MRI radiomics to monitor therapeutic outcome of sorafenib plus IHA transcatheter NK cell combination therapy in hepatocellular carcinoma

Natural Killer Cell-Associated Radiogenomics Model for Hepatocellular Carcinoma: Integrating CD2 and Enhanced CT-Derived Radiomics Signatures

RATIONALE AND OBJECTIVES

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality. Natural Killer (NK) cells play a crucial role in immune defense against HCC, but their activity is often impaired by the tumor microenvironment (TME). This study aims to integrate radiomics and transcriptomics to develop a prognostic model linking NK cell characteristics to clinical outcomes in HCC.

METHODS

Transcriptomic data from five cohorts (734 HCC patients) from the Gene Expression Omnibus and The Cancer Genome Atlas databases were analyzed using the Microenvironment Cell Populations-counter algorithm. NK cell-related prognostic biomarkers were identified via weighted gene co-expression network analysis and LASSO-Cox regression. Radiomics models were established using CT imaging features from 239 patients in three datasets from The Cancer Imaging Archive and Shanghai East Hospital. HCC radiogenomic subtypes were proposed by integrating genetic biomarkers and radiomics models.

RESULTS

CD2 expression was identified as an independent NK cell-related prognostic biomarker, with a positive impact on prognosis and a strong correlation with NK cell-associated biological processes in HCC. A robust radiomics model was constructed, and the integration of CD2 expression with radioscore identified potential radiogenomic subtypes of HCC.

CONCLUSION

Radiomics has potential to link TME immune phenotypes with HCC prognosis. CD2 is a key biomarker connecting NK cells with radiomic features, offering a new classification of HCC into radiogenomic subtypes. This approach supports the use of radiogenomics in personalized HCC treatment.

Enhancement of apoptosis in HCT116 and HepG2 cells by Coix lacryma-jobi var. lacryma-jobi seed extract in combination with sorafenib

Objective

Coix lacryma-jobi, a highly regarded Asian herb widely used in traditional Chinese medicine, is recognized for its dual benefits in promoting overall health and treating various diseases. While it exhibits moderate anticancer efficacy when used alone, this study investigated the enhanced anticancer potential of raw and cooked Coix lacryma-jobi var. lacryma-jobi (CL) seed extracts in combination with sorafenib against HCT116 and HepG2 cancer cell lines. The combination of sorafenib with other anticancer agents, including natural extracts, has garnered significant attention as a promising strategy for developing more effective cancer therapies.

Methods

Dry powders of raw (R) and cooked (C) CL seeds, obtained from a local commercial source in Thailand, were extracted and fractionated using ethanol (E), dichloromethane (D), ethyl acetate (A), and water (W) to produce eight fractions: CLRE, CLCE, CLRD, CLCD, CLRA, CLCA, CLRW, and CLCW. The coixol content in raw and cooked seed extracts was quantified and expressed as μg of coixol per gram of extract. The cytotoxic effects of these fractions were evaluated against HCT116 and HepG2 cells using the MTT assay. Fractions demonstrating the most significant cytotoxic responses were combined with sorafenib to evaluate their synergistic effects. Apoptosis induction and mitochondrial membrane potential (MMP) were assessed, and the underlying mechanism of apoptosis was explored by analyzing reactive oxygen species (ROS) generation and antioxidant protein expression levels. Additionally, the combination treatment's effect on the phosphatidylinositol-3 kinase (PI3K)/protein kinase B (AKT)/mechanistic target of rapamycin (mTOR) pathway was investigated.

Results

One gram of CLCE and CLCD extracts contained higher coixol levels (7.02 μg and 9.69 μg, respectively) compared to CLRE and CLRD (2.66 μg and 5.96 μg, respectively). Coixol content in CLRA, CLRW, and CLCW fractions was undetectable under the study conditions. All extract fractions exhibited IC50 values exceeding 1 mg/mL after 24- and 48-hour incubations with HCT116 and HepG2 cells, indicating limited cytotoxicity when used independently. CLRD and CLCD fractions were selected for combination studies at a concentration of 1 mg/mL, combined with sub-IC50 concentrations of sorafenib to minimize its side effects. This combination significantly increased cytotoxicity, inducing apoptosis in HCT116 and HepG2 cells by elevating ROS levels and reducing the expression of superoxide dismutase 2 and catalase. Furthermore, the combination treatment downregulated the PI3K/AKT/mTOR pathway, indicating a targeted anticancer mechanism.

