Purpose: During the radiation treatment planning process, one of the time-consuming procedures is the final high-resolution dose calculation, which obstacles the wide application of the emerging online adaptive radiotherapy techniques (OLART). There is an urgent desire for highly accurate and efficient dose calculation methods. This study aims to develop a dose super resolution -based deep learning model for fast and accurate dose prediction in clinical practice. Method: A Multi -stage Dose Super -Resolution Network (MDSR Net) architecture with sparse masks module and multistage progressive dose distribution restoration method were developed to predict high -resolution dose distribution using low -resolution data. A total of 340 VMAT plans from different disease sites were used, among which 240 randomly selected nasopharyngeal, lung, and cervix cases were used for model training, and the remaining 60 cases from the same sites for model benchmark testing, and additional 40 cases from the unseen site (breast and rectum) was used for model generalizability evaluation. The clinical calculated dose with a grid size of 2 mm was used as baseline dose distribution. The input included the dose distribution with 4 mm grid size and CT images. The model performance was compared with HD U -Net and cubic interpolation methods using Dose -volume histograms (DVH) metrics and global gamma analysis with 1%/1 mm and 10% low dose threshold. The correlation between the prediction error and the dose, dose gradient, and CT values was also evaluated. Results: The prediction errors of MDSR were 0.06 - 0.84% of D- mean indices, and the gamma passing rate was 83.1 - 91.0% on the benchmark testing dataset, and 0.02 - 1.03% and 71.3 - 90.3% for the generalization dataset respectively. The model performance was significantly higher than the HD U -Net and interpolation methods (p < 0.05). The mean errors of the MDSR model decreased (monotonously by 0.03 - 0.004%) with dose and increased (by 0.01 - 0.73%) with the dose gradient. There was no correlation between prediction errors and the CT values. Conclusion: The proposed MDSR model achieved good agreement with the baseline high -resolution dose distribution, with small prediction errors for DVH indices and high gamma passing rate for both seen and unseen sites, indicating a robust and generalizable dose prediction model. The model can provide fast and accurate high -resolution dose distribution for clinical dose calculation, particularly for the routine practice of OLART.

Aims This study aimed to develop a method for predicting short-term outcomes of lung cancer patients treated with intensity-modulated radiotherapy (IMRT) using radiomic features detected through computed tomography images. Methods A prediction model was developed based on a dataset of radiomic features obtained from 132 patients with lung cancer receiving IMRT. Dimension reduction was performed for the features using the maximum-relevance and minimum-redundancy (mRMR) algorithm, and the least absolute shrinkage and selection operator (LASSO) regression model was utilized to optimize feature selection for the IMRT-sensitivity prediction model. The model was constructed using binary logistic regression analysis and was evaluated using the concordance index (C-index), calibration plots, receiver operating characteristic curve, and decision curve analysis. Results Fifty features were selected from 1348 radiomic features using the mRMR method. Of these, three radiomic features were selected by LASSO logistic regression to construct the radiomics nomogram. The C-index of the model was 0.776 (95% confidence interval: 0.689–0.862) and 0.791 (95% confidence interval: 0.607–0.974) in the training and validation cohorts, respectively. Decision curve analysis showed that the radiomics nomogram was clinically useful. Conclusion Radiomic features have the potential to be applied to predict the short-term efficacy of IMRT in patients with inoperable lung cancer.

目的 探究一种以剂量体积直方图参数为优化变量,使用多目标优化算法的剂量优化配准算法,以提高锥形线束CT(CBCT)图像验证匹配的准确性.方法 回顾性分析哈尔滨医科大学附属肿瘤医院2022年收治的6例肺癌和5例宫颈癌患者的28套CBCT图像.以骨性配准的结果为剂量配准算法的初始点,在其周围的三维空间内计算各位移点的通量加权灰度均方差,并使用无监督k均值聚类方法筛选候选位移点.使用有限尺寸笔形束算法计算各候选位移点的三维剂量分布,并提取直方图剂量指标作为多目标优化算法的优化变量.使用多目标遗传算法求解Pareto最优解集后,根据预设的目标权重方案求解最优唯一解.结果 采用剂量优化配准后,肺癌病例计划靶区(PTV)的D90％、D95％、D98％、Dmean、适形度(CI)指标,较常规配准策略分别平均提高0.23 Gy、0.49 Gy、1.05 Gy、0.15 Gy、0.03,危及器官的剂量差异无统计学意义.宫颈癌病例PTV的D90、D95％、D98％、Dmean、CI指标,较常规配准策略分别平均提高0.72 Gy、1.15 Gy、2.53 Gy、0.24 Gy、0.05,部分危及器官评估指标降低1.06～1.81 Gy.结论 剂量优化配准方法可有效提高靶区覆盖度并降低危及器官剂量,减少刚性配准算法的残余误差,可作为自适应放疗技术的流程之一.

