Deep Learning-Based Prediction of Future Extrahepatic Metastasis and Macrovascular Invasion in Hepatocellular Carcinoma

Bibliometrics of artificial intelligence applications in hepatobiliary surgery from 2014 to 2024

BACKGROUND

In recent years, the rapid development of artificial intelligence (AI) in hepatobiliary surgery research has led to an increase in articles exploring its benefits. We performed a bibliometric analysis of AI applications in hepatobiliary surgery to better delineate the contemporary state of AI application in hepatobiliary surgery and potential future trajectories.

AIM

To provide clinical practitioners with a reliable reference point. It offers a detailed overview of the development of AI in hepatobiliary surgery by systematically examining the contributions of authors, countries, institutions, journals, and keywords in this domain over the last 10 years.

METHODS

The academic resources utilized in this study were obtained from the Web of Science Core Collection database. The search results were subsequently integrated and imported into CiteSpace and VOSviewer software for the purpose of visual analysis.

RESULTS

The study analyzed 2552 publications during 2014–2024. These publications collectively garnered 32 628 citations, averaging 15.66 citations per paper. The top contributor to this field was China. The USA had the highest citation count. The author with the highest citation count was Summers RM. In terms of the number of articles published, the leading journals were Medical Physics. Excluding the subject search terms, the most frequently used keywords included “classification”, “CT and “diagnosis”.

CONCLUSION

This bibliometric analysis indicates that research on AI in hepatobiliary surgery has entered a period of rapid development, particularly in the domain of disease imaging diagnostics.

Radiomics and machine learning based on preoperative MRI for predicting extrahepatic metastasis in hepatocellular carcinoma patients treated with transarterial chemoembolization

Purpose

To develop and validate a radiomics machine learning (Rad-ML) model based on preoperative MRI to predict extrahepatic metastasis (EHM) in hepatocellular carcinoma (HCC) patients receiving transarterial chemoembolization (TACE) treatment.

Methods

A total of 355 HCC patients who received multiple TACE procedures were split at random into a training set and a test set at a 7:3 ratio. Radiomic features were calculated from tumor and peritumor in arterial phase and portal venous phase, and were identified using intraclass correlation coefficient, maximal relevance and minimum redundancy, and least absolute shrinkage and selection operator techniques. Cox regression analysis was employed to determine the clinical model. The best-performing algorithm among eight machine learning methods was used to construct the Rad-ML model. A nomogram combining clinical and Rad-ML parameters was used to develop a combined model. Model performance was evaluated using C-index, decision curve analysis, calibration plot, and survival analysis.

Results

In clinical model, elevated neutrophil to lymphocyte ratio and alpha-fetoprotein were associated with faster EHM. The XGBoost-based Rad-ML model demonstrated the best predictive performance for EHM. When compared to the clinical model, both the Rad-ML model and the combination model performed better (C-indexes of 0.61, 0.85, and 0.86 in the training set, and 0.62, 0.82, and 0.83 in the test set, respectively). However, the combined model's and the Rad-ML model's prediction performance did not differ significantly. The most influential feature was peritumoral waveletHLL_firstorder_Minimum in AP, which exhibited an inverse relationship with EHM risk.

Conclusions

Our study suggests that the preoperative MRI-based Rad-ML model is a valuable tool to predict EHM in HCC patients treated with TACE.

Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer

BACKGROUND

The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet.

PURPOSE

This multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value.

METHODS

A total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS).

RESULTS

The combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p < 0.05). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p < 0.0001) in the internal survival cohort and 0.715 (0.650-0.779, p < 0.0001) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p < 0.05) that was missed by preoperative diagnosis.

CONCLUSIONS

The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.

Efficacy and safety of regorafenib as second-line treatment for patients with hepatocellular carcinoma and macrovascular invasion and(or) extrahepatic metastasis

Background

Macrovascular invasion and(or) extrahepatic metastasis are the main clinical characteristics of Chinese patients with hepatocellular carcinoma (HCC) after entering the second-line treatment. The aim of this study was to explore the efficacy and safety of regorafenib as a second-line treatment for these patients with HCC.

