User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions

PurposePrecision cancer medicine is dependent on accurate prediction of disease and treatment outcome, requiring integration of clinical, imaging and interventional knowledge. User controlled pipelines are capable of feature integration with varied levels of human interaction. In this work we present two pipelines designed to combine clinical, radiomic (quantified imaging), and RTx-omic (quantified radiation therapy (RT) plan) information for prediction of locoregional failure (LRF) in head and neck cancer (H&N).MethodsPipelines were designed to extract information and model patient outcomes based on clinical features, computed tomography (CT) imaging, and planned RT dose volumes. We predict H&N LRF using: 1) a highly user-driven pipeline that leverages modular design and machine learning for feature extraction and model development; and 2) a pipeline with minimal user input that utilizes deep learning convolutional neural networks to extract and combine CT imaging, RT dose and clinical features for model development.ResultsClinical features with logistic regression in our highly user-driven pipeline had the highest precision recall area under the curve (PR-AUC) of 0.66 (0.33–0.93), where a PR-AUC = 0.11 is considered random. CONCLUSIONS: Our work demonstrates the potential to aggregate features from multiple specialties for conditional-outcome predictions using pipelines with varied levels of human interaction. Most importantly, our results provide insights into the importance of data curation and quality, as well as user, data and methodology bias awareness as it pertains to result interpretation in user controlled pipelines.     

Influence of adjuvant radiotherapy on circulating epithelial tumor cells and circulating cancer stem cells in primary non-metastatic breast cancer

Background: There is an unmet need to identify biomarkers that directly reflect response to adjuvant radiotherapy (RT). Circulating epithelial tumor cells (CETCs) represent the liquid component of solid tumors and are responsible for metastatic relapse. CETC subsets with cancer stem cell characteristics, circulating cancer stem cells (cCSCs), play a pivotal role in the metastatic cascade. Monitoring the most aggressive subpopulation of CETCs could reflect the aggressiveness of the remaining tumor burden. There is limited data on the detection and monitoring changes in CETC and cCSC numbers during RT in early breast cancer.Methods: CETC numbers were analyzed prior to, at midterm and at the end of RT in 52 primary non-metastatic breast cancer patients. Hormone receptor status was determined in CETCs prior to and at the end of RT. For the identification of cCSCs cell suspensions from the peripheral blood of patients were cultured in vitro under conditions favoring growth of tumorspheres.Results: Hormone receptor status in CETCs before RT was comparable to that in primary tumor tissue. Prior to RT numbers of CETCs correlated with aggressiveness of primary tumors. cCSCs could be successfully identified and monitored during RT. Prior to RT patients treated with neoadjuvant chemotherapy had significantly higher numbers of CETCs and tumorspheres compared to patients after adjuvant chemotherapy. During RT, the number of CETCs decreased continuously in patients after neoadjuvant chemotherapy but not after adjuvant chemotherapy.Conclusion: Monitoring the number of CETCs and the CETC subset with cancer stem cell properties during RT may provide additional clinically useful prognostic information.     

The double face of cold in cancer

In a recent paper published in Nature, multiple evidence is provided that cold exposure causes tumor growth restriction in mice, by activating brown adipose tissue metabolism and by subsequent cancer cells’ glucose starvation. The paper shows a tumor growth inhibition by 80% for multiple cancer types in mice exposed to 4 °C in comparison with mice exposed to 30 °C. These results are very promising since cost effective protocols could be designed for future clinical trials, for several cancer forms. In this commentary, an extensive analysis is performed on the potential of these results. Some previous published studies are discussed as well, showing differences in tumor growth for mice housed in different external temperatures.     

Improved prediction of radiation pneumonitis by combining biological and radiobiological parameters using a data-driven Bayesian network analysis

