Test the Effectiveness of Quantitative Linear-Quadratic-Based (qLQB) Model on Evaluating Irradiation-Induced Liver Injury (ILI) Against Normal Tissue Complication Probability (NTCP)

• Main Authors: Han Bai (Author), Li Wang (Author), Wenhui Li (Author), Xuhong Liu (Author), Yaoxiong Xia (Author), Li Chang (Author)
• Format: Book
• Published: SAGE Publishing, 2020-09-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1559-3258; 10.1177/1559325820961721
• Keywords: Therapeutics. Pharmacology; RM1-950
• Source: Dose-Response, Vol 18 (2020)

Objectives: To test the effectiveness of quantitative linear-quadratic-based (qLQB) model on evaluating irradiation-induced liver injury (ILI) and establish the relation between the damaged ratio/percent (DRP) in qLQB model and normal tissue complication probility (NTCP). Materials and Methods: We established the qLQB model to calculate the ratio/percent (RP) between damaged cell/functional subunit (FSU) and entire cell/FSU of liver for radiation dose response, tested the qLQB against the Lyman-Kutcher-Burman (LKB) model, and established relation between the RP and NTCP through analyzing the dose of 32 patients with cancer of abdominal cavity who were treated with radiation therapy at our department. Based on varied α/β and varied parameters for NTCP, we put the calculated results into varied arrays for the next analysis. We named the 2 groups of RPs: RP1 (α/β = 3.0, α = 0.03) and RP2 (α/β = 8.0, α = 0.26), and named the 2 groups of NTCPs: NTCP1 (n = 0.32, m = 0.15, TD50(1) = 4000 cGy) and NTCP2 (n = 1.10, m = 0.28, TD50(1) = 4050 cGy). Results: Spearman correlation analysis was used to analyze the correlations among the groups, the results were as follows: RP1 vs NTCP1, rs = 0.83827, p < 0.0001; RP1 vs NTCP2, rs = 0.83827, p < 0.0001; RP2 vs NTCP2, rs = 0.79289, p < 0.0001; and RP2 vs NTCP1, rs = 0.79289, p < 0.0001. Conclusions: There is a significant correlation between RP value and NTCP for evaluating ILI, and there is no difference between qLQB model and LKB model on evaluating ILI.