ABSTRACT
Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.
Subject(s)
Machine Learning , Neoplasms/diagnostic imaging , Neoplasms/pathology , Artificial Intelligence , Biomarkers, Tumor , Diagnosis, Computer-Assisted/methods , Humans , Neoplasms/mortality , Neoplasms/radiotherapy , Prognosis , Radiation Oncology , Treatment OutcomeABSTRACT
PURPOSE: Metastases to the thyroid gland are uncommon and they represent 1-3% of all thyroid malignancy. The aim of this study is to analyze the diagnostic problems and the role of surgery in metastatic lesions to the thyroid. METHODS: We retrospectively analyzed all patients who had undergone thyroidectomy at our Center. Out of more than 5000 thyroidectomies performed, only 9 cases had metastases to the thyroid gland. RESULTS: The most common primary tumor arises from kidney and lung. Non-thyroid malignancies diagnosis was obtained with US, FNAC and PET-CT. Surgery was performed in all our series (except for one case) and the expectancy of life after surgery is related to the primary tumor and comorbidities of patients. CONCLUSION: The thyroid gland can be a rare site of metastases for many tumors, especially in an abnormal thyroid gland. Surgery is associated with an improved survival and the extension of surgery depends on the extension of the neoplastic lesion.
Subject(s)
Thyroid Neoplasms/diagnosis , Thyroid Neoplasms/pathology , Thyroid Neoplasms/surgery , Adult , Aged , Aged, 80 and over , Biopsy, Fine-Needle , Diagnosis, Differential , Female , Humans , Incidence , Italy/epidemiology , Male , Middle Aged , Neoplasm Metastasis , Positron Emission Tomography Computed Tomography , Retrospective Studies , Survival Analysis , Thyroid Gland/pathology , Thyroid Gland/surgery , Thyroid Neoplasms/epidemiology , Thyroidectomy/statistics & numerical data , UltrasonographyABSTRACT
The production of artificial epidermis using reabsorbable polymeric matrices is one of possible goals; one of most used strategies in this field is the polymer substrate functionalitation using specific growth factors, in order to accelerate and improve keratinocyte adhesion and proliferation. In this study films of poly(D,L)lactide (P(D,L)LA), have been functionalized with various concentrations of galactose (GAL, 1-5-10%, w/v) conjugated with poly-L-lysine (PLL) using 1-etil-3-(3-diaminopropil) carbodiimide (EDC) as a coupling agent. GAL is a disaccharide present in the extracellular matrix (ECM) and it is bind by Galectines, a family of cell receptors whose activation regulate the cell-matrix interaction and cell growth and apoptosis. One of these receptors, Galectin-7 (Gal-7), is selectively expressed by human keratinocytes. Spontaneously immortalized human keratinocytes (HaCaT) that express high level of Gal-7 were allowed to adhere for 4 h in serum free condition on control P(D,L)LA (PLA), and on PLA-GAL and cell proliferation; the production of matrix metalloproteinases (MMP-2, MMP-9, and MMP-28), involved in cellular migration and tissue homeostasis have been analyzed after 24 h. The presence of GAL onto the polymer surface increased both cell adhesion, spreading and proliferation along with MMP-9 and MMP-28 production, suggesting that polymer functionalization using GAL could be an useful tool for the production of an artificial epidermis.
Subject(s)
Coated Materials, Biocompatible/pharmacology , Epidermal Cells , Epidermis/growth & development , Galactose/pharmacology , Keratinocytes/cytology , Keratinocytes/physiology , Lactic Acid/chemistry , Polymers/chemistry , Tissue Engineering/methods , Cell Adhesion/drug effects , Cell Culture Techniques/methods , Cell Proliferation/drug effects , Cells, Cultured , Coated Materials, Biocompatible/chemistry , Galactose/chemistry , Humans , Keratinocytes/drug effects , Matrix Metalloproteinases/metabolism , Polyesters , Skin, Artificial , Surface PropertiesABSTRACT
We present the MOlecular NETwork (MONET) ontology as a model to integrate data from different networks that govern cell function. To achieve this, different existing ontologies were analyzed and an integrated ontology was built in a way to make it possible to share and reuse knowledge, support interoperability between systems, and also allow the formulation of hypotheses through inferences. By studying the cell as an entity of a myriad of elements and networks of interactions, we aim to offer a means to understand the large-scale characteristics responsible for the behavior of the cell and to enable new biological insights.