Grade 2 and higher radiation pneumonitis (RP2) is a potentially fatal toxicity that limits efficacy of radiation therapy (RT). We wished to identify a combined biomarker signature of circulating miRNAs and cytokines which, along with radiobiological and clinical parameters, may better predict a targetable RP2 pathway. In a prospective clinical trial of response-adapted RT for patients (n = 39) with locally advanced non-small cell lung cancer, we analyzed patients’ plasma, collected pre- and during RT, for microRNAs (miRNAs) and cytokines using array and multiplex enzyme linked immunosorbent assay (ELISA), respectively. Interactions between candidate biomarkers, radiobiological, and clinical parameters were analyzed using data-driven Bayesian network (DD-BN) analysis. We identified alterations in specific miRNAs (miR-532, -99b and -495, let-7c, -451 and -139-3p) correlating with lung toxicity. High levels of soluble tumor necrosis factor alpha receptor 1 (sTNFR1) were detected in a majority of lung cancer patients. However, among RP patients, within 2 weeks of RT initiation, we noted a trend of temporary decline in sTNFR1 (a physiological scavenger of TNFα) and ADAM17 (a shedding protease that cleaves both membrane-bound TNFα and TNFR1) levels. Cytokine signature identified activation of inflammatory pathway. Using DD-BN we combined miRNA and cytokine data along with generalized equivalent uniform dose (gEUD) to identify pathways with better accuracy of predicting RP2 as compared to either miRNA or cytokines alone. This signature suggests that activation of the TNFα-NFκB inflammatory pathway plays a key role in RP which could be specifically ameliorated by etanercept rather than current therapy of non-specific leukotoxic corticosteroids.     

Increased local vascular endothelial growth factor expression associated with antitumor activity of proteasome inhibitor

Inhibition of the proteasome, a multicatalytic proteinase complex, is an attractive approach to cancer therapy. Here we report that a selective inhibitor of the chymotrypsin-like activity of the proteasome, PSI (N-benzyloxycarbonyl-Ile-Glu(O-t-butyl)-Ala-leucinal) may inhibit growth of solid tumors not only through apoptosis induction, but also indirectly--through inhibition of angiogenesis. Two murine tumors: colon adenocarcinoma (C-26) and Lewis lung carcinoma (3LL) were chosen to study the antitumor effect of PSI. In an in vivo model of local tumor growth, PSI exerted significant antitumor effects against C-26 colon carcinoma, but not against 3LL lung carcinoma. Retardation of tumor growth was observed in mice treated with both 10 nmoles and 100 nmoles doses of PSI and in the latter group prolongation of the survival time of tumor-bearing mice was observed. PSI inhibited angiogenesis in the C-26 growing tumors with no such effect in 3LL tumors. Unexpectedly, that activity was associated with upregulation of vascular endothelial growth factor (VEGF) at the level of mRNA expression and protein production in C-26 tumors treated with PSI. C-26 cells treated with PSI produced increased amounts of VEGF in vitro in a dose- and time-dependent manner. We demonstrated that in C-26 colon adenocarcionoma higher VEGF production may render endothelial cells susceptible to the proapoptotic activity of PSI and is associated with inhibition of tumor growth.     

Combined immunotherapy with Listeria monocytogenes-based PSA vaccine and radiation therapy leads to a therapeutic response in a murine model of prostate cancer

Radiation therapy (RT) is an integral part of prostate cancer treatment across all stages and risk groups. Immunotherapy using a live, attenuated, Listeria monocytogenes-based vaccines have been shown previously to be highly efficient in stimulating anti-tumor responses to impact on the growth of established tumors in different tumor models. Here, we evaluated the combination of RT and immunotherapy using Listeria monocytogenes-based vaccine (ADXS31-142) in a mouse model of prostate cancer. Mice bearing PSA-expressing TPSA23 tumor were divided to 5 groups receiving no treatment, ADXS31-142, RT (10?Gy), control Listeria vector and combination of ADXS31-142 and RT. Tumor growth curve was generated by measuring the tumor volume biweekly. Tumor tissue, spleen, and sera were harvested from each group for IFN-?? ELISpot, intracellular cytokine assay, tetramer analysis, and immunofluorescence staining. There was a significant tumor growth delay in mice that received combined ADXS31-142 and RT treatment as compared with mice of other cohorts and this combined treatment causes complete regression of their established tumors in 60?% of the mice. ELISpot and immunohistochemistry of CD8+?cytotoxic T Lymphocytes (CTL) showed a significant increase in IFN-?? production in mice with combined treatment. Tetramer analysis showed a fourfold and a greater than 16-fold increase in PSA-specific CTLs in animals receiving ADXS31-142 alone and combination treatment, respectively. A similar increase in infiltration of CTLs was observed in the tumor tissues. Combination therapy with RT and Listeria PSA vaccine causes significant tumor regression by augmenting PSA-specific immune response and it could serve as a potential treatment regimen for prostate cancer.     

