title: The use of artificial intelligence technology to predict lymph node spread in men with clinically localized prostate carcinoma.
abstract: 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 (4,133 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 < or = 6 for Gleason sum and < or = 10.6 ng/mL for PSA. The empiric analysis yielded a clinical TNM stage low risk cutoff value of < or = 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 < or = 1%; therefore, pelvic lymph node dissection may be avoided safely. The implications of these findings in surgical and nonsurgical treatment are significant.
authors.affiliation: Section of Urologic Oncology, University of Colorado Health Sciences Center, Denver 80262, USA.
authors.forename: E D
authors.initials: ED
authors.lastname: Crawford
authors.forename: J T
authors.initials: JT
authors.lastname: Batuello
authors.forename: P
authors.initials: P
authors.lastname: Snow
authors.forename: E J
authors.initials: EJ
authors.lastname: Gamito
authors.forename: D G
authors.initials: DG
authors.lastname: McLeod
authors.forename: A W
authors.initials: AW
authors.lastname: Partin
authors.forename: N
authors.initials: N
authors.lastname: Stone
authors.forename: J
authors.initials: J
authors.lastname: Montie
authors.forename: R
authors.initials: R
authors.lastname: Stock
authors.forename: J
authors.initials: J
authors.lastname: Lynch
authors.forename: J
authors.initials: J
authors.lastname: Brandt
mesh_terms: D000135:Acid Phosphatase / Q000032:analysis; D000465:Algorithms; D001185:Artificial Intelligence; D001707:Biopsy, Needle; D002277:Carcinoma / Q000556:secondary*; D003363:Cost Control; D019992:Databases as Topic; D003661:Decision Support Techniques; D003663:Decision Trees; D005188:False Negative Reactions; D006801:Humans; D008197:Lymph Node Excision / Q000009:adverse effects / Q000191:economics; D008207:Lymphatic Metastasis / Q000473:pathology*; D008297:Male; D009367:Neoplasm Staging; D017430:Prostate-Specific Antigen / Q000032:analysis; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012307:Risk Factors
chemical_list: D000135:Acid Phosphatase; D017430:Prostate-Specific Antigen