Automated Analysis in Feature Modelling and Product Configuration

The automated analysis of feature models is one of the thriving topics of research in the software product line and variability management communities that has attracted more attention in the last years. A recent literature review reported that more than 30 analysis operations have been identi ed and di erent analysis mechanisms have been proposed. Product con guration is a well established research eld with more than 30 years of successful applications in di erent industrial domains. Our hypothesis, that is not really new, is that these two independent areas of research have interesting synergies that have not been fully explored. To try to explore the potential synergies systematically, in this paper we provide a rapid review to bring together these previously disparate streams of work. We de ne a set of research questions and give a preliminary answer to some of them. We conclude that there are many research opportunities in the synergy of these independent areas.Ministerio de Ciencia e Innovación TIN2009- 07366Junta de Andalucía TIC-590

Validation Study for Non-Invasive Prediction of IDH Mutation Status in Patients with Glioma Using In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

The isocitrate dehydrogenase (IDH) mutation status is an indispensable prerequisite for diagnosis of glioma (astrocytoma and oligodendroglioma) according to the WHO classification of brain tumors 2021 and is a potential therapeutic target. Usually, immunohistochemistry followed by sequencing of tumor tissue is performed for this purpose. In clinical routine, however, non-invasive determination of IDH mutation status is desirable in cases where tumor biopsy is not possible and for monitoring neuro-oncological therapies. In a previous publication, we presented reliable prediction of IDH mutation status employing proton magnetic resonance spectroscopy (1H-MRS) on a 3.0 Tesla (T) scanner and machine learning in a prospective cohort of 34 glioma patients. Here, we validated this approach in an independent cohort of 67 patients, for which 1H-MR spectra were acquired at 1.5 T between 2002 and 2007, using the same data analysis approach. Despite different technical conditions, a sensitivity of 82.6% (95% CI, 61.2-95.1%) and a specificity of 72.7% (95% CI, 57.2-85.0%) could be achieved. We concluded that our 1H-MRS based approach can be established in a routine clinical setting with affordable effort and time, independent of technical conditions employed. Therefore, the method provides a non-invasive tool for determining IDH status that is well-applicable in an everyday clinical setting