18F-FDG Micro PET/CT imaging to evaluate the effect of BRCA1 knockdown on MDA-MB231 breast cancer cell radiosensitivity

ObjectiveRadioresistance of tumor cells is a major factor associated with failure of radiotherapy (RT). This study aimed to investigate the effect of BRCA1 knockdown on MDA-MB231 breast cancer cell radiosensitivity.Materials and methodsShort hairpin RNA (shRNA) was used to knockdown BRCA1 gene in MDA-MB231 cells. Cell viability and proliferative capacity were assessed by CCK-8 and colony formation assays, respectively. We established xenograft models in nude mice to evaluate tumor volume and tumor weight. The mice were imaged by ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) before and after RT to evaluate changes in maximum standardized uptake value (SUV_max) and tumor SUV_max/muscle SUV_max (TMR). Changes in HIF-1α, Glut-1 and Ki-67 were analyzed and the correlation between ¹⁸F-FDG uptake and tumor biology was analyzed.ResultsCompared with the control cells, RT significantly reduced cell viability and colony formation capacity in cells with the BRCA1 gene knockdown. In vivo assays showed that there was obvious delay in the tumor growth in the shBRCA1+RT group compared with the control group. ¹⁸F-FDG Micro PET/CT indicated a reduction in glucose metabolism in the shBRCA1+RT group, with statistically significant differences in both the SUV_max and TMR. The data showed the expression of HIF-1α, Glut-1 and Ki-67 was downregulated in the shBRCA1+RT group, and both SUVmax and TMR had significant correlation with tumor biology.ConclusionThese results demonstrated that BRCA1 knockdown improves the sensitivity of MDA-MB231 breast cancer cells to RT. In addition, ¹⁸F-FDG PET/CT imaging allows non-invasive analysis of tumor biology and assessment of radiosensitivity.     

The PSI semantic validator: A framework to check MIAPE compliance of proteomics data

The Human Proteome Organization's Proteomics Standards Initiative (PSI) promotes the development of exchange standards to improve data integration and interoperability. PSI specifies the suitable level of detail required when reporting a proteomics experiment (via the Minimum Information About a Proteomics Experiment), and provides extensible markup language (XML) exchange formats and dedicated controlled vocabularies (CVs) that must be combined to generate a standard compliant document. The framework presented here tackles the issue of checking that experimental data reported using a specific format, CVs and public bio‐ontologies (e.g. Gene Ontology, NCBI taxonomy) are compliant with the Minimum Information About a Proteomics Experiment recommendations. The semantic validator not only checks the XML syntax but it also enforces rules regarding the use of an ontology class or CV terms by checking that the terms exist in the resource and that they are used in the correct location of a document. Moreover, this framework is extremely fast, even on sizable data files, and flexible, as it can be adapted to any standard by customizing the parameters it requires: an XML Schema Definition, one or more CVs or ontologies, and a mapping file describing in a formal way how the semantic resources and the format are interrelated. As such, the validator provides a general solution to the common problem in data exchange: how to validate the correct usage of a data standard beyond simple XML Schema Definition validation. The framework source code and its various applications can be found at http://psidev.info/validator .     

Combined metformin-salicylate treatment provides improved anti-tumor activity and enhanced radiotherapy response in prostate cancer; drug synergy at clinically relevant doses

BackgroundThere is need for well-tolerated therapies for prostate cancer (PrCa) secondary prevention and to improve response to radiotherapy (RT). The anti-diabetic agent metformin (MET) and the aspirin metabolite salicylate (SAL) are shown to activate AMP-activated protein kinase (AMPK), suppress de novo lipogenesis (DNL), the mammalian target of rapamycin (mTOR) pathway and reduce PrCa proliferation in-vitro. The purpose of this study was to examine whether combined MET+SAL treatment could provide enhanced PrCa tumor suppression and improve response to RT.MethodsAndrogen-sensitive (22RV1) and resistant (PC3, DU-145) PrCa cells and PC3 xenografts were used to examine whether combined treatment with MET+SAL can provide improved anti-tumor activity compared to each agent alone in non-irradiated and irradiated PrCa cells and tumors. Mechanisms of action were investigated with analysis of signaling events, mitochondria respiration and DNL activity assays.ResultsWe observed that PrCa cells are resistant to clinically relevant doses of MET. Combined MET + SAL treatment provides synergistic anti-proliferative activity at clinically relevant doses and enhances the anti-proliferative effects of RT. This was associated with suppression of oxygen consumption rate (OCR), activation of AMPK, suppression of acetyl-CoA carboxylase (ACC)-DNL and mTOR-p70^s6k/4EBP1 and HIF1α pathways. MET + SAL reduced tumor growth in non-irradiated tumors and enhanced the effects of RT.ConclusionMET+SAL treatment suppresses PrCa cell proliferation and tumor growth and enhances responses to RT at clinically relevant doses. Since MET and SAL are safe, widely-used and inexpensive agents, these data support the investigation of MET+SAL in PrCa clinical trials alone and in combination with RT.     

