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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.

BackgroundHepatocellular 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.MethodsSprague 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.ResultsA 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).ConclusionsOur study underscored the significant potential of texture-based MRI imaging features in the early assessment of multiple HCC treatment outcomes.

Background Point-of-care (POC) MRI is a bedside imaging technology with fewer than five units in clinical use in the United States and a paucity of scientific studies on clinical applications. Purpose To evaluate the clinical and operational impacts of deploying POC MRI in emergency department (ED) and intensive care unit (ICU) patient settings for bedside neuroimaging, including the turnaround time. Materials and Methods In this preliminary retrospective study, all patients in the ED and ICU at a single academic medical center who underwent noncontrast brain MRI from January 2021 to June 2021 were investigated to determine the number of patients who underwent bedside POC MRI. Turnaround time, examination limitations, relevant findings, and potential CT and fixed MRI findings were recorded for patients who underwent POC MRI. Descriptive statistics were used to describe clinical variables. The Mann-Whitney U test was used to compare the turnaround time between POC MRI and fixed MRI examinations. Results Of 638 noncontrast brain MRI examinations, 36 POC MRI examinations were performed in 35 patients (median age, 66 years [IQR, 57-77 years]; 21 women), with one patient undergoing two POC MRI examinations. Of the 36 POC MRI examinations, 13 (36%) occurred in the ED and 23 (64%) in the ICU. There were 12 of 36 (33%) POC MRI examinations interpreted as negative, 14 of 36 (39%) with clinically significant imaging findings, and 10 of 36 (28%) deemed nondiagnostic for reasons such as patient motion. Of 23 diagnostic POC MRI examinations with comparison CT available, three (13%) demonstrated acute infarctions not apparent on CT scans. Of seven diagnostic POC MRI examinations with subsequent fixed MRI examinations, two (29%) demonstrated missed versus interval subcentimeter infarctions, while the remaining demonstrated no change. The median turnaround time of POC MRI was 3.4 hours in the ED and 5.3 hours in the ICU. Conclusion Point-of-care (POC) MRI was performed rapidly in the emergency department and intensive care unit. A few POC MRI examinations demonstrated acute infarctions not apparent at standard-of-care CT examinations. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Anzai and Moy in this issue.

RATIONALE AND OBJECTIVES: Irreversible electroporation (IRE) is a promising non-thermal ablation technique for the treatment of patients with hepatocellular carcinoma. Early differentiation of the IRE zone from surrounding reversibly electroporated (RE) penumbra is vital for the evaluation of treatment response. In this study, an advanced statistical learning framework was developed by evaluating standard MRI data to differentiate IRE ablation zones, and to correlate with histological tumor biomarkers. MATERIALS AND METHODS: Fourteen rabbits with VX2 liver tumors were scanned following IRE ablation and forty-six features were extracted from T1w and T2w MRI. Following identification of key imaging variables through two-step feature analysis, multivariable classification and regression models were generated for differentiation of IRE ablation zones, and correlation with histological markers reflecting viable tumor cells, microvessel density, and apoptosis rate. The performance of the multivariable models was assessed by measuring accuracy, receiver operating characteristics curve analysis, and Spearman correlation coefficients. RESULTS: The classifiers integrating four radiomics features of T1w, T2w, and T1w+T2w MRI data distinguished IRE from RE zones with an accuracy of 97%, 80%, and 97%, respectively. Also, pixelwise classification models of T1w, T2w, and T1w+T2w MRI labeled each voxel with an accuracy of 82.8%, 66.5%, and 82.9%, respectively. Regression models obtained a strong correlation with behavior of viable tumor cells (0.62 ≤ r2 ≤ 0.85, p < 0.01), apoptosis (0.40 ≤ r2 ≤ 0.82, p < 0.01), and microvessel density (0.48 ≤ r2 ≤ 0.58, p < 0.01). CONCLUSION: MRI radiomics features provide descriptive power for early differentiation of IRE and RE zones while observing strong correlations among multivariable MRI regression models and histological tumor biomarkers.

Sorafenib is an oral multikinase inhibitor approved by the US Food and Drug Administration for treatment of the patients with surgically unresectable hepatocellular carcinoma (HCC). Sorafenib mitigates angiogenesis by targeting vascular endothelial growth factor receptors and platelet-derived growth factor receptors in endothelial cells and pericytes. Moreover, it suppresses cell proliferation via blockage of B-RAF and RAF1 of the mitogen-activated protein kinase pathway in tumor cells. Sorafenib has been the standard molecular targeted medication in the treatment of advanced-stage HCC patients ineligible for potentially curative interventional (radiofrequency or microwave ablation) or palliative trans-arterial chemoembolization (TACE) therapies for over a decade. However, it only increases overall survival by less than 3 months, and systemic exposure to sorafenib causes clinically significant toxicities (about 50% of patients). Given the high frequency and severity of these toxicities, sorafenib dose must be often reduced or discontinued altogether. In this review, we discussed the mechanism of sorafenib-associated adverse events and their management during HCC treatment.

