Metastasis risk prediction model in osteosarcoma using metabolic imaging phenotypes: A multivariable radiomics model

• Published in: PloS one Vol. 14; no. 11; p. e0225242
• Main Authors: Sheen, Heesoon, Kim, Wook, Byun, Byung Hyun, Kong, Chang-Bae, Song, Won Seok, Cho, Wan Hyeong, Lim, Ilhan, Lim, Sang Moo, Woo, Sang-Keun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.11.2019; Public Library of Science (PLoS)
• Subjects: Adult; Biology and Life Sciences; Biomedical materials; Bone cancer; Bone Neoplasms - diagnostic imaging; Bone Neoplasms - pathology; Bone surgery; Bone tumors; Cancer; Cancer therapies; Cancer treatment; Chemotherapy; Clinical outcomes; Colonization; Departments; Diagnostic imaging; Feature extraction; Female; Fluorine isotopes; Fluorodeoxyglucose F18; Genetic aspects; Health aspects; Heterogeneity; Humans; Hypoxia; Lung cancer; Male; Medical diagnosis; Medical imaging; Medical prognosis; Medicine and Health Sciences; Metabolism; Metastases; Metastasis; Neoplasm Metastasis; Nuclear medicine; Operating systems (Software); Orthopedics; Osteosarcoma; Osteosarcoma - diagnostic imaging; Osteosarcoma - pathology; Patients; Phenotype; Phenotypes; Physical Sciences; Positron emission; Positron emission tomography; Positron-Emission Tomography - methods; Positron-Emission Tomography - standards; Prediction models; Prognosis; Radiology; Radiomics; Radiopharmaceuticals; Regression analysis; Regression models; Research and Analysis Methods; Risk assessment; Sarcoma; Statistical analysis; Surgery; Survival; Tomography; Tumors; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0225242

Osteosarcoma (OS) is the most common primary bone tumor affecting humans and it has extreme heterogeneity. Despite modern therapy, it recurs in approximately 30-40% of patients initially diagnosed with no metastatic disease, with the long-term survival rates of patients with recurrent OS being generally 20%. Thus, early prediction of metastases in OS management plans is crucial for better-adapted treatments and survival rates. In this study, a radiomics model for metastasis risk prediction in OS was developed and evaluated using metabolic imaging phenotypes. The subjects were eighty-three patients with OS, and all were treated with surgery and chemotherapy for local control. All patients underwent a pretreatment 18F-FDG-PET scan. Forty-five features were extracted from the tumor region. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved cross validation in the following four steps leading to final prediction model construction: (1) feature set reduction and selection; (2) model coefficients computation through train and validation processing; and (3) prediction performance estimation. The multivariable logistic regression model was developed using two radiomics features, SUVmax and GLZLM-SZLGE. The trained and validated multivariable logistic model based on probability of endpoint (P) = 1/ (1+exp (-Z)) was Z = -1.23 + 1.53*SUVmax + 1.68*GLZLM-SZLGE with significant p-values (SUVmax: 0.0462 and GLZLM_SZLGE: 0.0154). The final multivariable logistic model achieved an area under the curve (AUC) receiver operating characteristics (ROC) curve of 0.80, a sensitivity of 0.66, and a specificity of 0.88 in cross validation. The SUVmax and GLZLM-SZLGE from metabolic imaging phenotypes are independent predictors of metastasis risk assessment. They show the association between 18F-FDG-PET and metastatic colonization knowledge. The multivariable model developed using them could improve patient outcomes by allowing aggressive treatment in patients with high metastasis risk.