目的:研究呼吸运动的波形、幅度及肿瘤体积对胸部肿瘤立体定向放射治疗剂量分布的影响,为临床选取呼吸管理措施提供参考.方法:选取30例胸部肿瘤患者,用QUASARTM程控呼吸运动仪拖动二维矩阵PTW 1000SRS模拟肺部肿瘤在头脚方向运动,在不同呼吸信号(正弦波、呼吸训练后平稳信号、真实不规则信号)和呼吸幅度(0 mm、2 mm、5 mm、8 mm、10 mm)时采集等中心层面剂量分布,分析采集数据与放疗计划系统计算所得剂量分布差异,Pearson法分析γ通过率差值Wijk与体积的相关性.结果:呼吸运动对胸部肿瘤立体定向体部放射治疗计划产生的剂量误差主要分布在靶区边缘剂量高梯度区,靶区边缘外侧为剂量热点,内侧为冷点,呼吸幅度为2 mm时,不同呼吸模式下的平均γ通过率均>99％(99.17％ ±0.68％ ～99.40％ ±0.55％),差异<0.5％,Wijk与体积呈弱负相关(r=-0.11,r=-0.19,r=-0.086,P>0.05);呼吸幅度为5 mm时,平均 γ 通过率均>94％(94.64％ ±1.07％ ～96.30％ ±1.16％),Wijk与体积大小呈中度负相关(r=-0.53,r=-0.55,r=-0.42,P<0.05);呼吸幅度为8 mm时,平均γ通过率均<90％(85.23％ ±3.11％ ～86.95％ ±4.34％),不能满足临床需求,Wijk与靶区体积中度负相关及强相关(r=-0.69,r=-0.59,r=-0.66,P<0.001);呼吸幅度10 mm时,平均γ通过率均<80％(72.84％ ±5.10％ ～77.59％ ±5.33％),Wijk与靶区体积强负相关(r=-0.79,r=-0.73,r=-0.75,P<0.001).结论:呼吸运动模式对胸部肿瘤立体定向体部放射治疗技术(Stereotactic Body Radiation Therapy,SBRT)的影响由剂量模糊效应和肿瘤移动与多叶光栅运动之间的相互作用效应共同贡献,剂量热点多分布在靶区边缘外侧,冷点多分布在内侧.呼吸幅度较大(≥5 mm)时,小体积肿瘤受呼吸运动模式的影响更显著,呼吸训练可改善呼吸波形,呼吸管理可减少呼吸幅度,平稳的呼吸模式能提高γ通过率,提升SBRT计划的精准度.

通过临床实习,学生有机会学习理论知识和实践,并巩固和加深对理论知识的理解,但要掌握专业技能,提高综合能力.临床教学水平的高低直接关系到能否培养合格人才的社会需求,这是深化和继续教育的基础,是搞活工作的任务.提出了中医药疾病文献数据语义网络模型构想的思想优势,实现了中医药文献数据疾病的整合共享,进一步揭示了中医药疾病信息内容的优势.本文探讨了基于经验的临床教学模式,改革型临床教学模式,以期为临床实践提供参考.

