A Multiparametric Method Based on Clinical and CT-Based Radiomics to Predict the Expression of p53 and VEGF in Patients With Spinal Giant Cell Tumor of Bone

Abstract

PurposeThis project aimed to assess the significance of vascular endothelial growth factor (VEGF) and p53 for predicting progression-free survival (PFS) in patients with spinal giant cell tumor of bone (GCTB) and to construct models for predicting these two biomarkers based on clinical and computer tomography (CT) radiomics to identify high-risk patients for improving treatment.Material and MethodsA retrospective study was performed from April 2009 to January 2019. A total of 80 patients with spinal GCTB who underwent surgery in our institution were identified. VEGF and p53 expression and clinical and general imaging information were collected. Multivariate Cox regression models were used to verify the prognostic factors. The radiomics features were extracted from the regions of interest (ROIs) in preoperative CT, and then important features were selected by the SVM to build classification models, evaluated by 10-fold crossvalidation. The clinical variables were processed using the same method to build a conventional model for comparison.ResultsThe immunohistochemistry of 80 patients was obtained: 49 with high-VEGF and 31 with low-VEGF, 68 with wild-type p53, and 12 with mutant p53. p53 and VEGF were independent prognostic factors affecting PFS found in multivariate Cox regression analysis. For VEGF, the Spinal Instability Neoplastic Score (SINS) was greater in the high than low groups, p < 0.001. For p53, SINS (p = 0.030) and Enneking stage (p = 0.017) were higher in mutant than wild-type groups. The VEGF radiomics model built using 3 features achieved an area under the curve (AUC) of 0.88, and the p53 radiomics model built using 4 features had an AUC of 0.79. The conventional model built using SINS, and the Enneking stage had a slightly lower AUC of 0.81 for VEGF and 0.72 for p53.Conclusionp53 and VEGF are associated with prognosis in patients with spinal GCTB, and the radiomics analysis based on preoperative CT provides a feasible method for the evaluation of these two biomarkers, which may aid in choosing better management strategies.

A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers

“…The average ± standard deviation (median, range) sample size of the included studies was of 97 ± 56 (92, 29–215). Five studies were based on CT [ 20 - 22 , 25 , 28 ], three were conducted with MRI [ 23 , 24 , 26 ], respectively, and the left one study used both CT and MRI [ 27 ]. The included nine articles covered a vast range of clinical questions of GCTB (Fig.…”

“…Seven models attempted to differentiate GCTB from other types of tumors, including aneurysmal bone cyst [ 21 , 24 ], chordoma [ 25 - 27 ], neurogenic tumor [ 28 ], or metastatic tumor [ 26 ], but only one model compared the performance of radiomics with radiologists’ assessment and showed significant improvement [ 24 ]. One model was developed for expression of p53 and VEGF in GCTB, and provided better performance than clinical scoring or staging system [ 23 ]. One model was built for prognostic purpose for early recurrence of spinal GCTB [ 22 ].…”

Prognostic Value and Quantitative CT Analysis in RANKL Expression of Spinal GCTB in the Denosumab Era: A Machine Learning Approach

“…This radiomic signature could assist surgeons in evaluating patient prognosis before surgery and predicting drug treatment response, which would be helpful for clinical decision-making. Previous findings have also applied radiomics for preoperative differential diagnosis, recurrence prediction, and biomarker assessment of GCTB [ 32 , 33 , 34 ]. Although our study is preliminary, this may be a promising method for improving the precise treatment of spinal GCTB.…”

Research status and progress of radiomics in bone and soft tissue tumors: A review

“…[94] Emerging evidence indicated that macroscopic heterogeneity assessed by medical imaging can reflect the aggressiveness of the biological phenotype. In patients with GCTB, Wang et al [95] evaluated the effects of VEGF and P53 on the prediction of PFS in GCTB patients. The preoperative CT imaging was also taken to establish models for predicting these 2 biomarkers.…”