Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction.
Marianna IngleseMatteo FerranteTommaso BoccatoAllegra ContiChiara A PistoleseOreste C BuonomoRolando M D'AngelilloNicola ToschiPublished in: Journal of personalized medicine (2023)
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 18 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.
Keyphrases
- positron emission tomography
- pet imaging
- computed tomography
- contrast enhanced
- magnetic resonance imaging
- neoadjuvant chemotherapy
- pet ct
- lymph node metastasis
- deep learning
- healthcare
- ejection fraction
- high resolution
- acute myeloid leukemia
- end stage renal disease
- emergency department
- lymph node
- machine learning
- locally advanced
- systematic review
- electronic health record
- early stage
- radiation therapy
- mass spectrometry
- dual energy
- young adults
- social media
- drug induced
- sentinel lymph node
- optical coherence tomography
- data analysis
- rectal cancer
- big data