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

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Abstract

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.

Section snippets

Material and methods

Between 1992 and 1999, 141 consecutive patients (102 male, 39 female) underwent lung resection at the Thoracic Surgical Division of the Hospital de Clı́nicas from the Universidad de Buenos Aires. Mean age was 57.73 years (range, 16 to 84 years). Surgical procedures included 57 lobectomies, 40 pneumonectomies, and 44 wedge resections. All of them were standard, nonextended resections, with mediastinal node sampling.

Ninety-six preoperative clinical, laboratory, and spirometric categorical and

Results

Patient population was distributed in risk groups correlated with the Torrington and Goldman risk indices, as shown in Table 3. Postoperative mortality was progressively higher in patients in Torrington grades II and III, as shown in Table 4. There were four postoperative deaths among the 28 test patients.

The following dependent variables correlated with the independent variable postoperative death at a p less than 0.01 level: ventricular extrasystoles (p < 0.049; df = 1; F ratio, 3.93);

Comment

Medical applications of artificial NN are mostly based on their ability to handle classification problems: multiple examples are presented to the system together with the known answer, and the NN is thus allowed to learn by adaptation using various paradigms 8, 9, 10, 11, 12, 13, 14. Trained NN can then prospectively classify information from new patients. Neural networks learn by finding subtle association between multiple elements of information that are not immediately apparent to a trained

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