Fisher discriminant model based on LASSO logistic regression for computed tomography imaging diagnosis of pelvic rhabdomyosarcoma in children

作者全名："Tian, Lu; Li, Xiaomeng; Zheng, Helin; Wang, Longlun; Qin, Yong; Cai, Jinhua"

作者地址："[Tian, Lu] Chongqing Med Univ, Childrens Hosp, Dept Radiol, Chongqing 400014, Peoples R China; [Tian, Lu] Minist Educ, Key Lab Child Dev & Disorders, Chongqing Int Sci & Technol Cooperat Ctr Child De, Chongqing 400014, Peoples R China; [Tian, Lu] Key Lab Pediat Chongqing, Chongqing 400014, Peoples R China; [Li, Xiaomeng; Zheng, Helin; Wang, Longlun; Qin, Yong; Cai, Jinhua] Chongqing Med Univ, Childrens Hosp, Chongqing, Peoples R China"

通信作者："Qin, Y; Cai, JH (通讯作者)，Chongqing Med Univ, Childrens Hosp, Chongqing, Peoples R China."

来源：SCIENTIFIC REPORTS

ESI学科分类：Multidisciplinary

WOS号：WOS:000854870500005

JCR分区：Q1

影响因子：4.6

年份：2022

卷号：12

期号：1

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结束页：　

文献类型：Article

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摘要："Computed tomography (CT) has been widely used for the diagnosis of pelvic rhabdomyosarcoma (RMS) in children. However, it is difficult to differentiate pelvic RMS from other pelvic malignancies. This study aimed to analyze and select CT features by using least absolute shrinkage and selection operator (LASSO) logistic regression and established a Fisher discriminant analysis (FDA) model for the quantitative diagnosis of pediatric pelvic RMS. A total of 121 pediatric patients who were diagnosed with pelvic neoplasms were included in this study. The patients were assigned to an RMS group (n = 36) and a non-RMS group (n = 85) according to the pathological results. LASSO logistic regression was used to select characteristic features, and an FDA model was constructed for quantitative diagnosis. Leave-one-out cross-validation and receiver operating characteristic (ROC) curve analysis were used to evaluate the diagnostic ability of the FDA model. Six characteristic variables were selected by LASSO logistic regression, all of which were CT morphological features. Using these CT features, the following diagnostic models were established: (RMS group)G(1) = -14.283 + 6.613x(1) + 5.333x(2) + 5.753x(3) + 12.361x(4) + 8.095x(5) - 0.715x(6); (Non-RMS group)G(2) = -2.008 + 3.539x(1) + 1.080x(2) + 1.154x(3) + 2.307x(4) + 1.656x(5) + 1.380x(6), where x(1), x(2), ... and x(6) are lower than normal muscle density (1 =yes; 0 =no), multinodular fusion (1 =yes; 0 =no), enhancement at surrounding blood vessels (1 =yes; 0 = no), heterogeneous progressive centripetal enhancement (1 =yes; 0 =no), ring enhancement (1 = yes; 0= no), and hemorrhage (1 = yes; 0 = no), respectively. The calculated area under the ROC curve (AUC) of the model was 0.992 (0.982-1.000), with a sensitivity of 94.4%, a specificity of 96.5%, and an accuracy of 95.9%. The calculated sensitivity, specificity and accuracy values were consistent with those from cross-validation. An FDA model based on the CT morphological features of pelvic RMS was established and could provide an easy and efficient method for the diagnosis and differential diagnosis of pelvic RMS in children."

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