Prognostic value of fluorodeoxyglucose positron emission tomography derived metabolic parameters and textural features in pediatric sarcoma

Abstract

The aim of this study was to evaluate the prognostic value of PET-derived metabolic features and textural parameters of primary tumors in pediatric sarcoma patients. MethodsThe imaging findings of 43 patients (14 girls and 29 boys; age 11.4 ± 4.4 years) who underwent 18-fluorodeoxyglucose positron emission tomography (PET)/computed tomography for primary staging prior to therapy between 2005 and 2020 were retrospectively evaluated. The diagnoses were osteosarcoma in 10, rhabdomyosarcoma in 10, and Ewing sarcoma in 23 patients. PET metabolic data and textural features of primary tumors were obtained. Cox proportional hazards regression models were used to identify predictors for progression-free survival and overall survival. Survival curves were estimated by using the Kaplan-Meier method. ResultsDistant metastases were detected in primary staging in 13 patients (30.2%). The median follow-up duration after diagnosis was 28 months (range: 10-171 months). In multivariate Cox regression analysis, the presence of distant metastasis and neighborhood grey-level difference matrix_Contrast (ngldm_Contrast) were found as independent predictors for both progression-free survival and overall survival.Grey-level zone length matrix_Zone-length nonuniformity (glzlm_ZLNU) was also found as an independent predictor for overall survival. The Kaplan-Meier survival analysis showed that higher ngldm_Contrast and glzlm_ZLNU values of primary tumors were significantly associated with shorter progression-free survival and overall survival. ConclusionIn addition to the presence of distant metastasis at initial diagnosis, textural features of primary tumors may be used as prognostic biomarkers to identify patients with worse prognosis in pediatric sarcoma.

Advancing Pediatric Sarcomas through Radiomics: A Systematic Review and Prospective Assessment Using Radiomics Quality Score (RQS) and Methodological Radiomics Score (METRICS)

“…Giraudo C (2022) [17] 53.9 Moderate 65.7 Good Ding Y (2022) [16] 60.5 Good 64.9 Good Aydos U (2022) [12] 55.6 Moderate 57.6 Moderate Bouhamama A (2022) [14] 80.3 Excellent 82.4 Excellent Yang Y (2022) [20] 80.0 Excellent 84.7 Excellent Aydos U (2023) [11] 59.0 Moderate 57.6 Moderate…”

“…Among these, 31 articles were excluded based on predefined criteria: 6 were review articles, 22 were studies that were unrelated to radiomics, 2 were not focused on pediatric populations, 1 was not written in English, and 2 were centered on volumetric diffusion-weighted imaging (DWI) analysis, with an additional article focused on deep learning and another one involving methods that deviate from the standard radiomics approach in terms of feature extraction and analysis techniques. Finally, following the application of inclusion and exclusion criteria, a total of 10 reports were included in the review process (refer to Figure 1 for the selection process flowchart) [11][12][13][14][15][16][17][18][19][20].…”