title: Real time decision support system for diagnosis of rare cancers, trained in parallel, on a graphics processing unit.
abstract: In the present study a new strategy is introduced for designing and developing of an efficient dynamic Decision Support System (DSS) for supporting rare cancers decision making. The proposed DSS operates on a Graphics Processing Unit (GPU) and it is capable of adjusting its design in real time based on user-defined clinical questions in contrast to standard CPU implementations that are limited by processing and memory constrains. The core of the proposed DSS was a Probabilistic Neural Network classifier and was evaluated on 140 rare brain cancer cases, regarding its ability to predict tumors' malignancy, using a panel of 20 morphological and textural features Generalization was estimated using an external 10-fold cross-validation. The proposed GPU-based DSS achieved significantly higher training speed, outperforming the CPU-based system by a factor that ranged from 267 to 288 times. System design was optimized using a combination of 4 textural and morphological features with 78.6% overall accuracy, whereas system generalization was 73.8%±3.2%. By exploiting the inherently parallel architecture of a consumer level GPU, the proposed approach enables real time, optimal design of a DSS for any user-defined clinical question for improving diagnostic assessments, prognostic relevance and concordance rates for rare cancers in clinical practice.
authors.affiliation: School of Engineering and Design, Brunel University West London, Uxbridge, Middlesex, UB8 3PH, UK. Konstantinos.Sidiropoulos@brunel.ac.uk
authors.forename: Konstantinos
authors.initials: K
authors.lastname: Sidiropoulos
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Glotsos
authors.forename: Spiros
authors.initials: S
authors.lastname: Kostopoulos
authors.forename: Panagiota
authors.initials: P
authors.lastname: Ravazoula
authors.forename: Ioannis
authors.initials: I
authors.lastname: Kalatzis
authors.forename: Dionisis
authors.initials: D
authors.lastname: Cavouras
authors.forename: John
authors.initials: J
authors.lastname: Stonham
mesh_terms: D001254:Astrocytoma / Q000175:diagnosis*; D001932:Brain Neoplasms / Q000175:diagnosis*; D002467:Cell Nucleus / Q000473:pathology; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006651:Histocytochemistry; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D035583:Rare Diseases / Q000175:diagnosis; D015203:Reproducibility of Results; D012984:Software