Dosimetric comparison of ZAP-X, Gamma Knife, and CyberKnife stereotactic radiosurgery for single brain metastasis.

PURPOSE: To evaluate the dosimetric characteristics of ZAP-X stereotactic radiosurgery (SRS) for single brain metastasis by comparing with two mature SRS platforms. METHODS: Thirteen patients with single brain metastasis treated with CyberKnife (CK) G4 were selected retrospectively. The prescription dose for the planning target volume (PTV) was 18-24 Gy for 1-3 fractions. The PTV volume ranged from 0.44 to 11.52 cc.Treatment plans of thirteen patients were replanned using the ZAP-X plan system and the Gamma Knife (GK) ICON plan system with the same prescription dose and organs at risk (OARs) constraints. The prescription dose of PTV was normalized to 70% for both ZAP-X and CK, while it was 50% for GK. The dosimetric parameters of three groups included the plan characteristics (CI, GI, GSI, beams, MUs, treatment time), PTV (D2, D95, D98, Dmin, Dmean, Coverage), brain tissue (volume of 100%-10% prescription dose irradiation V100%-V10%, Dmean) and other OARs (Dmax, Dmean),all of these were compared and evaluated. All data were read and analyzed with MIM Maestro. One-way ANOVA or a multisample Friedman rank sum test was performed, where p < 0.05 indicated significant differences. RESULTS: The CI of GK was significantly lower than that of ZAP-X and CK. Regarding the mean value, ZAP-X had a lower GI and higher GSI, but there was no significant difference among the three groups. The MUs of ZAP-X were significantly lower than those of CK, and the mean value of the treatment time of ZAP-X was significantly shorter than that of CK. For PTV, the D95, D98, and target coverage of CK were higher, while the mean of Dmin of GK was significantly lower than that of CK and ZAP-X. For brain tissue, ZAP-X showed a smaller volume from V100% to V20%; the statistical results of V60% and V50% showed a difference between ZAP-X and GK, while the V40% and V30% showed a significant difference between ZAP-X and the other two groups; V10% and Dmean indicated that GK was better. Excluding the Dmax of the brainstem, right optic nerve and optic chiasm, the mean value of all other OARs was less than 1 Gy. For the brainstem, GK and ZAP-X had better protection, especially at the maximum dose. CONCLUSION: For the SRS treating single brain metastasis, all three treatment devices, ZAP-X system, CyberKnife G4 system, and GammaKnife system, could meet clinical treatment requirements. The newly platform ZAP-X could provide a high-quality plan equivalent to or even better than CyberKnife and Gamma Knife, with ZAP-X presenting a certain dose advantage, especially with a more conformal dose distribution and better protection for brain tissue. As the ZAP-X systems get continuous improvements and upgrades, they may become a new SRS platform for the treatment of brain metastasis.

Application of MR images in radiotherapy planning for brain tumor based on deep learning.

PURPOSE: Explore the function and dose calculation accuracy of MRI images in radiotherapy planning through deep learning methods. METHODS: 131 brain tumor patients undergoing radiotherapy with previous MR and CT images were recruited for this study. A new series of MRI from the aligned MR was firstly registered to CT images strictly using MIM software and then resampled. A deep learning method (U-NET) was used to establish a MRI-to-CT conversion model, for which 105 patient images were used as the training set and 26 patient images were used as the tuning set. Data from additional 8 patients were collected as the test set, and the accuracy of the model was evaluated from a dosimetric standpoint. RESULTS: Comparing the synthetic CT images with the original CT images, the difference in dosimetric parameters D98, D95, D2 and Dmean of PTV in 8 patients was less than 0.5%. The gamma passed rates of PTV and whole body volume were: 1%/1 mm: 93.96%±6.75%, 2%/2 mm: 99.87%±0.30%, 3%/3 mm: 100.00%±0.00%; and 1%/1 mm: 99.14%±0.80%, 2%/2 mm: 99.92%±0.08%, 3%/3 mm: 99.99%±0.01%. CONCLUSION: MR images can be used both in delineation and treatment efficacy evaluation and in dose calculation. Using the deep learning way to convert MR image to CT image is a viable method and can be further used in dose calculation.

