Open AccessAssessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology
Author(s) -
Terranova Nadia,
Girard Pascal,
Ioannou Konstantinos,
Klinkhardt Ute,
Munafo Alain
Publication year - 2018
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12284
Subject(s) - dynamics (music) , cetuximab , computer science , categorical variable , irinotecan , metric (unit) , lesion , cluster analysis , categorization , similarity (geometry) , artificial intelligence , java , contrast (vision) , pattern recognition (psychology) , cancer , machine learning , colorectal cancer , medicine , pathology , image (mathematics) , psychology , pedagogy , operations management , economics , programming language
Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of tumor sizes (TS). However, differences in lesion dynamics might be predictive of tumor progression. To exploit this information, we have developed a novel and flexible approach for the non‐parametric analysis of iTLs, which integrates knowledge from signal processing and machine learning. We called this new methodology ClassIfication Clustering of Individual Lesions (CICIL). We used CICIL to assess similarities among the TS dynamics of 3,223 iTLs measured in 1,056 patients with metastatic colorectal cancer treated with cetuximab combined with irinotecan, in two phase II studies. We mainly observed similar dynamics among lesions within the same tumor site classification. In contrast, lesions in anatomic locations with different features showed different dynamics in about 35% of patients. The CICIL methodology has also been implemented in a user‐friendly and efficient Java‐based framework.