title: Outcome assessment of patients with metastatic renal cell carcinoma under systemic therapy using artificial neural networks.
abstract: BACKGROUND
  The outcome of patients with advanced renal cell carcinoma (RCC) under systemic therapy shows remarkable variability, and there is a need to identify prognostic parameters that allow individual prognostic stratification and selection of optimal therapy. Artificial neural networks (ANN) are software systems that can be trained to recognize complex data patterns. In this study, we used ANNs to identify poor prognosis of patients with RCC based on common clinical parameters available at the beginning of systemic therapy.
  
  
  METHODS
  Data from patients with RCC who started systemic therapy were collected prospectively in a single center database; 175 data sets with follow-up data (median, 36 months) were available for analysis. Age, sex, body mass index, performance status, histopathologic parameters, time interval between primary tumor and detection of metastases, type of systemic therapy, number of metastases, and metastatic sites were used as input data for the ANN. The target variable was overall survival after 36 months. Logistic regression models were constructed by using the same variables.
  
  
  RESULTS
  Death after 36 months occurred in 26% of the patients in the tyrosine kinase inhibitors group and in 37% of the patients in the immunotherapy group (P = .22). ANN achieved 95% overall accuracy and significantly outperformed logistic regression models (78% accuracy). Pathologic T classification, invasion of vessels, and tumor grade had the highest impact on the network's decision.
  
  
  CONCLUSIONS
  ANN is a promising approach for individual risk stratification of patients with advanced RCC under systemic therapy, based on clinical parameters, and can help to optimize the therapeutic strategy.
authors: Department of Urology, Ludwig-Maximilians University, Munich, Germany. alexander.buchner@med.uni-muenchen.de
authors: Alexander
authors: A
authors: Buchner
authors: Martin
authors: M
authors: Kendlbacher
authors: Philipp
authors: P
authors: Nuhn
authors: Cordula
authors: C
authors: Tüllmann
authors: Nicolas
authors: N
authors: Haseke
authors: Christian G
authors: CG
authors: Stief
authors: Michael
authors: M
authors: Staehler
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D002292:Carcinoma, Renal Cell / Q000401:mortality* / Q000473:pathology / Q000628:therapy; D003131:Combined Modality Therapy; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007680:Kidney Neoplasms / Q000401:mortality* / Q000473:pathology / Q000628:therapy; D008297:Male; D008875:Middle Aged; D009392:Nephrectomy; D016571:Neural Networks, Computer; D011446:Prospective Studies; D015996:Survival Rate; D016896:Treatment Outcome; D055815:Young Adult