ObjectivesAccurate differentiation of temporary vs. permanent changes occurring following irreversible electroporation (IRE) holds immense importance for the early assessment of ablative treatment outcomes. Here, we investigated the benefits of advanced statistical learning models for an immediate evaluation of therapeutic outcomes by interpreting quantitative characteristics captured with conventional MRI.MethodsThe preclinical study integrated twenty-six rabbits with anatomical and perfusion MRI data acquired with a 3T clinical MRI scanner. T1w and T2w MRI data were quantitatively analyzed, and forty-six quantitative features were computed with four feature extraction methods. The candidate key features were determined by graph clustering following the filtering-based feature selection technique, RELIEFF algorithm. Kernel-based support vector machines (SVM) and random forest (RF) classifiers interpreting quantitative features of T1w, T2w, and combination (T1w+T2w) MRI were developed for replicating the underlying characteristics of the tissues to distinguish IRE ablation regions for immediate assessment of treatment response. Accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve were used to evaluate classification performance.ResultsFollowing the analysis of quantitative variables, three features were integrated to develop a SVM classification model, while five features were utilized for generating RF classifiers. SVM classifiers demonstrated detection accuracy of 91.06%, 96.15%, and 98.04% for individual and combination MRI data, respectively. Besides, RF classifiers obtained slightly lower accuracy compared to SVM which were 95.06%, 89.40%, and 94.38% respectively.ConclusionsQuantitative models integrating structural characteristics of conventional T1w and T2w MRI data with statistical learning techniques identified IRE ablation regions allowing early assessment of treatment status.

Hepatocellular carcinoma (HCC) is the third most frequent source of deaths associated with cancer after lung cancer in the world despite recent innovative treatment techniques. Liver transplantation, hepatic resection, and percutaneous ablation techniques hold great promise as potentially curative treatments for patients at early stages. Nevertheless, most of the patients are not suitable for these curative treatments due to their advanced disease stages at the time of diagnosis. Food and Drug Administration (FDA) approved tyrosine kinase inhibitor, sorafenib is a standard therapy for advanced-stage HCC patients which extends overall survival for several months. However, its therapeutic efficacy is restricted by adverse events and drug resistance which limits the number of patients benefiting from this systemic chemotherapeutic drug. During the last decade, novel approaches including but not limited to immunotherapies, ablation methods, and chemotherapeutic drugs were proposed to enhance sensitivity to sorafenib, improve therapeutic efficacy, and prohibit adverse events through novel delivery routes, utilization of nanoparticle carriers, and combination with other therapeutic agents. However, studies are still being conducted to optimize the efficiency of sorafenib and reduce its adverse events. In this review paper, we examine research studies evaluating novel delivery methods to reduce drug-related cytotoxicity to improve patient tolerance to sorafenib and its therapeutic efficacy in patients with HCC. Moreover, therapeutic approaches with the synergistic potential to combine with sorafenib are briefly summarized.

Hepatocellular carcinoma (HCC) is among the most lethal cancer types despite great advancement in overall survival of the patients over the last decades. Surgical resection or partial hepatectomy has been approved as the curative treatment for early-stage HCC patients however only up to 30% of them are eligible for the procedures. Natural killer (NK) cells are cytotoxic lymphocytes recognized for killing virally infected cells and improving immune functions for defending the body against malignant cells. Although autologous NK cells failed to demonstrate significant clinical benefit, transfer of allogeneic adoptive NK cells arises as a promising approach for the treatment of solid tumors. The immunosuppressive tumor microenvironment and inadequate homing efficiency of NK cells to tumors can inhibit adoptive transfer immunotherapy (ATI) efficacy. However, potential of the NK cells is challenged by the transfection efficiency. The local ablation techniques that employ thermal or chemical energy have been investigated for the destruction of solid tumors for three decades and demonstrated promising benefits for individuals not eligible for surgical resection or partial hepatectomy. Irreversible electroporation (IRE) is one of the most recent minimally invasive ablation methods that destruct the cell within the targeted region through non-thermal energy. IRE destroys the tumor cell membrane by delivering high-frequency electrical energy in short pulses and overcomes tumor immunosuppression. The previous studies demonstrated that IRE can induce immune changes which can facilitate activation of specific immune responses and improve transfection efficiency. In this review paper, we have discussed the mechanism of NK cell immunotherapy and IRE ablation methods for the treatment of HCC patients and the combinatorial benefits of NK cell immunotherapy and IRE ablation.