[Study on Automatic Plan Method for Radiotherapy after Breast-conserving Surgery Based on TiGRT System].

To study an automatic plan(AP) method for radiotherapy after breast-conserving surgery based on TiGRT system and and compare with manual plan (MP). The dosimetry parameters of 10 patients and the evaluation of scoring table were analyzed, it was found that the targets dose of AP were better than that of MP, but there was no statistical difference except for CI, The V5, V20 and V30 of affected lungs and whole lungs in AP were lower than all that in MP, the Dmean of hearts was slightly higher than that of MP, but the difference was not statistically significant, the MU of AP was increase by 16.1% compared with MP, the score of AP evaluation was increase by 6.1% compared with MP. So the AP could be programmed and automated while ensuring the quality of the plan, and can be used to design the plans for radiotherapy after breast-conserving surgery.

Hypofractionated radiotherapy in ten fractions for postmastectomy patients: a phase II study compared with another hypofractionation schedule with sixteen fractions.

BACKGROUND: The purpose of this phase II study was to evaluate the feasibility of hypofractionated radiotherapy (HFRT) with a dose of 36.5 Gy in 10 fractions in postmastectomy patients. METHODS: From March 2014 to December 2015, 85 patients with locally advanced breast cancer were eligible to participate in this study with a schedule of 36.5 Gy in 10 fractions. Intensity-modulated radiation therapy (IMRT) was delivered to the chest wall with or without the supraclavicular region. The primary endpoint was radiation-related toxicities. The secondary endpoints were locoregional failure-free survival (LRFFS), disease-free survival (DFS) and overall survival (OS). And the outcomes were compared with our retrospective study of 72 patients with 42.5 Gy in 16 fractions. RESULTS: The median follow-up was 69.0 (range 66.5-71.5) months in the 36.5 Gy group and 93.0 (range 91.9-94.1) months in the 42.5 Gy group, respectively. Radiation-related toxicities were mainly grade 1, although a few patients had grade 2 plexopathy (1.2%) and acute skin toxicity (1.2%) in the 36.5 Gy group, and grade 2 acute skin toxicity (5.6%) and lymphedema (4.2%) in the 42.5 Gy group. There were no significant differences between the groups in acute and late toxicities. For all the patients, the 5-year LRFFS, DFS and OS were 97.7 and 100.0%, 93.1 and 90.3%, 98.8 and 97.2%, respectively, without significant differences between the groups. CONCLUSION: Postmastectomy HFRT with a schedule of 36.5 Gy in 10 fractions was feasible, with mild toxicities and excellent 5-year clinical outcome. TRIAL REGISTRATION: Trial registration number: ChiCTR-ONRC-14004391 . Date of registration: 9/3/2014.

CT based automatic clinical target volume delineation using a dense-fully connected convolution network for cervical Cancer radiation therapy.

BACKGROUND: It is very important to accurately delineate the CTV on the patient's three-dimensional CT image in the radiotherapy process. Limited to the scarcity of clinical samples and the difficulty of automatic delineation, the research of automatic delineation of cervical cancer CTV based on CT images for new patients is slow. This study aimed to assess the value of Dense-Fully Connected Convolution Network (Dense V-Net) in predicting Clinical Target Volume (CTV) pre-delineation in cervical cancer patients for radiotherapy. METHODS: In this study, we used Dense V-Net, a dense and fully connected convolutional network with suitable feature learning in small samples to automatically pre-delineate the CTV of cervical cancer patients based on computed tomography (CT) images and then we assessed the outcome. The CT data of 133 patients with stage IB and IIA postoperative cervical cancer with a comparable delineation scope was enrolled in this study. One hundred and thirteen patients were randomly designated as the training set to adjust the model parameters. Twenty cases were used as the test set to assess the network performance. The 8 most representative parameters were also used to assess the pre-sketching accuracy from 3 aspects: sketching similarity, sketching offset, and sketching volume difference. RESULTS: The results presented that the DSC, DC/mm, HD/cm, MAD/mm, ∆V, SI, IncI and JD of CTV were 0.82 ± 0.03, 4.28 ± 2.35, 1.86 ± 0.48, 2.52 ± 0.40, 0.09 ± 0.05, 0.84 ± 0.04, 0.80 ± 0.05, and 0.30 ± 0.04, respectively, and the results were greater than those with a single network. CONCLUSIONS: Dense V-Net can correctly predict CTV pre-delineation of cervical cancer patients and can be applied in clinical practice after completing simple modifications.

