Liver stiffness in magnetic resonance elastography is prognostic for sorafenib-treated advanced hepatocellular carcinoma

Liver injury related to vascular endothelial growth factor tyrosine kinase inhibitors: a pharmacovigilance analysis of the USA FDA adverse event reporting system (FAERS) database

BACKGROUND

While vascular endothelial growth factor tyrosine kinase inhibitors (VEGFR-TKIs) are known to cause adverse events like cardiotoxicity and haematotoxicity, their impact on liver injury remains understudied. This study evaluates the association between VEGFR-TKIs and liver injury using data from the FDA Adverse Event Reporting System (FAERS) database from 2006 to 2024.

RESEARCH DESIGN AND METHODS

Nine VEGFR-TKIs (Axitinib, Vandetanib, Cabozantinib, Lenvatinib, Pazopanib, Ponatinib, Regorafenib, Sunitinib, Sorafenib) were analyzed. Disproportionality and Bayesian analyses identified cases of VEGFR-TKI-induced liver injury, assessing onset time, mortality, and hospitalization rates.

RESULTS

8,619 cases of liver injury were identified. Pazopanib had the highest association with liver injury (reporting odds ratio 3.9). The median onset of liver injury was 21 days. Mortality was 28.5%, with Sorafenib linked to the highest mortality (48.6%). Lenvatinib had the highest hospitalization rate (56%).

CONCLUSION

VEGFR-TKIs are associated with liver injury. Close monitoring is required to mitigate the risks of hospitalization and early mortality during treatment.

3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center study

BACKGROUND

Besides tumorous information, synergistic liver parenchyma assessments may provide additional insights into the prognosis of hepatocellular carcinoma (HCC). This study aimed to investigate whether 3D synergistic tumor-liver analysis could improve the prediction accuracy for HCC prognosis.

METHODS

A total of 422 HCC patients from six centers were included. Datasets were divided into training and external validation datasets. Besides tumor, we also performed automatic 3D assessment of liver parenchyma by extracting morphological and high-dimensional data, respectively. Subsequently, we constructed a tumor model, a tumor-liver model, a clinical model and an integrated model combining information from clinical factors, tumor and liver parenchyma. Their discrimination and calibration were compared to determine the optimal model. Subgroup analysis was conducted to test the robustness, and survival analysis was conducted to identify high- and low-risk populations.

RESULTS

The tumor-liver model was superior to the tumor model in terms of both discrimination (training dataset: 0.747 vs. 0.722; validation dataset: 0.719 vs. 0.683) and calibration. Moreover, the integrated model was superior to the clinical model and tumor-liver model, particularly in discrimination (training dataset: 0.765 vs. 0.695 vs. 0.747; validation dataset: 0.739 vs. 0.628 vs. 0.719). The AUC of the integrated model was not influenced by AFP level, BCLC stage, Child-Pugh grade, and treatment style in training (6 months p value: 0.245-0.452; 12 months p value: 0.357-0.845) and validation (6 months p value: 0.294-0.638; 12 months p value: 0.365-0.937) datasets. With a risk score of 1.06, high- and low-risk populations demonstrated significant difference for progression-free survival (p < 0.001 in both datasets).

CONCLUSIONS

Combined with clinical factors, 3D synergistic tumor-liver assessment improved the efficacy prediction of HCC.

Elastic modulus-reflected liver lesion stiffness relates to worse prognosis in pancreatic cancer patients with liver metastasis

BACKGROUND

Liver stiffness relates to more advanced tumor status and poor outcomes in primary liver cancer, while its prognostic role in pancreatic cancer with liver metastasis is unclear. Therefore, the current study aimed to explore the correlation of elastic modulus (EM)-reflected liver lesion stiffness with clinical characteristics, tumor markers, and survival among pancreatic cancer patients with liver metastasis.

METHODS

Fifty-four pancreatic cancer patients with liver metastasis were enrolled, and the EM of liver metastasis and peripheral liver tissue was measured by two-dimensional shear wave elastography. Relative EM was calculated as the ratio of EM in liver metastasis to that in peripheral liver tissue, which reflected the relative liver lesion stiffness.

