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Abstract：
UNASSIGNED: Segmentation and analysis of organs at risks (OARs) and tumor volumes are integral concepts in the development of radiotherapy treatment plans and prediction of patients\' treatment outcomes.
UNASSIGNED: To develop a research tool, PAHPhysRAD, that can be used to semi- and fully automate segmentation of OARs. In addition, the proposed software seeks to extract 3214 radiomic features from tumor volumes and user-specified dose-volume parameters.
UNASSIGNED: Developed within MATLAB, PAHPhysRAD provides a comprehensive suite of segmentation tools, including manual, semi-automatic, and automatic options. For semi-autosegmentation, meta AI\'s Segment Anything Model was incorporated using the bounding box methods. Autosegmentation of OARs and tumor volume are implemented through a module that enables the addition of models in Open Neural Network Exchange format. To validate the radiomic feature extraction module in PAHPhysRAD, radiomic features extracted from gross tumor volume of 15 non-small cell lung carcinoma patients were compared against the features extracted from 3D Slicer™. The dose-volume parameters extraction module was validated using the dose volume data extracted from 28 tangential field-based breast treatment planning datasets. The volume receiving ≥20 Gy (V20) for ipsilateral lung and the mean doses received by the heart and ipsilateral lung, were compared against the parameters extracted from Eclipse.
UNASSIGNED: The Wilcoxon signed-rank test revealed no significant difference between the majority of the radiomic features derived from PAHPhysRAD and 3D Slicer. The average mean lung and heart doses calculated in Eclipse were 5.51 ± 2.28 Gy and 1.64 ± 1.98 Gy, respectively. Similarly, the average mean lung and heart doses calculated in PAHPhysRAD were 5.45 ± 2.89 Gy and 1.67 ± 2.08 Gy, respectively.
UNASSIGNED: The MATLAB-based graphical user interface, PAHPhysRAD, offers a user-friendly platform for viewing and analyzing medical scans with options to extract radiomic features and dose-volume parameters. Its versatility, compatibility, and potential for further development make it an asset in medical image analysis.
UNASSIGNED: To develop a research tool, PAHPhysRAD, that can be used to semi- and fully automate segmentation of OARs. In addition, the proposed software seeks to extract 3214 radiomic features from tumor volumes and user-specified dose-volume parameters.
UNASSIGNED: Developed within MATLAB, PAHPhysRAD provides a comprehensive suite of segmentation tools, including manual, semi-automatic, and automatic options. For semi-autosegmentation, meta AI\'s Segment Anything Model was incorporated using the bounding box methods. Autosegmentation of OARs and tumor volume are implemented through a module that enables the addition of models in Open Neural Network Exchange format. To validate the radiomic feature extraction module in PAHPhysRAD, radiomic features extracted from gross tumor volume of 15 non-small cell lung carcinoma patients were compared against the features extracted from 3D Slicer™. The dose-volume parameters extraction module was validated using the dose volume data extracted from 28 tangential field-based breast treatment planning datasets. The volume receiving ≥20 Gy (V20) for ipsilateral lung and the mean doses received by the heart and ipsilateral lung, were compared against the parameters extracted from Eclipse.
UNASSIGNED: The Wilcoxon signed-rank test revealed no significant difference between the majority of the radiomic features derived from PAHPhysRAD and 3D Slicer. The average mean lung and heart doses calculated in Eclipse were 5.51 ± 2.28 Gy and 1.64 ± 1.98 Gy, respectively. Similarly, the average mean lung and heart doses calculated in PAHPhysRAD were 5.45 ± 2.89 Gy and 1.67 ± 2.08 Gy, respectively.
UNASSIGNED: The MATLAB-based graphical user interface, PAHPhysRAD, offers a user-friendly platform for viewing and analyzing medical scans with options to extract radiomic features and dose-volume parameters. Its versatility, compatibility, and potential for further development make it an asset in medical image analysis.
摘要：
危险器官（OAR）和肿瘤体积的分割和分析是制定放射治疗计划和预测患者治疗结果的不可或缺的概念。
■为了开发一种研究工具，PAHPhysRAD,可用于半自动和全自动OAR分割。此外,所提出的软件试图从肿瘤体积和用户指定的剂量体积参数中提取3214个影像组学特征.
■在MATLAB中开发，PAHPhysRAD提供了一套全面的分割工具，包括手册,半自动,和自动选项。对于半自动分割，元AI的分段任意模型是使用边界框方法合并的。OAR和肿瘤体积的自动分割是通过一个模块实现的，该模块可以添加开放式神经网络交换格式的模型。为了验证PAHPhysRAD中的影像组学特征提取模块，将从15例非小细胞肺癌患者的大体肿瘤体积中提取的影像组学特征与从3DSlicer™中提取的特征进行比较.使用从28个基于切向场的乳房治疗计划数据集提取的剂量体积数据来验证剂量-体积参数提取模块。同侧肺的接受量≥20Gy（V20）以及心脏和同侧肺接受的平均剂量，与从Eclipse中提取的参数进行了比较。
■Wilcoxon符号秩检验显示，来自PAHPhysRAD和3D切片器的大多数放射学特征之间没有显着差异。在Eclipse中计算的平均肺和心脏剂量为5.51±2.28Gy和1.64±1.98Gy，分别。同样，在PAHPhysRAD中计算的平均肺和心脏剂量为5.45±2.89Gy和1.67±2.08Gy，分别。
■基于MATLAB的图形用户界面，PAHPhysRAD,提供了一个用户友好的平台，用于查看和分析医疗扫描，并提供提取放射学特征和剂量体积参数的选项。它的多功能性，兼容性,和进一步发展的潜力使其成为医学图像分析的资产。
■为了开发一种研究工具，PAHPhysRAD,可用于半自动和全自动OAR分割。此外,所提出的软件试图从肿瘤体积和用户指定的剂量体积参数中提取3214个影像组学特征.
■在MATLAB中开发，PAHPhysRAD提供了一套全面的分割工具，包括手册,半自动,和自动选项。对于半自动分割，元AI的分段任意模型是使用边界框方法合并的。OAR和肿瘤体积的自动分割是通过一个模块实现的，该模块可以添加开放式神经网络交换格式的模型。为了验证PAHPhysRAD中的影像组学特征提取模块，将从15例非小细胞肺癌患者的大体肿瘤体积中提取的影像组学特征与从3DSlicer™中提取的特征进行比较.使用从28个基于切向场的乳房治疗计划数据集提取的剂量体积数据来验证剂量-体积参数提取模块。同侧肺的接受量≥20Gy（V20）以及心脏和同侧肺接受的平均剂量，与从Eclipse中提取的参数进行了比较。
■Wilcoxon符号秩检验显示，来自PAHPhysRAD和3D切片器的大多数放射学特征之间没有显着差异。在Eclipse中计算的平均肺和心脏剂量为5.51±2.28Gy和1.64±1.98Gy，分别。同样，在PAHPhysRAD中计算的平均肺和心脏剂量为5.45±2.89Gy和1.67±2.08Gy，分别。
■基于MATLAB的图形用户界面，PAHPhysRAD,提供了一个用户友好的平台，用于查看和分析医疗扫描，并提供提取放射学特征和剂量体积参数的选项。它的多功能性，兼容性,和进一步发展的潜力使其成为医学图像分析的资产。
