Ct-based intratumoral and peritumoral radiomics for predicting prognosis in osteosarcoma: A multicenter study

被引:2
|
作者
Su, Qiushi [1 ]
Wang, Ning [2 ]
Wang, Bingyan [3 ]
Wang, Yanmei [4 ]
Dai, Zhengjun [5 ]
Zhao, Xia [6 ]
Li, Xiaoli [1 ]
Li, Qiyuan [1 ]
Yang, Guangjie [7 ]
Nie, Pei [1 ]
机构
[1] Qingdao Univ, Affiliated Hosp, Dept Radiol, Qingdao, Shandong, Peoples R China
[2] Shandong First Med Univ, Shandong Prov Hosp, Dept Radiol, Jinan, Shandong, Peoples R China
[3] Qingdao Univ, Dept Ultrasound, Affiliated Hosp, Qingdao, Shandong, Peoples R China
[4] GE Healthcare, Shanghai, Peoples R China
[5] Huiying Med Technol Co Ltd, Sci Res Dept, Beijing, Peoples R China
[6] Shandong Univ Tradit Chinese Med, Affiliated Hosp, Dept Radiol, Jinan, Shandong, Peoples R China
[7] Qingdao Univ, Dept Nucl Med, Affiliated Hosp, Qingdao, Shandong, Peoples R China
关键词
Osteosarcoma; Prognosis; Radiomics; Computerized tomography; METASTATIC OSTEOSARCOMA; SURVIVAL; NOMOGRAM;
D O I
10.1016/j.ejrad.2024.111350
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose: To evaluate the performance of CT -based intratumoral, peritumoral and combined radiomics signatures in predicting prognosis in patients with osteosarcoma. Methods: The data of 202 patients (training cohort:102, testing cohort:100) with osteosarcoma admitted to the two hospitals from August 2008 to February 2022 were retrospectively analyzed. Progression free survival (PFS) and overall survival (OS) were used as the end points. The radiomics features were extracted from CT images, three radiomics signatures(RS intratumoral, RS peritumoral, RS combined)were constructed based on intratumoral, peritumoral and combined radiomics features, respectively, and the radiomics score (Rad-score) were calculated. Kaplan -Meier survival analysis was used to evaluate the relationship between the Rad-score with PFS and OS, the Harrell's concordance index (C -index) was used to evaluate the predictive performance of the radiomics signatures. Results: Finally, 8, 6, and 21 features were selected for the establishment of RS intratumoral, RS peritumoral, and RS combined, respectively. Kaplan -Meier survival analysis confirmed that the Rad-scores of the three RSs were significantly correlated with the PFS and OS of patients with osteosarcoma. Among the three radiomics signatures, RS combined had better predictive performance, the C -index of PSF prediction was 0.833 in the training cohort and 0.814 in the testing cohort, the C -index of OS prediction was 0.796 in the training cohort and 0.764 in the testing cohort. Conclusions: CT -based intratumoral, peritumoral and combined radiomics signatures can predict the prognosis of patients with osteosarcoma, which may assist in individualized treatment and improving the prognosis of osteosarcoma patients.
引用
收藏
页数:7
相关论文
共 50 条
  • [1] Intratumoral and peritumoral CT radiomics in predicting prognosis in patients with chondrosarcoma: a multicenter study
    Li, Qiyuan
    Wang, Ning
    Wang, Yanmei
    Li, Xiaoli
    Su, Qiushi
    Zhang, Jing
    Zhao, Xia
    Dai, Zhengjun
    Wang, Yao
    Sun, Li
    Xing, Xuxiao
    Yang, Guangjie
    Gao, Chuanping
    Nie, Pei
    INSIGHTS INTO IMAGING, 2024, 15 (01)
  • [2] Intratumoral and peritumoral CT radiomics in predicting prognosis in patients with chondrosarcoma: a multicenter study
    Qiyuan Li
    Ning Wang
    Yanmei Wang
    Xiaoli Li
    Qiushi Su
    Jing Zhang
    Xia Zhao
    Zhengjun Dai
    Yao Wang
    Li Sun
    Xuxiao Xing
    Guangjie Yang
    Chuanping Gao
    Pei Nie
    Insights into Imaging, 15
  • [3] Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer
    Chen, Xingchi
    Zhuang, Zijian
    Pen, Lin
    Xue, Jing
    Zhu, Haitao
    Zhang, Lirong
    Wang, Dongqing
    ABDOMINAL RADIOLOGY, 2024, 49 (05) : 1363 - 1375
  • [4] The CT-based intratumoral and peritumoral machine learning radiomics analysis in predicting lymph node metastasis in rectal carcinoma
    Hang Yuan
    Xiren Xu
    Shiliang Tu
    Bingchen Chen
    Yuguo Wei
    Yanqing Ma
    BMC Gastroenterology, 22
  • [5] The CT-based intratumoral and peritumoral machine learning radiomics analysis in predicting lymph node metastasis in rectal carcinoma
    Yuan, Hang
    Xu, Xiren
    Tu, Shiliang
    Chen, Bingchen
    Wei, Yuguo
    Ma, Yanqing
    BMC GASTROENTEROLOGY, 2022, 22 (01)
  • [6] Intratumoral and peritumoral PET/CT-based radiomics for non-invasively and dynamically predicting immunotherapy response in NSCLC
    Lin, Xianwen
    Liu, Zhiwei
    Zhou, Kun
    Li, Yuedan
    Huang, Genjie
    Zhang, Hao
    Shu, Tingting
    Huang, Zhenhua
    Wang, Yuanyuan
    Zeng, Wei
    Liao, Yulin
    Bin, Jianping
    Shi, Min
    Liao, Wangjun
    Zhou, Wenlan
    Huang, Na
    BRITISH JOURNAL OF CANCER, 2025, 132 (06) : 558 - 568
  • [7] A multicenter study: predicting KRAS mutation and prognosis in colorectal cancer through a CT-based radiomics nomogram
    Li, Manman
    Yuan, Yiwen
    Zhou, Hui
    Feng, Feng
    Xu, Guodong
    ABDOMINAL RADIOLOGY, 2024, 49 (06) : 1816 - 1828
  • [8] CT-based intratumoral and peritumoral radiomics nomogram to predict spread through air spaces in lung adenocarcinoma with diameter ≤3 cm: A multicenter study
    Su, Yangfan
    Tao, Junli
    Lan, Xiaosong
    Liang, Changyu
    Huang, Xuemei
    Zhang, Jiuquan
    Li, Kai
    Chen, Lihua
    EUROPEAN JOURNAL OF RADIOLOGY OPEN, 2025, 14
  • [9] CT-Based Peritumoral and Intratumoral Radiomics as Pretreatment Predictors of Atypical Responses to Immune Checkpoint Inhibitor Across Tumor Types: A Preliminary Multicenter Study
    He, Shuai
    Feng, Yuqing
    Lin, Qi
    Wang, Lihua
    Wei, Lijun
    Tong, Jing
    Zhang, Yuwei
    Liu, Ying
    Ye, Zhaoxiang
    Guo, Yan
    Yu, Tao
    Luo, Yahong
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [10] Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study
    Chen, Weiyue
    Lin, Guihan
    Feng, Ye
    Chen, Yongjun
    Li, Yanjun
    Li, Jianbin
    Mao, Weibo
    Jing, Yang
    Kong, Chunli
    Hu, Yumin
    Chen, Minjiang
    Xia, Shuiwei
    Lu, Chenying
    Tu, Jianfei
    Ji, Jiansong
    CANCER IMAGING, 2025, 25 (01)