Yuichiro Narita

Correlation between tumor size and blood volume in lung tumors: a prospective study on dual-energy gemstone spectral CT imaging

The purpose of this study was to investigate the relationship between tumor size and blood volume for patients with lung tumors, using dual-energy computed tomography (DECT) and a gemstone spectral imaging (GSI) viewer. During the period from March 2011 to March 2013, 50 patients with 57 medically inoperable lung tumors underwent DECT before stereotactic body radiotherapy (SBRT) of 50–60 Gy in 5–6 fractions. DECT was taken for pretreatment evaluation. The region-of-interest for a given spatial placement of the tumors was set, and averages for CT value, water density and iodine density were compared with tumor size. The average values for iodine density in tumors of ≤2 cm, 2–3 cm, and >3 cm maximum diameter were 24.7, 19.6 and 16.0 (100 µg/cm3), respectively. The average value of the iodine density was significantly lower in larger tumors. No significant correlation was detected between tumor size and average CT value or between tumor size and average water density. Both the average water density and the average CT value were affected by the amount of air in the tumor, but the average iodine density was not affected by air in the tumor. The average water density and the average CT value were significantly correlated, but the average iodine density and the average CT value showed no significant correlation. The blood volume of tumors can be indicated by the average iodine density more accurately than it can by the average CT value. The average iodine density as assessed by DECT might be a non-invasive and quantitative assessment of the radio-resistance ascribable to the hypoxic cell population in a tumor.

A kernel-based method for markerless tumor tracking in kV fluoroscopic images

Markerless tracking of respiration-induced tumor motion in kilo-voltage (kV) fluoroscopic image sequence is still a challenging task in real time image-guided radiation therapy (IGRT). Most of existing markerless tracking methods are based on a template matching technique or its extensions that are frequently sensitive to non-rigid tumor deformation and involve expensive computation. This paper presents a kernel-based method that is capable of tracking tumor motion in kV fluoroscopic image sequence with robust performance and low computational cost. The proposed tracking system consists of the following three steps. To enhance the contrast of kV fluoroscopic image, we firstly utilize a histogram equalization to transform the intensities of original images to a wider dynamical intensity range. A tumor target in the first frame is then represented by using a histogram-based feature vector. Subsequently, the target tracking is then formulated by maximizing a Bhattacharyya coefficient that measures the similarity between the tumor target and its candidates in the subsequent frames. The numerical solution for maximizing the Bhattacharyya coefficient is performed by a mean-shift algorithm. The proposed method was evaluated by using four clinical kV fluoroscopic image sequences. For comparison, we also implement four conventional template matching-based methods and compare their performance with our proposed method in terms of the tracking accuracy and computational cost. Experimental results demonstrated that the proposed method is superior to conventional template matching-based methods.

LW6, a hypoxia-inducible factor 1 inhibitor, selectively induces apoptosis in hypoxic cells through depolarization of mitochondria in A549 human lung cancer cells

Hypoxia-inducible factor 1 (HIF-1) activates the transcription of genes that act upon the adaptation of cancer cells to hypoxia. LW6, an HIF-1 inhibitor, was hypothesized to improve resistance to cancer therapy in hypoxic tumors by inhibiting the accumulation of HIF-1α. A clear anti-tumor effect under low oxygen conditions would indicate that LW6 may be an improved treatment strategy for cancer in hypoxia. In the present study, the HIF-1 inhibition potential of LW6 on the growth and apoptosis of A549 lung cancer cells in association with oxygen availability was evaluated. LW6 was observed to inhibit the expression of HIF-1α induced by hypoxia in A549 cells at 20 mM, independently of the von Hippel-Lindau protein. In addition, at this concentration, LW6 induced hypoxia-selective apoptosis together with a reduction in the mitochondrial membrane potential. The intracellular reactive oxygen species levels increased in LW6-treated hypoxic A549 cells and LW6 induced a hypoxia-selective increase of mitochondrial O2•−. In conclusion, LW6 inhibited the growth of hypoxic A549 cells by affecting the mitochondria. The inhibition of the mitochondrial respiratory chain is suggested as a potentially effective strategy to target apoptosis in cancer cells.