[Automatic segmentation of head and neck organs at risk based on three-dimensional U-NET deep convolutional neural network].

The segmentation of organs at risk is an important part of radiotherapy. The current method of manual segmentation depends on the knowledge and experience of physicians, which is very time-consuming and difficult to ensure the accuracy, consistency and repeatability. Therefore, a deep convolutional neural network (DCNN) is proposed for the automatic and accurate segmentation of head and neck organs at risk. The data of 496 patients with nasopharyngeal carcinoma were reviewed. Among them, 376 cases were randomly selected for training set, 60 cases for validation set and 60 cases for test set. Using the three-dimensional (3D) U-NET DCNN, combined with two loss functions of Dice Loss and Generalized Dice Loss, the automatic segmentation neural network model for the head and neck organs at risk was trained. The evaluation parameters are Dice similarity coefficient and Jaccard distance. The average Dice Similarity coefficient of the 19 organs at risk was 0.91, and the Jaccard distance was 0.15. The results demonstrate that 3D U-NET DCNN combined with Dice Loss function can be better applied to automatic segmentation of head and neck organs at risk.

[Automated Pre-delineation of CTV in Patients with Cervical Cancer Using Dense V-Net].

We use a dense and fully connected convolutional network with good feature learning in small samples, to automatically pre-deline CTV of cervical cancer patients based on CT images and evaluate the effect. The CT data of stage IB and IIA postoperative cervical cancer with similar delineation scope were selected to be used to evaluate the pre-sketching accuracy from three aspects:sketching similarity, sketching offset and sketching volume difference. It has been proved that the 8 most representative parameters are superior to those with single network and reported internationally before. Dense V-Net can accurately predict CTV pre-delineation of cervical cancer patients, which can be used clinically after simple modification by doctors.

[Acceptance Test and Image Quality Assurance of MRI Simulator Equipment].

MRI simulator(MRI-Sim) images have unique clinical advantages with higher resolution of soft tissue and clearer visualization of tissue boundaries. Thus, the precise positioning of the tumor target area can be achieved and it is widely used in the field of radiotherapy. This article focuses on the acceptance test project and image quality assurance work of MRI-Sim equipment. The obtained ACR phantom images were used to analyze various image quality assurance indicators, and the results all reached the set standards, thereby ensuring that the obtained images meet the requirements of clinical applications.

[Results of Five-year Quality Control Program and the Change of QA Mode for Helical Tomotherapy Machine].

Helical tomotherapy (HT), as a new IMRT technology, utilizes a fan beam of radiation for treatment. It combines the main characteristics of a modern CT scanner and a linear accelerator to achieve the function of helical tomotherapy. Due to the complexity of the system with a highly integrated and automated features, monitoring its operation, continuing to improve the stability and reliability, and simplifying its quality control procedures has become an important part of quality assurance (QA) for HT. Based on the results of afi ve-year quality control (QC) program, and the initial application of new QA equipment, this study wil summarize the standardization mode of its QA and explore the changes of QA mode.

[Patient-specific dose verification method using ArcCHECK for total marrow irradiation with intensity modulated arc therapy].

To investigate the patient-specific dose verification method using ArcCHECK for total marrow irradiation (TMI) with Volumetric Modulated Arc Therapy (VMAT) and Helical Tomotherapy (HT). The kVCT images collected from 8 patients were respectively designed for RapidArc and Tomotherapy plans in total marrow irradiation. ArcCHECK was used for dose verification for the head-neck, chest-abdomen and pelvic. The merging function of ArcCHECK was used in VMAT and the method of double plans (reference and delivery plans) were used in HT. The γ-analysis passing rates for the head-neck, chest-abdomen, pelvic were 98.9% ± 1.9%, 98.4% ± 1.8%, 97.4% ± 2.1% for VMAT plans and 94.3% ± 1.5%, 96.5 ± 1.2%, 94.1% ± 1.9% for HT plans. The results show that using the merging function of ArcCHECK can achieve the dose verification well for VMAT plans with TMI. The method of double plans was done for the dose verification of HT plans with TMI as well as the plans with the targets keeping away from the set-up center.