RESULTS

The median relative EM of liver metastasis was 7.8 (interquartile range: 4.8-10.7) folds. Relative EM of liver metastasis was correlated with primary pancreatic cancer location (P = 0.048), the presence of extra lung metastasis (P = 0.040), liver metastasis ≥ 3 cm (P = 0.007), and the absence of extraskeletal metastasis (P = 0.036); but it was not correlated with tumor markers such as CA199, CA125, or CEA (all P > 0.05). Encouragingly, high relative EM of liver metastasis (cut off by median value) was correlated with poor progression-free survival (PFS) (P = 0.032) but not overall survival (OS) (P = 0.285). Multivariable Cox analysis showed that high relative EM of liver metastasis (hazard ratio (HR) = 1.768, P = 0.048) and multiple metastases (HR = 2.262, P = 0.036) independently predicted decreased PFS, but only abnormal CEA independently forecasted decreased OS (HR = 2.390, P = 0.027).

CONCLUSION

Elastic modulus reflected liver lesion stiffness may predict a worse prognosis in pancreatic cancer patients with liver metastasis.

Magnetic resonance elastography-derived stiffness: potential imaging biomarker for differentiation of benign and malignant pancreatic masses

OBJECTIVE

This study sought to determine the diagnostic performance of magnetic resonance elastography (MRE) for pancreatic solid masses, compared with diffusion-weighted imaging (DWI) and serum CA19-9, to establish a threshold for differentiating between pancreatic ductal adenocarcinoma (PDAC) and benign tumors in pancreas.

MATERIALS AND METHODS

Between July 2021 to January 2023, 75 adult patients confirmed with pancreatic solid tumors were enrolled in this prospective and consecutive study. All patients underwent MRE and DWI examinations that were both performed with a spin echo-EPI sequence. Stiffness maps and apparent diffusion coefficient (ADC) maps were generated, with MRE-derived mass stiffness and stiffness ratio (computing as the ratio of mass stiffness to the parenchyma stiffness) and DWI-derived ADC values obtained by placing regions of interest over the focal tumors on stiffness and ADC maps. Further analysis of comparing diagnostic performances was assessed by calculating the area under ROC curves.

RESULTS

PDAC had significantly higher tumor stiffness [3.795 (2.879-4.438) kPa vs. 2.359 (2.01-3.507) kPa, P = 0.0003], stiffness ratio [1.939 (1.562-2.511) vs. 1.187 (1.031-1.453), P < 0.0001] and serum CA19-9 level [276 (31.73-1055) vs. 10.45 (7.825-14.15), P < 0.0001] than other pancreatic masses. Mass stiffness, stiffness ratio and serum CA19-9 showed good diagnostic performance for differentiation with AUC of 0.7895, 0.8392 and 0.9136 respectively. The sensitivity/specificity/positive predictive value/negative predictive value for differentiating malignant from benign pancreatic tumors with mass stiffness (cutoff, > 2.8211 kPa) and stiffness ratio (cutoff, > 1.5117) were 78.4/66.7/82.9/60% and 77.8/83.3/90.3/65.2% respectively. The combined performance of Mass stiffness, stiffness ratio and serum CA19-9 got an AUC of 0.9758.

CONCLUSION

MRE holds excellent clinical potential in discriminating pancreatic ductal adenocarcinoma from other pancreatic solid masses according to their mechanical properties.

