Lei Xu1, Pengfei Yang2, Kun Hu3, Yan Wu4, Meng Xu-Welliver5, Yidong Wan1, Chen Luo1, Jing Wang1, Jinhua Wang6, Jiale Qin7, Yi Rong8, Tianye Niu3. 1. Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Institute of Translational Medicine, Zhejiang University, Hangzhou, China. 2. College of Biomedical Engineering &Instrument Science, Zhejiang University, Hangzhou, China. 3. Nuclear & Radiological Engineering and Medical Physics Programs, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA. 4. Department of Orthopedics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China. 5. Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA. 6. Department of Radiology, Jiangxi Maternal and Child Health Hospital, Nanchang, China. 7. Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China. 8. Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA.
Abstract
BACKGROUND: This study aimed to determine the impact of including radiomics analysis of non-tumorous bone region of interest in improving the performance of pathological response prediction to chemotherapy in high-grade osteosarcomas (HOS), compared to radiomics analysis of tumor region alone. METHODS: This retrospective study included 157 patients diagnosed with HOS between November 2013 and November 2017 (age range, 5-44 years; mean age, 16.99 ±7.42 years), in which 69 and 88 patients were diagnosed as pathological good response (pGR) and non-pGR, respectively. Radiomics features were extracted from tumor and non-tumorous bone regions based on diagnostic CT images. Pathological response classifiers were developed and validated via leave-one-out cross validation (LOOCV) and independent validation methods by using the area under the receiver operating characteristic curve (AUC) value as the figure of merit. RESULTS: Using the LOOCV, the classifiers combining features from tumor and non-tumorous regions showed better prediction performance than those from tumor region alone (AUC, 0.8207±0.0043 vs. 0.7799±0.0044). The combined classifier also showed better performance than the tumor feature-based classifier in both training and validation datasets [training dataset: 0.791, 95% confidence interval (CI), 0.706-0.860 vs. 0.766, 95% CI, 0.679-0.840; validation dataset: 0.816, 95% CI, 0.662-0.920 vs. 0.766, 95% CI, 0.606-0.885]. CONCLUSIONS: Radiomics analysis of combined tumor and non-tumorous bone features showed improved performance of pathological response prediction to chemotherapy in HOS compared to that of tumor features alone. Moreover, the proposed classifier had the potential to predict pathological response to chemotherapy for HOS patients. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: This study aimed to determine the impact of including radiomics analysis of non-tumorous bone region of interest in improving the performance of pathological response prediction to chemotherapy in high-grade osteosarcomas (HOS), compared to radiomics analysis of tumor region alone. METHODS: This retrospective study included 157 patients diagnosed with HOS between November 2013 and November 2017 (age range, 5-44 years; mean age, 16.99 ±7.42 years), in which 69 and 88 patients were diagnosed as pathological good response (pGR) and non-pGR, respectively. Radiomics features were extracted from tumor and non-tumorous bone regions based on diagnostic CT images. Pathological response classifiers were developed and validated via leave-one-out cross validation (LOOCV) and independent validation methods by using the area under the receiver operating characteristic curve (AUC) value as the figure of merit. RESULTS: Using the LOOCV, the classifiers combining features from tumor and non-tumorous regions showed better prediction performance than those from tumor region alone (AUC, 0.8207±0.0043 vs. 0.7799±0.0044). The combined classifier also showed better performance than the tumor feature-based classifier in both training and validation datasets [training dataset: 0.791, 95% confidence interval (CI), 0.706-0.860 vs. 0.766, 95% CI, 0.679-0.840; validation dataset: 0.816, 95% CI, 0.662-0.920 vs. 0.766, 95% CI, 0.606-0.885]. CONCLUSIONS: Radiomics analysis of combined tumor and non-tumorous bone features showed improved performance of pathological response prediction to chemotherapy in HOS compared to that of tumor features alone. Moreover, the proposed classifier had the potential to predict pathological response to chemotherapy for HOS patients. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Entities:
Keywords:
High-grade osteosarcoma (HOS); added value; neoadjuvant chemotherapy response; non-tumorous bone features
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