Prediction Of Radiosensitivity Research Articles

Prediction of Radiosensitivity in Head and Neck Squamous Cell Carcinoma Based on Multiple Omics Data.

Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor. Radiotherapy (RT) is an important treatment for HNSCC, but not all patients derive survival benefit from RT due to the individual differences on radiosensitivity. A prediction model of radiosensitivity based on multiple omics data might solve this problem. Compared with single omics data, multiple omics data can illuminate more systematical associations between complex molecular characteristics and cancer phenotypes. In this study, we obtained 122 differential expression genes by analyzing the gene expression data of HNSCC patients with RT (N = 287) and without RT (N = 189) downloaded from The Cancer Genome Atlas. Then, HNSCC patients with RT were randomly divided into a training set (N = 149) and a test set (N = 138). Finally, we combined multiple omics data of 122 differential genes with clinical outcomes on the training set to establish a 12-gene signature by two-stage regularization and multivariable Cox regression models. Using the median score of the 12-gene signature on the training set as the cutoff value, the patients were divided into the high- and low-score groups. The analysis revealed that patients in the low-score group had higher radiosensitivity and would benefit from RT. Furthermore, we developed a nomogram to predict the overall survival of HNSCC patients with RT. We compared the prognostic value of 12-gene signature with those of the gene signatures based on single omics data. It suggested that the 12-gene signature based on multiple omics data achieved the best ability for predicting radiosensitivity. In conclusion, the proposed 12-gene signature is a promising biomarker for estimating the RT options in HNSCC patients.

Gene signature for prediction of radiosensitivity in human papillomavirus-negative head and neck squamous cell carcinoma.

PurposeThe probability of recurrence of cancer after adjuvant or definitive radiotherapy in patients with human papillomavirus-negative (HPV(–)) head and neck squamous cell carcinoma (HNSCC) varies for each patient. This study aimed to identify and validate radiation sensitivity signature (RSS) of patients with HPV(–) HNSCC to predict the recurrence of cancer after radiotherapy.Materials and MethodsClonogenic survival assays were performed to assess radiosensitivity in 14 HNSCC cell lines. We identified genes closely correlated with radiosensitivity and validated them in The Cancer Genome Atlas (TCGA) cohort. The validated RSS were analyzed by ingenuity pathway analysis (IPA) to identify canonical pathways, upstream regulators, diseases and functions, and gene networks related to radiosensitive genes in HPV(–) HNSCC.ResultsThe survival fraction of 14 HNSCC cell lines after exposure to 2 Gy of radiation ranged from 48% to 72%. Six genes were positively correlated and 35 genes were negatively correlated with radioresistance, respectively. RSS was validated in the HPV(–) TCGA HNSCC cohort (n = 203), and recurrence-free survival (RFS) rate was found to be significantly lower in the radioresistant group than in the radiosensitive group (p = 0.035). Cell death and survival, cell-to-cell signaling, and cellular movement were significantly enriched in RSS, and RSSs were highly correlated with each other.ConclusionWe derived a HPV(–) HNSCC-specific RSS and validated it in an independent cohort. The outcome of adjuvant or definitive radiotherapy in HPV(–) patients with HNSCC can be predicted by analyzing their RSS, which might help in establishing a personalized therapeutic plan.

Predicting response to radiotherapy in tumors with PET/CT: when and how?

Radiotherapy is one of the main methods for tumor treatment, with the improved radiotherapy delivery technique to combat cancer, there is a growing interest for finding effective and feasible ways to predict tumor radiosensitivity. Based on a series of changes in metabolism, microvessel density, hypoxic microenvironment, and cytokines of tumors after radiotherapy, a variety of radiosensitivity detection methods have been studied. Among the detection methods, positron emission tomography-computed tomography (PET/CT) is a feasible tool for response evaluation following definitive radiotherapy for cancers with a high negative predictive value. The prognostic or predictive value of PET/CT is currently being studied widely. However, there are many unresolved issues, such as the optimal probe of PET/CT for radiosensitivity prediction, the selection of the most useful PET/CT parameters and their optimal cut-offs such as total lesion glycolysis (TLG), metabolic tumor volume (MTV) and standardized uptake value (SUV), and the optimal timing of PET/CT pre-treatment, during or following RT. Different radiosensitivity of tumors, modes of radiotherapy action and fraction scheduling may complicate the appropriate choice. In this study, we will discuss the diverse methods for evaluating radiosensitivity, and will also focus on the selection of the optimal probe, timing, cut-offs and parameters of PET/CT for evaluating the radiotherapy response.