Tumor matrix stiffness provides fertile soil for cancer stem cells

Matrix stiffness is a mechanical characteristic of the extracellular matrix (ECM) that increases from the tumor core to the tumor periphery in a gradient pattern in a variety of solid tumors and can promote proliferation, invasion, metastasis, drug resistance, and recurrence. Cancer stem cells (CSCs) are a rare subpopulation of tumor cells with self-renewal, asymmetric cell division, and differentiation capabilities. CSCs are thought to be responsible for metastasis, tumor recurrence, chemotherapy resistance, and consequently poor clinical outcomes. Evidence suggests that matrix stiffness can activate receptors and mechanosensor/mechanoregulator proteins such as integrin, FAK, and YAP, modulating the characteristics of tumor cells as well as CSCs through different molecular signaling pathways. A deeper understanding of the effect of matrix stiffness on CSCs characteristics could lead to development of innovative cancer therapies. In this review, we discuss how the stiffness of the ECM is sensed by the cells and how the cells respond to this environmental change as well as the effect of matrix stiffness on CSCs characteristics and also the key malignant processes such as proliferation and EMT. Then, we specifically focus on how increased matrix stiffness affects CSCs in breast, lung, liver, pancreatic, and colorectal cancers. We also discuss how the molecules responsible for increased matrix stiffness and the signaling pathways activated by the enhanced stiffness can be manipulated as a therapeutic strategy for cancer.

Clinical application of Magnetic resonance elastography in hepatocellular carcinoma: from diagnosis to prognosis

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and a major public health problem worldwide. Liver fibrosis is closely correlated with liver functional reserve and the risk of HCC development. Meanwhile, malignant tumors generally have high cellularity compared to benign tumors, which results in increased stiffness. Magnetic resonance elastography (MRE) has emerged as a new non-invasive technique for assessing tissue stiffness with excellent diagnostic accuracy, not only for assessing liver fibrosis but also for measuring tumor stiffness. Recent studies provide new evidence that MRE may play an important role in the management of patients with HCC and show several novel clinical applications, such as predicting the development of HCC, differentiating between benign/malignant liver lesions (FLL) and HCC pathological grades, assessing treatment response, and predicting recurrence after treatment, although some findings are controversial. Therefore, we conducted this review to summarize these novel applications of MRE in HCC patients and also discuss their limitations and future advancement.

Molecular and functional imaging in cancer-targeted therapy: current applications and future directions

Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.

Prognostic Role of Molecular and Imaging Biomarkers for Predicting Advanced Hepatocellular Carcinoma Treatment Efficacy

Hepatocellular carcinoma (HCC) is the sixth most common malignancy worldwide and the fourth cause of tumor-related death. Imaging biomarkers are based on computed tomography, magnetic resonance, and contrast-enhanced ultrasound, and are widely applied in HCC diagnosis and treatment monitoring. Unfortunately, in the field of molecular biomarkers, alpha-fetoprotein (AFP) is still the only recognized tool for HCC surveillance in both diagnostic and follow-up purposes. Other molecular biomarkers have little roles in clinical practice regarding HCC, mainly for the detection of early-stage HCC, monitoring the response to treatments and analyzing tumor prognosis. In the last decades no important improvements have been achieved in this field and imaging biomarkers maintain the primacy in HCC diagnosis and follow-up. Despite the still inconsistent role of molecular biomarkers in surveillance and early HCC detection, they could play an outstanding role in prognosis estimation and treatment monitoring with a potential reduction in health costs faced by standard radiology. An important challenge resides in identifying sufficiently sensitive and specific biomarkers for advanced HCC for prognostic evaluation and detection of tumor progression, overcoming imaging biomarker sensitivity. The aim of this review is to analyze the current molecular and imaging biomarkers in advanced 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.

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.

Quantitative magnetic resonance imaging for focal liver lesions: bridging the gap between research and clinical practice

Magnetic resonance imaging (MRI) is highly important for the detection, characterization, and follow-up of focal liver lesions. Several quantitative MRI-based methods have been proposed in addition to qualitative imaging interpretation to improve the diagnostic work-up and prognostics in patients with focal liver lesions. This includes DWI with apparent diffusion coefficient measurements, intravoxel incoherent motion, perfusion imaging, MR elastography, and radiomics. Multiple research studies have reported promising results with quantitative MRI methods in various clinical settings. Nevertheless, applications in everyday clinical practice are limited. This review describes the basic principles of quantitative MRI-based techniques and discusses the main current applications and limitations for the assessment of focal liver lesions.