TY - JOUR
T1 - A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials
AU - Soliman, Moataz A.S.
AU - Kelahan, Linda C.
AU - Magnetta, Michael
AU - Savas, Hatice
AU - Agrawal, Rishi
AU - Avery, Ryan J.
AU - Aouad, Pascale
AU - Liu, Benjamin
AU - Xue, Yue
AU - Chae, Young K.
AU - Salem, Riad
AU - Benson, Al B.
AU - Yaghmai, Vahid
AU - Velichko, Yuri S.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - PURPOSE: Variability in computed tomography images intrinsic to individual scanners limits the application of radiomics in clinical and research settings. The development of reproducible and generalizable radiomics-based models to assess lesions requires harmonization of data. The purpose of this study was to develop, test, and analyze the efficacy of a radiomics data harmonization model. MATERIALS AND METHODS: Radiomic features from biopsy-proven untreated hepatic metastasis (N = 380) acquired from 167 unique patients with pancreatic, colon, and breast cancers were analyzed. Radiomic features from volume-match 551 samples of normal liver tissue and 188 hepatic cysts were included as references. A novel linear mixed effect model was used to identify effects associated with lesion size, tissue type, and scanner model. Six separate machine learning models were then used to test the effectiveness of radiomic feature harmonization using multivariate analysis. RESULTS: Proposed model identifies and removes scanner-associated effects while preserving cancer-specific functional dependence of radiomic features on the tumor size. Data harmonization improves the performance of classification models by reducing the scanner-associated variability. For example, the multiclass logistic regression model, LogitBoost, demonstrated the improvement in sensitivity in the range from 15% to 40% for each type of liver metastasis, whereas the overall model accuracy and the kappa coefficient increased by 5% and 8% accordingly. CONCLUSION: The model removed scanner-associated effects while preserving cancer-specific functional dependence of radiomic features.
AB - PURPOSE: Variability in computed tomography images intrinsic to individual scanners limits the application of radiomics in clinical and research settings. The development of reproducible and generalizable radiomics-based models to assess lesions requires harmonization of data. The purpose of this study was to develop, test, and analyze the efficacy of a radiomics data harmonization model. MATERIALS AND METHODS: Radiomic features from biopsy-proven untreated hepatic metastasis (N = 380) acquired from 167 unique patients with pancreatic, colon, and breast cancers were analyzed. Radiomic features from volume-match 551 samples of normal liver tissue and 188 hepatic cysts were included as references. A novel linear mixed effect model was used to identify effects associated with lesion size, tissue type, and scanner model. Six separate machine learning models were then used to test the effectiveness of radiomic feature harmonization using multivariate analysis. RESULTS: Proposed model identifies and removes scanner-associated effects while preserving cancer-specific functional dependence of radiomic features on the tumor size. Data harmonization improves the performance of classification models by reducing the scanner-associated variability. For example, the multiclass logistic regression model, LogitBoost, demonstrated the improvement in sensitivity in the range from 15% to 40% for each type of liver metastasis, whereas the overall model accuracy and the kappa coefficient increased by 5% and 8% accordingly. CONCLUSION: The model removed scanner-associated effects while preserving cancer-specific functional dependence of radiomic features.
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U2 - 10.1200/CCI.22.00023
DO - 10.1200/CCI.22.00023
M3 - Article
C2 - 36332157
AN - SCOPUS:85141889797
SN - 2473-4276
VL - 6
SP - e2200023
JO - JCO Clinical Cancer Informatics
JF - JCO Clinical Cancer Informatics
ER -