Incorporating biologic measurements (SF(2), CFE) into a tumor control probability model increases their prognostic significance: a study in cervical carcinoma treated with radiation therapy.

PURPOSE To assess whether incorporation of measurements of surviving fraction at 2 Gy (SF(2)) and colony-forming efficiency (CFE) into a tumor control probability (tcp) model increases their prognostic significance. METHODS AND MATERIALS Measurements of SF(2) and CFE were available from a study on carcinoma of the cervix treated with radiation alone. These measurements, as well as tumor volume, dose, and treatment time, were incorporated into a Poisson tcp model (tcp(alpha,rho)). Regression analysis was performed to assess the prognostic power of tcp(alpha,rho) vs. the use of either tcp models with biologic parameters fixed to best-fit estimates (but incorporating individual dose, volume, and treatment time) or the use of SF(2) and CFE measurements alone. RESULTS In a univariate regression analysis of 44 patients, tcp(alpha,rho) was a better prognostic factor for both local control and survival (p < 0.001 and p = 0.049, respectively) than SF(2) alone (p = 0.009 for local control, p = 0.29 for survival) or CFE alone (p = 0.015 for local control, p = 0.38 for survival). In multivariate analysis, tcp(alpha,rho) emerged as the most important prognostic factor for local control (p < 0.001, relative risk of 2.81). After allowing for tcp(alpha,rho), CFE was still a significant independent prognostic factor for local control, whereas SF(2) was not. The sensitivities of tcp(alpha,rho) and SF(2) as predictive tests for local control were 87% and 65%, respectively. Specificities were 70% and 77%, respectively. CONCLUSIONS A Poisson tcp model incorporating individual SF(2), CFE, dose, tumor volume, and treatment time was found to be the best independent prognostic factor for local control and survival in cervical carcinoma patients.

[1]  R. Pötter,et al.  Intratumoral pO2-measurements as predictive assay in the treatment of carcinoma of the uterine cervix. , 1999, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[2]  D J Brenner,et al.  Dose, volume, and tumor-control predictions in radiotherapy. , 1993, International journal of radiation oncology, biology, physics.

[3]  S. A. Roberts,et al.  Intrinsic radiosensitivity and prediction of patient response to radiotherapy for carcinoma of the cervix. , 1993, British Journal of Cancer.

[4]  P. Stanton,et al.  Prediction of radiotherapy response of cervical carcinoma through measurement of proliferation rate. , 1996, British Journal of Cancer.

[5]  R K Sachs,et al.  A convenient extension of the linear-quadratic model to include redistribution and reoxygenation. , 1995, International journal of radiation oncology, biology, physics.

[6]  P Okunieff,et al.  Clinical implications of heterogeneity of tumor response to radiation therapy. , 1992, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[7]  S Webb,et al.  A model for calculating tumour control probability in radiotherapy including the effects of inhomogeneous distributions of dose and clonogenic cell density. , 1993, Physics in medicine and biology.

[8]  S. Tucker,et al.  Improved models of tumour cure. , 1996, International journal of radiation biology.

[9]  R K Sachs,et al.  The link between low-LET dose-response relations and the underlying kinetics of damage production/repair/misrepair. , 1997, International journal of radiation biology.

[10]  P Vaupel,et al.  Association between tumor hypoxia and malignant progression in advanced cancer of the uterine cervix. , 1996, Cancer research.

[11]  David E. Matthews,et al.  Using and Understanding Medical Statistics , 1984 .

[12]  M. Zaider,et al.  Tumour control probability: a formulation applicable to any temporal protocol of dose delivery. , 2000, Physics in medicine and biology.

[13]  J. Bourhis,et al.  Predictive assays of radiation response in patients with head and neck squamous cell carcinoma: a review of the Institute Gustave Roussy experience. , 1997, International journal of radiation oncology, biology, physics.

[14]  J D Fenwick,et al.  Predicting the radiation control probability of heterogeneous tumour ensembles: data analysis and parameter estimation using a closed-form expression. , 1998, Physics in medicine and biology.

[15]  A. Nahum,et al.  An analysis of the relationship between radiosensitivity and volume effects in tumor control probability modeling. , 2000, Medical physics.

[16]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.

[17]  C. Ling,et al.  Analysis of biopsy outcome after three-dimensional conformal radiation therapy of prostate cancer using dose-distribution variables and tumor control probability models. , 2000, International journal of radiation oncology, biology, physics.

[18]  T. Björk-Eriksson,et al.  Tumor radiosensitivity (SF2) is a prognostic factor for local control in head and neck cancers. , 2000, International journal of radiation oncology, biology, physics.

[19]  Edward J. Dudewicz,et al.  Modern Mathematical Statistics , 1988 .

[20]  J. Whitehead,et al.  A FORTRAN program for the design and analysis of sequential clinical trials. , 1983, Computers and biomedical research, an international journal.

[21]  S. A. Roberts,et al.  The independence of intrinsic radiosensitivity as a prognostic factor for patient response to radiotherapy of carcinoma of the cervix. , 1997, British Journal of Cancer.