Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with limited therapeutic options and is often complicated by treatment resistance. Recent evidence suggests that memory-like natural killer (pNK) cells may offer a potent immunotherapeutic approach, particularly when combined with sorafenib; however, the optimal delivery method and ability to non-invasively monitor treatment effects remain unclear. This study aimed to evaluate the efficacy of sorafenib and pNK cell combination therapy across different administration routes and to develop machine learning models for non-invasive monitoring of the tumor microenvironment (TME). Two distinct rat HCC models (N1–S1 and McA-RH7777) were used to address the heterogeneity of HCC characteristics. At the beginning of tumor xenograft implantation, the animals were randomly assigned to control, sorafenib, pNK, or combination groups. The N1–S1 model received hepatic arterial delivery of pNK cells, whereas the McA-RH7777 tumor model underwent systemic administration via the tail vein. Tumor growth was monitored weekly using quantitative MRI. Histopathological analysis was performed on H&E, CD56, CD31, and TUNEL-stained tissue slides to evaluate the TME. Quantitative texture features were extracted from multiparametric MRI data and used to train multiple radiofrequency regression models to predict histopathological changes. The N1–S1 tumor model demonstrated superior responsiveness to all therapies compared with the McA-RH7777 model. Histopathological analysis revealed significant baseline differences, with N1–S1 tumors exhibiting higher NK cell infiltration, greater micro-vessel density, and higher spontaneous apoptosis. Combining local NK cell delivery with therapy arrested tumor growth in the N1–S1 model. The machine learning models demonstrated high predictive accuracy, with R2 values surpassing 0.90 in predicting key histopathological markers. The T1w and T2w models exhibited strong individual predictive capabilities, while the combined model demonstrated incremental benefits. The superior therapeutic response of the N1–S1 tumor model is attributed to a more immunogenic and vascularized TME, with the local administration of pNK cells being highly effective. This study validated the use of quantitative MRI and machine learning as powerful, non-invasive tools for predicting and monitoring TME changes, offering a promising framework for personalized HCC treatment and overcoming the limitations of conventional assessment methods.

The tumor microenvironment (TME) of hepatocellular carcinoma (HCC) has garnered significant attention, especially with the rise of immunotherapy as a treatment strategy. Radiomics, an innovative technique, offers valuable insights into the intricate structure of the TME. This review highlights recent advancements in radiomics for analyzing the HCC TME, identifies key areas that warrant further research, and explores comprehensive multi-omics approaches that extend the potential of radiomics to new frontiers.

Background: Assessing the efficacy of combination therapies in hepatocellular carcinoma (HCC) requires both accurate tumor delineation and biologically validated prediction of therapeutic response. Conventional MRI-based criteria, which rely primarily on tumor size, often fail to capture treatment efficacy due to tumor heterogeneity and pseudo-progression. This study aimed to develop and biologically validate a multi-task deep learning model that simultaneously segments HCC tumors and predicts treatment outcomes using clinically relevant multi-parametric MRI in a preclinical rat model. Methods: Orthotopic HCC tumors were induced in rats assigned to Control, Sorafenib, NK cell immunotherapy, and combination treatment groups. Multi-parametric MRI (T1w, T2w, and contrast enhanced MRI) scans were performed weekly. We developed a U-Net++ architecture incorporating a pre-trained EfficientNet-B0 encoder, enabling simultaneous segmentation and classification tasks. Model performance was evaluated through Dice coefficients and area under the receiver operator characteristic curve (AUROC) scores, and histological validation (H&E for viability, TUNEL for apoptosis) assessed biological correlations using linear regression analysis. Results: The multi-task model achieved precise tumor segmentation (Dice coefficient = 0.92, intersection over union (IoU) = 0.86) and reliably predicted therapeutic outcomes (AUROC = 0.97, accuracy = 85.0%). MRI-derived deep learning biomarkers correlated strongly with histological markers of tumor viability and apoptosis (root mean squared error (RMSE): viability = 0.1069, apoptosis = 0.013), demonstrating that the model captures biologically relevant imaging features associated with treatment-induced histological changes. Conclusions: This multi-task deep learning framework, validated against histology, demonstrates the feasibility of leveraging widely available clinical MRI sequences for non-invasive monitoring of therapeutic response in HCC. By linking imaging features with underlying tumor biology, the model highlights a translational pathway toward more clinically applicable strategies for evaluating treatment efficacy.

