鼻咽癌容积调强剂量验证与计划复杂性的相关性

目的:研究鼻咽癌容积调强剂量验证γ通过率与计划复杂性之间的相关性。方法:选取106例鼻咽癌容积调强计划,采用二维电离室矩阵Matrixx进行剂量验证,比较测量的和计划的剂量分布,评价在不同标准(3%/3 mm、3%/2 mm、3%/1 mm、2%/2 mm)下的通过率。采用调强复杂性指数(MCS)定量评价计划的复杂性,并分析计划验证γ通过率与MCS间的关系。结果:鼻咽癌容积调强计划在3%/3 m、3%/2 mm、3%/1 mm、2%/2 mm标准下的通过率分别为(98.49±0.95)%、(95.92±1.71)%、(89.74±2.44)%、(90.58±2.87)%。计划的平均MCS值为0.210±0.019,与通过率间(3%/3m、3%/2 mm、3%/1 mm、2%/2 mm标准)的Pearson相关性系数分别为0.333(P<0.001)、0.303(P=0.002)、0.347(P<0.001)、0.267(P=0.006)。结论:鼻咽癌容积调强计划验证γ通过率与MCS之间有相关性,但相关性较弱。

Software Analytics of Open Source Business Software

This paper applies software analytics to open source code. Open-source software gives both individuals and businesses the flexibility to work with different parts of available code to modify it or incorporate it into their own project. The open source software market is growing. Major companies such as AWS, Facebook, Google, IBM, Microsoft, Netflix, SAP, Cisco, Intel, and Tesla have joined the open source software community. In this study, a sample of 40 open source applications was selected. Traditional McCabe software metrics including cyclomatic and essential complexities were examined. An analytical comparison of this set of metrics and derived metrics for high risk software was utilized as a basis for addressing risk management in the adoption and integration decisions of open source software. From this comparison, refinements were added, and contemporary concepts of design and data metrics derived from cyclomatic complexity were integrated into a classification scheme for software quality. It was found that 84% of the sample open source applications were classified as moderate low risk or low risk indicating that open source software exhibits low risk characteristics. The 40 open source applications were the base data for the model resulting in a technique which is applicable to any open source code regardless of functionality, language, or size.

Software Metric Analysis of Open-Source Business Software

Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.

VMAT计划复杂性的定量评估方法探讨

目的：提出新的定量评估指标———弧形野调制复杂度分数（ AMCS），并分析6种量化计划复杂性指标对VMAT剂量验证的影响。方法定量计算127例患者VMAT计划复杂性指标：平均射野面积（ ABA）、平均叶片路径（ ALT）、平均射野宽度（ ABW）、调制复杂度分数（ MCS）、叶片路径？调制复杂度分数（ LTMCS）和AMCS。采用Delta4系统测量VMAT剂量验证γ通过率。 Pearson法分析ABA、ALT、ABW、MCS、LTMCS和AMCS与3 mm／3％下γ通过率相关性，以及6个指标间的相互关系。结果 ABA、ALT、ABW、MCS、LTMCS和AMCS平均值分别为（73．5±24．1） cm2、（69．6±9．8） cm、（3．63±1．03） cm、0．33±0．05、0．14±0．04和0．16±0．05，且均与γ通过率相关（P＝0．000），其中AMCS与γ通过率相关性最大（ r＝0．637）；AMCS＜平均值时γ通过率＜95％的射野比为60．7％，＞平均值时为9．9％。 ABA、ABW与ALT无相关性（P＝0．720、0．073），其余指标间均相关（P＝0．000～0．003）。结论 AMCS指标是一种适合用于定量评估VMAT计划复杂性的方法，可用于预测VMAT剂量验证结果，在VMAT计划审核中用于比较多个计划的复杂度以帮助选择最佳计划。

基于复杂性评分VMAT计划质量控制

目的探讨容积调强弧形治疗不同计划系统、不同多叶准直器(MLC)类型和不同治疗部位的计划复杂性差异,提出复杂性评分用于计划质量控制。方法统计Monaco和Eclipse系统,Agility、M-MLC和高分辨率MLC,鼻咽癌、肺癌和宫颈癌12个复杂性指标,计算复杂性指标Spearman相关系数,执行主成分分析将原数据集维数降至前两个主成分并解释其物理意义,计算复杂性评分并为计划质控建立容差和干预限值,分析复杂性指标与γ验证通过率相关性。结果Monaco与Eclipse除射束孔径子区域个数外,其他复杂性指标均有显著性差异,Monaco MLC子野更规则但机器跳数更高、叶片间距更小、运动距离更长,高分辨率MLC由于叶片宽度更窄显著提高MLC形状相关复杂性指标。主成分分析前两个主成分包含原数据集中超过80%的可变性,复杂性评分为前两个主成分加权均值。不同设备和部位复杂性评分不一样,以均值加标准差为容差限值,均值加2倍标准差为干预限值。复杂性特征和复杂性评分与γ通过率相关系数较小,呈弱相关或不相关,却有统计学意义。结论不同计划系统、MLC类型和治疗部位复杂性指标存在较大差异,复杂性评分为有用质控工具。