A triphasic constrained mixture model of engineered tissue formation under in vitro dynamic mechanical conditioning

While it has become axiomatic that mechanical signals promote in vitro engineered tissue formation, the underlying mechanisms remain largely unknown. Moreover, efforts to date to determine parameters for optimal extracellular matrix (ECM) development have been largely empirical. In the present work, we propose a two-pronged approach involving novel theoretical developments coupled with key experimental data to develop better mechanistic understanding of growth and development of dense connective tissue under mechanical stimuli. To describe cellular proliferation and ECM synthesis that occur at rates of days to weeks, we employ mixture theory to model the construct constituents as a nutrient-cell-ECM triphasic system, their transport, and their biochemical reactions. Dynamic conditioning protocols with frequencies around 1 Hz are described with multi-scale methods to couple the dissimilar time scales. Enhancement of nutrient transport due to pore fluid advection is upscaled into the growth model, and the spatially dependent ECM distribution describes the evolving poroelastic characteristics of the scaffold-engineered tissue construct. Simulation results compared favorably to the existing experimental data, and most importantly, distinguish between static and dynamic conditioning regimes. The theoretical framework for mechanically conditioned tissue engineering (TE) permits not only the formulation of novel and better-informed mechanistic hypothesis describing the phenomena underlying TE growth and development, but also the exploration/optimization of conditioning protocols in a rational manner.     

Enzastaurin,an inhibitor of PKCβ, Enhances Antiangiogenic Effects and Cytotoxicity of Radiation against Endothelial Cells

PURPOSE: Angiogenesis plays an important role in pancreas cancer pathobiology. Pancreatic tumor cells secrete vascular endothelial growth factor (VEGF), activating endothelial cell protein kinase C beta (PKCβ) that phosphorylates GSK3β to suppress apoptosis and promote endothelial cell proliferation and microvessel formation. We used Enzastaurin (Enz) to test the hypothesis that inhibition of PKCβ results in radiosensitization of endothelial cells in culture and in vivo. MATERIALS/METHODS: We measured PKCβ phosphorylation, VEGF pathway signaling, colony formation, and capillary sprout formation in primary human dermal microvessel endothelial cells (HDMECs) after Enz or radiation (RT) treatment. Microvessel density and tumor volume of human pancreatic cancer xenografts in nude mice were measured after treatment with Enz, RT, or both. RESULTS: Enz inhibited PKCβ and radiosensitized HDMEC with an enhancement ratio of 1.31 ± 0.05. Enz combined with RT reduced HDMEC capillary sprouting to a greater extent than either agent alone. Enz prevented radiation-induced GSK3β phosphorylation of serine 9 while having no direct effect on VEGFR phosphorylation. Treatment of xenografts with Enz and radiation produced greater reductions in microvessel density than either treatment alone. The reduction in microvessel density corresponded with increased tumor growth delay. CONCLUSIONS: Enz-induced PKCβ inhibition radiosensitizes human endothelial cells and enhances the antiangiogenic effects of RT. The combination of Enz and RT reduced microvessel density and resulted in increased growth delay in pancreatic cancer xenografts, without increase in toxicity. These results provide the rationale for combining PKCβ inhibition with radiation and further investigating such regimens in pancreatic cancer.     