Methods

We selected 253 patients with primary liver cancer who were treated in Henan Cancer Hospital from June 2017 to September 2020. According to the inclusion and exclusion criteria, 63 patients with HCC with macrovascular invasion and/or extrahepatic metastasis were finally included. The clinical data of patients were obtained by consulting the electronic medical record system and through telephone follow-up. The median overall survival (mOS), duration of drug use, and disease control rate (DCR) of patients were evaluated, and the Cox regression model was used to analyze the risk factors of prognosis.

Results

The mOS of 63 patients with HCC administered regorafenib as second-line treatment was 9.6 months, the duration of drug use was 3.8 months, and the DCR was 59% (37/63). Cox multivariate analysis showed that overall survival (OS) was closely related to the level of alpha-fetoprotein (AFP) and treatment method but not to the type of first-line drug. The mOS of patients with AFP ≥400 ng/mL was 7.4 months, which was significantly lower than that of those with AFP <400 ng/mL (12.5 months) (P=0.0052). The mOS of patients treated with regorafenib alone was 6.8 months, which was significantly lower than that of those treated with regorafenib combined with immunotherapy (24.3 months) and intervention therapy (17.5 months) (P<0.0001). The mOS of patients using regorafenib as second-line treatment in the first-line sorafenib group and first-line nonsorafenib group were 9.5 and 9.6 months, respectively (P=0.9766). The grade ≥3 adverse events (AEs) with an incidence of more than 10% included hand-foot syndrome, increased bilirubin, decreased albumin, and elevated transaminase, with incidences of 22%, 14%, 11%, and 10%, respectively.

Conclusions

As second-line treatment for patients with HCC with macrovascular invasion and(or) extrahepatic metastasis, regorafenib has definite efficacy and tolerable adverse reactions. It is the preferred drug for the second-line treatment of patients with advanced HCC.

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

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

Technological Advancements in Interventional Oncology

Interventional radiology, and particularly interventional oncology, represents one of the medical subspecialties in which technological advancements and innovations play an utterly fundamental role. Artificial intelligence, consisting of big data analysis and feature extrapolation through computational algorithms for disease diagnosis and treatment response evaluation, is nowadays playing an increasingly important role in various healthcare fields and applications, from diagnosis to treatment response prediction. One of the fields which greatly benefits from artificial intelligence is interventional oncology. In addition, digital health, consisting of practical technological applications, can assist healthcare practitioners in their daily activities. This review aims to cover the most useful, established, and interesting artificial intelligence and digital health innovations and updates, to help physicians become more and more involved in their use in clinical practice, particularly in the field of interventional oncology.

Prior Knowledge-Aware Fusion Network for Prediction of Macrovascular Invasion in Hepatocellular Carcinoma

Macrovascular invasion (MaVI) is a major threat to survival in hepatocellular carcinoma (HCC), which should be treated as early as possible to ensure safety and efficacy. In this aspect, MaVI prediction can be helpful. However, MaVI prediction is difficult because of the inter-class similarity and intra-class variation of HCC in computed tomography (CT) images. Moreover, existing methods fail to include clinical priori knowledge associated with HCC, leading to incomprehensive information extraction. In this paper, we proposed a prior knowledge-aware fusion network (PKAFnet) to accurately achieve MaVI prediction in CT images. First, a perception module was presented to extract features related to tumor marginal heterogeneity in the graph domain, which contributed to rotation invariance and captured intensity variations of tumor margin. Second, a tumor segmentation network was built to obtain global information of a 3D tumor image and information associated with tumor internal heterogeneity in the image domain. Finally, multi-domain features associated with the tumor margin and tumor region were combined by using a multi-domain attentional feature fusion module. Thus, by incorporating MaVI-related prior knowledge, our PKAFnet can alleviate overfitting, which can improve the discriminative ability. The proposed PKAFnet was validated on a multi-center dataset, and remarkable performance was achieved in an independent testing set. Moreover, the interpretability of perception module and segmentation network were presented in our paper, which illustrated the effectiveness and credibility of PKAFnet. Therefore, the proposed method showed great application potential for MaVI prediction.