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

BACKGROUND. The current study assesses artificial intelligence methods to identify prostate carcinoma patients at low risk for lymph node spread. If patients can be assigned accurately to a low risk group, unnecessary lymph node 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 patients) to derive low risk cutoff values for Gleason sum and prostate specific antigen (PSA) level. An empiric analysis was used to derive a low risk cutoff 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 ≤ 6 for Gleason sum and ≤ 10.6 ng/mL for PSA. The empiric analysis yielded a clinical TNM stage low risk cutoff value of ≤ T2a. When these cutoff values were applied to the larger database, 44% of patients were classified as being at low risk for lymph node metastases [0.8% false-negative rate). When the same cutoff values were applied to the smaller databases, between 11 and 43% of patients were classified 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 identified accurately using a simple decision algorithm that considers preoperative PSA, Gleason sum. and clinical TNM stage. The risk of lymph node metastases in these patients is ≤ 1%; therefore, pelvic lymph node dissection may be avoided safely. The implications of these findings in surgical and nonsurgical treatment are significant. (C) 2000 American Cancer Society.