Background/Objectives: Hepatocellular carcinoma (HCC) remains a significant global health issue due to its high mortality rate and resistance to standard treatments. Sorafenib, the first-line systemic therapy for unresectable HCC, shows limited effectiveness due to resistance and severe side effects. Recent studies suggest that combining sorafenib with immunotherapy, particularly natural killer (NK) cells, may improve treatment outcomes. Methods: This study examined the effectiveness of sorafenib combined with NK cells pretreated with interleukin-12 (IL-12) and interleukin-18 (IL-18) in a rat HCC model. Tumor progression and treatment outcomes were assessed using MRI and histological analysis. Results: The results show that combination therapy significantly reduced tumor growth, increased tumor cell density, and inhibited angiogenesis and fibrosis in the tumor microenvironment. The sorafenib- and IL-12/IL-18-pretreated NK cell combination enhanced tumor inhibition by overcoming drug resistance and modulating the immune response. Conclusions: This study suggests that this combination therapy could be a promising strategy for treating HCC, offering both direct antitumor effects and modification of the tumor microenvironment for better clinical outcomes.

Previously, we introduced photomagnetic imaging (PMI) that synergistically utilizes laser light to slightly elevate the tissue temperature and magnetic resonance thermometry (MRT) to measure the induced temperature. The MRT temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction algorithm. In the MRT maps, the presence of abnormalities such as tumors would create a notable high contrast due to their higher hemoglobin levels. In this study, we present a new artificial intelligence-based image reconstruction algorithm that improves the accuracy and spatial resolution of the recovered absorption maps while reducing the recovery time. Technically, a supervised machine learning approach was used to detect and delineate the boundary of tumors directly from the MRT maps based on their temperature contrast to the background. This information was further utilized as a soft functional a priori in the standard PMI algorithm to enhance the absorption recovery. Our new method was evaluated on a tissue-like phantom with two inclusions representing tumors. The reconstructed absorption map showed that the well-trained neural network not only increased the PMI spatial resolution but also improved the accuracy of the recovered absorption to as low as a 2% percentage error, reduced the artifacts by 15%, and accelerated the image reconstruction process approximately 9-fold.

Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and limited treatment options. This review examines the challenges and potential of using immunotherapy to treat pancreatic cancer, highlighting the role of artificial intelligence (AI) as a promising tool to enhance early detection and monitor the effectiveness of these therapies. By synthesizing recent advancements and identifying gaps in the current research, this review aims to provide a comprehensive overview of how AI and immunotherapy can be integrated to develop more personalized and effective treatment strategies. The insights from this review may guide future research efforts and contribute to improving patient outcomes in pancreatic cancer management.

This preclinical study explored the synergistic potential of sorafenib and NK cell chemoimmunotherapy to combat hepatocellular carcinoma (HCC) in a rat model. We aimed to enhance NK cell cytotoxicity through IL-12/18 cytokines supplementation and elucidate the underlying molecular mechanisms driving this collaborative antitumor action. Twenty-four Sprague-Dawley rats were divided into distinct treatment groups, receiving sorafenib via gavage and NK cells via catheterization of the proper hepatic artery. Tumor growth and treatment response were monitored through weekly MRI scans, including T1w, T2w, DCE, and DWI sequences. Histological examinations assessed tumor cell viability, apoptosis fraction, and microvessel density. The combined therapy demonstrated significant inhibition of tumor growth, angiogenesis, and induction of durable antitumor immunity compared to either modality alone. DCE-MRI and DWI revealed distinct alterations in tumor microvasculature, highlighting the effectiveness of the combination. Our findings highlight the promise of sorafenib-augmented NK cell chemoimmunotherapy as a potential therapeutic strategy for HCC management. The targeted delivery of IL-12/18 cytokines supplemented NK cells effectively enhanced cytotoxicity within the tumor microenvironment, leading to improved antitumor responses. Further investigation in clinical trials is warranted to validate these findings in human patients and explore the translational potential of this approach.

To engineer cytotoxic functionality of natural killer (NK) cells via activation of interleukin (IL)-12 and IL-18 cytokines against hepatocellular carcinoma (HCC) and evaluate therapeutic response of novel NK cell immunotherapy administered via intrahepatic arterial delivery in a rat HCC model.

