Shedding light on the effect of radiation therapy on circulating tumor cells

Many common treatments for cancer – including radiation therapy (RT) – have the unfortunate side effect of promoting the spread of cancer to other organs [1-3]. While the ‘pro-metastatic’ effects of RT have been known for some time, it has garnered renewed attention in recent years in part due to the widespread study of circulating tumor cells (CTCs). In hematogenous metastasis, CTCs detach from the primary tumor and spread via the blood to other organs and tissues of the body. There are three main hypotheses for RT induced metastasis (RTIM) as reviewed in [1]: i) RT causes disruption of the primary tumor and vasculature, which leads to immediate shedding of CTCs, iii) RT induces biomolecular changes in tumor cells, such as epithelial to mesenchymal transition, leading to increased CTC shedding over time as the tumor cells die, and, iii) Systemic effects, such as the elimination of suppressive signaling molecules by the primary tumor resulting in the proliferation of existent but previously dormant micro-metastases [3]. Our team recently developed a new instrument called ‘Diffuse in vivo Flow Cytometry’ (DiFC; figure 1) [4]. The main advantage of DiFC is that it samples large circulating blood volumes (hundreds of µL per minute), allowing in vivo detection of very rare CTCs. DiFC uses specially designed fiber-optic probe bundles with built-in filters and lenses for efficient collection of weak fluorescent signals and blocking of tissue autofluorescence. As labeled cells pass through the DiFC field of view, transient fluorescent peaks are detected. A custom signal processing algorithm allowed us to determine the number, direction, speed, and depth of circulating cells, and reject false alarm signals from motion artifacts. For example, we recently showed that DiFC allowed detection of early dissemination of green fluorescent protein (GFP)-labeled multiple myeloma cells in a disseminated xenograft model at CTC burdens below 1 cell per mL, as well as rare CTC clusters (fig. 1). Please click Additional Files below to see the full abstract

Application of a spring-dashpot system to clinical lung tumor motion data

A spring-dashpot system based on the Voigt model was developed to model the correlation between abdominal respiratory motion and tumor motion during lung radiotherapy. The model was applied to clinical data comprising 52 treatment beams from 10 patients, treated on the Mitsubishi Real-Time Radiation Therapy system, Sapporo, Japan. In Stage 1, model parameters were optimized for individual patients and beams to determine reference values and to investigate how well the model can describe the data. In Stage 2, for each patient the optimal parameters determined for a single beam were applied to data from other beams to investigate whether a beam-specific set of model parameters is sufficient to model tumor motion over a course of treatment. In Stage 1 the baseline root mean square (RMS) residual error for all individually-optimized beam data was 0.90 plus or minus 0.40 mm. In Stage 2, patient-specific model parameters based on a single beam were found to model the tumor position closely, even for irregular beam data, with a mean increase with respect to Stage 1 values in RMS error of 0.37 mm. On average the obtained model output for the tumor position was 95% of the time within an absolute bound of 2.0 mm and 2.6 mm in Stage 1 and 2, respectively. The model was capable of dealing with baseline, amplitude and frequency variations of the input data, as well as phase shifts between the input tumor and output abdominal signals. These results indicate that it may be feasible to collect patient-specific model parameters during or prior to the first treatment, and then retain these for the rest of the treatment period. The model has potential for clinical application during radiotherapy treatment of lung tumors

Dosimetric validation of a magnetic resonance image gated radiotherapy system using a motion phantom and radiochromic film.

PurposeMagnetic resonance image (MRI) guided radiotherapy enables gating directly on the target position. We present an evaluation of an MRI-guided radiotherapy system's gating performance using an MRI-compatible respiratory motion phantom and radiochromic film. Our evaluation is geared toward validation of our institution's clinical gating protocol which involves planning to a target volume formed by expanding 5 mm about the gross tumor volume (GTV) and gating based on a 3 mm window about the GTV.MethodsThe motion phantom consisted of a target rod containing high-contrast target inserts which moved in the superior-inferior direction inside a body structure containing background contrast material. The target rod was equipped with a radiochromic film insert. Treatment plans were generated for a 3 cm diameter spherical planning target volume, and delivered to the phantom at rest and in motion with and without gating. Both sinusoidal trajectories and tumor trajectories measured during MRI-guided treatments were used. Similarity of the gated dose distribution to the planned, motion-frozen, distribution was quantified using the gamma technique.ResultsWithout gating, gamma pass rates using 4%/3 mm criteria were 22-59% depending on motion trajectory. Using our clinical standard of repeated breath holds and a gating window of 3 mm with 10% target allowed outside the gating boundary, the gamma pass rate was 97.8% with 3%/3 mm gamma criteria. Using a 3 mm window and 10% allowed excursion, all of the patient tumor motion trajectories at actual speed resulting in at least 95% gamma pass rate at 4%/3 mm.ConclusionsOur results suggest that the device can be used to compensate respiratory motion using a 3 mm gating margin and 10% allowed excursion results in conjunction with repeated breath holds. Full clinical validation requires a comprehensive evaluation of tracking performance in actual patient images, outside the scope of this study

Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy

Purpose: To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. Methods: Given a set of volumetric images of a patient at N breathing phases as the training data, we perform deformable image registration between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, we can generate new DVFs, which, when applied on the reference image, lead to new volumetric images. We then can reconstruct a volumetric image from a single projection image by optimizing the PCA coefficients such that its computed projection matches the measured one. The 3D location of the tumor can be derived by applying the inverted DVF on its position in the reference image. Our algorithm was implemented on graphics processing units (GPUs) to achieve real-time efficiency. We generated the training data using a realistic and dynamic mathematical phantom with 10 breathing phases. The testing data were 360 cone beam projections corresponding to one gantry rotation, simulated using the same phantom with a 50% increase in breathing amplitude. Results: The average relative image intensity error of the reconstructed volumetric images is 6.9% +/- 2.4%. The average 3D tumor localization error is 0.8 mm +/- 0.5 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for reconstructing a volumetric image from each projection is 0.24 seconds (range: 0.17 and 0.35 seconds). Conclusions: We have shown the feasibility of reconstructing volumetric images and localizing tumor positions in 3D in near real-time from a single x-ray image.Comment: 8 pages, 3 figures, submitted to Medical Physics Lette

Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer

Background: PET-based texture features have been used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity of texture features to tumor motion by comparing static (3D) and respiratory-gated (4D) PET imaging. Methods: Twenty-six patients (34 lesions) received 3D and 4D [18F]FDG-PET scans before the chemo-radiotherapy. The acquired 4D data were retrospectively binned into five breathing phases to create the 4D image sequence. Texture features, including Maximal correlation coefficient (MCC), Long run low gray (LRLG), Coarseness, Contrast, and Busyness, were computed within the physician-defined tumor volume. The relative difference (δ3D-4D) in each texture between the 3D- and 4D-PET imaging was calculated. Coefficient of variation (CV) was used to determine the variability in the textures between all 4D-PET phases. Correlations between tumor volume, motion amplitude, and δ3D-4D were also assessed. Results: 4D-PET increased LRLG ( = 1%–2%, p0.08) compared to 3D-PET. Nearly negligible variability was found between the 4D phase bins with CV<5% for MCC, LRLG, and Coarseness. For Contrast and Busyness, moderate variability was found with CV = 9% and 10%, respectively. No strong correlation was found between the tumor volume and δ3D-4D for the texture features. Motion amplitude had moderate impact on δ for MCC and Busyness and no impact for LRLG, Coarseness, and Contrast. Conclusions: Significant differences were found in MCC, LRLG, Coarseness, and Busyness between 3D and 4D PET imaging. The variability between phase bins for MCC, LRLG, and Coarseness was negligible, suggesting that similar quantification can be obtained from all phases. Texture features, blurred out by respiratory motion during 3D-PET acquisition, can be better resolved by 4D-PET imaging. 4D-PET textures may have better prognostic value as they are less susceptible to tumor motion

Evaluation of the need for simultaneous orthogonal gated setup imaging

Image‐guided patient setup for respiratory‐gated radiotherapy often relies on a pair of respiratory‐gated orthogonal radiographs, acquired one after the other. This study quantifies the error due to changes in the internal/external correlation which may affect asynchronous (non‐simultaneous) imaging. The dataset from eight patients includes internal and external coordinates acquired at 30Hz during multi‐fraction SBRT treatments using the Mitsubishi RTRT system coupled with an external surrogate gating device. We performed a computational simulation of the position of an implanted fiducial marker in an asynchronous orthogonal image set. A comparison is made to the reference position, the actual 3D fiducial location at the initial time point, as would be obtainable by simultaneous orthogonal setup imaging at that time point. The time interval between the two simulated radiographic acquisitions was set to a minimum of 30, 60 or 90 seconds, based on our clinical experience. The setup position is derived from a combination of both the initial (AP) and the final (LR) simulated 2D images in the following way: LRsetup=LRinitial,SIsetup=SIinitial+(SIfinal−SIinitial)/2,APsetup=APfinal. The 3D error is then the magnitude of the vector from the initial (reference) position to the setup position. The calculation was done for every exhale phase in the data for which there was another one at least 30, 60 or 90 seconds later, at an amplitude within 0.5 mm from the first. A correlation between the time interval and the 3D error was also sought. The mean 3D error is found to be roughly equivalent for time intervals (tinterval) of 30, 60 and 90 seconds between the orthogonal simulated images (0.8 mm, 0.8 mm, 0.6 mm, respectively). The 3D error is less than 1, 2 and 3 mm for 77%, 89% and 98% of the data points, respectively. The actual time between simulated images turned out to be very close to tinterval, with 90% of the second simulated image acquisitions being completed within 38, 68 and 95 seconds of the first simulated image for tinterval of 30, 60 and 90 seconds, respectively. No correlation was found between the length of the time interval and the 3D error. When acquiring respiratory‐gated radiographs for patient setup, only small errors should be expected if those images are not taken simultaneously. PACS number: 87.55.n

Diaphragm as an anatomic surrogate for lung tumor motion

Lung tumor motion due to respiration poses a challenge in the application of modern three-dimensional conformal radiotherapy. Direct tracking of the lung tumor during radiation therapy is very difficult without implanted fiducial markers. Indirect tracking relies on the correlation of the tumor's motion and the surrogate's motion. The present paper presents an analysis of the correlation between the tumor motion and the diaphragm motion in order to evaluate the potential use of diaphragm as a surrogate for tumor motion. We have analyzed the correlation between diaphragm motion and superior-inferior lung tumor motion in 32 fluoroscopic image sequences from 10 lung cancer patients. A simple linear model and a more complex linear model that accounts for phase delays between the two motions have been used. Results show that the diaphragm is a good surrogate for tumor motion prediction for most patients, resulting in an average correlation factor of 0.94 and 0.98 with each model respectively. The model that accounts for delays leads to an average localization prediction error of 0.8mm and an error at the 95% confidence level of 2.1mm. However, for one patient studied, the correlation is much weaker compared to other patients. This indicates that, before using diaphragm for lung tumor prediction, the correlation should be examined on a patient-by-patient basis.Comment: Accepted by Physics in Medicine and Biolog