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

Autoři: Heesoon Sheen aff001;  Wook Kim aff001;  Byung Hyun Byun aff002;  Chang-Bae Kong aff003;  Won Seok Song aff003;  Wan Hyeong Cho aff003;  Ilhan Lim aff002;  Sang Moo Lim aff002;  Sang-Keun Woo aff001
Působiště autorů: Division of applied RI, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea aff001;  Departments of Nuclear Medicine, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea aff002;  Department of Orthopedic Surgery, Korea Institute of Radiology and Medical Sciences, Seoul, Republic of Korea aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
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.

Methods and findings

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.

Klíčová slova:

Bone imaging – Cancer detection and diagnosis – Cancer chemotherapy – Cancer treatment – Metastasis – Positron emission tomography – Osteosarcoma


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Článek vyšel v časopise


2019 Číslo 11