A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

Discrimination of radiosensitive and radioresistant murine lymphoma cells by Raman spectroscopy and SERS.

Intrinsic radiosensitivity is a biological parameter known to influence the response to radiation therapy in cancer treatment. In this study, Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) were successfully used in conjunction with principal component analysis (PCA) to discriminate between radioresistant (LY-R) and radiosensitive (LY-S) murine lymphoma sublines (L5178Y). PCA results for normal Raman analysis showed a differentiation between the radioresistant and radiosensitive cell lines based on their specific spectral fingerprint. In the case of SERS with gold nanoparticles (AuNPs), greater spectral enhancements were observed in the radioresistant subline in comparison to its radiosensitive counterpart, suggesting that each subline displays different interaction with AuNPs. Our results indicate that spectroscopic and chemometric techniques could be used as complementary tools for the prediction of intrinsic radiosensitivity of lymphoma samples.

The Cell Cycle G2/M Block Is an Indicator of Cellular Radiosensitivity.

Background:Determination of the radiosensitivity of a specific tumor is essential to its precision tumor radiotherapy, but the measurement of cellular radiosensitivity with a routine colony forming assay is both labor- and time-consuming. An alternative option allowing rapid and precise prediction of radiosensitivity is necessary.Methods:In this study, we exposed 4 in vitro cultured cell lines to various doses of X-rays or carbon ions and then measured their radiosensitivities with a routine colony-forming assay, and monitored the kinetics of cell cycle distribution with routine propidium iodine staining and flow cytometry.Results:Based on the results, we correlated cellular radiosensitivity with a dynamic assay of cell cycle distribution, specifically, the negative correlation of cellular radiosensitivity with the accumulated G2/M arrested cells at 48 hours after exposure. The higher the proportion of accumulated G2/M arrested cells at 48 hours after exposure, the lower the radiosensitivity of the cell line, that is, the higher radioresistance of the cell line.Conclusion:These findings provide an optional application of regular cell cycle analysis for the prediction of tumor radiosensitivity.

A Novel Methylation Signature Predicts Radiotherapy Sensitivity in Glioma

Background: Glioblastoma (GBM) is the most common and malignant cancer in the central nervous system, and radiotherapy is wildly applied in GBM treatment. However, the sensitivity of radiotherapy varies in different patients. In order to solve this clinical dilemma, a radiosensitivity prediction signature was constructed based on genomic methylation. Methods: In this study, a total of 1044 primary GBM samples with clinical and methylation microarray data were involved. LASSO-COX, GSVA, Kaplan-Meier survive curve and COX regression were performed in construction and verification of predictive models. R language was used as the main tool for statistical analysis and graphical work. Findings: Through the integration analysis of methylation and survival data in primary GBM, a novel prognostic and radio-sensitivity prediction signature was constructed. Interpretation: This signature was stable in prognosis prediction in TCGA and CGGA databases. The possible mechanism was also explored, this signature was closely related to DNA repair functions. Importantly, this signature could predict whether GBM patients could benefit from radiotherapy. In conclusion, our study built a radiosensitivity prediction signature for GBM patients based on five methylated probes with great potential for clinical application. Funding: National Natural Science Foundation of China (No. 81672479). Declaration of Interest: The authors declare no potential conflicts of interest. Ethical Approval: This study was approved by Beijing Tiantan Hospital institutional review board (IRB).

Silencing of XRCC4 increases radiosensitivity of triple-negative breast cancer cells.

