Will traditional biopsy be substituted by radiomics and liquid biopsy for breast cancer diagnosis and characterisation?
Filippo PesapaneMatteo Basilio SuterAnna RotiliSilvia PencoOlga NigroMarta CremonesiMassimo BellomiBarbara Alicja Jereczek-FossaGraziella PinottiEnrico CassanoPublished in: Medical oncology (Northwood, London, England) (2020)
The diagnosis of breast cancer currently relies on radiological and clinical evaluation, confirmed by histopathological examination. However, such approach has some limitations as the suboptimal sensitivity, the long turnaround time for recall tests, the invasiveness of the procedure and the risk that some features of target lesions may remain undetected, making re-biopsy a necessity. Recent technological advances in the field of artificial intelligence hold promise in addressing such medical challenges not only in cancer diagnosis, but also in treatment assessment, and monitoring of disease progression. In the perspective of a truly personalised medicine, based on the early diagnosis and individually tailored treatments, two new technologies, namely radiomics and liquid biopsy, are rising as means to obtain information from diagnosis to molecular profiling and response assessment, without the need of a biopsied tissue sample. Radiomics works through the extraction of quantitative peculiar features of cancer from radiological data, while liquid biopsy gets the whole of the malignancy's biology from something as easy as a blood sample. Both techniques hopefully will identify diagnostic and prognostic information of breast cancer potentially reducing the need for invasive (and often difficult to perform) biopsies and favouring an approach that is as personalised as possible for each patient. Nevertheless, such techniques will not substitute tissue biopsy in the near future, and even in further times they will require the aid of other parameters to be correctly interpreted and acted upon.
Keyphrases
- ultrasound guided
- artificial intelligence
- fine needle aspiration
- big data
- clinical evaluation
- papillary thyroid
- lymph node metastasis
- machine learning
- deep learning
- squamous cell
- childhood cancer
- squamous cell carcinoma
- magnetic resonance
- mass spectrometry
- minimally invasive
- young adults
- single molecule
- social media
- breast cancer risk