Megakaryocytic differentiation in human chronic myelogenous leukemia K562 cells induced by ionizing radiation in combination with phorbol 12-myristate 13-acetate

Differentiation-induction therapy is an attractive approach in leukemia treatment. It has been suggested that the accumulation of intracellular reactive oxygen species (ROS) is involved in megakaryocytic differentiation induced by phorbol 12-myristate 13-acetate (PMA) in the K562 leukemia cell line. Therefore, a ROS-inducible technique could be a powerful method of differentiation induction. Accordingly, we hypothesized that ionizing radiation contributes to the acceleration of megakaryocytic differentiation through the accumulation of intracellular ROS in leukemia cells. In the present study, ionizing radiation was shown to promote PMA-induced megakaryocytic differentiation. Cells with high CD41 expression sustained intracellular ROS levels effectively. The enhancement of differentiation by ionizing radiation was found to be regulated through the mitogen-activated protein kinase (MAPK) pathway, involving both extracellular signal-regulated protein kinase 1/2 (ERK1/2) and p38 MAPK. Ionizing radiation also controlled mRNA expression of the oxidative stress response gene heme oxygenase-1 (HO1). Consequently, we concluded that intracellular ROS, increased by ionizing radiation, modulate megakaryocytic differentiation downstream of the MAPK pathway.

Markerless Lung Tumor Motion Tracking by Dynamic Decomposition of X-Ray Image Intensity

We propose a new markerless tracking technique of lung tumor motion by using an X-ray fluoroscopic image sequence for real-time image-guided radiation therapy (IGRT). A core innovation of the new technique is to extract a moving tumor intensity component from the fluoroscopic image intensity. The fluoroscopic intensity is the superimposition of intensity components of all the structures passed through by the X-ray. The tumor can then be extracted by decomposing the fluoroscopic intensity into the tumor intensity component and the others. The decomposition problem for more than two structures is ill posed, but it can be transformed into a well-posed one by temporally accumulating constraints that must be satisfied by the decomposed moving tumor component and the rest of the intensity components. The extracted tumor image can then be used to achieve accurate tumor motion tracking without implanted markers that are widely used in the current tracking techniques. The performance evaluation showed that the extraction error was sufficiently small and the extracted tumor tracking achieved a high and sufficient accuracy less than 1 mm for clinical datasets. These results clearly demonstrate the usefulness of the proposed method for markerless tumor motion tracking.

A Time-Varying Seasonal Autoregressive Model-Based Prediction of Respiratory Motion for Tumor following Radiotherapy

To achieve a better therapeutic effect and suppress side effects for lung cancer treatments, latency involved in current radiotherapy devices is aimed to be compensated for improving accuracy of continuous (not gating) irradiation to a respiratory moving tumor. A novel prediction method of lung tumor motion is developed for compensating the latency. An essential core of the method is to extract information valuable for the prediction, that is, the periodic nature inherent in respiratory motion. A seasonal autoregressive model useful to represent periodic motion has been extended to take into account the fluctuation of periodic nature in respiratory motion. The extended model estimates the fluctuation by using a correlation-based analysis for adaptation. The prediction performance of the proposed method was evaluated by using data sets of actual tumor motion and compared with those of the state-of-the-art methods. The proposed method demonstrated a high performance within submillimeter accuracy. That is, the average error of 1.0 s ahead predictions was 0.931 ± 0.055 mm. The accuracy achieved by the proposed method was the best among those by the others. The results suggest that the method can compensate the latency with sufficient accuracy for clinical use and contribute to improve the irradiation accuracy to the moving tumor.

Respiratory motion prediction for tumor following radiotherapy by using time-variant seasonal autoregressive techniques

We develop a new prediction method of respiratory motion for accurate dynamic radiotherapy, called tumor following radiotherapy. The method is based on a time-variant seasonal autoregressive (TVSAR) model and extended to further capture time-variant and complex nature of various respiratory patterns. The extended TVSAR can represent not only the conventional quasi-periodical nature, but also the residual components, which cannot be expressed by the quasi-periodical model. Then, the residuals are adaptively predicted by using another autoregressive model. The proposed method was tested on 105 clinical data sets of tumor motion. The average errors were 1.28 ± 0.87 mm and 1.75 ± 1.13 mm for 0.5 s and 1.0 s ahead prediction, respectively. The results demonstrate that the proposed method can outperform the state-of-the-art prediction methods.