Rationale and objectivesTo develop classification and regression models interpreting tumor characteristics obtained from structural (T1w and T2w) magnetic resonance imaging (MRI) data for early detection of dendritic cell (DC) vaccine treatment effects and prediction of long-term outcomes for LSL-KrasG12D; LSL-Trp53R172H; Pdx-1-Cre (KPC) transgenic mice model of pancreatic ductal adenocarcinoma.Materials and methodsEight mice were treated with DC vaccine for 3 weeks while eight KPC mice were used as untreated control subjects. The reproducibility of the computed 264 features was evaluated using the intraclass correlation coefficient. Key variables were determined using a three-step feature selection approach. Support vector machines classifiers were generated to differentiate treatment-related changes on tumor tissue following first- and third weeks of the DC vaccine therapy. The multivariable regression models were generated to predict overall survival (OS) and histological tumor markers of KPC mice using quantitative features.ResultsThe quantitative features computed from T1w MRI data have better reproducibility than T2w MRI features. The KPC mice in treatment and control groups were differentiated with a longitudinally increasing accuracy (first- and third weeks: 87.5% and 93.75%). The linear regression model generated with five features of T1w MRI data predicted OS with a root-mean-squared error (RMSE)

PurposeTo evaluate the effect of the anatomic size on 3D radiomic imaging features of the breast cancer hepatic metastases.Materials and methodsCT scans of 81 liver metastases from 54 patients with breast cancer were evaluated. Ten most common 3D radiomic features from the histogram and gray level co-occurrence matrix (GLCM) categories were calculated for the hepatic metastases (HM) and compared to normal liver (NL). The effect of size was evaluated by using linear mixed-effects regression models. The effect of size on different radiomic features was analyzed for both liver lesions and background liver.ResultsThree-dimensional radiomic features from GLCM demonstrate an important size dependence. The texture-feature size dependence was found to be different among feature categories and between the HM and NL, thus demonstrating a discriminatory power for the tissue type. Significant difference in the slope was found for GLCM homogeneity (NL slope = 0.004, slope difference 95% confidence interval [CI] 0.06-0.1, p

Pancreatic ductal adenocarcinoma (PDAC) is associated with highly immunosuppressive tumor microenvironment (TME) that can limit the efficacy of dendritic cell (DC) vaccine immunotherapy. Irreversible electroporation (IRE) is a local ablation approach. Herein, we test the hypothesis that IRE ablation can overcome TME immunosuppression to improve the efficacy of DC vaccination using KrasLSL-G12D-p53LSL-R172H-Pdx-1-Cre (KPC) orthotopic mouse model of PDAC. The median survival for mice treated with the combined IRE and DC vaccination was 77 days compared with sham control (35 days), DC vaccination (49 days), and IRE (44 days) groups (P = .006). Thirty-six percent of the mice treated with combination IRE and DC vaccination were still survival at the end of the study period (90 days) without visible tumor. The changes of tumor apparent diffusion coefficient (ΔADC) were higher in mice treated with combination IRE and DC vaccination than that of other groups (all P

Rationale and objectivesTo investigate whether transcatheter intraarterial perfusion (TRIP) magnetic resonance imaging (MRI) can differentiate reversible electroporation (RE) zones from irreversible electroporation (IRE) zones immediately after IRE procedure in the rabbit liver.Materials and methodsAll studies were approved by the institutional animal care and use committee and performed in accordance with institutional guidelines. A total of 13 healthy New Zealand White rabbits were used. After selective catheterization of the hepatic artery under X-ray fluoroscopy, we acquired TRIP-MRI at 20 minutes post-IRE using 3 mL of 5% intraarterial gadopentetate dimeglumine. Semi-quantitative (peak enhancement, PE; time to peak, TTP; wash-in slope, WIS; areas under the time-intensity curve, AUT, over 30, 60, 90, 120, 150, and 180 seconds after the initiation of enhancement) and quantitative (Ktrans, ve, and vp) TRIP-MRI parameters were calculated. The relationships between TRIP-MRI parameters and histological measurements and the differential ability of TRIP-MRI parameters was assessed.ResultsPE, AUT60, AUT90, AUT120, AUT150, AUT180, Ktrans, and ve were significantly higher in RE zones than in IRE zones (all P < 0.05), and AUC for these parameters ranged from 0.91(95% CI, 0.80, 1.00) to 0.99 (95% CI, 0.98, 1.00). There was no significant difference in AUC between any two parameters (Z, 0-1.47; P, 0.14-1.00). Hepatocyte apoptosis strongly correlated with PE, AUT60, AUT90, AUT120, AUT150, AUT180, Ktrans, and vp (the absolute value r, 0.6-0.7, all P < 0.0001).ConclusionAUT150 or AUT180 could be a potential imaging biomarker to differentiate RE from IRE zones, and TRIP-MRI permits to differentiate RE from IRE zones immediately after IRE procedure in the rabbit liver.

BackgroundPreoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models.MethodsA total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC).ResultsThe clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p

BackgroundThere is a lack of well-established clinical tools for predicting dendritic cell (DC) vaccination response of pancreatic ductal adenocarcinoma (PDAC). DC vaccine treatment efficiency was demonstrated using histological analysis in pre-clinical studies; however, its usage was limited due to invasiveness. In this study, we aimed to investigate the potential of MRI texture features for detection of early immunotherapeutic response as well as overall survival (OS) of PDAC subjects following dendritic cell (DC) vaccine treatment in LSL-KrasG12D;LSL-Trp53R172H;Pdx-1-Cre (KPC) transgenic mouse model of pancreatic ductal adenocarcinoma (PDAC).Materials and methodsKPC mice were treated with DC vaccines, and tumor growth was dynamically monitored. A total of a hundred and fifty-two image features of T2-weighted MRI images were analyzed using a kernel-based support vector machine model to detect treatment effects following the first and third weeks of the treatment. Moreover, univariate analysis was performed to describe the association between MRI texture and survival of KPC mice as well as histological tumor biomarkers.ResultsOS for mice in the treatment group was 54.8 ± 22.54 days while the control group had 35.39 ± 17.17 days. A subset of three MRI features distinguished treatment effects starting from the first week with increasing accuracy throughout the treatment (75% to 94%). Besides, we observed that short-run emphasis of approximate wavelet coefficients had a positive correlation with the survival of the KPC mice (r = 0.78, p

PurposeIrreversible electroporation (IRE) is a nonthermal tissue ablation technique that represents a promising treatment option for unresectable liver tumors, but the effectively treated zone cannot be reliably predicted. We investigate the potential benefit of transcatheter intra-arterial perfusion (TRIP) -MRI for the early noninvasive differentiation of IRE zone from surrounding reversibly electroporated (RE) zone.MethodsSeventeen rabbits with VX2 liver tumors were scanned with morphological and contrast-enhanced MRI sequences approximately 30 min after IRE tumor ablation. Quantitative TRIP-MRI perfusion parameters were evaluated in IRE zone and RE zone, defined according to histology. MRI and histology results were compared among zones using Wilcoxon rank-sum tests and correlations were evaluated by Pearson's correlation coefficient.ResultsThere were significant differences in area under the curve, time to peak, maximum and late enhancement, wash-in and wash-out rates in the tumor IRE zones compared with the boundary tumor RE zones and untreated tumors. Histology showed significantly fewer tumor cells, microvessels and significantly more apoptosis in tumor IRE zones compared with tumor RE zones (-51%, -66% and +185%, respectively) and untreated tumors (-60%, -67%, and +228%, respectively). A strong correlation was observed between MRI and histology measurements of IRE zones (r = 0.948) and RE zones (r = 0.951).ConclusionTRIP-MRI demonstrated the potential to detect immediate perfusion changes following IRE liver tumor ablation and effectively differentiate the IRE zone from the surrounding tumor RE zone.

It is unknown whether the route of administration impacts dendritic cell (DC)-based immunotherapy for pancreatic ductal adenocarcinoma (PDAC). We compared the effect of intraperitoneal (i.p.), subcutaneous (s.c.), and intratumoral (i.t.) administration of DC vaccine on induction of antitumor responses in a KPC mouse model of PDAC. Histological analysis and flow cytometry were used to evaluate tumor progression and antitumor immunity after different routes of DC vaccination. Using a flank mouse model of PDAC, we found that the i.t. route of DC vaccination had no significant effect on tumor growth rates compared with i.p. and s.c. routes (i.p. 6.66 ± 2.58% vs s.c. 6.79 ± 1.36% vs i.t. 8.57 ± 2.36%; P = 0.33). However, in an orthotopic PDAC model, i.p. injection of DC vaccine effectively suppressed tumor growth, inhibited tumor progression, and increased antitumor immunity compared with s.c. vaccination (tumor weight: i.p. 71.60 ± 15.55 mg vs control 200.40 ± 53.04 mg; P = 0.048; s.c. 151.40 ± 41.64 mg vs control 200.40 ± 53.04 mg; P = 0.49). Our study suggests that immunization via an i.p. route results in superior antitumor immune response and tumor suppression when compared with other routes.

