AI Article Synopsis

  • * This study introduces 'Dynomics,' an approach that analyzes dynamic PET scans alongside static data to evaluate breast cancer patients by extracting relevant features from both types of images.
  • * The findings show that this method significantly outperforms traditional imaging, achieving 94% accuracy for tumor classification and 86% for prognosis prediction, indicating it could enhance breast cancer diagnosis and treatment planning.

Article Abstract

Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of dynamic F-Fluorothymidine (FLT) PET scans from BC patients, extending conventional static radiomics methods to the time domain-termed as 'Dynomics'. Radiomic features were extracted from both static and dynamic PET images on lesion and reference tissue masks. The extracted features were used to train an XGBoost model for classifying tumor versus reference tissue and complete versus partial responders to neoadjuvant chemotherapy. The results underscored the superiority of dynamic and static radiomics over standard PET imaging, achieving accuracy of 94% in tumor tissue classification. Notably, in predicting BC prognosis, dynomics delivered the highest performance, achieving accuracy of 86%, thereby outperforming both static radiomics and standard PET data. This study illustrates the enhanced clinical utility of dynomics in yielding more precise and reliable information for BC diagnosis and prognosis, paving the way for improved treatment strategies.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303631PMC
http://dx.doi.org/10.3390/jpm13061004DOI Listing

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