The assessment of treatment outcomes of NK cell therapy in hepatocellular carcinoma (HCC) at an early stage remains a challenge due to the absence of immediate discernible alterations in tumor size. Our study explores the feasibility of employing machine learning models based on radiomic features to achieve a more accurate appraisal of treatment outcomes of NK cell delivery and its combination with Sorafenib treatment in HCC. Buffalo rats were implanted with NIS1 tumors, followed by the catheterization of the proper hepatic artery in NK cell immunotherapy and combination groups. The catheterization process involved the surgical exposure of the portal triad, temporary ligation of the common hepatic artery (CHA), permanent ligation of the gastroduodenal artery (GDA), and insertion of a microcatheter from the gastroduodenal artery into the proper hepatic artery. One week into the treatment regimen, T1W MRI data were collected with tumor regions subsequently identified. MRI features were extracted, normalized, and subjected to filtering. A two-step feature selection was performed on T1W features, including the elimination of highly correlated features and the application of the linear-kernel SVM algorithm. Binary classification models for NK vs. Control and the Combination of NK and Sorafenib vs. Control were constructed using SVM, XGBoost, and Random Forest algorithms, all with a 5-fold cross-validation. The performances were evaluated based on accuracy, areas under the curve (AUC) of the receiver operating characteristic curves (ROC), specificity, and sensitivity. For the NK cell immunotherapy group, the Random Forest model presents an accuracy of 88%, an AUC of 87%, a sensitivity of 90%, and a specificity of 83%. For the combination of NK cell and Sorafenib treatment group, the Random Forest model demonstrates better performances with an accuracy of 96%, an AUC of 100%, a sensitivity of 93%, and a specificity of 100%. In general, T1W MRI radiomic features provide promising insights into the effects of NK cell therapy on HCC treatment, with a further improvement when NK therapy was combined with Sorafenib. The results underscore the potential of employing quantitative MRI analysis as a means to monitor and assess the effectiveness of NK cell therapies in HCC.

The early assessment of treatment outcomes in hepatocellular carcinoma (HCC) post-NK cell therapy remains challenging due to the lack of immediate observable changes in tumor size. In this study, we aim to investigate the potential of using machine learning models based on radiomic features for the precise evaluation of HCC treatment outcomes after combination therapy of intraarterial transcatheter NK cell delivery and Sorafenib administration. N1S1 tumors were implanted in Twenty-four Sprague-Dawley rats. The animal models were assigned to four groups: control group (n=6), intraarterial transcatheter NK cell delivery group (n=6), Sorafenib group (n=6) and intraarterial transcatheter NK cell plus sorafenib (combination) therapy group (n=6). Animals in the NK cell immunotherapy and combination groups underwent catheterization of the proper hepatic artery. The NK therapy treatments were performed through intra-hepatic artery (IHA) local NK cell delivery. For Sorafenib treatment, animals were administered sorafenib daily for a week. The animals underwent weekly MRI examinations for 3 weeks and multi-parametric MRI data were collected. T2-weighted MRI texture features were extracted using five approaches. Feature selection was subsequently performed, eliminating highly correlated features and employing Recursive Feature Elimination (RFECV). Kernel-based Support Vector Machine (SVM) and Random Forest (RF) were developed to differentiate the treatment response and their performance was evaluated via accuracy and area under curve (AUC) of receiver operating characteristic (ROC) curve. Six T2w MRI features were identified as key predictor for predictive model. Random Forest model demonstrated superior performance for identifying the therapeutic efficacy. In the training set, the model achieved an accuracy of 100% and AUC of 1.00, along with a sensitivity of 98.7% and specificity of 97.3%. For the validation set, the model exhibited an accuracy of 96.0%, an AUC of 1.00, a sensitivity of 95.6%, and a specificity of 91.1% for early prediction of therapeutic response. T2-weighted MRI radiomic features provide insights into the effects of combination of sorafenib and NK cell immunotherapy for HCC, emphasizing the potential of quantitative MRI analysis for dynamic monitoring and early assessment of combination therapies in HCC.

The purpose of our study was to test the following hypotheses in a rat model with liver tumors: (1) Compared with sorafenib only group, natural killer (NK) cell only group and control group, the combined treatment group can effectively slow down the tumor growth rate. (2) The growth rates of tumor blood vessels in the four different treatment groups vary greatly, which means that the combined treatment group not only reduces the growth rate of the tumors but also inhibits the function of the tumors. In this study, we randomly divided 24 rat HCC models (N1S1 liver tumors implanted into Sprague-Dawley rats) into 4 groups: transcatheter IHA NK infusion combination group, oral sorafenib group, IHA NK infusion group and transcatheter saline infusion control group. For rats in the transcatheter IHA NK infusion combination group and IHA NK infusion group, we anesthetized the rats and performed a small laparotomy. Heparin, 4.0 × 106 preconditioned NK cells (PNK), and PBS have been delivered from the IHA to the liver lobes. For the transcatheter IHA saline infusion control group, PBS was infused, which was equivalent to the volume of the IHA group, and the rest of procedure was performed as same as the IHA group. T2w and DCE studies were performed one week after implantation and one week after drug treatment. Finally, the MRI data were analyzed to evaluate the efficacy of different groups. According to the changes in tumor size in T2w images, after one week of treatment, the transcatheter IHA NK infusion combination group was significantly better than the other three groups in inhibiting tumor growth. Although the IHA NK infusion group and oral sorafenib group were not as good as the transcatheter IHA NK infusion combination group, they showed significant inhibition of tumor growth compared with the transcatheter IHA saline infusion control group. At the same time, we can observe on the DCE images that the maximum enhancement (ME) value of the transcatheter IHA NK infusion combination group was significantly lower than that of the other groups. This shows that the IHA NK infusion combination group significantly inhibited the formation of new blood vessels in tumors and reduced tumor function. This study demonstrates that the combination of sorafenib and NK cells is a promising approach for the treatment of advanced HCC. Not only can it effectively slow down the growth of tumors, but it also significantly inhibits the formation of blood vessels within tumors. And the combination of the two can also effectively reduce the side effects of high concentrations of sorafenib and NK cells.