Background: Radiotherapy is an important locoregional treatment, and its effect on triple-negative breast cancer (TNBC) needs to be enhanced. The aim of the present study was to investigate the potential effects of XRCC4 on radiosensitivity of TNBC. Methods: The RNAi technique was implemented to establish the TNBC stable cell line with XRCC4 knockdown. MTT assay was used to detect the effect of XRCC4 knockdown on cell proliferation. Western blot and immunohistochemistry assays were employed to identify protein expression. Colony assay was performed to detect the effect of XRCC4 knockdown on the colony formation ability of TNBC cells with radiation treatment. Comet assay was conducted to evaluate the influence of XRCC4 silencing on DNA repair activity in ionizing radiation. In addition, we performed a survival analysis based on data in TCGA database. Results: XRCC4 knockdown by lentivirus-mediated shRNA had no significant effect on proliferation of TNBC cells. Knockdown of XRCC4 could substantially increase the sensitivity of TNBC cells to ionizing radiation. The DNA damage level was detected to be increased in the XRCC4 knockdown group, indicating there was a significant repair delay in the XRCC4-deleted cells. Clinical sample analysis exhibited that there were various XRCC4 expression in different patients with TNBC. Moreover, survival analysis showed that high expression of XRCC4 was significantly associated with poor progression-free survival after radiotherapy in TNBC patients. Conclusion: Our findings suggest that XRCC4 knockdown sensitizes TNBC cells to ionizing radiation, and could be considered as a novel predictor of radiosensitivity and a promising target for TNBC.

A Bayesian Approach for Prediction of Patient Radiosensitivity

A priori identification of the small proportion of radiation therapy patients who prove to be severely radiosensitive is a long-held goal in radiation oncology. A number of published studies indicate that analysis of the DNA damage response after exvivo irradiation of peripheral blood lymphocytes, using the γ-H2AX assay to detect DNA damage, provides a basis for a functional assay for identification of the small proportion of severely radiosensitive cancer patients undergoing radiotherapy. We introduce a new, more rigorous, integrated approach to analysis of radiation-induced γ-H2AX response, using Bayesian statistics. This approach shows excellent discrimination between radiosensitive and non-radiosensitive patient groups described in a previously reported data set. Bayesian statistical analysis provides a more appropriate and reliable methodology for future prospective studies.

MicroRNA-9 functions as a tumor suppressor and enhances radio-sensitivity in radio-resistant A549 cells by targeting neuropilin 1.

Radiotherapy is commonly used to treat lung cancer but may not kill all cancer cells, which may be attributed to the radiotherapy resistance that often occurs in non-small cell lung cancer (NSCLC). At present, the molecular mechanism of radio-resistance remains unclear. Neuropilin 1 (NRP1), a co-receptor for vascular endothelial growth factor (VEGF), was demonstrated to be associated with radio-resistance of NSCLC cells via the VEGF-phosphoinositide 3-kinase-nuclear factor-κB pathway in our previous study. It was hypothesized that certain microRNAs (miRs) may serve crucial functions in radio-sensitivity by regulating NRP1. Bioinformatics predicted that NRP1 was a potential target of miR-9, and this was validated by luciferase reporter assays. Functionally, miR-9-transfected A549 cells exhibited a decreased proliferation rate, increased apoptosis rate and attenuated migratory and invasive abilities. Additionally, a high expression of miR-9 also significantly enhanced the radio-sensitivity of A549 cells in vitro and in vivo. These data improve understanding of the mechanisms of cell radio-resistance, and suggest that miR-9 may be a molecular target for the prediction of radio-sensitivity in NSCLC.

Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature.

Soft tissue sarcomas are rare and aggressive tumors arising from connective tissues. Adjuvant radiotherapy is a commonly used treatment approach for the majority of sarcomas. We attempted to identify a gene signature that can predict radiosensitive patients who are most likely to have a better treatment response from radiotherapy, compared with disease progression. Using the publicly available data of soft tissue sarcoma from The Cancer Genome Atlas, we developed a cross-validation procedure to identify a predictive gene signature for radiosensitivity. The results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved treatment response. We further provide supportive evidence to validate our sensitivity prediction. Results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved survival than patients who did not. ROC analysis showed that the developed gene signature had a powerful prediction on treatment response. We further found that predicted radiosensitive patients who received radiotherapy had a significantly reduced rate of new tumor events. Finally, we validated our gene signature using a hierarchical cluster analysis, and found that the predicted sensitivities were well-matched with results from the cluster analysis. These results are consistent with our expectation, suggesting that the identified gene signature and radiosensitivity prediction are effective. The genes involved in the signature may provide a molecular basis for prognostic studies and radiotherapy target discovery.

