Tumor Regression Model of Cervical Cancer – Letter

Abstract

We read with interest the article by Huang and colleagues (“Predicting Outcomes in Cervical Cancer: A Kinetic Model of Tumor Regression during Radiation Therapy”), which appeared in the January 15, 2010 issue of Cancer Research (1). In their experience the tumor regression rate can be accurately evaluated by sequential magnetic resonance imaging (MRI) exams obtained before, during, and after the course of radiation therapy.Effectiveness of therapy is currently assessed by imaging techniques, such as computerized tomography (CT) and MRI, which rely on morphological changes (tumor shrinkage, signal intensity, etc.). In our opinion, however, imaging methods that rely on different parameters, such as metabolic activity or vascularity, that is on function rather than on anatomy, might be more suitable.Given that functional changes are expected to precede morphological changes, the authors' assumption that “currently available methods … do not allow determination of treatment success or failure until many months/years after therapy” should be, in our opinion, revised in light of the newer evaluation methods. In fact, with cervical cancer, recent studies have shown the prognostic value of positron emission tomography (PET) with the glucose analog [18F-FDG] coupled with low dose CT [18F-FDG PET-CT] after completion of radiotherapy or neoadjuvant chemotherapy (2–4), as well as in early response assessment (5). In this regard, a research study on patients with locally advanced cervical carcinoma has recently been undertaken in our institution, by means of 18F-FDG PET-CT scans obtained before, during (at 2 weeks from the beginning), and after preoperative chemo-radiotherapy, in comparison with the histologic findings at subsequent surgery.On the basis of the abovementioned studies and on our preliminary observations, changes of tumor metabolic activity in response to ongoing therapy can be accurately detected, even in the early PET scan done within the second week of treatment, thus allowing earlier outcome prediction and helping in the selection of the most effective treatment regimen for individual patients.See the Response, p. 6104.No potential conflicts of interest were disclosed.