Predicting the growth of glioblastoma multiforme spheroids using a multiphase porous media model

Tumor spheroids constitute an effective in vitro tool to investigate the avascular stage of tumor growth. These three-dimensional cell aggregates reproduce the nutrient and proliferation gradients found in the early stages of cancer and can be grown with a strict control of their environmental conditions. In the last years, new experimental techniques have been developed to determine the effect of mechanical stress on the growth of tumor spheroids. These studies report a reduction in cell proliferation as a function of increasingly applied stress on the surface of the spheroids. This work presents a specialization for tumor spheroid growth of a previous more general multiphase model. The equations of the model are derived in the framework of porous media theory, and constitutive relations for the mass transfer terms and the stress are formulated on the basis of experimental observations. A set of experiments is performed, investigating the growth of U-87MG spheroids both freely growing in the culture medium and subjected to an external mechanical pressure induced by a Dextran solution. The growth curves of the model are compared to the experimental data, with good agreement for both the experimental settings. A new mathematical law regulating the inhibitory effect of mechanical compression on cancer cell proliferation is presented at the end of the paper. This new law is validated against experimental data and provides better results compared to other expressions in the literature.     

Growth laws in cancer: implications for radiotherapy

Comparing the conventional Gompertz tumor growth law (GL) with the "Universal" law (UL), which has recently been proposed and applied to cancer, we have investigated the implications of the growth laws for various radiotherapy regimens. According to the GL, the surviving tumor cell fraction could be reduced ad libitum, independent of the initial tumor mass, simply by increasing the number of treatments. In contrast, if tumor growth dynamics follows the Universal scaling law, there is a lower limit of the surviving fraction that cannot be reduced further regardless of the total number of treatments. This finding can explain the so-called tumor size effect and re-emphasizes the importance of early diagnosis because it implies that radiotherapy may be successful provided that the tumor mass at treatment onset is rather small. Taken together with our previous work, the implications of these findings include revisiting standard radiotherapy regimens and treatment protocols overall.     

Constrained parametric model for simultaneous inference of two cumulative incidence functions

We propose a parametric regression model for the cumulative incidence functions (CIFs) commonly used for competing risks data. The model adopts a modified logistic model as the baseline CIF and a generalized odds‐rate model for covariate effects, and it explicitly takes into account the constraint that a subject with any given prognostic factors should eventually fail from one of the causes such that the asymptotes of the CIFs should add up to one. This constraint intrinsically holds in a nonparametric analysis without covariates, but is easily overlooked in a semiparametric or parametric regression setting. We hence model the CIF from the primary cause assuming the generalized odds‐rate transformation and the modified logistic function as the baseline CIF. Under the additivity constraint, the covariate effects on the competing cause are modeled by a function of the asymptote of the baseline distribution and the covariate effects on the primary cause. The inference procedure is straightforward by using the standard maximum likelihood theory. We demonstrate desirable finite‐sample performance of our model by simulation studies in comparison with existing methods. Its practical utility is illustrated in an analysis of a breast cancer dataset to assess the treatment effect of tamoxifen, adjusting for age and initial pathological tumor size, on breast cancer recurrence that is subject to dependent censoring by second primary cancers and deaths.     

Multi-state survival models with treatment effects and biomarkers: Simulations for study design assessment

BackgroundComparative effectiveness studies of cancer therapeutics in observational data face confounding by patterns of clinical treatment over time. The validity of survival analysis in longitudinal health records depends on study design choices including index date definition and model specification for covariate adjustment.MethodsOverall survival in cancer is a multi-state transition process with mortality and treatment switching as competing risks. Parametric Weibull regression quantifies proportionality of hazards across lines of therapy in real-world cohorts of 12 solid tumor types. Study design assessments compare alternative analytic models in simulations with realistic disproportionality. The multi-state simulation framework is adaptable to alternative treatment effect profiles and exposure patterns.ResultsEvent-specific hazards of treatment-switching and death are not proportional across lines of therapy in 12 solid tumor types. Study designs that include all eligible lines of therapy per subject showed lower bias and variance than designs that select one line per subject. Confounding by line number was effectively mitigated across a range of simulation scenarios by Cox proportional hazards models with stratified baseline hazards and inverse probability of treatment weighting.ConclusionQuantitative study design assessment can inform the planning of observational research in clinical oncology by demonstrating the potential impact of model misspecification. Use of empirical parameter estimates in simulation designs adapts analytic recommendations to the clinical population of interest.     

