The use of artificial intelligence technology to predict lymph node spreadin men with clinically localized prostate carcinoma

BACKGROUND. The current study assesses artificial intelligence methods to i dentify prostate carcinoma patients at low risk for lymph node spread. If p atients can be assigned accurately to a low risk group, unnecessary lymph n ode dissections can be avoided, thereby reducing morbidity and costs. METHODS. A rule-derivation technology for simple decision-tree analysis was trained and validated using patient data from a large database (4133 patie nts) to derive low risk cutoff, values for Gleason sum and prostate specifi c antigen (PSA) level. An empiric analysis was used to derive a low risk cu toff value for clinical TNM stage. These cutoff Values then were applied to 2 additional, smaller databases (227 and 330 patients, respectively) from separate institutions. RESULTS. The decision-tree protocol derived cutoff values of less than or e qual to 6 for Gleason sum and less than or equal to 10.6 ng/mL for PSA. The empiric analysis yielded a clinical TNM stage low risk cutoff value of les s than or equal to T2a. When these cutoff values were applied to the larger database, 44% of patients were classified as being at low risk for lymph n ode metastases (0.8% false-negative rate). When the same cutoff values were applied to the smaller databases, between II and 43% of patients were clas sified as low risk with a false-negative rate of between 0.0 and 0.7%. CONCLUSIONS, The results of the current study indicate that a population of prostate carcinoma patients at low risk for lymph node metastases can be i dentified accurately using a simple decision algorithm that considers preop erative PSA, Gleason sum, and clinical TNM stage. The risk of lymph node me tastases in these patients is less than or equal to 1%; therefore, pelvic l ymph node dissection may be avoided safely. The implications of these findi ngs in surgical and nonsurgical treatment are significant. (C) 2000 America n Cancer Society.