RESUMEN
The 5-year survival of non-small cell lung cancer patients can be as low as 1% in advanced stages. For patients with resectable disease, the successful choice of preoperative chemotherapy is critical to eliminate micrometastasis and improve operability. In silico experimentations can suggest the optimal treatment protocol for each patient based on their own multiscale data. A determinant for reliable predictions is the a priori estimation of the drugs' cytotoxic efficacy on cancer cells for a given treatment. In the present work a mechanistic model of cancer response to treatment is applied for the estimation of a plausible value range of the cell killing efficacy of various cisplatin-based doublet regimens. Among others, the model incorporates the cancer related mechanism of uncontrolled proliferation, population heterogeneity, hypoxia and treatment resistance. The methodology is based on the provision of tumor volumetric data at two time points, before and after or during treatment. It takes into account the effect of tumor microenvironment and cell repopulation on treatment outcome. A thorough sensitivity analysis based on one-factor-at-a-time and latin hypercube sampling/partial rank correlation coefficient approaches has established the volume growth rate and the growth fraction at diagnosis as key features for more accurate estimates. The methodology is applied on the retrospective data of thirteen patients with non-small cell lung cancer who received cisplatin in combination with gemcitabine, vinorelbine or docetaxel in the neoadjuvant context. The selection of model input values has been guided by a comprehensive literature survey on cancer-specific proliferation kinetics. The latin hypercube sampling has been recruited to compensate for patient-specific uncertainties. Concluding, the present work provides a quantitative framework for the estimation of the in-vivo cell-killing ability of various chemotherapies. Correlation studies of such estimates with the molecular profile of patients could serve as a basis for reliable personalized predictions.
RESUMEN
Current methods for imaging joint motion are limited to either two-dimensional (2D) video fluoroscopy, or to animated motions from a series of static three-dimensional (3D) images. 3D movement patterns can be detected from biplane fluoroscopy images matched with computed tomography images. This involves several x-ray modalities and sophisticated 2D to 3D matching for the complex wrist joint. We present a method for the acquisition of dynamic 3D images of a moving joint. In our method a 3D-rotational x-ray (3D-RX) system is used to image a cyclically moving joint. The cyclic motion is synchronized to the x-ray acquisition to yield multiple sets of projection images, which are reconstructed to a series of time resolved 3D images, i.e., four-dimensional rotational x ray (4D-RX). To investigate the obtained image quality parameters the full width at half maximum (FWHM) of the point spread function (PSF) via the edge spread function and the contrast to noise ratio between air and phantom were determined on reconstructions of a bullet and rod phantom, using 4D-RX as well as stationary 3D-RX images. The CNR in volume reconstructions based on 251 projection images in the static situation and on 41 and 34 projection images of a moving phantom were 6.9, 3.0, and 2.9, respectively. The average FWHM of the PSF of these same images was, respectively, 1.1, 1.7, and 2.2 mm orthogonal to the motion and parallel to direction of motion 0.6, 0.7, and 1.0 mm. The main deterioration of 4D-RX images compared to 3D-RX images is due to the low number of projection images used and not to the motion of the object. Using 41 projection images seems the best setting for the current system. Experiments on a postmortem wrist show the feasibility of the method for imaging 3D dynamic joint motion. We expect that 4D-RX will pave the way to improved assessment of joint disorders by detection of 3D dynamic motion patterns in joints.