Background and Objective:Pancreatic ductal adenocarcinoma (PDAC) is 3rd most lethal cancer in the USA leading to a median survival of six months and less than 5% 5-year overall survival (OS). As the only potentially curative treatment, surgical resection is not suitable for up to 90% of the patients with PDAC due to late diagnosis. Highly fibrotic PDAC with an immunosuppressive tumor microenvironment restricts cytotoxic T lymphocyte (CTL) infiltration and functions causing limited success with systemic therapies like dendritic cell (DC)-based immunotherapy. In this study, we investigated the potential benefits of irreversible electroporation (IRE) ablation therapy in combination with DC vaccine therapy against PDAC. Methods:We performed a literature search to identify studies focused on DC vaccine therapy and IRE ablation to boost therapeutic response against PDAC indexed in PubMed, Web of Science, and Scopus until February 20th, 2023. Key Content and Findings:IRE ablation destructs tumor structure while preserving extracellular matrix and blood vessels facilitating local inflammation. The studies demonstrated IRE ablation reduces tumor fibrosis and promotes CTL tumor infiltration to PDAC tumors in addition to boosting immune response in rodent models. The administration of the DC vaccine following IRE ablation synergistically enhances therapeutic response and extends OS rates compared to the use of DC vaccination or IRE alone. Moreover, the implementation of data-driven approaches further allows dynamic and longitudinal monitoring of therapeutic response and OS following IRE plus DC vaccine immunoablation. Conclusions:The combination of IRE ablation and DC vaccine immunotherapy is a potent strategy to enhance the therapeutic outcomes in patients with PDAC.

Abstract Background Heterogeneity of hepatocellular carcinoma (HCC) presents significant challenges for therapeutic strategies and necessitates combinatorial treatment approaches to counteract suppressive behavior of tumor microenvironment and achieve improved outcomes. Here, we employed cytokines to induce memory-like behavior in natural killer (NK) cells, thereby enhancing their cytotoxicity against HCC. Additionally, we evaluated the potential benefits of combining sorafenib with this newly developed memory-like NK cell (pNK) immunochemotherapy in a preclinical model. Methods HCC tumors were grown in SD rats using subcapsular implantation. Interleukin 12/18 cytokines were supplemented to NK cells to enhance cytotoxicity through memory activation. Tumors were diagnosed using MRI, and animals were randomly assigned to control, pNK immunotherapy, sorafenib chemotherapy, or combination therapy groups. NK cells were delivered locally via the gastrointestinal tract, while sorafenib was administered systemically. Therapeutic responses were monitored with weekly multi-parametric MRI scans over three weeks. Afterward, tumor tissues were harvested for histopathological analysis. Structural and functional changes in tumors were evaluated by analyzing MRI and histopathology data using ANOVA and pairwise T-test analyses. Results The tumors were allowed to grow for six days post-cell implantation before treatment commenced. At baseline, tumor diameter averaged 5.27 mm without significant difference between groups (p = 0.16). Both sorafenib and combination therapy imposed greater burden on tumor dimensions compared to immunotherapy alone in the first week. By the second week of treatment, combination therapy had markedly expanded its therapeutic efficacy, resulting in the most significant tumor regression observed (6.05 ± 1.99 vs. 13.99 ± 8.01 mm). Histological analysis demonstrated significantly improved cell destruction in the tumor microenvironment associated with combination treatment (63.79%). Interestingly, we observed fewer viable tumor regions in the sorafenib group (38.9%) compared to the immunotherapy group (45.6%). Notably, there was a significantly higher presence of NK cells in the tumor microenvironment with combination therapy (34.79%) compared to other groups (ranging from 2.21 to 26.50%). Although the tumor sizes in the monotherapy groups were similar, histological analysis revealed a stronger response in pNK cell immunotherapy group compared to the sorafenib group. Conclusions Experimental results indicated that combination therapy significantly enhanced treatment response, resulting in substantial tumor growth reduction in alignment with histological analysis.