Dinaciclib is a small molecule cyclin-dependent kinase inhibitor with the potential to treat multiple cancers. To better understand its cytotoxic action in pancreatic ductal adenocarcinoma (PDAC), we evaluated dinaciclib therapeutic effects in the transgenic mouse model (LSL-KrasG12D/+ ; LSL-Trp53R172H/+ ; Pdx-1-Cre mice; KPC mice). Tumor growth and microenvironment were dynamically monitored by magnetic resonance imaging (MRI). Dinaciclib therapy significantly delayed tumor progression (P < 0.001) and prolonged survival (P = 0.007) in KPC mice. In vitro assays showed that dinaciclib exerted antiproliferative effects on PDAC cells by increasing surface calreticulin expression and release of ATP. Dinaciclib treatment inhibited proliferation and induced apoptosis in KPC tumor as assessed by Ki67 and cleaved caspase 3, respectively. Particularly, the tumor infiltrating CD8+ T cells were increased after dinaciclib treatment in KPC mice. Additionally, the mean apparent diffusion coefficient values of KPC tumor calculated from diffusion weighted MR images were significantly lower after dinaciclib treatment (P = 0.033). These finding suggest that dinaciclib as a single agent can inhibit tumor growth and improve the overall survival in KPC mice.

There is a lack of a well-established approach for assessment of early treatment outcomes for modern therapies for pancreatic ductal adenocarcinoma (PDAC) e.g. dinaciclib or dendritic cell (DC) vaccination. Here, we developed multivariate models using MRI texture features to detect treatment effects following dinaciclib drug or DC vaccine therapy in a transgenic mouse model of PDAC including 21 LSL-KrasG12D ; LSL-Trp53R172H ; Pdx-1-Cre (KPC) mice used as untreated control subjects (n=8) or treated with dinaciclib (n=7) or DC vaccine (n=6). Support vector machines (SVM) technique was performed to build a linear classifier with three variables for detection of tumor tissue changes following drug or vaccine treatments. Besides, multivariate regression models were generated with five variables to predict survival behavior and histopathological tumor markers (Fibrosis, CK19, and Ki67). The diagnostic performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUC) and decision curve analyses. The regression models were evaluated with adjusted r-squared (Radj 2). SVM classifier successfully distinguished changes in tumor tissue with an accuracy of 95.24% and AUC of 0.93. The multivariate models generated with five variables were strongly associated with histopathological tumor markers, fibrosis (Radj 2=0.82, P

PurposeIrreversible electroporation (IRE) is a promising new ablation method for hepatocellular carcinoma (HCC) treatment with few side-effects; however, tissue perfusion and differentiation between treatment zones have not been sufficiently studied. In this project, we analyzed HCC tumor perfusion changes immediately after IRE treatment using transcatheter intraarterial perfusion (TRIP)-MRI to monitor treatment zone margins.Materials and methodsAll protocols were approved by the institutional animal care and use committee. A total of 34 rabbits were used for this prospective study: tumor liver group (n=17), normal liver group (n=14), and 3 for growing VX2 tumors. All procedures and imaging were performed under anesthesia. VX2 tumors were grown by injection of VX2 cells into rabbit hindlimbs. Liver tumors were induced by percutaneous US-guided injection of VX2 tumor fragments into liver. For digital subtraction angiography (DSA), a 2F catheter was advanced through left hepatic artery via femoral artery access, followed by contrast injection. All rabbits underwent baseline anatomic MRI, then IRE procedure or IRE probe placement only, and lastly post-procedure anatomic and TRIP-MRI. Liver tissues were dissected immediately after imaging for histology. All statistical analyses were performed on GraphPad Prism, with P

The purpose of this study was to develop a radiomics signature for distinguishing stage in advanced colon cancer (CC). 195 colon cancer patients were enrolled in this study (stage III, n = 146 vs. stage IV, n = 49) and divided into training cohort (n = 136) and validation cohort (n = 59). A total of 286 radiomic features were extracted from tumor and LN images. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) technique. The relationship between radiomics signature and CC staging was explored using a kernel-based support vector machine (SVM) classifier model. The classification performance was assessed by accuracy and the receiver operating characteristics (ROC) curve. A total of 5 features (2 for tumor and 3 for LN) were selected among 286 features. Radiomics signature built from extracted features successfully differentiated stage III from stage IV CC with no known distant metastases on imaging preoperatively. Furthermore, the SVM classifier model generated using tumor and LN images together achieved better performance than the tumor alone, with accuracies of 86.03% vs. 78.68% and 83.05% vs. 76.27% in training and validation cohorts, respectively. In ROC analysis, the model showed a significant improvement for training (AUC 89.16% vs. 69.5%) and validation cohorts (AUC 75.15% vs. 55%) in comparison with the combined analysis and the tumor alone. In conclusion, the radiomics signature based on preoperative CT may distinguish stage III from stage IV CC with no known distant metastases. In addition, the radiomic features from combined images achieved better classification performance than tumor alone.

