Search

Background: The TNM staging system originated as a response to the need for an accurate, consistent, universal cancer outcome prediction system. Since the TNM staging system was introduced in the 1950s, new prognostic factors have been identified and new methods for integrating prognostic factors have been developed. This study compares the prediction accuracy of the TNM staging system with that of artificial neural network statistical models., Methods: For 5-year survival of patients with breast or colorectal carcinoma, the authors compared the TNM staging system's predictive accuracy with that of artificial neural networks (ANN). The area under the receiver operating characteristic curve, as applied to an independent validation data set, was the measure of accuracy., Results: For the American College of Surgeons' Patient Care Evaluation (PCE) data set, using only the TNM variables (tumor size, number of positive regional lymph nodes, and distant metastasis), the artificial neural network's predictions of the 5-year survival of patients with breast carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.720; ANN, 0.770; P < 0.001). For the National Cancer Institute's Surveillance, Epidemiology, and End Results breast carcinoma data set, using only the TNM variables, the artificial neural network's predictions of 10-year survival were significantly more accurate than those of the TNM staging system (TNM, 0.692; ANN, 0.730; P < 0.01). For the PCE colorectal data set, using only the TNM variables, the artificial neural network's predictions of the 5-year survival of patients with colorectal carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.737; ANN, 0.815; P < 0.001). Adding commonly collected demographic and anatomic variables to the TNM variables further increased the accuracy of the artificial neural network's predictions of breast carcinoma survival (0.784) and colorectal carcinoma survival (0.869)., Conclusions: Artificial neural networks are significantly more accurate than the TNM staging system when both use the TNM prognostic factors alone. New prognostic factors can be added to artificial neural networks to increase prognostic accuracy further. These results are robust across different data sets and cancer sites.

A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a piecewise-continuous function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system.

Pulmonary vascular resistance is an important determinant of cardiac output after the Fontan procedure and is adversely affected by elevated mean airway pressure. High-frequency jet ventilation (HFJV) is an alternate form of mechanical ventilation which supports gas exchange at lower mean airway pressure. This study was performed to determine if HFJV could lower mean airway pressure and pulmonary vascular resistance and result in an increase in cardiac output after the Fontan procedure. We prospectively evaluated 13 patients ranging in age from 0.9 to 8.5 years (mean, 3.9 years) and in weight from 6.2 to 20.1 kg (mean, 13.9 kg). Right atrial, left atrial and pulmonary artery catheters were used to measure hemodynamic parameters. Cardiac index was measured by dye dilution technique, and pulmonary vascular resistance was calculated. The patients were stabilized on mechanical ventilation to achieve a PaCO2 = 30 +/- 5 mm Hg, and baseline hemodynamic and respiratory measurements were made. HFJV was begun at settings adjusted to achieve similar gas exchange. Respiratory and hemodynamic measurements were repeated after 30-60 minutes of HFJV. Mechanical ventilation was then resumed at baseline settings, and measurements were repeated 0.5-1 hour later. There was no significant change in gas exchange. HFJV resulted in a 50% reduction in mean airway pressure (9.2 +/- 0.2 cm H2O to 4.6 +/- 0.1 cm H2O, p less than 0.001), a 59% reduction in pulmonary vascular resistance (3.82 +/- 0.36 to 1.52 +/- 0.16 Woods units, p less than 0.001), and a 25% increase in cardiac index (2.32 +/- 0.12 l/min/m2 to 2.91 +/- 0.12 l/min/m2, p less than 0.001).(ABSTRACT TRUNCATED AT 250 WORDS)