Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloDifferential diagnosis of pleural mesothelioma using Logic Learning Machine
Anno di pubblicazione2015
Autore/iParodi S.; Filiberti R.; Marroni P.; Libener R.; Ivaldi G.;Mussap M.; Ferrari E.; Manneschi E.; Montani E.; Muselli M.;
Affiliazioni autoriInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Via De Marini, 6, 16149 Genoa, Italy Epidemiology, Biostatistics and Clinical Trials, IRCCS AOU San Martino-IST, L.go R. Benzi, 10, 16132 Genoa, Italy Laboratory Medicine Service, IRCCS AOU San Martino-IST, L.go R. Benzi, 10, 16132 Genoa, Italy Pathology Unit, Azienda Ospedaliera Nazionale SS. Antonio e Biagio e Cesare Arrigo, Via Venezia 16, 15121 Alessandria, Italy Department of Pneumology, AO Villa Scassi, Corso Scassi, 1, 16149 Genoa, Italy IMPARA Srl, Piazza Borgo Pila 39, 16129 Genoa, Italy
Autori CNR e affiliazioni
  • inglese
AbstractBackground 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.
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RivistaBMC bioinformatics
Attiva dal 2000
Editore: BioMed Central, - [London]
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1471-2105
Titolo chiave: BMC bioinformatics
Titolo proprio: BMC bioinformatics
Titolo abbreviato: BMC bioinformatics
Titoli alternativi:
  • BioMed Central bioinformatics
  • Bioinformatics
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Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
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Parole chiavemesothelioma, logic learning machine, decision tree, neural network
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Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli/Attività/Sottoprogetti CNR-
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Differential diagnosis of pleural mesothelioma using Logic Learning Machine
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Tipo documento: application/pdf