[The Dose Effect of Isocenter Selection during IMRT Dose Verification with the 2D Chamber Array].

To investigate the dose effect of isocenter difference during IMRT dose verification with the 2D chamber array. The samples collected from 10 patients were respectively designed for IMRT plans, the isocenter of which was independently defined as P(o), P(x) and P(y). P(o) was fixed on the target center and the other points shifted 8cm from the target center in the orientation of x/y. The PTW729 was used for 2D dose verification in the 3 groups which beams of plans were set to 0 degrees. The γ-analysis passing rates for the whole plan and each beam were gotten using the different standards in the 3 groups, The results showed the mean passing rate of γ-analysis was highest in the P(o) group, and the mean passing rate of the whole plan was better than that of each beam. In addition, it became worse with the increase of dose leakage between the leaves in P(y) group. Therefore, the determination of isocenter has a visible effect for IMRT dose verification of the 2D chamber array, The isocenter of the planning design should be close to the geometric center of target.

[Use of megavoltage CT(MVCT) in helical tomotherapy for head and neck dose calculation].

OBJECTIVE: To evaluate the feasibility and accuracy of using Megavoltage CT(MVCT) for head and neck dose calculation. METHODS: The cheese Phantom was imaged using MVCT scanner, and the MVCT value density calibration curve was established. Conventional CT and MVCT image of nasopharyngeal carcinoma was acquired respectively, and IMRT plan was designed on conventional CT image of NPC patient. The conventional CT plan was copied to MVCT image. The dose distribution was calculated for tumor and normal tissue using the MVCT value density calibration curve, and compared with that of conventional CT. Ten NPC patients were collected for dose verification of IMRT plan on MVCT images. RESULTS: The MVCT numbers depended linearly on the electron density of the sample, and the stability of the MVCT numbers to electron density was good.The error between the measured dose and calculated dose in measured point was less than 3%.The isodose distribution was well agreement with that calculated by planning system. CONCLUSIONS: Performing dose recalculation using MVCT of Tomotherapy in head and neck region was feasible.and the dose distributions on kVCT and MVCT were in excellent agreement.

Gait classification for early detection and severity rating of Parkinson's disease based on hybrid signal processing and machine learning methods.

Parkinson's disease (PD) is one of the cognitive degenerative disorders of the central nervous system that affects the motor system. Gait dysfunction represents the pathology of motor symptom while gait analysis provides clinicians with subclinical information reflecting subtle differences between PD patients and healthy controls (HCs). Currently neurologists usually assess several clinical manifestations of the PD patients and rate the severity level according to some established criteria. This is highly dependent on clinician's expertise which is subjective and ineffective. In the present study we address these issues by proposing a hybrid signal processing and machine learning based gait classification system for gait anomaly detection and severity rating of PD patients. Time series of vertical ground reaction force (VGRF) data are utilized to represent discriminant gait information. First, phase space of the VGRF is reconstructed, in which the properties associated with the nonlinear gait system dynamics are preserved. Then Shannon energy is used to extract the characteristic envelope of the phase space signal. Third, Shannon energy envelope is decomposed into high and low resonance components using dual Q-factor signal decomposition derived from tunable Q-factor wavelet transform. Note that the high Q-factor component consists largely of sustained oscillatory behavior, while the low Q-factor component consists largely of transients and oscillations that are not sustained. Fourth, variational mode decomposition is employed to decompose high and low resonance components into different intrinsic modes and provide representative features. Finally features are fed to five different types of machine learning based classifiers for the anomaly detection and severity rating of PD patients based on Hohen and Yahr (HY) scale. The effectiveness of this strategy is verified using a Physionet gait database consisting of 93 idiopathic PD patients and 73 age-matched asymptomatic HCs. When evaluated with 10-fold cross-validation method for early PD detection and severity rating, the highest classification accuracy is reported to be 98.20% and 96.69%, respectively, by using the support vector machine classifier. Compared with other state-of-the-art methods, the results demonstrate superior performance and support the validity of the proposed method.

