Article

Tumor response kinetics after schedule-dependent paclitaxel chemoradiation treatment for inoperable non-mall cell lung cancer: A model for low-dose chemotherapy radiosensitization

Departments of Radiation Oncology, James P. Wilmot Cancer Center, University of Rochester Medical Center, Rochester, New York, USA.
Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer (Impact Factor: 5.8). 06/2008; 3(6):563-8. DOI: 10.1097/JTO.0b013e3181729dbe
Source: PubMed

ABSTRACT Poor local disease control remains a major obstacle for inoperable non-small cell lung cancer (NSCLC) after radiotherapy. We previously reported results of a phase I/II clinical study based on preclinical investigations of paclitaxel radiation interactions for inoperable locally advanced NSCLC, which yielded remarkable local tumor responses and durable in-field tumor control using schedule-dependent low-dose paclitaxel for radiosensitization. Given our unique results, we analyzed the tumor response kinetics and conducted a statistical modeling of tumor response to characterize this regimen.
A total of 104 chest CT scans from 27 patients treated in the clinical trial were evaluated. Tumor volumes were calculated by three-dimensional measurements of pretreatment and serial post-therapy CT scans. A nonlinear mixed effects model was used to model response kinetics.
The average tumor volume reduction at 1 month post-therapy was 69.9 +/- 22.6% (standard deviation), and was 80.6 +/- 17.9% at the last follow-up. The nonlinear mixed effects model predicts that tumor volume will ultimately shrink by at least 75% for more than 75% of patients treated by this regimen. The model also suggests that maximum shrinkage is reached within 2 months after treatment.
Tumor volume response kinetics revealed a rapid shrinkage of gross tumors using schedule-dependent pulsed low-dose paclitaxel radiosensitization. This is contrary to the protracted tumor regression process observed in radiation alone or other chemoradiation combinations. Statistical modeling may prove useful in characterizing and comparing different therapeutic regimens.

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