Category: esters-buliding-blocks. The mechanism of aromatic electrophilic substitution of aromatic heterocycles is consistent with that of benzene. Compound: cis-Diammine(1,1-cyclobutanedicarboxylato)platinum(II), is researched, Molecular C6H12N2O4Pt, CAS is 41575-94-4, about Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer. Author is Roy, Sudipta; Whitehead, Timothy D.; Li, Shunqiang; Ademuyiwa, Foluso O.; Wahl, Richard L.; Dehdashti, Farrokh; Shoghi, Kooresh I..
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 neg. breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clin. study to predict response to therapy using machine learning (ML) algorithms. TNBC patients and subtype-matched PDX were recruited into a co-clin. 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 preclin. 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. Sixty-four out of 131 preclin. imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclin. PDX trial. In the clin. 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. We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clin. imaging trial.
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