Abstract 1794: Lymphocyte apoptosis as a predictive biomarker for radiotherapy de-intensification in EBV-associated nasopharynx cancer

Abstract Aims: Emerging evidence in viral-associated oro- and naso-pharynx cancers supports the concept of radiotherapy (RT) dose de-intensification in these radiosensitive tumours. This notion is particularly relevant in the background that a majority of head and neck cancer patients develop severe late toxicities despite modern precision RT. A reasonable strategy could thus entail stratifying patients at risk of late toxicities to dose de-intensification. Here, we investigated the utility of a lymphocyte apoptosis (RILA) assay to predict for late toxicities in EBV-positive nasopharynx cancer patients. We also test if a multi-modal approach incorporating DNA damage induction and repair improves the prediction of clinical radiosensitivity. Methods: Assays were assessed retrospectively among survivors of a randomised controlled trial (NCC-0902), where patients were assigned to weekly cisplatin-RT with or without neoadjuvant gemcitabine, carboplatin, and paclitaxel. Late toxicities were assessed by CTCAE v.2 at the following intervals - 2-mo year 1, 4-mo year 2, 6-mo years 3-5, and annually thereafter; and considered severe if CTCAE v.2 ≥Grade 2. RILA was based on a fluorogenic inhibitor of caspases assay (48 h post-8 Gy). DNA damage induction and repair were assessed by semi-automated scoring of ϒH2AX foci at 30 min post-1 Gy and 24 h post-4 Gy, respectively. Results: Median follow-up of patients was 5.7 (range 4.6-7.4) years. Clinical and treatment indices, including assigned study arm and RT dosimetry to normal tissue structures, were balanced between patients with and without late toxicities (p >0.05). We observed a trend between decreasing RILA scores and increased risks of late toxicities; odds ratio (OR) 1.27 (95% CI = 0.92-1.72) for lowest quartile cut-off, corresponding to an AUC of 0.608 for prediction accuracy (p = 0.049). A multi-modal approach incorporating DNA damage induction and repair did not improve upon the predictive accuracy of RILA for clinical radiosensitivity. Independently, DNA damage induction and repair were not associated with risks of late toxicities; OR 1.03 (95% CI = 0.69-1.53, p = 0.901) and 1.04 (95% CI = 0.73-1.49, p = 0.829), respectively. Interestingly, onset of late toxicities was correlated to an improved disease-free survival in our cohort (5-y DFS = 94.1% vs 73.3%, no late toxicity, p = 0.003), which suggests a common mechanism underlying tumour and normal tissue radiosensitivity in EBV-positive nasopharynx cancer. Conclusions: Herein, we report on the potential utility of a RILA assay in stratifying for patients at risk of late RT-induced toxicities. The clinical implication of this assay is further exemplified by the observation of a superior survival in these sensitive individuals, which supports the approach of selecting this subgroup for treatment de-intensification. Citation Format: Kevin L.M. Chua, Shihabuddeen Waseem Ahamed, Ma Than Than Shwe, Li-lian Kwok, Joseph T.S. Wee, Terence W.K. Tan, Prakash M. Hande, Kai Rothkamm, Melvin L.K. Chua. Lymphocyte apoptosis as a predictive biomarker for radiotherapy de-intensification in EBV-associated nasopharynx cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1794. doi:10.1158/1538-7445.AM2017-1794

Plasma miR-145 as a novel biomarker for the diagnosis and radiosensitivity prediction of human cervical cancer.

ObjectivesThe objective of this study was to evaluate levels of plasma miR-145 in patients with cervical cancer (CC) and investigate its biomarker potential.MethodsUsing qRT-PCR, we compared plasma miR-145 levels in 120 patients with CC, 120 patients with cervical intraepithelial neoplasia (CIN), and 120 healthy volunteers. The association between plasma miR-145 expression and clinicopathological factors, including radiation response, was also analyzed.ResultsPlasma miR-145 levels were lower in CC patient than in CIN patients and healthy controls. Low levels were significantly associated with poor cancer differentiation, lymph node metastasis, HPV, and advanced FIGO stage. CC patients who achieved complete response to radiotherapy had higher plasma miR-145 levels than incomplete responders. ROC analysis confirmed that plasma miR-145 is a candidate biomarker for detecting CC and differentiating complete responders from incomplete responders.ConclusionsPlasma miR-145 is reduced in CC and is a novel candidate biomarker for diagnosing CC and predicting radiosensitivity.

