Real time decision support system for diagnosis of rare cancers, trained in parallel, on a graphics processing unit

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• 作者：Konstantinos Sidiropoulos^a ; ^{Konstantinos.Sidiropoulos@brunel.ac.uk} ; ^{ksidis@gmail.com} ; ; Dimitrios Glotsos^b ; Spiros Kostopoulos^b ; Panagiota Ravazoula^c ; Ioannis Kalatzis^b ; Dionisis Cavouras^b ; John Stonham^a
• 关键词：Decision support system ; Parallel processing ; Graphics processing unit (GPU) ; Rare brain cancers
• 刊名：Computers in Biology and Medicine
• 出版年：2012
• 期刊代码：70_00104825
• 类别：med
• 出版时间：April, 2012
• 卷：42
• 期：4
• 页码：376-386
• 文件大小：1803 K

摘要

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%卤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.