.

MATERIALS AND METHODS

A systematic literature search was performed through Pubmed/MEDLINE, Embase and the Cochrane Library. Original studies published in English that reported on software-based assessment of ablation margins (AM) following PTA of liver tumors were selected. Studies were analyzed with respect to design, number of patients and tumors, tumor type, PTA technique, tumor size, target registration error, study outcome(s) (subtypes: feasibility, comparative, clinical impact, predictive or survival), and follow-up period.

RESULTS

Twenty-nine articles (one multi-center and two prospective studies) were included. The majority were feasibility (26/29, 89.7%) or predictive (23/29, 79.3%) studies. AM was a risk factor of local tumor progression (LTP) in 25 studies (25/29, 86.2%). In nine studies (9/29, 31%) visual assessment overestimated AM compared with software-aided assessment. LTP occurred at the location of the thinnest margin in nine studies (9/29, 31%). Time for registration and analysis was heterogeneously reported, ranging between 5-30 min. Mean target registration error was reported in seven studies (7/29, 24.1%) at 1.62 mm (range: 1.20-2.23 mm). Inter-operator reproducibility was high (kappa range: 0.686-1). Ascites, liver deformation and inconspicuous tumor were major factors of co-registration error.

CONCLUSION

Available studies present a low level of evidence overall, since most of them are feasibility, retrospective and single-center studies.

Deep learning in hepatocellular carcinoma: Current status and future perspectives

Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.