期刊
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
卷 49, 期 2, 页码 550-562出版社
SPRINGER
DOI: 10.1007/s00259-021-05489-8
关键词
Triple-negative breast cancer (TNBC); FDG-PET; Radiomics; Co-clinical imaging; Quantitative imaging; Machine learning
资金
- NCI [U24CA209837, U24CA253531, U54CA224083, U2CCA233303, K12CA167540]
- Siteman Cancer Center (SCC) [P30CA091842]
- Mallinckrodt Institute of Radiology
The study aimed to optimize radiomic features using patient-derived tumor xenografts (PDX) in predicting therapy response in TNBC, and to implement these features in a co-clinical study using machine learning algorithms to predict therapy response.
Purpose We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. Methods TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naive Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUVmean, SUVmax, and lean body mass-normalized SULpeak measures. Results Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUVmean, SUVmax, and SULpeak measures. Conclusions We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial.
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