Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer

• Published in: European journal of nuclear medicine and molecular imaging Vol. 49; no. 2; pp. 550 - 562
• Main Authors: Roy, Sudipta, Whitehead, Timothy D., Li, Shunqiang, Ademuyiwa, Foluso O., Wahl, Richard L., Dehdashti, Farrokh, Shoghi, Kooresh I.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2022; Springer Nature B.V
• Subjects: Algorithms; Bayes Theorem; Bayesian analysis; Body mass; Breast cancer; Breast Neoplasms; Cardiology; Chemotherapy; Cross correlation; Feature extraction; Female; Fluorodeoxyglucose F18; Heterogeneity; Humans; Imaging; Lean body mass; Learning algorithms; Machine learning; Medical imaging; Medicine; Medicine & Public Health; Neoadjuvant Therapy; Nuclear Medicine; Oncology; Oncology – General; Original; Original Article; Orthopedics; Patients; Performance prediction; Positron emission; Positron emission tomography; Positron-Emission Tomography - methods; Radiology; Radiomics; Regression analysis; Reproducibility of Results; Robustness; Signatures; Support vector machines; Tomography; Triple Negative Breast Neoplasms - diagnostic imaging; Triple Negative Breast Neoplasms - drug therapy; Tumors; Xenografts; Xenotransplantation; FDG-PET; Co-clinical imaging; Triple-negative breast cancer (TNBC); Quantitative imaging; Machine learning; Radiomics
• ISSN: 1619-7070; 1619-7089
• DOI: 10.1007/s00259-021-05489-8

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 SUV mean , SUV max , and lean body mass-normalized SUL peak 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 SUV mean , SUV max , and SUL peak 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.