Natural killer (NK) cells play a pivotal role in host immunity against different malignancies, including pancreatic ductal adenocarcinoma (PDAC). Our study aimed to evaluate the antitumor effects of NK cell-based adoptive transfer immunotherapy for PDAC in an orthotopic mouse model. Orthotopic KrasLSL-G12D p53LSL-R172H Pdx1-Cre (KPC) mice were used to evaluate the therapeutic efficacy. Mouse NK cells (LNK cells) (1×106) were intravenously injected to tumor-bearing mice once a week for 3 weeks. MRI measurements (tumor volume and apparent diffusion coefficient (ADC) values) and survival were compared between control and LNK treated tumors. Flow cytometry and enzyme-linked immunosorbent assay (ELISA) were used to determine LNK cells cytotoxicity and IFN-γ level, respectively. LNK cells can produce a higher level of IFN-γ and more effectively lyse PDAC cells compared with spleen NK cells in vitro. LNK-cell adoptive transfer therapy elicited potent in vivo antitumor activity, resulting in delayed tumor growth (P=0.033) in KPC mice. The ADC values at the last timepoint ((0.94±0.06)×10-3 mm2/s) were significantly higher than that at first timepoint ((0.75±0.04)×10-3 mm2/s) in treated tumors (P

As the major thermogenic tissue in body, the brown adipose tissue (BAT) was recently identified as an important factor to induce the rapid weight loss and malnutrition in malignancy. Current methods for detecting and quantifying brown adipose tissue (BAT) are in limited use. The aim of this study was to evaluate the changes of BAT tissue and its function in the development of pancreatic ductal adenocarcinoma (PDAC) by using magnetic resonance imaging (MRI). Ten-week-old female C57BL/6 mice were inoculated orthotopically with Pan02 tumor cells. R2* maps and two-point Dixon MRI were performed weekly for evaluation of BAT function and volume, respectively. The T2-weighted MRI was applied weekly for monitoring tumor growth. Meanwhile, the body weight was measured daily as another indication of malnutrition. The UCP1 levels in BAT and white adipose tissue (WAT) were assessed. The serum IL-6 was also measured as the biomarker of cancer-associated cachexia. T2-weighted MRI indicated the rapid tumor growth from week 3 to week 5 after tumor cell inoculation. The water-fat separated MRI could clearly identify and quantify the BAT. The function and volume of BAT could be monitored by weekly MRI measurement in tumor-bearing mice. The total body weights of PDAC tumor-bearing mice were relatively stable, however, was significantly lower than that of control C57BL/6 mice. The results of this study demonstrated the feasibility of detection and quantification of BAT in vivo by MRI during the development of pancreatic cancer.

The purpose of our study was to investigate the hypothesis that DWI-MRI and DCE-MRI cab be used to distinguish between IRE and RE zones of IRE treatment in a rabbit liver model. 6 rabbits underwent baseline and post-procedure MR imaging with DWI and DCE-MRI as well as IRE (10 pulses, 2000 V, 10 µs/pulse, 10 ms between pulses). Rabbits were euthanized immediately after post-procedure MRI to acquire liver tissue for histology. Liver tissues were fixed and then stained with HE and TUNEL. T1w and T2w intensities in different treatment zones were calculated and normalized to paraspinal muscle signal. ADC maps were generated from DWI. AUC, PE, TTP, WIS, Ktrans, Kep, and VE were calculated from DCE-MRI. Apoptosis index was calculated from TUNEL stained tissues. P

The aim of this study was to develop and validate a new non-invasive artificial intelligence (AI) model based on preoperative computed tomography (CT) data to predict the presence of liver metastasis (LM) in colon cancer (CC). A total of forty-eight eligible CC patients were enrolled, including twenty-four patients with LM and twenty-four patients without LM. Six clinical factors and one hundred and fifty-two tumor image features extracted from CT data were utilized to develop three models: clinical, radiomics, and hybrid (a combination of clinical and radiomics features) using support vector machines with 5-fold cross-validation. The performance of each model was evaluated in terms of accuracy, specificity, sensitivity, and area under the curve (AUC). For the radiomics model, a total of four image features utilized to construct the model resulting in an accuracy of 83.87% for training and 79.50% for validation. The clinical model that employed two selected clinical variables had an accuracy of 69.82% and 69.50% for training and validation, respectively. The hybrid model that combined relevant image features and clinical variables improved accuracy of both training (90.63%) and validation (85.50%) sets. In terms of AUC, hybrid (0.96; 0.87) and radiomics models (0.91; 0.85) demonstrated a significant improvement compared with the clinical model (0.71; 0.69), and the hybrid model had the best prediction performance. In conclusion, the AI model developed using preoperative conventional CT data can accurately predict LM in CC patients without additional procedures. Furthermore, combining image features with clinical characteristics greatly improved the model's prediction performance. We have thus generated a promising tool that allows guidance and individualized surveillance of CC patients with high risks of LM.