Significance of p53 in cervical cancer and its influence on radiosensitivity

Radiotherapy is a major local treatment for cervical cancer. However, local uncontrollability due to radioresistance is still common. Therefore, the prediction of radiosensitivity is quite beneficial to develop an optimal treatment strategy for individual patients. Multiple factors could influence the radiosensitivity of cells, and p53 status is one of them. The upstream or downstream molecules of p53 could also be regulated to affect the radiosensitivity of cervical cancer. The aim of the review is to analyze the difference in p53 status between different types of cervical cancer and to discuss how p53 regulates the response to radiotherapy. Key words: p53 gene; Cervical neoplasms; Radiosensitivity; Regulate

Nuclei Size Distribution as a Predictor for Radiosensitivity with 192Ir Brachytherapy

Nuclei Size Distribution as a Predictor for Radiosensitivity with 192Ir Brachytherapy

Establishment of a logistic regression model for the predicting of radio-sensitivity of preoperative radiotherapy in rectal cancer

Objective To investigate the correlation between the biomarkers related to radio-sensitivity and preoperative radiotherapy in rectal cancer patients, and to establish a logistic regression model to predict the effect of the preoperative radiotherapy through detecting the expression levels of the molecular markers. Methods 33 patients with rectal cancer who received preoperative radiotherapy from January 2010 to January 2015 were retrospectively analyzed. Patients' information was also collected including the serum level of carcino-embryonic antigen(CEA), the immune-histochemical expression levels of vascular endothelial growth factor (VEGF), epidermal growth factor receptor (EGFR), thymidylate synthase (TS) and Ki-67, and image data (CT or magnetic resonance imaging) before radiotherapy, preoperative clinical staging and the postoperative pathologic staging. According to the postoperative pathological remission, the treatment effects of preoperative radiotherapy included effective (CR+PR) and ineffective (PD+SD) were evaluated. The relationship between these molecular markers and the curative effect of preoperative radiotherapy was analyzed by logistic regression analysis using SPSS v17.0 software, and a logistic curative effect prediction model was established. Results As a result of single factor and multiple factors logistic binary regression analysis, CEA, VEGF and Ki-67 were recognized as the interested factors for the radio-sensitivity predicting in patients with rectal cancer who received preoperative radiotherapy. A molecular markers predictive model for radio-sensitivity in preoperative radiotherapy in rectal cancer is as follow: log P=1.700-0.276×CEA-0.238×VEGF-0.135×EGFR+1.377×TS+0.080×Ki-67. Serum CEA level and the expression of VEGF might associate with radio-resistant, and the expression of Ki-67 might associate with better reaction to preoperative radiotherapy. Conclusion The levels of serum CEA, VEGF and Ki-67 may be the predictors of radio-sensitivity in rectal cancer patients who received preoperative radiotherapy. Key words: Rectal neoplasms; Radiotherapy; Tumor markers, biological; Radiation tolerance

Research progress of radiotherapy and molecular classifications of gastric cancer

Adjuvant radiotherapy (RT) can improve the rate of loco-regional control for patients with gastric cancer (GC), while the selection of patients plays a key role. As the research moves along, several relatively comprehensive molecular classifications emerged such as the TCGA classification and the ACRG classification. Studies have demonstrated that molecular classifications are closely related to the clinicopathologic characteristics, prognosis and treatment response. However, there is not recognized molecular classification of GC presently. It is a great challenge for radiation oncologists to make use of the individual bioinformation and accurately select patients who would benefit from RT. Meanwhile, precision RT could also be achieved with the prediction of radiosensitivity, combination of RT with targeted therapy and the application of ctDNA within the field of RT. Key words: Stomach neoplasms/RT/PA; DNA, neoplasm; Review

Insulin-like growth factor binding protein-3 is a new predictor of radiosensitivity on esophageal squamous cell carcinoma.