Conclusion

The combination of CLCD with sorafenib demonstrates significant potential as a strategy for future anticancer therapies. This CL seed extract, cultivated and commercially available in Thailand, shows promise as a natural supplement to enhance the efficacy of chemotherapy in upcoming clinical anticancer applications.

The dual role of CXCL9/SPP1 polarized tumor-associated macrophages in modulating anti-tumor immunity in hepatocellular carcinoma

Introduction

The main challenge for cancer therapy lies in immuno-suppressive tumor micro-environment. Reprogramming tumor-associated macrophages (TAMs) into an anti-tumor phenotype is a promising strategy.

Methods

A comprehensive analysis by combing multi-regional single-cell, bulk and spatial transcriptome profiling with radiomics characterization was conducted to dissect the heterogeneity of TAMs and resolve the landscape of the CXCL9:SPP1 (CS) macrophage polarity in HCC.

Results

TAMs were particularly increased in HCC. SPP1+ TAMs and CXCL9+ TAMs were identified as the dominant subtypes with different evolutionary trajectories. SPP1+ TAMs, located in the tumor core, co-localized with cancer-associated fibroblasts to promote tumor growth and further contributed to worse prognosis. In contrast, CXCL9+ TAMs, located in the peritumoral region, synergized with CD8+ T cells to create an immunostimulatory micro-environment. For the first time, we explored the applicability of CS polarity in HCC tumors and revealed several key transcription factors involved in shaping this polarity. Moreover, CS polarity could serve as a potential indicator of prognostic and micro-environmental status for HCC patients. Based on medical imaging data, we developed a radiomics tool, RCSP (Radiogenomics-based CXCL9/SPP1 Polarity), to assist in non-invasively predicting the CS polarity in HCC patients.

Conclusion

Our research sheds light on the regulatory roles of SPP1+ TAMs and CXCL9+ TAMs in the micro-environment and provides new therapeutic targets or insights for the reprogramming of targeted macrophages in HCC.

Reliability of radiomics features as imaging biomarkers for evaluating brain aging: A study based on myelin protein and diffusion tensor imaging

Radiomics has made considerable progress in neurodegenerative diseases. However, previous studies only explored the feasibility of radiomics in clinical applications. Therefore, the objective of this study was to obtain the most relevant radiomics features with the aging changes of myelin proteins and compare their diagnostic performances with the diffusion tensor imaging (DTI) parameters to identify the reliability of these features as imaging biomarkers for assessing brain aging. Thirty middle-aged and thirty old-aged mice were assigned to the training set to explore the most relevant features of myelin proteins and their diagnostic performances. Ten middle-aged and ten old-aged mice were assigned to the testing set to further validate the reproducibility of the features. T2-weighted imaging and DTI were conducted to obtain white matter radiomics features and DTI parameters. Myelin proteins, including proteolipid protein (PLP) and myelin basic protein (MBP), were examined by immunofluorescence staining in the regions of the whole brain, cortex, corpus callosum, striatum, and anterior commissure. The Pearson correlation analysis was used to observe the correlations between radiomics features and myelin proteins. The four most relevant features with the top four correlation coefficients were selected to compare their diagnostic performances with the DTI parameters, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD), and radial diffusivity (RD). Wavelet-HLL_glszm_ZoneEntropy, wavelet-HLL_gldm_DependenceEntropy, wavelet-LHL_glszm_ZoneEntropy, and log-sigma-2-0-mm-3D_gldm_DependenceEntropy were the four most relevant features, which had moderately significant correlations with PLP. The area under the receiver operating characteristic curves (AUC) of the four features were 0.940, 0.917, 0.831, and 0.964 in the training set, and 0.880, 0.840, 0.860, and 0.880 in the testing set. The AUCs of FA, MD, AxD, and RD were 0.864, 0.743, 0.673, and 0.778 in the training set, and 0.780, 0.710, 0.670, and 0.730 in the testing set. These results demonstrated that radiomics features of white matter displayed significant correlations with myelin proteins and their performances were comparable or even superior to DTI parameters, which ensured their reliability as non-invasive imaging biomarkers for evaluating brain aging.