Evaluation of Dose Calculation Based on Cone-Beam CT Using Different Measuring Correction Methods for Head and Neck Cancer Patients.

Purpose: To investigate and compare 2 cone-beam computed tomography (CBCT) correction methods for CBCT-based dose calculation. Materials and Methods: Routine CBCT image sets of 12 head and neck cancer patients who received volumetric modulated arc therapy (VMAT) treatment were retrospectively analyzed. The CBCT images obtained using an on-board imager (OBI) at the first treatment fraction were firstly deformable registered and padded with the kVCT images to provide enough anatomical information about the tissues for dose calculation. Then, 2 CBCT correction methods were developed and applied to correct CBCT Hounsfield unit (HU) values. One method (HD method) is based on protocol-specific CBCT HU to physical density (HD) curve, and the other method (HM method) is based on histogram matching (HM) of HU value. The corrected CBCT images (CBCTHD and CBCTHM for HD and HM methods) were imported into the original planning system for dose calculation based on the HD curve of kVCT (the planning CT). The dose computation result was analyzed and discussed to compare these 2 CBCT-correction methods. Results: Dosimetric parameters, such as the Dmean, Dmax and D5% of the target volume in CBCT plan doses, were higher than those in the kVCT plan doses; however, the deviations were less than 2%. The D2%, in parallel organs such as the parotid glands, the deviations from the CBCTHM plan dose were less than those of the CBCTHD plan dose. The differences were statistically significant (P < .05). Meanwhile, the V30 value based on the HM method was better than that based on the HD method in the oral cavity region (P = .016). In addition, we also compared the γ passing rates of kVCT plan doses with the 2 CBCT plan doses, and negligible differences were found. Conclusion: The HM method was more suitable for head and neck cancer patients than the HD one. Furthermore, with the CBCTHM-based method, the dose calculation result better matches the kVCT-based dose calculation.

Induction chemotherapy with albumin-bound paclitaxel plus lobaplatin followed by concurrent radiochemotherapy for locally advanced esophageal cancer.

BACKGROUND: Albumin-bound paclitaxel (ABP) has been used as second- and higher-line treatments for advanced esophageal cancer, and its efficacy and safety have been well demonstrated. Lobaplatin (LBP) is a third-generation platinum antitumor agent; compared with the first two generations of platinum agents, it has lower toxicity and has been approved for the treatment of breast cancer, small cell lung cancer, and chronic granulocytic leukemia. However, its role in the treatment of esophageal cancer warrants further investigations. AIM: To investigate the efficacy and safety of induction chemotherapy with ABP plus LBP followed by concurrent radiochemotherapy (RCT) for locally advanced esophageal cancer. METHODS: Patients with pathologically confirmed advanced esophageal squamous cell carcinoma (ESCC) at our hospital were enrolled in this study. All patients were treated with two cycles of induction chemotherapy with ABP plus LBP followed by concurrent RCT: ABP 250 mg/m2, ivgtt, 30 min, d1, every 3 wk; and LBP, 30 mg/m2, ivgtt, 2 h, d1, every 3 wk. A total of four cycles were scheduled. The dose of the concurrent radiotherapy was 56-60 Gy/28-30 fractions, 1.8-2.0 Gy/fraction, and 5 fractions/wk. RESULTS: A total of 29 patients were included, and 26 of them completed the treatment protocol. After the induction chemotherapy, the objective response rate (ORR) was 61.54%, the disease control rate (DCR) was 88.46%, and the progressive disease (PD) rate was 11.54%; after the concurrent RCT, the ORR was 76.92%, the DCR was 88.46%, and the PD rate was 11.54%. The median progression-free survival was 11.1 mo and the median overall survival was 15.83 mo. Cox multivariate analysis revealed that two cycles of induction chemotherapy followed by concurrent RCT significantly reduced the risk of PD compared with two cycles of chemotherapy alone (P = 0.0024). Non-hematologic toxicities were tolerable, and the only grade 3 non-hematologic toxicity was radiation-induced esophagitis (13.79%). The main hematologic toxicity was neutropenia, and no grade 4 adverse event occurred. CONCLUSION: Induction chemotherapy with ABP plus LBP followed by concurrent RCT is effective in patients with locally advanced ESCC, with mild adverse effects. Thus, this protocol is worthy of clinical promotion and application.