The promise of dendritic cell (DC)-based immunotherapy has been established by two decades of translational research. However, long-term benefits of DC vaccination are reported in only scattered patients with pancreatic ductal adenocarcinoma (PDAC). Here we optimize DC vaccination and evaluate its safety and antitumor efficacy in the genetically engineered PDAC model (KrasLSL-G12D p53LSL-R172H Pdx-1-Cre (KPC mice)). KPC transgenic mice and orthotopic models using KPC cell lines were treated with DC vaccine via an intraperitoneal route. Tumor growth and microenvironment were dynamically monitored by magnetic resonance imaging (MRI). Histological analysis and flow cytometry were used to evaluate tumor-directed T cell immunity of these mice. DC vaccine via intraperitoneal injection suppressed tumor progression (P = 0.030) and significantly prolonged survival time (P = 0.028) in KPC mice. Vaccinated KPC mice displayed an increased antitumor T cell response indicated by a higher IFN-γ production (P = 0.016) and tumor-specific cytotoxicity (P = 0.027). Particularly, the mean apparent diffusion coefficient (ADC) values of KPC tumor calculated from diffusion weighted MRI (DW-MRI) were significantly higher in DC vaccine group than that in control group (P < 0.001). More interestingly, we observed that ADC positively correlated with fibrosis in KPC tumor (R2 = 0.463, P = 0.015). Our study demonstrated that the immunization with our improved DC vaccine can elicit a strong tumor-specific immune response and tumor suppression in PDAC.

While natural killer (NK) cell-based adoptive transfer immunotherapy (ATI) provides only modest clinical success in cancer patients. This study was hypothesized that MRI-guided transcatheter intra-hepatic arterial (IHA) infusion permits local delivery to liver tumors to improve outcomes during NK-based ATI in a rat model of hepatocellular carcinoma (HCC). Mouse NK cells were labeled with clinically applicable iron nanocomplexes. Twenty rat HCC models were assigned to three groups: transcatheter IHA saline infusion as the control group, transcatheter IHA NK infusion group, and intravenous (IV) NK infusion group. MRI studies were performed at baseline and at 24 h, 48 h, and 8 days postinfusion. There was a significant difference in tumor R2* values between baseline and 24 h following the selective transcatheter IHA NK delivery to the tumors (P = 0.039) when compared to IV NK infusion (P = 0.803). At 8 days postinfusion, there were significant differences in tumor volumes between the control, IV, and IHA NK infusion groups (control vs. IV, P = 0.196; control vs. IHA, P

Purpose To test the hypothesis that biomarkers of fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET) can be used for the early detection of therapeutic response to irreversible electroporation (IRE) of liver tumor in a rodent liver tumor model. Materials and Methods The institutional animal care and use committee approved this study. Rats were inoculated with McA-RH7777 liver tumor cells in the left median and left lateral lobes. Tumors were allowed to grow for 7 days to reach a size typically at least 5 mm in longest diameter, as verified with magnetic resonance (MR) imaging. IRE electrodes were inserted, and eight 100-μsec, 2000-V pulses were applied to ablate the tumor tissue in the left median lobe. Tumor in the left lateral lobe served as a control in each animal. PET/computed tomography (CT) and MR imaging measurements were performed at baseline and 3 days after IRE for each animal. Additional MR imaging measurements were obtained 14 days after IRE. After 14-day follow-up MR imaging, rats were euthanized and tumors harvested for hematoxylin-eosin, CD34, and caspase-3 staining. Change in the maximum standardized uptake value (ΔSUVmax) was calculated 3 days after IRE. The maximum lesion diameter change (ΔDmax) was measured 14 days after IRE by using axial T2-weighted imaging. ΔSUVmax and ΔDmax were compared. The apoptosis index was calculated by using caspase-3-stained slices of apoptotic tumor cells. Pearson correlation coefficients were calculated to assess the relationship between ΔSUVmax at 3 days and ΔDmax (or apoptosis index) at 14 days after IRE treatment. Results ΔSUVmax, ΔDmax, and apoptosis index significantly differed between treated and untreated tumors (P < .001 for all). In treated tumors, there was a strong correlation between ΔSUVmax 3 days after IRE and ΔDmax 14 days after IRE (R = 0.66, P = .01) and between ΔSUVmax 3 days after IRE and apoptosis index 14 days after IRE (R = 0.57, P = .04). Conclusion 18F-FDG PET imaging biomarkers can be used for the early detection of therapeutic response to IRE treatment of liver tumors in a rodent model. © RSNA, 2017.

