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Lee, Geewon,Lee, Ho Yun,Park, Hyunjin,Schiebler, Mark L.,van Beek, Edwin J.R.,Ohno, Yoshiharu,Seo, Joon Beom,Leung, Ann Elsevier 2017 European journal of radiology Vol.86 No.-
<P><B>Abstract</B></P> <P>With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. <I>Radiomics</I> is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Radiomics is the post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. </LI> <LI> Radiomics features can reflect the spatial complexity, genomic heterogeneity, and subregional identification of lung cancer. </LI> <LI> Currently available radiomic features can be divided into four major categories. </LI> <LI> The major challenge is to integrate radiomic data with clinical, pathological, and genomic information. </LI> </UL> </P>
Lee, Geewon,I, Hoseok,Kim, Seong-Jang,Jeong, Yeon Joo,Kim, In Joo,Pak, Kyoungjune,Park, Do Yun,Kim, Gwang Ha Society of Nuclear Medicine 2014 The Journal of nuclear medicine Vol.55 No.8
<P>This was a study to compare the diagnostic efficacies of endoscopic ultrasonography (EUS), CT, PET/MR imaging, and PET/CT for the preoperative local and regional staging of esophageal cancer, with postoperative pathologic stage used as the reference standard. <B>Methods:</B> During 1 y, 19 patients with resectable esophageal cancer were enrolled and underwent preoperative EUS, CT, PET/CT, and PET/MR imaging. A chest radiologist and nuclear medicine physician retrospectively reviewed the images and assigned tumor and lymph node stages according to the seventh version of the TNM system and the American Joint Committee on Cancer staging system. Four patients who were treated nonsurgically were excluded from data analysis. The efficacies of EUS, CT, PET/CT, and PET/MR imaging were compared. <B>Results:</B> Primary tumors were correctly staged in 13 (86.7%), 10 (66.7%), and 5 (33.3%) patients at EUS, PET/MR imaging, and CT, respectively (<I>P</I> value ranging from 0.021 to 0.375). The accuracy of determining T1 lesions was 86.7%, 80.0%, and 46.7% for EUS, PET/MR imaging, and CT, respectively. For distinguishing T3 lesions, the accuracy was 93.3% for EUS and 86.7% for both PET/MR imaging and CT. For lymph node staging, the accuracy was 83.3%, 75.0%, 66.7%, and 50.0% for PET/MR imaging, EUS, PET/CT, and CT, respectively. In addition, area-under-the-curve values were 0.800, 0.700, 0.629, and 0.543 for PET/MR imaging, EUS, PET/CT, and CT, respectively. <B>Conclusion:</B> PET/MR imaging demonstrated acceptable accuracy for T staging compared with EUS and, although not statistically significant, even higher accuracy than EUS and PET/CT for prediction of N staging. With adjustments in protocols, PET/MR imaging may provide an important role in preoperative esophageal cancer staging in the future.</P>
CT Radiomics in Thoracic Oncology: Technique and Clinical Applications
Lee, Geewon,Bak, So Hyeon,Lee, Ho Yun The Korea Society of Nuclear Medicine 2018 핵의학 분자영상 Vol.52 No.2
Precision medicine offers better treatment options and improved survival for cancer patients based on individual variability. As the success of precision medicine depends on robust biomarkers, the requirement for improved imaging biomarkers that reflect tumor biology has grown exponentially. Radiomics, the field of study in which high-throughput data are generated and large amounts of advanced quantitative features are extracted from medical images, has shown great potential as a source of quantitative biomarkers in the field of oncology. Radiomics provides quantitative information about the morphology, texture, and intratumoral heterogeneity of the tumor itself as well as features related to pulmonary function. Hence, radiomics data can be used to build descriptive and predictive clinical models that relate imaging characteristics to tumor biology phenotypes. In this review, we describe the workflow of CT radiomics, types of CT radiomics, and its clinical application in thoracic oncology.
센서퓨전 기반의 Auto High Beam 시스템 구현
김지원(Geewon Kim),강현우(Hyunwoo Kang),신주석(Juseok Shin) 한국자동차공학회 2022 한국자동차공학회 부문종합 학술대회 Vol.2022 No.6
An AHB system was implemented using a camera and radar sensor in an embedded system. In this paper, vehicles, motorcycles, and street-lights were detected through a deep learning model, and in particular, it recognized whether the surroundings were bright by street-lights. In the case of deep learning models, the model is designed to be lightweight so that it can be operated in an embedded system. It also used radar sensors to detect objects that exist at a distance of 100m or more, which cannot be detected by deep learning models. The performance of the implemented AHB system was verified through various test scenarios, and it was confirmed that the high beam was normally controlled in 38 out of 40 test scenarios.
Characterizing graphs of maximum matching width at most 2
Jeong, Jisu,Ok, Seongmin,Suh, Geewon Elsevier 2018 Discrete Applied Mathematics Vol.248 No.-
<P><B>Abstract</B></P> <P>The maximum matching width is a width-parameter that is defined on a branch-decomposition over the vertex set of a graph. The size of a maximum matching in the bipartite graph is used as a cut-function. In this paper, we characterize the graphs of maximum matching width at most 2 using the minor obstruction set. Also, we compute the exact value of the maximum matching width of a grid.</P>