Chang-Bae Kong

조상현, et al. J Korean Orthop Assoc. 2024 Feb;59(1):15-21. https://doi.org/10.4055/jkoa.2024.59.1.15

조상현, et al. J Korean Orthop Assoc. 2024 Jun;59(3):201-207. https://doi.org/10.4055/jkoa.2024.59.3.201

공창배, et al. J Korean Orthop Assoc. 2024 Apr;59(2):111-116. https://doi.org/10.4055/jkoa.2024.59.2.111

Purpose: This study evaluated the treatment outcomes of spine stereotactic body radiation therapy (SBRT) in sarcoma patients. Materials and methods: A total of 44 sarcoma patients and 75 spinal lesions (6 primary tumors, 69 metastatic tumors) treated with SBRT were retrospectively reviewed between 2006 and 2017. The median radiation dose was 33 Gy (range, 18–45 Gy) in 3 fractions (range, 1–5) prescribed to the 75% isodose line. Results: The median follow-up duration was 18.2 months. The 1-year local control was 76.4%, and patients treated with single vertebral body were identified as a favorable prognostic factor on multivariate analyses. Progression-free survival at 1 year was 31.9%, with the interval between initial diagnosis and SBRT and extent of disease at the time of treatment being significant prognostic factors. The 1-year overall survival was 80.5%, and PTV and visceral metastases were independently associated with inferior overall survival. Conclusion: SBRT for spinal sarcoma is effective in achieving local control, particularly when treating a single vertebral level with a limited extent of disease involvement, resulting in an excellent control rate. The extent of disease at the time of SBRT is significantly correlated with survival outcomes and should be considered when treating spine sarcoma.

Purpose This study evaluated the treatment outcomes of spine stereotactic radiosurgery (SRS) in sarcoma patients. Materials and methods A total of 44 sarcoma patients and 75 spinal lesions (6 primary tumors, 69 metastatic tumors) treated with SRS were retrospectively reviewed between 2006 and 2017. The median radiation dose was 33 Gy (range, 18–45 Gy) in 3 fractions (range, 1–5) prescribed to the 75% isodose line. Results The median follow-up duration was 18.2 months. The 1-year local control was 76.4%, and patients treated with multiple vertebral bodies and large planning target volume (PTV) size were identified as unfavorable prognostic factors on multivariate analyses. Progression-free survival at 1 year was 31.9%, with the interval between initial diagnosis and SRS and extent of disease at the time of treatment being significant prognostic factors. The 1-year overall survival was 80.5%, and PTV > 16 cc and visceral metastases were independently associated with inferior overall survival. Conclusion SRS for spinal sarcoma is effective in achieving local control, particularly when treating a single vertebral level or a small treatment volume, resulting in an excellent control rate. The extent of disease at the time of SRS is significantly correlated with survival outcomes and should be considered when treating spine sarcoma.

조완형, et al. J Korean Orthop Assoc. 2022 Oct;57(5):385-391. https://doi.org/10.4055/jkoa.2022.57.5.385

공창배, et al. J Korean Orthop Assoc. 2022 Aug;57(4):300-306. https://doi.org/10.4055/jkoa.2022.57.4.300

We compared the accuracy of prediction of the response to neoadjuvant chemotherapy (NAC) in osteosarcoma patients between machine learning approaches of whole tumor utilizing fluorine−¹⁸fluorodeoxyglucose (¹⁸F-FDG) uptake heterogeneity features and a convolutional neural network of the intratumor image region. In 105 patients with osteosarcoma, ¹⁸F-FDG positron emission tomography/computed tomography (PET/CT) images were acquired before (baseline PET0) and after NAC (PET1). Patients were divided into responders and non-responders about neoadjuvant chemotherapy. Quantitative ¹⁸F-FDG heterogeneity features were calculated using LIFEX version 4.0. Receiver operating characteristic (ROC) curve analysis of ¹⁸F-FDG uptake heterogeneity features was used to predict the response to NAC. Machine learning algorithms and 2-dimensional convolutional neural network (2D CNN) deep learning networks were estimated for predicting NAC response with the baseline PET0 images of the 105 patients. ML was performed using the entire tumor image. The accuracy of the 2D CNN prediction model was evaluated using total tumor slices, the center 20 slices, the center 10 slices, and center slice. A total number of 80 patients was used for k-fold validation by five groups with 16 patients. The CNN network test accuracy estimation was performed using 25 patients. The areas under the ROC curves (AUCs) for baseline PET maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and gray level size zone matrix (GLSZM) were 0.532, 0.507, 0.510, and 0.626, respectively. The texture features test accuracy of machine learning by random forest and support vector machine were 0.55 and 0. 54, respectively. The k-fold validation accuracy and validation accuracy were 0.968 ± 0.01 and 0.610 ± 0.04, respectively. The test accuracy of total tumor slices, the center 20 slices, center 10 slices, and center slices were 0.625, 0.616, 0.628, and 0.760, respectively. The prediction model for NAC response with baseline PET0 texture features machine learning estimated a poor outcome, but the 2D CNN network using ¹⁸F-FDG baseline PET0 images could predict the treatment response before prior chemotherapy in osteosarcoma. Additionally, using the 2D CNN prediction model using a tumor center slice of ¹⁸F-FDG PET images before NAC can help decide whether to perform NAC to treat osteosarcoma patients.

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 (¹⁸F-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. ¹⁸F-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.