The Zap-X Radiosurgical System in the Treatment of Intracranial Tumors: A Technical Case Report.

BACKGROUND AND IMPORTANCE: The Zap-X system (Zap Surgical Systems Inc, San Carlos, California) is a radically new surgical robot designed for brain and head and neck radiosurgery. It represents the first new dedicated brain stereotactic radiosurgery platform in almost half a century optimizing the goals of safety, speed, and accuracy. The Zap-X system was used in a required Chinese National Medical Products Administration clinical study. In early January 2020, 2 patients were treated with the Zap-X robot prior to a national COVID-19 lockdown. Both were closely followed via clinical exam and magnetic resonance imaging (MRI) imaging. Prospectively collected data were used to generate this report. CLINICAL PRESENTATION: Two female patients, each harboring either a trigeminal schwannoma or petroclival meningioma, were treated with the Zap-X robot. Respective tumor volumes were 2.60 and 4.02 cm3. A radiation dose of 13 Gy was prescribed to the 50% isodose line. At 8 mo of follow-up, preoperative symptoms were either resolved or stable and MRI imaging demonstrated a 31% and 56% reduction in lesion volume, respectively. In both patients, symptoms improved, and tumor volumes decreased, whereas no major complication was observed. CONCLUSION: Given only 2 patients and short-term follow-up, any conclusions about the safety and efficacy of the Zap-X radiosurgery robot are preliminary. However, in the absence of any other published outcomes to date, this small case series may be of interest to many radiosurgical specialists.

[Analysis of megavoltage computed tomography imaging on a helical tomotherapy unit].

OBJECTIVE: To evaluate the image quality of megavoltage computed Tomography imaging. METHODS: The HU uniformity and linearity, image noise, spatial resolution, low contrast resolution and spatial linearity in MVCT mode were evaluated with Catphan 600 phantom, and the factor of pitch was also evaluated. Influencing factors of image quality were also discussed. RESULTS: The MVCT values depended linearly on the physical density of the sample. The MVCT values uniformity was good. The spatial resolution was 4 lp/cm. The use of an MV Beam for imaging results in the loss of low contrast resolution, but it is sufficient for pretreatment image guidance. The geometric accuracy was good. CONCLUSIONS: The image quality of MVCT is less than that of KVCT, but is good enough for IGRT.

Neoadjuvant chemoradiotherapy plus surgery in the treatment of potentially resectable thoracic esophageal squamous cell carcinoma.

BACKGROUND: In recent years, neoadjuvant chemoradiotherapy (NCRT) combined with surgery has been gradually applied in patients with locally advanced thoracic esophageal cancer, but its effectiveness and safety remains unclear. In this clinical trial, we prospectively investigated the efficacy and safety of NCRT plus surgery in the treatment of thoracic esophageal squamous cell carcinoma (TESCC). AIM: To investigate the efficacy and safety of NCRT combined with surgery in the treatment of potentially resectable TESCC. METHODS: Thirty patients with advanced TESCC hospitalized in our hospital from July 2016 to June 2019 were prospectively studied. All patients received NCRT, which included intensity modulated conformal radiotherapy (40-44 Gy/20-22f, 2 Gy/f) and chemotherapy (paclitaxel 150-175 mg/m2d1, 22 + lobaplatin 25-30 mg/m2d2, 23 for two cycles). Surgery was performed after radiotherapy and chemotherapy. The effectiveness and safety of these treatments were observed. RESULTS: Among these 30 patients, complete response was achieved in two cases (6.7%) and partial response in 26 cases (86.7%), yielding an objective response rate of 100%. All patients underwent radical surgery successfully. The R0 resection rate was 100%, and the pathologic complete response rate was 33.3%. The incidence of grade III- IV granulocytopenia was 10% during the NCRT, and anastomotic leakage occurred in one patient after surgery. CONCLUSION: For patients with potentially resectable TESCC, NCRT can effectively reduce the tumor size, increase R0 resection rate, and achieve obvious pathological degradation, with mild adverse reactions. Thus, it is worthy of wider clinical application.

