Pharmacometric Modeling of Liver Metastases' Diameter, Volume, and Density and Their Relation to Clinical Outcome in Imatinib‐Treated Patients With Gastrointestinal Stromal Tumors

Abstract

Three‐dimensional and density‐based tumor metrics have been suggested to better discriminate tumor response to treatment than unidimensional metrics, particularly for tumors exhibiting nonuniform size changes. In the developed pharmacometric modeling framework based on data from 77 imatinib‐treated gastrointestinal patients, the time‐courses of liver metastases' maximum transaxial diameters, software‐calculated actual volumes (Vactual) and calculated ellipsoidal volumes were characterized by logistic growth models, in which imatinib induced a linear dose‐dependent size reduction. An indirect response model best described the reduction in density. Substantial interindividual variability in the drug effect of all response assessments and additional interlesion variability in the drug effect on density were identified. The predictive ability of longitudinal tumor unidimensional and three‐dimensional size and density on overall survival (OS) and progression‐free survival (PFS) were compared using parametric time‐to‐event models. Death hazard increased with increasing Vactual. This framework may guide early clinical interventions based on three‐dimensional tumor responses to enhance benefits for patients with gastrointestinal stromal tumors (GIST).

Exposure-Response Analysis of Necitumumab Efficacy in Squamous Non-Small Cell Lung Cancer Patients

“…Benefits and limitations of this approach was investigated in depth in a simulation study by Ribba et al, 34 and recent applications have demonstrated the feasability in liver and renal cancer. 35,36 This study demonstrates that application of simultaneous TGI-OS modeling can be done also within lung cancer, and be used for dose justification as well as evaluation of alternative dose regimens. It should be noted that this work investigated the timecourse only of target lesions, while it can be assumed that also nontarget lesions would contribute to hazard.…”

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival

“…Despite enriching data analysis in oncology, lesion heterogeneity has been often ignored or disregarded in the TS modeling, as also highlighted by the limited number of published works in this field (9,10). This is due to the complex models and methods needed to consider differences between organs or tissues, as well as the intra-tumor heterogeneity within an anatomic area.…”

“…Tumor heterogeneity is one of the factors involved in tumor resistance (7) and tumor metastasis (8). Recently, some modeling works in the oncology area successfully included tumor heterogeneity (9) and related this lesions' variability to OS (10). Not including tumor heterogeneity into TS models may hide iTLs resistance development or other differences in response to treatment.…”

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

“…129 Therefore, the question of modeling interlesion variability (ILV) within the same host is of important relevance and has started to attract the attention of mathematical modelers in recent years. [130][131][132][133] In a model including a hierarchical ILV layer for the kinetics of standard uptake values from PET imaging in sunitinib-treated gastrointestinal stromal tumors, Schindler et al found a significant ILV for the drug effect, although smaller than interpatient variability. 134 This finding was also observed in another study modeling ILV and comparing the OS predictive power of model metrics (time-to-growth) derived from either diameters or volumes (computed from semi-automated tumor segmentation) in 918 patients with metastatic colorectal cancer.…”