| Autor: |
Kim BC; Department of Nuclear Medicine, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea., Kim J; Radiological & Medico-Oncological Sciences, University of Science & Technology, Seoul 34113, Korea., Kim K; Division of Applied RI, Korea Institute of Radiological and Medical Science, Seoul 01812, Korea., Byun BH; Department of Nuclear Medicine, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea., Lim I; Department of Nuclear Medicine, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea., Kong CB; Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul 03080, Korea., Song WS; Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul 03080, Korea., Koh JS; Department of Pathology, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea., Woo SK; Radiological & Medico-Oncological Sciences, University of Science & Technology, Seoul 34113, Korea.; Division of Applied RI, Korea Institute of Radiological and Medical Science, Seoul 01812, Korea. |
| Abstrakt: |
Chemotherapy response and metastasis prediction play important roles in the treatment of pediatric osteosarcoma, which is prone to metastasis and has a high mortality rate. This study aimed to estimate the prediction model using gene expression and image texture features. 18F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) images of 52 pediatric osteosarcoma patients were used to estimate the machine learning algorithm. An appropriate algorithm was selected by estimating the machine learning accuracy. 18 F-FDG PET/CT images of 21 patients were selected for prediction model development based on simultaneous KI67 and EZRIN expression. The prediction model for chemotherapy response and metastasis was estimated using area under the curve (AUC) maximum image texture features (AUC_max) and gene expression. The machine learning algorithm with the highest test accuracy in chemotherapy response and metastasis was selected using the random forest algorithm. The chemotherapy response and metastasis test accuracy with image texture features was 0.83 and 0.76, respectively. The highest test accuracy and AUC of chemotherapy response with AUC_max, KI67 , and EZRIN were estimated to be 0.85 and 0.89, respectively. The highest test accuracy and AUC of metastasis with AUC_max, KI67 , and EZRIN were estimated to be 0.85 and 0.8, respectively. The metastasis prediction accuracy increased by 10% using radiogenomics data. |
