Sorafenib plus memory like natural killer cell combination therapy in hepatocellular carcinoma.

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.

Self-Guided Algorithm for Fast Image Reconstruction in Photo-Magnetic Imaging: Artificial Intelligence-Assisted Approach.

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.

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

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.

Identification of Myocardial Scarring Using Contrast-Free Cardiac MRI in Patients With Autoimmune Rheumatic Diseases.

BACKGROUND: Late gadolinium enhancement (LGE) cardiac MRI is the method of choice in revealing the presence of myocardial scarring, but its availability remains limited in clinical practice. PURPOSE: To assess myocardial scarring in patients with autoimmune rheumatic diseases (ARDs) using contrast-free cardiac MRI with a radiomics model. STUDY TYPE: Retrospective. POPULATION: One hundred ninety-two patients (mean age, 41 years ± 15, 62 men) with or without ARDs, grouped into a training set of 153 patients and a testing set of 39 patients. FIELD STRENGTH/SEQUENCE: 3.0 T/ cine imaging with a balanced steady-state free precession sequence, T1 mapping with a modified Look-Locker inversion recovery sequence, and LGE imaging with a phase-sensitive inversion recovery gradient echo sequence. ASSESSMENT: LGE assessment was the reference standard for identifying myocardial scarring. Based on motion features extracted from cine images and tissue characterization features extracted from native T1 maps, a fully automated radiomics model with T1, cine MRI, or combined inputs was developed. STATISTICAL TESTS: Logistic regression model was used to detect myocardial scarring using contrast-free cardiac MRI parameters. Receiver operating characteristic curves were analyzed to assess the accuracy, sensitivity, and specificity in detecting myocardial scarring. Sensitivities of the models were further assessed in patients with various myocardial scarring proportions. Z-statistic and dice coefficient were assessed to compare the performance. P-values <0.05 were considered significant. RESULTS: The multivariable regression model exhibited an accuracy of 85.3%, a sensitivity of 93.5%, and a specificity of 50.0%. The radiomics model with T1 and cine MRI input exhibited an accuracy of 75.7%, a sensitivity of 60.9%, and a specificity of 85.5%. Moreover, the radiomics model showed a sensitivity of 90.9% among patients with >25% myocardial scarring. DATA CONCLUSIONS: The proposed radiomics model allowed for the identification of myocardial scarring similar to LGE, but on contrast-free cardiac MRI in patients with ARDs. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

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.

Adverse events of sorafenib in hepatocellular carcinoma treatment.

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.

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.

Combination of natural killer cell-based immunotherapy and irreversible electroporation for the treatment of hepatocellular carcinoma.

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.

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.

Combination of NK-based immunotherapy and sorafenib against hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most frequent malignancy of the liver, which is considered the fourth leading cause of cancer-related death in the United States. Liver transplant and surgical resection are curative treatments for HCC, but only 10-15% of HCC patients are eligible candidates. The FDA-approved sorafenib is a multi-kinase inhibitor systemic therapy for advanced HCC that extends the overall survival by over 3 months when compared with placebo. Adoptive transfer of Natural Killer (NK) cells holds great promise for clinical cancer treatment. However, only limited clinical benefit has been achieved in cancer patients. Therefore, there is currently considerable interest in development of the combination of sorafenib and NK cells for the treatment of HCC patients. However, the mechanism of how sorafenib affects the function of NK cells remains to be comprehensively clarified. In this paper, we will discuss NK cell-based immunotherapies that are currently under preclinical and clinical investigation and its potential combination with sorafenib for improving the survival of HCC patients.

Strategies to improve sorafenib efficacy during image-guided treatment of hepatocellular carcinoma.

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.

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.

Using MRI to quantify skeletal muscle pathology in Duchenne muscular dystrophy: A systematic mapping review.

There is a great demand for accurate non-invasive measures to better define the natural history of disease progression or treatment outcome in Duchenne muscular dystrophy (DMD) and to facilitate the inclusion of a large range of participants in DMD clinical trials. This review aims to investigate which MRI sequences and analysis methods have been used and to identify future needs. Medline, Embase, Scopus, Web of Science, Inspec, and Compendex databases were searched up to 2 November 2019, using keywords "magnetic resonance imaging" and "Duchenne muscular dystrophy." The review showed the trend of using T1w and T2w MRI images for semi-qualitative inspection of structural alterations of DMD muscle using a diversity of grading scales, with increasing use of T2map, Dixon, and MR spectroscopy (MRS). High-field (>3T) MRI dominated the studies with animal models. The quantitative MRI techniques have allowed a more precise estimation of local or generalized disease severity. Longitudinal studies assessing the effect of an intervention have also become more prominent, in both clinical and animal model subjects. Quality assessment of the included longitudinal studies was performed using the Newcastle-Ottawa Quality Assessment Scale adapted to comprise bias in selection, comparability, exposure, and outcome. Additional large clinical trials are needed to consolidate research using MRI as a biomarker in DMD and to validate findings against established gold standards. This future work should use a multiparametric and quantitative MRI acquisition protocol, assess the repeatability of measurements, and correlate findings to histologic parameters.

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.

Intraprocedural Transcatheter Intraarterial Perfusion (TRIP)-MRI for Evaluation of Irreversible Electroporation Therapy Response in a Rabbit Liver Tumor Model.

PURPOSE: Irreversible 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 METHODS: All 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<0.05 considered significant. RESULTS: IRE generated central IRE zone and peripheral reversible electroporation (RE) zone on anatomic MRI for both normal liver and liver tumor tissues. The semiquantitative analysis showed that IRE zone had the lowest AUC, PE, WIS, Ktrans, ve , and vp as well as the highest TTP, followed by RE zone, then untreated tissues. Receiver operating characteristic analysis showed that WIS and AUC60 had the highest AUCROC. Histologic analysis showed a positive correlation in viable area fraction between MRI and histologic measurements. IRE zone had the highest %apoptosis and lowest CD31+ staining. CONCLUSION: Our results demonstrated that intraprocedural TRIP-MRI can effectively differentiate IRE and RE zones after IRE ablation in normal liver and liver tumor tissues.

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.