Precise prediction of the radiation pneumonitis in lung cancer: an explorative preliminary mathematical model using genotype information.

Purpose: Radiation pneumonitis (RP) is the most significant dose-limiting toxicity and is one major obstacle for lung cancer radiotherapy. Grade ≥2 RP usually needs clinical interventions and serve RP could be life threatening. Clinically, tissue response could be strikingly different even two similar patients after identical radiotherapy. Previous methods for the RP prediction can hardly distinguish substantial variations among individuals. Reliable predictive factors or methods emphasizing the individual differences are strongly desired by clinical radiation oncologists. The purpose of this study is to develop an approach for the personalized RP risk prediction. Experimental Design: One hundred eighteen lung cancer patients who received radiotherapy were enrolled. Seven hundred thousand single-nucleotide polymorphism (SNP) sites were assessed via Generalized Linear Models via Lasso and Elastic-Net Regularization (GLMNET) to determine their synergistic effects on the RP risk prediction. Non-genetic factors including patient's phenotypes and clinical interventional parameters were separately assessed by statistic test. Based on the results of the aforementioned analysis, a multiple linear regression model named Radiation Pneumonitis Index (RPI) was built, for the assessment of Grade ≥2RP risk. Results: Only previous surgery and fractional dose were discovered statistical significantly associated with grade ≥2RP. Thirty-nine effective SNPs for predicting the Grade ≥2RP risk were discovered and their coefficients of the synergistic effect were determined. The RPI score can successfully distinguish the RP≥2 population with 92.0% sensitivity and 100% specificity. Conclusions: Individual radiation sensitivity can be determined with genotype information and personalized radiotherapy could be achieved based on mathematical model result.

GSTP1 Ile105Val polymorphism might be associated with the risk of radiation pneumonitis among lung cancer patients in Chinese population: A prospective study.

Background: Growing data suggest that DNA damage repair and detoxification pathways play crucial roles in radiation-induced toxicities. To determine whether common functional single-nucleotide polymorphisms (SNPs) in candidate genes from these pathways can be used as predictors of radiation pneumonitis (RP), we conducted a prospective study to evaluate the associations between functional SNPs and risk of RP. Methods: We recruited a total of 149 lung cancer patients who had received intensity modulated radiation therapy (IMRT). GSTP1 and XRCC1 were genotyped using the SurPlexTM-xTAG method in all patients. RP events were prospectively scored using the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE), version 4.0. Kaplan-Meier analysis was used to determine the cumulative probability of RP of grade ≥ 2. Cox proportional hazard regression was performed to identify clinical variables and SNPs associated with risk of RP grade ≥ 2, using univariate and multivariate analysis, respectively. Results: With a median follow-up of 9 months, the incidence of RP of grade ≥ 2 was 38.3%. A predicting role in RP was observed for the GSTP1 SNP (adjusted hazard ratio 3.543; 95% CI 1.770-7.092; adjusted P< 0.001 for the Ile/Val and Val/Val genotypes versus Ile/Ile genotype). Whereas, we found that patients with XRCC1 399Arg/Gln and Gln/Gln genotypes had a lower risk of RP compares with those carrying Arg/Arg genotype (adjusted HR 0.653; 95% CI 0.342-1.245), but with no statistical significance observed (adjusted P = 0.195). Conclusions: Our results suggested a novel association between GSTP1 SNP 105Ile/Val and risk of RP development, which suggests the potential use of this genetic polymorphism as a predictor of RP. In addition, genetic polymorphisms of XRCC1 399Arg/Gln may also be associated with RP.