目的:评价左乳腺癌保乳术后三种放疗方式(3DCRT,IMRT,VMAT)的剂量学特点.方法:选取本院2015年5月至2016年2月期间20例早期左乳腺癌保乳术后放疗患者,所有靶区及危及器官均由同一高级放疗医师勾画,包括临床靶区(CTV)、计划靶区(PTV)及危及器官(OAR),并由同一高级放疗物理师分别设计3DCRT、IMRT、VMAT三种治疗计划,处方剂量为50 Gy.比较三种计划的计划靶区(PTV)的靶区均匀性指数(HI)及适形度指数(CI),最大剂量(Dmax)、平均剂量(Dmean)、最小剂量(Dmin);肺、心脏的K,V10,V20,V30,V40,Dmax,Dmean及Dmin等.结果:3DCRT、IMRT、VMAT三种放疗计划适形度指数(CI)分别为0.75±0.08、0.84±0.04和0.89±0.04(P ＜0.05),均匀性指数(HI)分别为0.11±0.12、0.11±0.08和0.10±0.09.VMAT与IMRT计划降低了危及器官高剂量区体积,但相应增加了低剂量区体积,尤其VMAT计划的心脏、患侧肺V5、V10明显增加(P＜0.05).结论:IMRT计划不仅提高了靶区的适形度,而且降低了心脏和肺的低剂量受照体积及平均剂量.因此,IMRT计划更适用于左乳腺癌保乳术后的放射治疗.

Oddi括约肌运动功能障碍是消化系统临床常见疾病之一,西医治疗副作用大,远期效果不理想,中医药对Oddi括约肌运动功能障碍有独到的认识和丰富的治疗经验.本文介绍了谢晶日教授治疗Oddi括约肌运动功能障碍的临证经验,治疗上谢晶日教授主张在中医特色辨证的基础之上进行脏腑分治,运用理气止痛以疏肝,调畅气机以健脾,祛瘀通络以利胆的治疗原则,且在临床上取得良好疗效.同时将中医内服治疗与穴位贴敷疗法相辅结合,并强调注重患者的情绪、心理及饮食调护,以巩固临床疗效.

Oddi括约肌运动功能障碍是消化系统临床常见疾病之一,主要由胆囊切除后所导致。中医学对Oddi括约肌运动功能障碍有独到的认识和丰富的治疗经验。笔者从循经选穴特点、中药特色贴敷、针贴结合特色、典型病例多角度介绍运用中医经典,针药结合治疗Oddi括约肌运动功能障碍的临床特色及经验。在Oddi括约肌运动功能障碍的治疗中应重辨证、识病因,以疏为本,以通为先,以健为辅,注重疏肝以调畅气机、利胆以通壅活络、健脾以扶土祛邪,并结合脉络同治,强调治疗持久持效之法。在针灸循经选穴治疗之后,结合特色中药穴位贴敷,辅以疏肝健脾利胆中药,着重应用原穴、背俞穴及八会穴以透腠理、充血脉、补脏腑,体现针药相结合的治疗特色,在临床上取得了较好的疗效。

PURPOSE:This study evaluates the correlation between the susceptibility of the γ passing rate of IMRT plans to the multi-leaf collimator (MLC) position errors and a quantitative plan complexity metric.METHODS:Twenty patients were selected for this study. For each patient, two IMRT plans were generated using sliding window and step-&-shoot techniques, respectively. Modulation complexity score (MCS) was calculated for all IMRT plans, and symmetric MLC leaf bank errors, ranging from 0.3 mm to 1 mm, were introduced. Original and modified plans were delivered using Varian's Clinac iX. The obtained dose distribution using ArcCHECK was then compared with the TPS calculated dose distribution of the original plans. 3D gamma analysis was performed for each verification with passing criteria of 2%/2 mm. The γ passing rate decreasing gradient were calculated to evaluate relationship between variation of γ passing rate due to MLC errors and complexity.RESULTS:A linear regression analysis was applied between γ gradient and complexity, and the results showed a linear correlation (R2 = 0.81 and 0.82 for open and closed MLC error types, respectively) indicating the more complex plans are more susceptible to MLC leaf bank errors. Meanwhile, correlation of re-normalized γ passing rate and complexity for all errors scenarios also presented a strong correlation (r > 0.75).CONCLUSION:The statistics results revealed variation relationship of dosimetry robust of plans with various complexities to MLC errors. Our results also suggested that the observed susceptibility is independent of the delivery techniques.

