Abstract：
:Applications of mathematical modeling can improve outcome predictions of cancer therapy. Here we present a kinetic model incorporating effects of radiosensitivity, tumor repopulation, and dead-cell resolving on the analysis of tumor volume regression data of 80 cervical cancer patients (stages 1B2-IVA) who underwent radiation therapy. Regression rates and derived model parameters correlated significantly with clinical outcome (P < 0.001; median follow-up: 6.2 years). The 6-year local tumor control rate was 87% versus 54% using radiosensitivity (2-Gy surviving fraction S(2) < 0.70 vs. S(2) > or = 0.70) as a predictor (P = 0.001) and 89% vs. 57% using dead-cell resolving time (T(1/2) < 22 days versus T(1/2) > or = 22 days, P < 0.001). The 6-year disease-specific survival was 73% versus 41% with S(2) < 0.70 versus S(2) > or = 0.70 (P = 0.025), and 87% vs. 52% with T(1/2) < 22 days versus T(1/2) > or = 22 days (P = 0.002). Our approach illustrates the promise of volume-based tumor response modeling to improve early outcome predictions that can be used to enable personalized adaptive therapy.
journal_name
Cancer Resjournal_title
Cancer researchauthors
Huang Z,Mayr NA,Yuh WT,Lo SS,Montebello JF,Grecula JC,Lu L,Li K,Zhang H,Gupta N,Wang JZdoi
10.1158/0008-5472.CAN-09-2501subject
Has Abstractpub_date
2010-01-15 00:00:00pages
463-70issue
2eissn
0008-5472issn
1538-7445pii
0008-5472.CAN-09-2501journal_volume
70pub_type
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