Computational diagnosis of canine lymphoma

2nd International Conference on Mathematical Modeling in Physical Sciences 2013

物理学;数学

Mirkes, E.M.^1 ; Alexandrakis, I.^2 ; Slater, K.^3 ; Tuli, R.^2 ; Gorban, A.N.^1

Department of Mathematics, University of Leicester, Leicester, LE1 7RH, United Kingdom^1

Avacta Animal Health, Avenue E, Thorp Arch Estate Wetherby, LS23 7GA, United Kingdom^2

PetScreen Ltd, Biocity, Pennyfoot Street, Nottingham, NG1 1GF, United Kingdom^3

关键词: Acute phase proteins;    C-reactive proteins;    Decision tree method;    Development and applications;    Differential diagnosis;    Preprocessing approaches;    Probability densities;    Sensitivity and specificity;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/490/1/012135/pdf
DOI  :  10.1088/1742-6596/490/1/012135

来源: IOP

【 摘 要 】

One out of four dogs will develop cancer in their lifetime and 20% of those will be lymphoma cases. PetScreen developed a lymphoma blood test using serum samples collected from several veterinary practices. The samples were fractionated and analysed by mass spectrometry. Two protein peaks, with the highest diagnostic power, were selected and further identified as acute phase proteins, C-Reactive Protein and Haptoglobin. Data mining methods were then applied to the collected data for the development of an online computer-assisted veterinary diagnostic tool. The generated software can be used as a diagnostic, monitoring and screening tool. Initially, the diagnosis of lymphoma was formulated as a classification problem and then later refined as a lymphoma risk estimation. Three methods, decision trees, kNN and probability density evaluation, were used for classification and risk estimation and several preprocessing approaches were implemented to create the diagnostic system. For the differential diagnosis the best solution gave a sensitivity and specificity of 83.5% and 77%, respectively (using three input features, CRP, Haptoglobin and standard clinical symptom). For the screening task, the decision tree method provided the best result, with sensitivity and specificity of 81.4% and >99%, respectively (using the same input features). Furthermore, the development and application of new techniques for the generation of risk maps allowed their user-friendly visualization.