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

Abstract

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) ; (Non-RMS group) , where , , … and 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.

CT and MR imaging features of soft tissue rhabdoid tumor: compared with rhabdomyosarcoma in children

“…In addition, the rapid growth and metabolism of malignant RT cells, as well as the excessive proliferation of blood vessels in the tumor, make calcium salt deposition possible (28). Most studies agree with us that calcification and hemorrhage are uncommon in children with RMS (29,30). As shown in the study, 63.6% (21/33) of RMSs and 18.8% (3/16) of soft tissue RTs displayed rim enhancement, which contributed significantly to the differential diagnosis between the two groups, and functioned as an independent predictor.…”

Comprehensive pan-cancer analysis and the regulatory mechanism of AURKA, a gene associated with prognosis of ferroptosis of adrenal cortical carcinoma in the tumor micro-environment

“…The least absolute shrinkage and selection operator (LASSO) is an L1-regularized linear regression approach. Using L1-regularization, part of the learned feature weights will be set to zero, achieving the goal of sparsity and feature selection ( Tian et al, 2022 ). Univariate Cox regression analysis of DEFGs was used to identify significant prognosis-related genes, followed by LASSO regression analysis to obtain independent genes.…”