In our fight against cancer the cancer diagnosis plays a crucial role. Failure of standard cancer therapies has dramatic effects on the health and quality of life of cancer patients and their families, both physically and emotionally. Although many of the current available anti-cancer therapeutic approaches may have significant results, they still may fail if cancer is not detected early and diagnosed appropriately. Therefore, from a societal point of view the benefit from the development of novel diagnostic tools/biomarkers for early cancer diagnosis can improve the efficacy of standard cancer therapies, leading to the desired outcome of prolonged disease-free survival. World Health Organization (WHO) launched the Global Action Plan for the Prevention and Control of Noncommunicable Diseases that aims to reduce by 25% premature mortality from cancer and other diseases (e.g. cardiovascular diseases, diabetes and chronic respiratory) by 2025[1]. Cancer rates are set to increase at an alarming rate globally and immediately actions should be taken. According to the WHO, among the areas where actions can make a difference to controlling cancer is the early detection so as to help treatment and palliative care. Cancer mortality can be reduced if cases are detected early[2]. In absence of any early detection, diagnosis and treatment intervention, patients are diagnosed at very late stages when curative treatment is no longer an option. 

Solution suggested by the Host Organization: Cancer development and progression are closely associated with changes in cellular phenotype and cytoarchitecture, extracellular matrix (ECM) structure and in the mechanical properties of both ECM and cancer cells. Very recent work from different ranchers worldwide have demonstrated that AI can be used on morphological characteristics of the cells from optical/fluorescence microscopy [1] or from Atomic Force Microscopy (AFM) [2] for cancer diagnosis. However, these techniques have not been combined. BioNanoTec aims to develop a novel user-friendly software for cancer diagnosis based on microscopy characterization of cells. BioNanoTec will combine AI and machine learning techniques for cancer diagnosis based on optical microscopy images and AFM nanomechanical characterization of the cells. This will enable overcoming the limitation of using data from only one technique (e.g., only nanomechanical characterization with AFM). Problem that the Host Organization faces: BioNanoTec members have a great experience on AFM characterization and have started developing the software using data from AFM nanomechanical characterization of different cell lines. The preliminary data are very encouraging and promising. Furthermore, they have developed a new technique for 3D AFM characterization of the cells and have obtained funding from Cyprus Research and Innovation (CONCEPT/0521/0069) that will help the development of the relevant technique. However, BioNanoTec have no access to optical microscopy images. A small number of figures that have been obtained are not appropriate for the development, training and testing of the software under development. BioNanoTech need a dataset with images and information from cells (of different cancer types) that will correlate their invasive properties with their morphological features.