Sorafenib, FDA-approved therapy for patients with advanced hepatocellular carcinoma (HCC), leads to limited improvement in overall survival. However, it may indirectly impact the expansion and activity of natural killer (NK) cells. While NK cell-based immunotherapies generally exhibit favorable safety profiles, their effectiveness in controlling solid tumor growth is constrained, primarily due to the absence of antigen specificity and suboptimal expansion and persistence within the tumor microenvironment. In this study, we postulated that enhancing NK cell functionality via cytokine activation could bolster their viability and cytotoxic capabilities, leading to an improved therapeutic response when combined with sorafenib. Memory-like (ML)-NK cells were generated through the supplementation of optimal concentrations of interleukin (IL)-12 and IL-18 cytokines. Following a single day of treatment, cytotoxicity against rat and human HCC cells was evaluated via flow cytometry analysis. A rat HCC model was developed in 30 animals via subcapsular implantation and assigned to control, NK, sorafenib, ML-NK, and combination groups. Sorafenib was administered orally, and NK cells were delivered via the intrahepatic artery. Tumor growth was measured one week after treatment evaluation. Therapeutic efficacy during in-vitro and in-vivo analysis was investigated through a one-way ANOVA test, followed by pairwise two-tailed Student t-tests, considering P < 0.05 statistically significant. The in-vitro experiment results demonstrated that sorafenib and conventional NK cell therapies induced more substantial cell death than the control group (P < 0.01). ML NK cells significantly improved cell death compared to conventional NK cell immunotherapy. Furthermore, sorafenib in combination with ML-NK cells significantly decreased the viability of HCC cells (P < 0.05) compared to sorafenib plus conventional NK cell combination therapy. In vivo experiments have shown that sorafenib and ML-NK cell immunotherapy reduced the growth rate of HCC tumors compared to conventional NK immunotherapy and control groups. Notably, a combination of sorafenib and ML-NK cell immunochemotherapy resulted in the most significant suppression of tumor growth when compared to other therapies. In conclusion, our experimental findings demonstrate that the concurrent administration of sorafenib and ML-NK immunotherapy enhances cytotoxicity against HCC by optimizing the therapeutic response through cytokine activation, resulting in a significant decrease in tumor growth.

Background Hepatocellular carcinoma (HCC) is a common liver malignancy with limited treatment options. Previous studies expressed the potential synergy of sorafenib and NK cell immunotherapy as a promising approach against HCC. MRI is commonly used to assess response of HCC to therapy. However, traditional MRI-based metrics for treatment efficacy are inadequate for capturing complex changes in the tumor microenvironment, especially with immunotherapy. In this study, we investigated potent MRI radiomics analysis to non-invasively assess early responses to combined sorafenib and NK cell therapy in a HCC rat model, aiming to predict multiple treatment outcomes and optimize HCC treatment evaluations. Methods Sprague Dawley (SD) rats underwent tumor implantation with the N1-S1 cell line. Tumor progression and treatment efficacy were assessed using MRI following NK cell immunotherapy and sorafenib administration. Radiomics features were extracted, processed, and selected from both T1w and T2w MRI images. The quantitative models were developed to predict treatment outcomes and their performances were evaluated with area under the receiver operating characteristic (AUROC) curve. Additionally, multivariable linear regression models were constructed to determine the correlation between MRI radiomics and histology, aiming for a noninvasive evaluation of tumor biomarkers. These models were evaluated using root-mean-squared-error (RMSE) and the Spearman correlation coefficient. Results A total of 743 radiomics features were extracted from T1w and T2w MRI data separately. Subsequently, a feature selection process was conducted to identify a subset of five features for modeling. For therapeutic prediction, four classification models were developed. Support vector machine (SVM) model, utilizing combined T1w + T2w MRI data, achieved 96% accuracy and an AUROC of 1.00 in differentiating the control and treatment groups. For multi-class treatment outcome prediction, Linear regression model attained 85% accuracy and an AUC of 0.93. Histological analysis showed that combination therapy of NK cell and sorafenib had the lowest tumor cell viability and the highest NK cell activity. Correlation analyses between MRI features and histological biomarkers indicated robust relationships (r = 0.94). Conclusions Our study underscored the significant potential of texture-based MRI imaging features in the early assessment of multiple HCC treatment outcomes.