Insulin-like growth factor binding protein-3 (IGFBP-3) plays an essential role in radiosensitivity of esophageal squamous cell carcinoma (ESCC). However, the underlying mechanism is not completely understood. Here, we observed that IGFBP-3 had favorable impact on the tumorigenicity of ESCC cells in nude mice by using an in vivo imaging system (IVIS) to monitor tumor growth treated with ionizing radiation (IR). Downregulation of IGFBP-3 expression enhanced tumor growth, inhibited anti-proliferative and apoptotic activity and result in IR resistance in vivo. Cell cycle antibody array suggested that silencing IGFBP-3 promoted transition from G0/G1 to S phase, perhaps though influencing Smad3 dephosphorylation and retinoblastoma protein (Rb) phosphorylation. Downregulation of P21 and P27, and upregulation of p-P27 (phospho-Thr187), cyclin-dependent kinase 2 (CDK2), and cyclin E1 might contribute to the G0/G1 to S phase transition promoted by IGFBP-3. Our results suggest that Smad3-P27/P21-cyclin E1/CDK2-phosphorylated retinoblastoma protein pathways might be involved in this IGFBP-3 mediated radiosensitivity transition in ESCC.

Comparison of methods to quantify histone H2AX phosphorylation and its usefulness for prediction of radiosensitivity

Purpose: The number of radio-induced double-strand breaks is correlated with the number of histone gamma-H2AX (γ-H2AX) foci. For this reason, foci quantification is a useful tool to measure radiation-induced DNA damage and the number of foci has been suggested as a predictive biomarker of radiosensitivity. The aim of the present study was to evaluate the reproducibility of different microscopic methodologies and flow cytometry analysis to score γ-H2AX induction, and its suitability to distinguish a radiosensitive (RS) cell line from a radioresistant (RR) one.Materials and methods: γ-H2AX analyses were performed by semi-automated and automated microscopic methods and by flow cytometry before and after irradiation in two human lymphoblastoid cell lines and in lymphocytes from three healthy donors.Results: Reproducible results were obtained by all the methodologies tested, although not all showed the same sensitivity. The RS cell line always showed higher foci counts and higher levels of immunofluorescence intensity after irradiation than the RR cell line.Conclusions: Our results suggest that microscopic methodologies with z-stage capacity give the most accurate results after 1 Gy irradiation. However, for high doses of ionizing radiation, flow cytometry gives reliable results. Further studies will be necessary to determine the usefulness of γ-H2AX analysis to predict adverse side reactions in radiotherapy patients.

Prediction of radiosensitivity in primary central nervous system germ cell tumors using dynamic contrast-enhanced magnetic resonance imaging.

To evaluate the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting tumor response to radiotherapy in patients with suspected primary central nervous system (CNS) germ cell tumors (GCTs). DCE-MRI parameters of 35 patients with suspected primary CNS GCTs were obtained prior to diagnostic radiation, using the Tofts and Kermode model. Radiosensitivity was determined in tumors diagnosed 2 weeks after radiation by observing changes in tumor size and markers as a response to MRI. Taking radiosensitivity as the gold standard, the cut-off value of DCE-MRI parameters was measured by receiver operating characteristic (ROC) curve. Diagnostic accuracy of DCE-MRI parameters for predicting radiosensitivity was evaluated by ROC curve. A significant elevation in transfer constant (K(trans)) and extravascular extracellular space (Ve) (P=0.000), as well as a significant reduction in rate constant (Kep) (P=0.000) was observed in tumors. K(trans), relative K(trans), and relative Kep of the responsive group were significantly higher than non-responsive groups. No significant difference was found in Kep, Ve, and relative Ve between the two groups. Relative K(trans) showed the best diagnostic value in predicting radiosensitivity with a sensitivity of 100%, specificity of 91.7%, positive predictive value (PPV) of 95.8%, and negative predictive value (NPV) of 100%. Relative K(trans) appeared promising in predicting tumor response to radiation therapy (RT). It is implied that DCE-MRI pre-treatment is a requisite step in diagnostic procedures and a novel and reliable approach to guide clinical choice of RT.