Early assessment of irreversible electroporation ablation outcomes by analyzing MRI texture: preclinical study in an animal model of liver tumor.

OBJECTIVES: Accurate 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. METHODS: The 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. RESULTS: Following 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. CONCLUSIONS: Quantitative 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.

Early Differentiation of Irreversible Electroporation Ablation Regions With Radiomics Features of Conventional MRI.

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.

Irreversible electroporation ablation overcomes tumor-associated immunosuppression to improve the efficacy of DC vaccination in a mice model of pancreatic cancer.

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 < .001); tumor ΔADC value positively correlated with tumor fibrosis fraction (R = 0.707, P < .001). IRE induced immunogenic cell death and alleviation of immunosuppressive components in PDAC TME when combined with DC vaccination, including increased tumor infiltration of CD8+ T cells and Granzyme B+ cells (P = .001, and P = .007, respectively). Our data show that IRE ablation can overcome TME immunosuppression to improve the efficacy of DC vaccination in PDAC. Combination IRE ablation and DC vaccination may enhance therapeutic efficacy for PDAC.

Detection of Immunotherapeutic Response in a Transgenic Mouse Model of Pancreatic Ductal Adenocarcinoma Using Multiparametric MRI Radiomics: A Preliminary Investigation.

RATIONALE AND OBJECTIVES: To 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 METHODS: Eight 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. RESULTS: The 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) <6 days. The proposed multivariate regression models predicted histological tumor markers with relative error <2.5% for fibrosis percentage (RMSE: 0.414), CK19+ area (RMSE: 0.027), and Ki67+ cells (RMSE: 0.190). CONCLUSION: Our results demonstrated that proposed models generated with quantitative MRI features can be used to detect early treatment-related changes in tumor tissue and predict OS of KPC mice following DC vaccination.

Effect of route of administration on the efficacy of dendritic cell vaccine in PDAC mice.

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.

Erratum: Magnetic resonance imaging monitoring therapeutic response to dendritic cell vaccine in murine orthotopic pancreatic cancer models.

[This corrects the article on p. 562 in vol. 9, PMID: 30949410.].

Preoperative prediction of perineural invasion and KRAS mutation in colon cancer using machine learning.

PURPOSE: Preoperative prediction of perineural invasion (PNI) and Kirsten RAS (KRAS) mutation in colon cancer is critical for treatment planning and patient management. We developed machine learning models for diagnosis of PNI and KRAS mutation in colon cancer patients by interpreting preoperative CT. METHODS: This retrospective study included 207 patients who received surgical resection in our institution. The underlying tumor characteristics were described by analyzing CT image texture quantitatively. The key radiomics features were determined with similarity analysis followed by RELIEFF method among 306 CT imaging features. Eight kernel-based support vector machines classifiers were constructed using individual (II, III, or IV) or multi-stage (II + III + IV) patient cohorts for predicting PNI and KRAS mutation. The model performances were evaluated using accuracy, receiver operating curve, and decision curve analyses. RESULTS: Multi-stage classifiers obtained AUC of 0.793 and 0.862 for detecting PNI and KRAS mutation for test cohort. Moreover, individual-stage classifiers demonstrated significantly improved diagnostic performance at all stages (IIAUC: [0.86; 0.99], IIIAUC: [0.99; 0.99], and IVAUC: [1.00; 1.00], respectively, for PNI and KRAS mutation in test cohort). Besides, stage II tumor is better described with coarse texture features while more detailed features are required for better characterization of advanced-stage tumors (III and IV) for diagnoses of PNI or KRAS mutation. CONCLUSION: Machine learning models developed using preoperative CT data can predict PNI and KRAS mutation in colon cancer patients with satisfactory performance. Individual-stage models better-characterized the relationship between CT features and PNI or KRAS mutation than multi-stage models and demonstrated good prediction scores.

Prediction of therapeutic outcome and survival in a transgenic mouse model of pancreatic ductal adenocarcinoma treated with dendritic cell vaccination or CDK inhibitor using MRI texture: a feasibility study.

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<0.001), CK19 (Radj 2=0.92, P<0.001) and Ki67 (Radj 2=0.97, P<0.001). Furthermore, the multivariate regression model successfully predicted survival of KPC mice by interpreting tumor characteristics from MRI data (Radj 2=0.91, P<0.001). The results demonstrated that MRI texture features had great potential to generate diagnosis and prognosis models for monitoring early treatment response following dinaciclib drug or DC vaccine treatment and also predicting histopathological tumor markers and long-term clinical outcomes.

Dinaciclib prolongs survival in the LSL-KrasG12D/+ ; LSL-Trp53R172H/+ ; Pdx-1-Cre (KPC) transgenic murine models of pancreatic ductal adenocarcinoma.

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.

Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study.

