Differential diagnosis of pleural mesothelioma using Logic Learning Machine

• Published in: BMC bioinformatics 2015-06, Vol.16 Suppl 9 (S9), p.S3-S3, Article S3
• Main Authors: Parodi, Stefano, Filiberti, Rosa, Marroni, Paola, Libener, Roberta, Ivaldi, Giovanni Paolo, Mussap, Michele, Ferrari, Enrico, Manneschi, Chiara, Montani, Erika, Muselli, Marco
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Biomarkers, Tumor - analysis; Cohort Studies; Decision Trees; Diagnosis, Differential; Female; Humans; Logic; Lung Neoplasms - diagnosis; Male; Mesothelioma - diagnosis; Mesothelioma, Malignant; Middle Aged; Neoplasm Metastasis; Neural Networks, Computer; Pleural Neoplasms - diagnosis
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-16-S9-S3

Tumour markers are standard tools for the differential diagnosis of cancer. However, the occurrence of nonspecific symptoms and different malignancies involving the same cancer site may lead to a high proportion of misclassifications. Classification accuracy can be improved by combining information from different markers using standard data mining techniques, like Decision Tree (DT), Artificial Neural Network (ANN), and k-Nearest Neighbour (KNN) classifier. Unfortunately, each method suffers from some unavoidable limitations. DT, in general, tends to show a low classification performance, whereas ANN and KNN produce a "black-box" classification that does not provide biological information useful for clinical purposes. Logic Learning Machine (LLM) is an innovative method of supervised data analysis capable of building classifiers described by a set of intelligible rules including simple conditions in their antecedent part. It is essentially an efficient implementation of the Switching Neural Network model and reaches excellent classification accuracy while keeping low the computational demand. LLM was applied to data from a consecutive cohort of 169 patients admitted for diagnosis to two pulmonary departments in Northern Italy from 2009 to 2011. Patients included 52 malignant pleural mesotheliomas (MPM), 62 pleural metastases (MTX) from other tumours and 55 benign diseases (BD) associated with pleurisies. Concentration of three tumour markers (CEA, CYFRA 21-1 and SMRP) was measured in the pleural fluid of each patient and a cytological examination was also carried out. The performance of LLM and that of three competing methods (DT, KNN and ANN) was assessed by leave-one-out cross-validation. LLM outperformed all other considered methods. Global accuracy was 77.5% for LLM, 72.8% for DT, 54.4% for KNN, and 63.9% for ANN, respectively. In more details, LLM correctly classified 79% of MPM, 66% of MTX and 89% of BD. The corresponding figures for DT were: MPM = 83%, MTX = 55% and BD = 84%; for KNN: MPM = 58%, MTX = 45%, BD = 62%; for ANN: MPM = 71%, MTX = 47%, BD = 76%. Finally, LLM provided classification rules in a very good agreement with a priori knowledge about the biological role of the considered tumour markers. LLM is a new flexible tool potentially useful for the differential diagnosis of pleural mesothelioma.