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    A framework for validating AI in precision medicine: considerations from the European ITFoC consortium
    (BMC, 2021-01-01) Tsopra, Rosy; Fernandez, Xose; Luchinat, Claudio; Alberghina, Lilia; Lehrach, Hans; Vanoni, Marco; Dreher, Felix; Sezerman, O. Ugur; Cuggia, Marc; de Tayrac, Marie; Miklasevics, Edvins; Itu, Lucian Mihai; Geanta, Marius; Ogilvie, Lesley; Godey, Florence; Boldisor, Cristian Nicolae; Campillo-Gimenez, Boris; Cioroboiu, Cosmina; Ciusdel, Costin Florian; Coman, Simona; Cubelos, Oliver Hijano; Itu, Alina; Lange, Bodo; Le Gallo, Matthieu; Lespagnol, Alexandra; Mauri, Giancarlo; Soykam, H. Okan; Rance, Bastien; Turano, Paola; Tenori, Leonardo; Vignoli, Alessia; Wierling, Christoph; Benhabiles, Nora; Burgun, Anita
    Background Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods The European ``ITFoC (Information Technology for the Future Of Cancer){''} consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the ``ITFoC Challenge{''}. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.
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    Gamma Knife Radiosurgery for Anterior Clinoid Process Meningiomas: A Series of 61 Consecutive Patients
    (ELSEVIER SCIENCE INC, 2020-01-01) Akyoldas, Goktug; Hergunsel, Omer Batu; Yilmaz, Meltem; Sengoz, Meric; Peker, Selcuk
    OBJECTIVE: Gamma Knife radiosurgery (GKRS) outcomes for anterior clinoid process (ACP) meningiomas have not been specifically reported within any meningioma series. We present the initial and largest series in the literature that describes the presenting features, radiosurgery parameters, and radiologic and long-term clinical outcomes for 61 patients with ACP meningiomas treated with GKRS. METHODS: Medical records were reviewed for 61 consecutive patients at a single center who underwent GKRS for ACP meningioma between 2008 and 2016. RESULTS: Of 61 patients with ACP meningiomas, 49 (80\%) were treated with GKRS as primary treatment, and 12 (20\%) were treated with GKRS as an adjuvant therapy. Before GKRS, 29 patients presented with visual impairment and 50 patients presented with headache. Median patient age was 54.9 years. Median tumor volume was 3.2 cm(3), and median margin dose was 12.0 Gy. The median radiologic follow-up time after GKRS was 75 months. During follow-up, tumor volume regressed in 37 cases (61\%) and remained unchanged in 24 cases (39\%). None of the patients experienced tumor volume progression. Tumor volume <3 cm(3) was an independent predictor of tumor volume regression after GKRS (univariate analysis, P = 0.047