A personalized stepwise dynamic predictive algorithm of the time to first treatment in chronic lymphocytic leukemia

Theodoros Moysiadis, Dimitris Koparanis, Konstantinos Liapis, Maria Ganopoulou, George Vrachiolias, Ioannis Katakis, Chronis Moyssiadis, Ioannis S. Vizirianakis, Lefteris Angelis, Konstantinos Fokianos, Ioannis Kotsianidis

Research output: Contribution to journalArticlepeer-review


Personalized prediction is ideal in chronic lymphocytic leukemia (CLL). Although refined models have been developed, stratifying patients in risk groups, it is required to accommodate time-dependent information of patients, to address the clinical heterogeneity observed within these groups. In this direction, this study proposes a personalized stepwise dynamic predictive algorithm (PSDPA) for the time-to-first-treatment of the individual patient. The PSDPA introduces a personalized Score, reflecting the evolution in the patient's follow-up, employed to develop a reference pool of patients. Score evolution's similarity is used to predict, at a selected time point, the time-to-first-treatment for a new patient. Additional patient's biological information may be utilized. The algorithm was applied to 20 CLL patients, indicating that stricter assessment criteria for the Score evolution's similarity, and biological similarity exploitation, may improve prediction. The PSDPA capitalizes on both the follow-up and the biological background of the individual patient, dynamically promoting personalized prediction in CLL.

Original languageEnglish
Article number107591
Issue number9
Publication statusPublished - 15 Sept 2023


  • Cancer systems biology
  • Computational bioinformatics
  • Health sciences
  • Mathematical biosciences

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