Objective We aimed to explore the difference of dosimetry among intensity modulated radia-tion therapy(IMRT),volumetric modulated arc therapy(VMAT)and helical tomotherapy(TOMO)in the radio-therapy of medulloblastoma.Methods Ten children's patients with the medulloblastoma were selected and de-signed in this study.A clinically feasible radiotherapy plan was designed for IMRT(5 fields),VMAT and TOMO. Conformality index(CI),homogeneity index(HI),V107,maximum dose(Dmax),organ at risk(OAR)dose-volume level,monitor units and treatment time were used to analyze in these groups.Results CI,HI,V107,Dmaxand OAR of target areas were significantly superior to VMAT and IMRT in the TOMO group of target PTV for whole brain full radiotherapy.The organ at risk dose-volume level in the TOMO group was lower than that in VMAT and IM-RT(5 fields)groups(P<0.05).The TOMO group also had the most monitor units and the longest treatment time (P<0.05).Hence,the patients in the TOMO group could irradiate completely the whole brain and full spinal cord without moving treatment couch to avoid the error from the man-made movement in VMAT and IMRT(5 fields)treatments.Conclusion In the radiotherapy of medulloblastoma,the dose distribution of patients in the TOMO group are superior to the VMAT and IMRT groups(5 fields).However,the number of monitor units and treatment time is significantly increased during treatment,and its clinical effect needed to be further studies.

为共享医生诊疗经验,从相似电子病历检索的角度探讨辅助诊断系统的建设,介绍系统功能模块组成和理论算法,包括主诉相似度、诊疗模式相似度、图像相似度、综合相似度计算算法.此方法能够较为全面地根据电子病历的内容计算相似度,检索出的电子病历为医生制订诊疗计划提供参考.

OBJECTIVE:To detect the accuracy of CT-MR image registration in the radiation therapy by comparing the volome of the GTV contoured on the CT,CT-MR and MR.METHODS:The scan time of the enhanced CT with 30 mgI/mL Iopamidol Injection at 5mm slice thickness was 10.6 s.The enhanced MR at 5 mm slice thickness which used 0.1 mmol/kg Gd-DTPA scaned the T1WI of the coronal section and the T1WI,T2WI,FLAIR of the transverse section.The parameters of the MR were as follows,T1WI:TR 440 ms,TE 14 ms;T2WI:TR 3 200 ms,TE 280 ms;FLAIR IR:IR 2 000 ms.The images of the 20 patients were transferred to the Eclipse workstation for image fusion.The GTV of each patient was contoured on CT(GTVCT),CT-MR(GTVCT-MR),MR(GTVMR).The volume of GTV on the all sets images and the accuracy of the two image fusion methods in Landmark were contrasted.RESULTS:The mean value of GTVCT,GTVCT-MR and GTVMR were(25.24±4.73) cm3,(21.8±5.31) cm3,(19.03±3.04) cm3(F=9.709,q=6.21,P=0.001).The veracity of the GTVCT-MR was better than that of the GTVCT(q=3.44,P0.05).There was no significant difference between GTVCT-MR and GTVMR(q=2.77,P0.05).The mean difference of the two kinds of methods were(1.39±0.64) and(1.97±1.0) mm.CONCLUSIONS:CT-MR image fusion can improve the stability and the accuracy on contouring GTV for lung cancer with brain metastasis.The method with the surface landmark performs better in IMRT.

BACKGROUND:Accurate target delineation in radiation therapy is a key component of the treatment regimen for brain metastasis for which CT/MRI fusion technology provides a feasible method. The aim of this study is to explore the role of CT/MRI image registration in target delineation for lung cancer with brain metastasis. METHODS:The image data of 31 patients were processed using Oncentra MasterPlan. The GTVs were delineated on CT and CT/MRI images, and their differences were compared to analyze the impact of the maximum average error and tumor edema on target delineation. RESULTS:The GTVs delineated on CT/MRI images were markedly smaller than those delineated on CT images. Target delineation was clearly influenced by edema. CONCLUSIONS:The technology of CT/MRI image registration can improve the accuracy of target delineation for lung cancer with brain metastasis.