CT radiomics for differentiating spinal bone island and osteoblastic bone metastases
10.13929/j.issn.1003-3289.2024.05.026
- VernacularTitle:CT影像组学鉴别脊柱骨岛与成骨型转移癌
- Author:
Xin WEN
1
,
2
;
Liping ZUO
;
Yong WANG
;
Ziyu TIAN
;
Fei LU
;
Shuo SHI
;
Lingyu CHANG
;
Yu JI
;
Ran ZHANG
;
Dexin YU
Author Information
1. 山东大学齐鲁医院放射科,山东济南 250012
2. 滨州市第二人民医院放射科,山东滨州 256800
- Keywords:
spine;
osteosclerosis;
neoplasm metastasis;
radiomics;
tomography,X-ray computed
- From:
Chinese Journal of Medical Imaging Technology
2024;40(5):758-763
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of CT radiomics for differentiating spinal bone islands(BI)and osteoblastic metastases(OBM).Methods Data of 109 BI lesions in 98 patients and 282 OBM lesions in 158 patients(including 103 OBM in 48 lung cancer cases,86 OBM in 52 breast cancer cases and 93 OBM in 58 prostate cancer cases)from 3 medical institutions were retrospectively analyzed.Data obtained from institution 1 were used as the internal dataset and divided into internal training set and internal validation set at a ratio of 7∶3,from institution 2 and 3 were used as external dataset.All datasets were divided into female data subset(including OBM of female lung cancer and breast cancer)and male data subset(including OBM of male lung cancer and prostate cancer).Radiomics features were extracted and screened to construct 3 different support vector machine(SVM)models,including model1 for distinguishing BI and OBM,model2 for differentiating OBM of female lung cancer and breast cancer,and model3 for differentiating OBM of male lung cancer and prostate cancer.Diagnostic efficacy of model1,CT value alone and 3 physicians(A,B,C)for distinguishing BI and OBM were assessed,as well as differentiating efficacy for different OBM of model2 and model3.Receiver operating characteristic(ROC)curves were drawn,and area under the curves(AUC)were calculated and compared.The differential diagnostic efficacy of model2 and model3 were also assessed with ROC analysis and AUC.Results AUC of model1 for distinguishing spinal OBM from BI in internal training set,internal validation set and external dataset was 0.99,0.98 and 0.86,respectively.In internal training set,model1 had higher AUC for distinguishing BI and OBM than that of physician A(AUC=0.78),B(AUC=0.87)and C(AUC=0.93)as well as that of mean CT value(AUC=0.78,all P＜0.05).AUC in internal training set,internal validation set and external dataset of model2 for identifying female lung cancer and breast cancer OBM was 0.79,0.75 and 0.73,respectively,of model3 for discriminating male lung cancer from prostate cancer OBM was 0.77,0.74 and 0.77,respectively.Conclusion CT radiomics SVM model might reliablely distinguish OBM and BI.
