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

doi:10.1016/j.isci.2023.107591

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

iScience. 2023 Aug 9;26(9):107591. doi: 10.1016/j.isci.2023.107591. eCollection 2023 Sep 15.

Affiliations

¹ Department of Hematology, University Hospital of Alexandroupolis, Democritus University of Thrace Medical School, 68100 Alexandroupolis, Greece.
² School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
³ Department of Computer Science, School of Sciences and Engineering, University of Nicosia, 2417 Nicosia, Cyprus.
⁴ School of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
⁵ School of Pharmacy, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
⁶ Department of Health Sciences, School of Life and Health Sciences, University of Nicosia, 2417 Nicosia, Cyprus.
⁷ Department of Mathematics & Statistics, University of Cyprus, 1678 Nicosia, Cyprus.

Abstract

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.

Keywords: Cancer systems biology; Computational bioinformatics; Health sciences; Mathematical biosciences.