SU-GG-J-58: Stereotactic Body Radiotherapy: Computer-Assisted Verification of a Lung Tumor Region Using EPID without Implanted Markers

2010 ◽  
Vol 37 (6Part10) ◽  
pp. 3158-3158
Author(s):  
H Arimura ◽  
Y Shioyama ◽  
K Nakamura ◽  
T Yoshitake ◽  
S Anai ◽  
...  
Keyword(s):  
Stereotactic Body Radiotherapy ◽  
Lung Tumor ◽  
Computer Assisted ◽  
Tumor Region

Clinical outcome of stereotactic body radiotherapy of 54 Gy in nine fractions for patients with localized lung tumor using a custom-made immobilization system

2007 ◽  
Vol 25 (6) ◽  
pp. 289-294 ◽  
Author(s):  
Masahiko Aoki ◽  
Yoshinao Abe ◽  
Hidehiro Kondo ◽  
Yoshiomi Hatayama ◽  
Hideo Kawaguchi ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Stereotactic Body Radiotherapy ◽  
Lung Tumor ◽  
Custom Made

scholarly journals Amalgamation of Clustering and Meta-heuristic Optimization Techniques for Automated MR Brain Analysis

2019 ◽  
Vol 9 (2S2) ◽  
pp. 642-647
Keyword(s):  
Computation Time ◽  
Medical Practitioner ◽  
Automated Analysis ◽  
Disease Diagnosis ◽  
Tumor Location ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Computer Assisted ◽  
Accurate Analysis ◽  
Tumor Region

Interest in computer-assisted image analysis in increasing among the radiologist as it provides them the additional information to take decision and also for better disease diagnosis. Traditionally, MR image is manually examined by medical practitioner through naked eye for the detection and diagnosis of tumor location, size, and intensity; these are difficult and not sufficient for accurate analysis and treatment. For this purpose, there is need for additional automated analysis system for accurate detection of normal and abnormal tumor region. This paper introduces the new semi-automated image processing method to identify the brain tumor region in Magnetic Resonance Image (MRI) using c means clustering technique along with meta-heuristic optimization, based on Jaya optimization algorithm. The resultant performance of the proposed algorithm (FCM +JA) is examined with the help of key analyzing parameters, MSE-Mean Square Error, PSNR-Peak Signal to Noise Ratio, DOI-Dice Overlap Index and CPU memory utilization. The experimental results of this method show better and enhanced tumor region display in reduced computation time.

Interfractional Variations of Lung Tumor in the Stereotactic Body Radiotherapy with Cine EPID at Treatment

2010 ◽  
Vol 78 (3) ◽  
pp. S682
Author(s):  
Y. Ueda ◽  
K. Tsujii ◽  
K. Shirai ◽  
M. Miyazaki ◽  
K. Miyagi ◽  
...  
Keyword(s):  
Stereotactic Body Radiotherapy ◽  
Lung Tumor

scholarly journals Computer-assisted framework for machine-learning–based delineation of GTV regions on datasets of planning CT and PET/CT images

2017 ◽  
Vol 58 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Koujiro Ikushima ◽  
Hidetaka Arimura ◽  
Ze Jin ◽  
Hidetake Yabu-uchi ◽  
Jumpei Kuwazuru ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ground Glass Opacity ◽  
Global Optimum ◽  
Image Features ◽  
Support Vector ◽  
Computer Assisted ◽  
Dice Similarity Coefficient ◽  
Radiation Oncologists ◽  
Tumor Region ◽  
Leave One Out

