{"title":"通过整合放射组学特征和神经网络,在计算机断层扫描中快速估算患者特定器官的剂量。","authors":"Wencheng Shao, Xin Lin, Ying Huang, Liangyong Qu, Weihai Zhuo, Haikuan Liu","doi":"10.21037/qims-24-645","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Computed tomography (CT) offers detailed cross-sectional images of internal anatomy for disease detection but carries a risk of solid cancer or blood malignancies due to exposure to X-ray radiation. This study aimed to develop a new method to quickly predict patient-specific organ doses from CT examinations by training neural networks (NNs) based on radiomics features.</p><p><strong>Methods: </strong>CT Digital Imaging and Communications in Medicine (DICOM) image data were exported to DeepViewer, a clinical autosegmentation software, to segment the regions of interest (ROIs) for patient organs. Radiomics feature extraction was performed based on the selected CT data and ROIs. Reference organ doses were computed using Monte Carlo (MC) simulations. Patient-specific organ doses were predicted by training a NN model based on radiomics features and reference doses. For the dose prediction performance, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R<sup>2</sup>) were evaluated on the test sets. The robustness of the NN model was evaluated via the random rearrangement of patient samples in the training and test sets.</p><p><strong>Results: </strong>The maximal difference between the reference and predicted doses was less than 1 mGy for all investigated organs. The range of MAPE was 1.68% to 5.2% for head organs, 11.42% to 15.2% for chest organs, and 5.0% to 8.0% for abdominal organs; the maximal R<sup>2</sup> values were 0.93, 0.86, and 0.89 for the head, chest, and abdominal organs, respectively.</p><p><strong>Conclusions: </strong>The radiomics feature-based NN model can achieve accurate prediction of patient-specific organ doses at a high speed of less than 1 second using a single central processing unit, which supports its use as a user-friendly online clinical application.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11485356/pdf/","citationCount":"0","resultStr":"{\"title\":\"Rapid patient-specific organ dose estimation in computed tomography scans via integration of radiomics features and neural networks.\",\"authors\":\"Wencheng Shao, Xin Lin, Ying Huang, Liangyong Qu, Weihai Zhuo, Haikuan Liu\",\"doi\":\"10.21037/qims-24-645\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Computed tomography (CT) offers detailed cross-sectional images of internal anatomy for disease detection but carries a risk of solid cancer or blood malignancies due to exposure to X-ray radiation. This study aimed to develop a new method to quickly predict patient-specific organ doses from CT examinations by training neural networks (NNs) based on radiomics features.</p><p><strong>Methods: </strong>CT Digital Imaging and Communications in Medicine (DICOM) image data were exported to DeepViewer, a clinical autosegmentation software, to segment the regions of interest (ROIs) for patient organs. Radiomics feature extraction was performed based on the selected CT data and ROIs. Reference organ doses were computed using Monte Carlo (MC) simulations. Patient-specific organ doses were predicted by training a NN model based on radiomics features and reference doses. For the dose prediction performance, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R<sup>2</sup>) were evaluated on the test sets. The robustness of the NN model was evaluated via the random rearrangement of patient samples in the training and test sets.</p><p><strong>Results: </strong>The maximal difference between the reference and predicted doses was less than 1 mGy for all investigated organs. The range of MAPE was 1.68% to 5.2% for head organs, 11.42% to 15.2% for chest organs, and 5.0% to 8.0% for abdominal organs; the maximal R<sup>2</sup> values were 0.93, 0.86, and 0.89 for the head, chest, and abdominal organs, respectively.</p><p><strong>Conclusions: </strong>The radiomics feature-based NN model can achieve accurate prediction of patient-specific organ doses at a high speed of less than 1 second using a single central processing unit, which supports its use as a user-friendly online clinical application.</p>\",\"PeriodicalId\":54267,\"journal\":{\"name\":\"Quantitative Imaging in Medicine and Surgery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11485356/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quantitative Imaging in Medicine and Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.21037/qims-24-645\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/9/26 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Imaging in Medicine and Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/qims-24-645","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/26 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
摘要
背景:计算机断层扫描(CT)可提供详细的内部解剖横截面图像,用于疾病检测,但由于暴露于 X 射线辐射,存在患实体癌或血液恶性肿瘤的风险。本研究旨在开发一种新方法,通过训练基于放射组学特征的神经网络(NNs),从 CT 检查中快速预测患者特定器官的剂量:CT数字成像和医学通信(DICOM)图像数据被导出到临床自动分割软件DeepViewer,以分割患者器官的感兴趣区(ROI)。根据选定的 CT 数据和 ROI 进行放射组学特征提取。使用蒙特卡洛(MC)模拟计算参考器官剂量。通过训练基于放射组学特征和参考剂量的 NN 模型,预测患者特定器官的剂量。在剂量预测性能方面,对测试集的相对均方根误差(RRMSE)、平均绝对百分比误差(MAPE)和决定系数(R2)进行了评估。通过随机重新排列训练集和测试集中的患者样本,对 NN 模型的鲁棒性进行了评估:所有研究器官的参考剂量和预测剂量之间的最大差异均小于 1 mGy。头部器官的 MAPE 范围为 1.68% 至 5.2%,胸部器官为 11.42% 至 15.2%,腹部器官为 5.0% 至 8.0%;头部、胸部和腹部器官的最大 R2 值分别为 0.93、0.86 和 0.89:基于放射组学特征的 NN 模型可以在单个中央处理单元上以小于 1 秒的高速准确预测患者特定器官的剂量,支持将其用作用户友好型在线临床应用。
Rapid patient-specific organ dose estimation in computed tomography scans via integration of radiomics features and neural networks.
Background: Computed tomography (CT) offers detailed cross-sectional images of internal anatomy for disease detection but carries a risk of solid cancer or blood malignancies due to exposure to X-ray radiation. This study aimed to develop a new method to quickly predict patient-specific organ doses from CT examinations by training neural networks (NNs) based on radiomics features.
Methods: CT Digital Imaging and Communications in Medicine (DICOM) image data were exported to DeepViewer, a clinical autosegmentation software, to segment the regions of interest (ROIs) for patient organs. Radiomics feature extraction was performed based on the selected CT data and ROIs. Reference organ doses were computed using Monte Carlo (MC) simulations. Patient-specific organ doses were predicted by training a NN model based on radiomics features and reference doses. For the dose prediction performance, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were evaluated on the test sets. The robustness of the NN model was evaluated via the random rearrangement of patient samples in the training and test sets.
Results: The maximal difference between the reference and predicted doses was less than 1 mGy for all investigated organs. The range of MAPE was 1.68% to 5.2% for head organs, 11.42% to 15.2% for chest organs, and 5.0% to 8.0% for abdominal organs; the maximal R2 values were 0.93, 0.86, and 0.89 for the head, chest, and abdominal organs, respectively.
Conclusions: The radiomics feature-based NN model can achieve accurate prediction of patient-specific organ doses at a high speed of less than 1 second using a single central processing unit, which supports its use as a user-friendly online clinical application.