Dendritic cell (DC) based immunotherapy is a promising approach for cancer treatment and has been approved in clinical settings for decades. Clinical trials have demonstrated relatively poor therapeutic efficacy. The efficacy of DC immunotherapy is strongly influenced by their ability to migrate to the draining lymph nodes (LNs). Therefore, it is critical to deliver DCs and monitor the in vivo biodistributions of DCs after administration. The purpose of this study is to determine whether a novel injection route of DCs improves DC migration to LNs, tissues, organs and lymphatics. In the present study, a modified method was investigated to acquire DCs from mouse bone marrow. Cultured antibody labeled DCs were analyzed by flow cytometry. India ink was used to visualize mouse abdominal LNs and PKH26 was utilized to label DCs for intraperitoneal (IP) injection, results were evaluated by histology. Our results showed that large amounts of DCs with a relatively high purity were acquired. IP injection of india ink marked the abdominal LNs and PKH26 labeled DCs showed IP was an effective administration route to increase the absorption of viable DCs, and different time points after IP inject showed no significant difference of the migrated DCs. The findings indicated that large amounts of high purity DCs can be acquired through our method and IP injection accelerates DCs migration to abdominal LNs, which can be directly translated to clinical settings, especially for abdominal cancers. This study makes a foundation for future researches of DC-based immunotherapy as a treatment modality against cancer.

BackgroundLiver cancer makes up a huge percentage of cancer mortality worldwide. Irreversible electroporation (IRE) is a relatively new minimally invasive nonthermal ablation technique for tumors that applies short pulses of high frequency electrical energy to irreversibly destabilize cell membrane to induce tumor cell apoptosis.MethodsThis review aims to investigate the studies regarding the use of IRE treatment in liver tumors and metastases to liver. We searched PubMed for all of IRE relevant English language articles published up to September 2016. They included clinical trials, experimental studies, observational studies, and reviews. This review manuscript is nothing with ethics issues and ethical approval is not provided.ResultsIn recent years, increasingly more studies in both preclinical and clinical settings have been conducted to examine the safety and efficacy of this new technique, shedding light on the crucial advantages and disadvantages that IRE possesses. Unlike the current leading thermal ablation techniques, such as radiofrequency ablation (RFA), microwave ablation (MWA), and cryoablation, IRE requires shorter ablation time without damaging adjacent important vital structures.ConclusionAlthough IRE has successfully claimed its valuable status in the field of hepatic cancer treatment both preclinical and clinical settings. In order to systemically test and establish its safety and efficacy for clinical applications, more studies still need to be conducted.

Irreversible electroporation (IRE) is a relatively new technique for tumor ablation. It has shown promising results in difficult cases where surgery is not recommended and delicate anatomic structures are present near or within the tumor. Currently, liver cancer is one of the most common targets for IRE treatment. Pre-operative and post-operative imaging has a key role in IRE procedures and research studies. Although ultrasound is usually the first choice, especially for intra-operative guidance, magnetic resonance imaging (MRI) plays an important role in the visualization and characterization of tumor before and after IRE in clinical and preclinical studies. However, the appearance of liver lesions after IRE with different MRI sequences has never been systematically investigated, and the most common practice is to limit the acquisition protocol to only contrast-enhanced T1-weighted images. In this work, the role of MRI in clinical and preclinical assessment of hepatic tumors treated with IRE is reviewed and discussed.

In recent decades, immunotherapy has undergone extensive developments for oncologic therapy applications. Dendritic cells (DCs) plays a fundamental role in cell-based vaccination immunotherapy against various types of cancer. It involves stimulating innate and adaptive immunity, in particular cytotoxic T-cell mediated antitumor effects, against targeted tumors and has been studied in both preclinical and clinical settings. Nevertheless, clinical outcomes have been unsatisfying. The antitumor response requires sufficient migration of viable DCs from primary administration site to targeted tumors through related lymphatics. The dynamics and mechanisms of the DCs migration still need further investigation. Here, we briefly introduce the current clinically applicable methods for manufacturing DC-based cancer vaccines and their most commonly used non-invasive, real-time tracking approaches. Furthermore, we propose a hypothesis that intraperitoneal injection may improve the efficiency of DC-based cancer vaccine.