title: Differential diagnosis of pleural mesothelioma using Logic Learning Machine.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  LLM is a new flexible tool potentially useful for the differential diagnosis of pleural mesothelioma.
authors.forename: Stefano
authors.initials: S
authors.lastname: Parodi
authors.forename: Rosa
authors.initials: R
authors.lastname: Filiberti
authors.forename: Paola
authors.initials: P
authors.lastname: Marroni
authors.forename: Roberta
authors.initials: R
authors.lastname: Libener
authors.forename: Giovanni Paolo
authors.initials: GP
authors.lastname: Ivaldi
authors.forename: Michele
authors.initials: M
authors.lastname: Mussap
authors.forename: Enrico
authors.initials: E
authors.lastname: Ferrari
authors.forename: Chiara
authors.initials: C
authors.lastname: Manneschi
authors.forename: Erika
authors.initials: E
authors.lastname: Montani
authors.forename: Marco
authors.initials: M
authors.lastname: Muselli
mesh_terms: D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D015331:Cohort Studies; D003663:Decision Trees; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008128:Logic; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D008654:Mesothelioma / Q000175:diagnosis*; D000086002:Mesothelioma, Malignant; D008875:Middle Aged; D009362:Neoplasm Metastasis; D016571:Neural Networks, Computer; D010997:Pleural Neoplasms / Q000175:diagnosis*
chemical_list: D014408:Biomarkers, Tumor