The escape of cancer from T lymphocytes: immunoselection of MHC class I loss variants harboring structural-irreversible “hard” lesions

The discovery of tumor antigens recognized by T lymphocytes has stimulated the development of a variety of cancer treatment protocols aimed at enhancing antitumor-specific T cell responses and tumor rejection. However, immunotherapy-mediated regression of established tumors and clearly positive clinical response to such treatment has not been achieved yet despite the induction of T cells directed against tumor antigens. The failure of the modern immunotherapy protocols can be explained by different tumor escape mechanisms that have been defined in various types of malignancy. The loss or downregulation of MHC class I antigens in tumor cells is one of the best analyzed mechanisms. In this review, we show experimental evidence obtained in our laboratory on human tumors and in a mouse cancer model suggesting that the molecular mechanism responsible for the MHC class I alteration in tumor cells might have a crucial impact on tumor recovery of normal H-2/HLA expression during the natural history of tumor development or after immunotherapy. When the preexisting molecular lesion underlying tumor MHC class I alteration is reversible (regulatory or soft), class I expression can be recovered leading to regression of tumor lesion. In contrast, if the HLA class I alteration is irreversible in nature (structural or hard), the lesion will progress killing the host. This is a new vision of the role of MHC class I alteration in tumors that can explain the failure of immunotherapy in a variety of different clinical protocols.     

A Novel 3-D Mineralized Tumor Model to Study Breast Cancer Bone Metastasis

Background

Metastatic bone disease is a frequent cause of morbidity in patients with advanced breast cancer, but the role of the bone mineral hydroxyapatite (HA) in this process remains unclear. We have developed a novel mineralized 3-D tumor model and have employed this culture system to systematically investigate the pro-metastatic role of HA under physiologically relevant conditions in vitro.

Methodology/Principal Findings

MDA-MB231 breast cancer cells were cultured within non-mineralized or mineralized polymeric scaffolds fabricated by a gas foaming-particulate leaching technique. Tumor cell adhesion, proliferation, and secretion of pro-osteoclastic interleukin-8 (IL-8) was increased in mineralized tumor models as compared to non-mineralized tumor models, and IL-8 secretion was more pronounced for bone-specific MDA-MB231 subpopulations relative to lung-specific breast cancer cells. These differences were pathologically significant as conditioned media collected from mineralized tumor models promoted osteoclastogenesis in an IL-8 dependent manner. Finally, drug testing and signaling studies with transforming growth factor beta (TGFβ) confirmed the clinical relevance of our culture system and revealed that breast cancer cell behavior is broadly affected by HA.

Conclusions/Significance

Our results indicate that HA promotes features associated with the neoplastic and metastatic growth of breast carcinoma cells in bone and that IL-8 may play an important role in this process. The developed mineralized tumor models may help to reveal the underlying cellular and molecular mechanisms that may ultimately enable more efficacious therapy of patients with advanced breast cancer.     

Synergistic Effect of Anti-Angiogenic and Radiation Therapy: Quantitative Evaluation with Dynamic Contrast Enhanced MR Imaging

Purpose

We assessed the effects of anti-angiogenic therapy (AAT) on radiation therapy (RT), evaluating the tumor growth and perfusion patterns on dynamic contrast enhanced MR (DCE-MR) images.

Methods

Thirteen nude mice with heterotopic xenograft cancer of human lung cancer cell line were used. To observe the interval change of the tumor size and demonstrate the time-signal intensity enhancement curve of the tumor, the mice were subdivided into four groups: control (n = 2), AAT (n = 2), RT (n = 5), and combined therapy (AART, n = 4). DCE-MR images were taken four weeks after treatment. Perfusion parameters were obtained based on the Brix model. To compare the interval size changes in the RT group with those in the AART group, repeated measures ANOVA was used. Perfusion parameters in both the RT and AART groups were compared using a Mann-Whitney U test.

Results

Tumor growth was more suppressed in AART group than in the other groups. Control group showed the rapid wash-in and wash-out pattern on DCE-MR images. In contrast to RT group with delayed and prolonged enhancement, both AAT and AART groups showed the rapid wash-in and plateau pattern. The signal intensity in the plateau/time to peak enhancement (P<0.016) and the maximum enhancement ratio (P<0.016) of AART group were higher than those of RT group.

Conclusions

AART showed synergistic effects in anticancer treatment. The pattern of the time-intensity curve on the DCE-MR images in each group implies that AAT might help maintain the perfusion in the cancer of AART group.