Research progress of MRI-based radiomics in hepatocellular carcinoma

Background

Primary liver cancer (PLC), notably hepatocellular carcinoma (HCC), stands as a formidable global health challenge, ranking as the sixth most prevalent malignant tumor and the third leading cause of cancer-related deaths. HCC presents a daunting clinical landscape characterized by nonspecific early symptoms and late-stage detection, contributing to its poor prognosis. Moreover, the limited efficacy of existing treatments and high recurrence rates post-surgery compound the challenges in managing this disease. While histopathologic examination remains the cornerstone for HCC diagnosis, its utility in guiding preoperative decisions is constrained. Radiomics, an emerging field, harnesses high-throughput imaging data, encompassing shape, texture, and intensity features, alongside clinical parameters, to elucidate disease characteristics through advanced computational techniques such as machine learning and statistical modeling. MRI radiomics specifically holds significant importance in the diagnosis and treatment of hepatocellular carcinoma (HCC).

Objective

This study aims to evaluate the methodology of radiomics and delineate the clinical advancements facilitated by MRI-based radiomics in the realm of hepatocellular carcinoma diagnosis and treatment.

Methods

A systematic review of the literature was conducted, encompassing peer-reviewed articles published between July 2018 and Jan 2025, sourced from PubMed and Google Scholar. Key search terms included Hepatocellular carcinoma, HCC, Liver cancer, Magnetic resonance imaging, MRI, radiomics, deep learning, machine learning, and artificial intelligence.

Results

A comprehensive analysis of 93 articles underscores the efficacy of MRI radiomics, a noninvasive imaging analysis modality, across various facets of HCC management. These encompass tumor differentiation, subtype classification, histopathological grading, prediction of microvascular invasion (MVI), assessment of treatment response, early recurrence prognostication, and metastasis prediction.

Conclusion

MRI radiomics emerges as a promising adjunctive tool for early HCC detection and personalized preoperative decision-making, with the overarching goal of optimizing patient outcomes. Nevertheless, the current lack of interpretability within the field underscores the imperative for continued research and validation efforts.

Myeloid response evaluated by noninvasive CT imaging predicts post-surgical survival and immune checkpoint therapy benefits in patients with hepatocellular carcinoma

Background

The potential of preoperative CT in the assessment of myeloid immune response and its application in predicting prognosis and immune-checkpoint therapy outcomes in hepatocellular carcinoma (HCC) has not been explored.

Methods

A total of 165 patients with pathological slides and multi-phase CT images were included to develop a radiomics signature for predicting the imaging-based myeloid response score (iMRS). Overall survival (OS) and recurrence-free survival (RFS) were assessed according to the iMRS risk group and validated in a surgical resection cohort (n = 98). The complementary advantage of iMRS incorporating significant clinicopathologic factors was investigated by the Cox proportional hazards analysis. Additionally, the iMRS in inferring the benefits of immune checkpoint therapy was explored in an immunotherapy cohort (n = 36).

Results

We showed that AUCs of the optimal radiomics signature for iMRS were 0.941 [95% confidence interval (CI), 0.909-0.973] and 0.833 (0.798-0.868) in the training and test cohorts, respectively. High iMRS was associated with poor RFS and OS. The prognostic performance of the Clinical-iMRS nomogram was better than that of a single parameter (p < 0.05), with a 1-, 3-, and 5-year C-index for RFS of 0.729, 0.709, and 0.713 in the training, test, and surgical resection cohorts, respectively. A high iMRS score predicted a higher proportion of objective response (vs. progressive disease or stable disease; odds ratio, 2.311; 95% CI, 1.144-4.672; p = 0.020; AUC, 0.718) in patients treated with anti-PD-1 and PD-L1.

Conclusions

iMRS may provide a promising method for predicting local myeloid immune responses in HCC patients, inferring postsurgical prognosis, and evaluating benefits of immune checkpoint therapy.

Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.