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Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer.
- Source :
-
European Journal of Nuclear Medicine & Molecular Imaging . Jan2022, Vol. 49 Issue 2, p550-562. 13p. 1 Color Photograph, 1 Diagram, 2 Charts, 5 Graphs. - Publication Year :
- 2022
-
Abstract
- 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), Naïve 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *TRIPLE-negative breast cancer
*CANCER chemotherapy
*CART algorithms
*NEOADJUVANT chemotherapy
*FEATURE extraction
*MACHINE learning
*REGRESSION analysis
*TREATMENT effectiveness
*RADIOPHARMACEUTICALS
*POSITRON emission tomography
*DEOXY sugars
*COMBINED modality therapy
*BREAST tumors
*ALGORITHMS
*EVALUATION
Subjects
Details
- Language :
- English
- ISSN :
- 16197070
- Volume :
- 49
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- European Journal of Nuclear Medicine & Molecular Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 154982387
- Full Text :
- https://doi.org/10.1007/s00259-021-05489-8