Abstract We have proposed a computer-assisted framework for machine-learning–based delineation of gross tumor volumes (GTVs) following an optimum contour selection (OCS) method. The key idea of the proposed framework was to feed image features around GTV contours (determined based on the knowledge of radiation oncologists) into a machine-learning classifier during the training step, after which the classifier produces the ‘degree of GTV’ for each voxel in the testing step. Initial GTV regions were extracted using a support vector machine (SVM) that learned the image features inside and outside each tumor region (determined by radiation oncologists). The leave-one-out-by-patient test was employed for training and testing the steps of the proposed framework. The final GTV regions were determined using the OCS method that can be used to select a global optimum object contour based on multiple active delineations with a LSM around the GTV. The efficacy of the proposed framework was evaluated in 14 lung cancer cases [solid: 6, ground-glass opacity (GGO): 4, mixed GGO: 4] using the 3D Dice similarity coefficient (DSC), which denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those determined using the proposed framework. The proposed framework achieved an average DSC of 0.777 for 14 cases, whereas the OCS-based framework produced an average DSC of 0.507. The average DSCs for GGO and mixed GGO were 0.763 and 0.701, respectively, obtained by the proposed framework. The proposed framework can be employed as a tool to assist radiation oncologists in delineating various GTV regions.

SU-GG-J-41: Automated Estimation of a Tumor Region and Its Displacement On EPID Cine Images Without Implanted Markers in Lung Stereotactic Body Radiotherapy

2008 ◽  
Vol 35 (6Part6) ◽  
pp. 2688-2688
Author(s):  
H Arimura ◽  
Y Egashira ◽  
Y Shioyama ◽  
K Nakamura ◽  
S Yoshidome ◽  
...  
Keyword(s):  
Stereotactic Body Radiotherapy ◽  
Tumor Region

SU-E-J-40: Automated Estimation of Lung Tumor Locations for Tumor-Based Patient Setup Using MV-CBCT Images in Stereotactic Body Radiotherapy

2013 ◽  
Vol 40 (6Part7) ◽  
pp. 158-158
Author(s):  
S Yoshidome ◽  
H Arimura ◽  
K Nakamura ◽  
Y Shioyama ◽  
K Atsumi ◽  
...  
Keyword(s):  
Stereotactic Body Radiotherapy ◽  
Lung Tumor ◽  
Patient Setup

scholarly journals Prognostic impact of average iodine density assessed by dual-energy spectral imaging for predicting lung tumor recurrence after stereotactic body radiotherapy

2016 ◽  
Vol 57 (4) ◽  
pp. 381-386 ◽  
Author(s):  
Masahiko Aoki ◽  
Katsumi Hirose ◽  
Mariko Sato ◽  
Hiroyoshi Akimoto ◽  
Hideo Kawaguchi ◽  
...  
Keyword(s):  
Local Control ◽  
Stereotactic Body Radiotherapy ◽  
Lung Tumor ◽  
Lung Metastases ◽  
Prognostic Significance ◽  
Lung Tumors ◽  
Dual Energy ◽  
Hypoxic Cell ◽  
Prognostic Impact ◽  
Lung Cancers

Abstract The purpose of this study was to investigate the prognostic significance of average iodine density as assessed by dual-energy computed tomography (DE-CT) for lung tumors treated with stereotactic body radiotherapy (SBRT). From March 2011 to August 2014, 93 medically inoperable patients with 74 primary lung cancers and 19 lung metastases underwent DE-CT prior to SBRT of a total dose of 45–60 Gy in 5–10 fractions. Of these 93 patients, nine patients had two lung tumors. Thus, 102 lung tumors were included in this study. DE-CT was performed for pretreatment evaluation. Regions of interest were set for the entire tumor, and average iodine density was obtained using a dedicated imaging software and evaluated with regard to local control. The median follow-up period was 23.4 months (range, 1.5–54.5 months). The median value of the average iodine density was 1.86 mg/cm 3 (range, 0.40–9.27 mg/cm 3 ). Two-year local control rates for the high and low average iodine density groups divided by the median value of the average iodine density were 96.9% and 75.7% ( P = 0.006), respectively. Tumors with lower average iodine density showed a worse prognosis, possibly reflecting a hypoxic cell population in the tumor. The average iodine density exhibited a significant impact on local control. Our preliminary results indicate that iodine density evaluated using dual-energy spectral CT may be a useful, noninvasive and quantitative assessment of radio-resistance caused by presumably hypoxic cell populations in tumors.

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