Radiomics, a burgeoning field within medical imaging, has gained momentum for its potential to provide nuanced insights into lesion characteristics. This study introduces a pioneering approach to benchmarking radiomic features, delving into their correlations with ground truth measurements and subsequent clustering patterns. By analyzing 59 simulated lesions mined from the publicly available QIDW Liver II Hybrid Dataset, a vast array of radiomic features were extracted and evaluated using Pyradiomics. A total of 2060 features are correlated with ground truth volume and contrast measurements, revealing intricate relationships. Novel co-clustering patterns emerge, underscoring the versatility and complexity of radiomic features. These findings not only contribute to lesion characterization precision but also advanced our understanding of radiomic intricacies. The presented research holds potential to refine radiomics-driven medical insights, paving the way for more informed clinical decision-making and improved patient care.
Immune checkpoint inhibitors (ICIs) are among the most effective classes of cancer immunotherapies yet only a small minority of patients derive clinical benefit. We are investigating the use of multi-spectral paired-agent imaging (mPAI) to quantify available PD-1 and PD-L1 receptor concentrations for the therapeutic binding of anti-PD-1 checkpoint inhibitors.
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