Radiomics models to predict bone marrow metastasis of neuroblastoma using CT  

作　　者：Xiong Chen Qinchang Chen Yuanfang Liu Ya Qiu Lin Lv Zhengtao Zhang Xuntao Yin Fangpeng Shu 

机构地区：[1]Department of Paediatric Urology,Guangzhou Women and Children's Medical Center,Guangzhou Medical University,Guangzhou,China [2]Department of Paediatric Surgery,Guangzhou Institute of Paediatrics,Guangzhou Medical University,Guangzhou,China [3]Department of Pediatric Cardiology,Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences,Guangdong Cardiovascular Institute,Guangdong Provincial Key Laboratory of Structural Heart Disease,Guangzhou,China [4]Department of Radiology,Sun Yat‐sen Memorial Hospital,Sun Yat‐sen University,Guangzhou,China [5]Department of Radiology,the First People's Hospital of Kashi Prefecture,Kashi,China [6]Medical School,Sun Yat‐sen University,Guangzhou,China [7]Department of Radiology,Guangzhou Women and Children's Medical Center,Guangzhou,China

出　　处：《Cancer Innovation》2024年第5期71-83,共13页肿瘤学创新（英文）

基　　金：Science and Technology Projects in Guangzhou,Grant/Award Number:202201011843;National Natural Science Foundation of China,Grant/Award Numbers:82103544,82202251。

摘　　要：Background:Bone marrow is the leading site for metastasis from neuroblastoma and affects the prognosis of patients with neuroblastoma.However,the accurate diagnosis of bone marrow metastasis is limited by the high spatial and temporal heterogeneity of neuroblastoma.Radiomics analysis has been applied in various cancers to build accurate diagnostic models but has not yet been applied to bone marrow metastasis of neuroblastoma.Methods:We retrospectively collected information from 187 patients pathologically diagnosed with neuroblastoma and divided them into training and validation sets in a ratio of 7:3.A total of 2632 radiomics features were retrieved from venous and arterial phases of contrastenhanced computed tomography(CT),and nine machine learning approaches were used to build radiomics models,including multilayer perceptron(MLP),extreme gradient boosting,and random forest.We also constructed radiomics‐clinical models that combined radiomics features with clinical predictors such as age,gender,ascites,and lymph gland metastasis.The performance of the models was evaluated with receiver operating characteristics(ROC)curves,calibration curves,and risk decile plots.Results:The MLP radiomics model yielded an area under the ROC curve(AUC)of 0.97(95%confidence interval[CI]:0.95–0.99)on the training set and 0.90(95%CI:0.82–0.95)on the validation set.The radiomics‐clinical model using an MLP yielded an AUC of 0.93(95%CI:0.89–0.96)on the training set and 0.91(95%CI:0.85–0.97)on the validation set.Conclusions:MLP‐based radiomics and radiomics‐clinical models can precisely predict bone marrow metastasis in patients with neuroblastoma.

关 键 词：bone marrow metastasis machine learning NEUROBLASTOMA radiomics 

分 类 号：R739.4[医药卫生—肿瘤]