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Ann Thorac Surg 2002;73:1576-1581
© 2002 The Society of Thoracic Surgeons

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Original articles: general thoracic

Neural networks as a prognostic tool of surgical risk in lung resections

^a Division of Thoracic Surgery and Division of Pulmonology, Hospital de Clínicas "José de San Martín," University of Buenos Aires, Buenos Aires, Argentina
^b Department of Pathology, UCLA School of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA

Accepted for publication January 5, 2002.

* Address reprint requests to Dr Hugo Esteva, Av. San Martín 1039, (1661) Bella Vista, Provincia de Buenos Aires, República Argentina
e-mail: hesteva{at}intramed.net.ar

Background. Assessment of surgical risk in patients undergoing pulmonary resection is a fundamental goal for thoracic surgeons. Usually used risk indices do not predict the individual outcome. Neural networks (NN) are artificial intelligence software models that have been used for estimation of several prognostic situations.

Methods. Ninety-six clinical and laboratory features from each one of 141 patients who underwent lung resection were retrospectively collected. The variables were used as input data for the software. Cases were divided into a training set (n = 113) and a test set (n = 28). Four NN models were trained using the data from the training set: (1) using all variables; (2) using only the Goldman and Torrington scores; (3) using all variables except for the two scores. A fourth NN was programmed with all variables to estimate the development of major postoperative complications. The trained NN models were tested with the test set data.

Results. The NN using all variables with or without the scores were able to correctly classify all 28 test cases against actual outcome. The NN using all variables also estimated major postoperative complications correctly in all 28 test cases. The NN using only two indices (Goldman and Torrington) yielded 6 of 28 errors in classification.

Conclusions. These data suggest that NN can integrate results from multiple data predicting the individual outcome for patients, rather than assigning them to less-precise risk group categories.

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