TY - JOUR
T1 - A personalized stepwise dynamic predictive algorithm of the time to first treatment in chronic lymphocytic leukemia
AU - Moysiadis, Theodoros
AU - Koparanis, Dimitris
AU - Liapis, Konstantinos
AU - Ganopoulou, Maria
AU - Vrachiolias, George
AU - Katakis, Ioannis
AU - Moyssiadis, Chronis
AU - Vizirianakis, Ioannis S.
AU - Angelis, Lefteris
AU - Fokianos, Konstantinos
AU - Kotsianidis, Ioannis
N1 - Funding Information:
The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 553 ).
Publisher Copyright:
© 2023 The Authors
PY - 2023/9/15
Y1 - 2023/9/15
N2 - 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.
AB - 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.
KW - Cancer systems biology
KW - Computational bioinformatics
KW - Health sciences
KW - Mathematical biosciences
UR - http://www.scopus.com/inward/record.url?scp=85168596694&partnerID=8YFLogxK
U2 - 10.1016/j.isci.2023.107591
DO - 10.1016/j.isci.2023.107591
M3 - Article
AN - SCOPUS:85168596694
SN - 2589-0042
VL - 26
JO - iScience
JF - iScience
IS - 9
M1 - 107591
ER -