Predicting Outcomes in Cervical Cancer: A Kinetic Model of Tumor Regression during Radiation Therapy

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-I...

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Bibliographic Details
Published in:Cancer research (Chicago, Ill.) Ill.), 2010-01, Vol.70 (2), p.463-470
Main Authors: ZHIBIN HUANG, MAYR, Nina A, WANG, Jian Z, YUH, William T. C, LO, Simon S, MONTEBELLO, Joseph F, GRECULA, John C, LANCHUN LU, KAILE LI, HUALIN ZHANG, GUPTA, Nilendu
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Antineoplastic agents
Biological and medical sciences
Female
Female genital diseases
Gynecology. Andrology. Obstetrics
Humans
Magnetic Resonance Imaging
Medical sciences
Middle Aged
Models, Biological
Multivariate Analysis
Neoplasm Staging
Pharmacology. Drug treatments
Prognosis
Treatment Outcome
Tumors
Uterine Cervical Neoplasms - pathology
Uterine Cervical Neoplasms - radiotherapy
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Summary: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.
ISSN:0008-5472
1538-7445
DOI:10.1158/0008-5472.CAN-09-2501