BACKGROUND: Preoperative 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. METHODS: A 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). RESULTS: The 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 < 0.02) according to the DeLong method. CONCLUSIONS: The texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.

Transcatheter Intraarterial Perfusion MRI Approaches to Differentiate Reversibly Electroporated Penumbra From Irreversibly Electroporated Zones in Rabbit Liver.

RATIONALE AND OBJECTIVES: To 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 METHODS: All 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. RESULTS: PE, 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). CONCLUSION: AUT150 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.

MRI radiomics for early prediction of response to vaccine therapy in a transgenic mouse model of pancreatic ductal adenocarcinoma.

BACKGROUND: There 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 METHODS: KPC 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. RESULTS: OS 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 < 0.001). Additionally, tissue-specific MRI texture features showed positive association with fibrosis percentage (r = 0.84, p < 0.002), CK19 positive percentage (r = - 0.97, p < 0.001), and Ki67 positive cells (r = 0.81, p < 0.02) as histological disease biomarkers. CONCLUSION: Our results demonstrate that MRI texture features can be used as imaging biomarkers for early detection of therapeutic response following DC vaccination in the KPC mouse model of PDAC. Besides, MRI texture can be utilized to characterize tumor microenvironment reflected with histology analysis.

Transcatheter intra-arterial perfusion (TRIP)-MRI biomarkers help detect immediate response to irreversible electroporation of rabbit VX2 liver tumor.

PURPOSE: Irreversible 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. METHODS: Seventeen 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. RESULTS: There 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). CONCLUSION: TRIP-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.

Dendritic cell immunotherapy induces anti-tumor effect in a transgenic mouse model of pancreatic ductal adenocarcinoma.

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.

Establishment of a new non-invasive imaging prediction model for liver metastasis in colon cancer.

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.

Dendritic cells in pancreatic cancer immunotherapy: Vaccines and combination immunotherapies.

Despite significant advances over the past decades of research, pancreatic cancer (PC) continues to have the worst 5-year survival of any malignancy. Dendritic cells (DCs) are the most potent professional antigen-presenting cells and are involved in the induction and regulation of antitumor immune responses. DC-based immunotherapy has been used in clinical trials for PC. Although safety, efficacy, and immune activation were reported in patients with PC, DC vaccines have not yet fulfilled their promise. Additional strategies for combinatorial approaches aimed to augment and sustain the antitumor specific immune response elicited by DC vaccines are currently being investigated. Here, we will discuss DC vaccination immunotherapies that are currently under preclinical and clinical investigation and potential combination approaches for treating and improving the survival of PC patients.

Diffusion-Weighted MR Imaging to Evaluate Immediate Response to Irreversible Electroporation in a Rabbit VX2 Liver Tumor Model.

PURPOSE: To evaluate the feasibility of diffusion-weighted imaging (DWI) in magnetic resonance imaging for quantitative measurement of responses following irreversible electroporation (IRE) in a rabbit liver tumor model. MATERIALS AND METHODS: Twelve rabbits underwent ultrasound-guided VX2 tumor implantation in the left medial and left lateral liver lobes. The tumors in the left medial lobe were treated with IRE, whereas those in the left lateral lobe served as internal controls. DWI was performed before and immediately after IRE. Tumors were then harvested for histopathologic staining. The apparent diffusion coefficient (ADC) and change in ADC (ΔADC) were calculated based on DWI. Tumor apoptosis index (AI) was assessed by terminal deoxynucleotidyl transferase dUTP nick-end labeling. These measurements from DWI and histopathology were compared between untreated and treated tumors. RESULTS: The ADC values, ΔADC, and AI showed statistically significant differences between treated and untreated tumors (P < .05 for all). ADC values were higher in treated tumors than in untreated tumors (1.08 × 10-3 mm2/s ± 0.15 vs 0.88 × 10-3 mm2/s ± 0.19; P = .042). CONCLUSIONS: DWI can be used to quantitatively evaluate treatment response in liver tumors immediately after IRE.

Natural killer cell-based adoptive transfer immunotherapy for pancreatic ductal adenocarcinoma in a KrasLSL-G12D p53LSL-R172H Pdx1-Cre mouse model.

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<0.001). ADC values were significantly different between control group and treated tumors at the last time point ((0.75±0.09)×10-3 mm2/s vs (0.94±0.06)×10-3 mm2/s, P=0.004) in KPC mice. Our data demonstrate the potential of NK cell-based adoptive transfer immunotherapy for PDAC treatment.

Radiomics signature for the preoperative assessment of stage in advanced colon cancer.

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.

MRI Assessment of Associations between Brown Adipose Tissue and Cachexia in Murine Pancreatic Ductal Adenocarcinoma.

OBJECTIVE: 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). METHODS: 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. RESULTS: 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. CONCLUSION: The results of this study demonstrated the feasibility of detection and quantification of BAT in vivo by MRI during the development of pancreatic cancer.