Volume registration based on 3-D phase correlation for tumor motion estimation in 4-D CT

This paper presents a three-dimensional (3-D) volume registration method that uses 3-D phase correlation to estimate the respiration-induced tumor motion in four-dimensional (4-D) thorax computed tomography (CT) for radiation therapy. The proposed method is an extension of 2-D phase correlation method to 3-D volume registration. Given two CT volumes obtained from different respiration stages, the tumor motion is modeled as a translational shift between the volumes. The 3-D phase correlation is obtained from the 3-D inverse Fourier transform of a normalized cross power spectrum of the volumes. The tumor motion along three directions is estimated by locating the highest peak in the 3-D phase correlation. In order to improve the estimation accuracy, we extend the 3-D phase correlation to sub-voxel accuracy. Experimental results demonstrate the effectiveness of the proposed method relative to a conventional 2-D phase correlation-based method.

Impact of dexamethasone, etoposide, ifosfamide and carboplatin as concurrent chemoradiotherapy agents for nasal natural killer/T-cell lymphoma

The nasal type of extranodal natural killer (NK)/T-cell lymphoma (NKTCL) is a rare aggressive lymphoma with poor prognosis. The reported 5-year overall survival for patients with localized nasal NKTCL treated with cyclophosphamide, hydroxydaunorubicin, oncovin and prednisone (CHOP) is <50%. Dexamethasone, etoposide, ifosfamide and carboplatin (DeVIC) chemotherapy was designed as a salvage chemotherapeutic regimen for aggressive lymphoma, comprising multidrug resistance (MDR) non-related agents and etoposide, which is considered to be effective against nasal NKTCL. An experimental chemoradiotherapy (CRT) is currently being designed using DeVIC as the concurrent chemotherapeutic regimen for nasal NKTCL. The aim of this study was to examine the initial outcome of this treatment and evaluate its effectiveness and feasibility. Six patients (age range, 29–82 years; median age, 68 years) were treated with CRT using DeVIC between April, 2004 and February, 2010. The median follow-up was 56 months (range, 11–80 months). All patients were administered 3–6 cycles of full-dose DeVIC regimen. The chemotherapy was administered concurrently with radiotherapy (RT) and was repeated every 3 weeks. RT was performed using 4-MV X-ray and the prescription dose was 46–50 Gy/23–25 fx (median, 50 Gy). After treatment, all patients were followed up at our hospital. A complete remission was achieved in 5 patients (83%) at 1 month after treatment. The 5-year overall survival and disease-free survival rates were 100%. No severe adverse effect (grade ≥3) was reported. In conclusion, the initial results of the experimental CRT with DeVIC for this type of aggressive lymphoma were very encouraging. Further investigation is required on concurrent CRT with 50 Gy/25 fx and 3 cycles of DeVIC comprising non-MDR agents and etoposide for nasal NKTCL.

WE‐A‐134‐03: A Kernel‐Based Method for Non‐Rigid Tumor Tracking in KV Image Sequence

PURPOSE To develop a fast algorithm to track the non-rigid lung tumor motion in KV X-ray image sequence for image-guided radiation therapy (IGRT). METHODS The KV X-ray image sequence was acquired on the Varian On-Board Imager (OBI) KV imaging system. As a pre-processing, a histogram equalization was employed to enhance the tumor contrast in the images. In the first frame, a target model containing tumor area was delineated manually, and its feature space was represented by its histogram weighted with an isotropic kernel. In the subsequent frames, the tumor location was estimated by maximizing a Bhattacharyya coefficient which measures the similarity between the target candidates in the current frame and the target model in the previous frame. The numerical solution of maximizing the Bhattacharyya coefficient was performed by using a mean-shift algorithm. RESULTS We implemented four conventional template matching algorithms to compare their performance with the proposed method. Experiments were conducted on four lung tumor kV image sequences of resolution 0.26 mm/pixel. Each sequence consists of 100 frames. The ground truths of the tumor motion were obtained by manual localization. Experimental results demonstrated that the proposed algorithm was superior to the conventional template matching algorithms in terms of its accuracy and computational cost. CONCLUSION This study aims at developing a robust and fast algorithm used for tracking the lung tumor for real-time IGRT. Due to the histogram representation of the target feature, the proposed method is robust against the tumors shape deformation. In addition, the proposed tracking algorithm is based on a kernel gradient estimation and its computational cost is much lower than that of the conventional template matching algorithms that involve in exhaustive search procedures. The proposed method shows the effectiveness of tracking tumor in KV image sequence and a promising prospect for MV image sequence.