The amount of clinical and biological data stored within clinical trials is growing exponentially. Data warehousing (DW) is useful for systematic global evaluation of information collected in trials: the highly translational FIL (Fondazione Italiana Linfomi)-MCL0208 trial has been used to test DW to improve data quality and to discover putative associations [Zaccaria, ASH ‘17]. In this study we developed an engineered prognostic model, focusing on easily accessible clinical variables. For this purpose, we exploited hierarchical clustering with the aim of seeking hidden patterns of interest in large datasets. Hence, these tools allowed us to develop a novel prognostic model: the engineered MIPI index (e-MIPI). Herein we present the first results, on baseline clinical characteristics: 1) clustering analysis and definition of a signature of predictive variables 2) construction of the e-MIPI to detect patients’ risk of relapse 3) comparison with known prognostic indexes for MCL 4) validation of the signature on an independent subset of patients.

The Engineered MIPI (e-MIPI), a Candidate Data-Mining Based Mantle Cell Lymphoma Prognostic Index Developed from the Dataset of the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

Zaccaria, Gian Maria;Ferrero, Simone;Evangelista, Andrea;Dogliotti, Irene;Ghislieri, Marco;Genuardi, Elisa;Ciccone, Giovannino;Ladetto, Marco
2018

Abstract

The amount of clinical and biological data stored within clinical trials is growing exponentially. Data warehousing (DW) is useful for systematic global evaluation of information collected in trials: the highly translational FIL (Fondazione Italiana Linfomi)-MCL0208 trial has been used to test DW to improve data quality and to discover putative associations [Zaccaria, ASH ‘17]. In this study we developed an engineered prognostic model, focusing on easily accessible clinical variables. For this purpose, we exploited hierarchical clustering with the aim of seeking hidden patterns of interest in large datasets. Hence, these tools allowed us to develop a novel prognostic model: the engineered MIPI index (e-MIPI). Herein we present the first results, on baseline clinical characteristics: 1) clustering analysis and definition of a signature of predictive variables 2) construction of the e-MIPI to detect patients’ risk of relapse 3) comparison with known prognostic indexes for MCL 4) validation of the signature on an independent subset of patients.
American Society of Hematology (ASH) - 61st Annual Meeting and Exposition
San Diego
01/12/2018 - 04/12/2018
Titolo non avvalorato
132
Supplement 1
2890
2890
http://www.bloodjournal.org/content/132/Suppl_1/2890.abstract
Zaccaria, Gian Maria; Ferrero, Simone; Passera, Roberto; Evangelista, Andrea; Loschirico, Mariella; Dogliotti, Irene; Ghislieri, Marco; Genuardi, Elisa; Bomben, Riccardo; Gattei, Valter; Ciccone, Giovannino; Tani, Monica; Gaidano, Gianluca; Volpetti, Stefano; Cabras, Maria Giuseppina; Di Renzo, Nicola; Merli, Francesco; Vallisa, Daniele; Spina, Michele; Pascarella, Anna; Latte, Giancarlo; Patti, Caterina; Pozzato, Gabriele; Fabbri, Alberto; Cortelazzo, Sergio; Ladetto, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1714231
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