財団法人セコム化学技術振興財団助成研究（平成 21 年度－24 年度）	 	 
Secom Science and Technology Foundation
Research Grant Report - Executive Summary
FY2009 to FY2012	 	 	 研究課題名：アスベスト曝露疾患の計算機支援画像診断の創出と臨床応用	 	 

Title:Development of Computer-Aided Diagnosis Systems for Asbestos-Related Diseases
and Its Clinical Application	 	 	 	 	 	 	 	 報告年日：2013 年 6 月	 June, 2013	 
研究代表者：徳島大学大学院ソシオテクノサイエンス研究部・准教授・河田佳樹	 
Institute of Technology and Science, The University of Tokushima
Yoshiki Kawata, Associate Professor

1Summary
Asbestos-related diseases remains a major public health problem. Asbestos are carcinogenic tohuman, and may cause mesothelioma and lung cancer. Currently, about 125 million people in
the world are exposed to asbestos. The World Health Organization (WHO) has estimated that
more than 107,000 people die every year from asbestos-related lung cancer, mesothelioma andasbestosis resulting from the asbestos exposure. The latency period of 30-50 years between
exposure and the development of clinical symptoms explains the continued rise in the numbersof asbestos-related diseases despite international recognition of the health risks. One of strategic
directions towards elimination of asbestos-related diseases is to improve early diagnosis andtreatment of asbestos-related diseases.
Cutting-edge medical technologies are being sought that will enable early detection andtreatment of asbestos-related diseases by finding minute changes in the human lung. Advancesin medical imaging technology make it possible to reveal the location of lesions at an earlystage. The morphological and functional images provided by imaging technologies arebecoming more complex in amount of information gained. Computer-aided diagnosis (CAD) is
considered as a solution to support physicians' interpretation of the expanding amount of image
information on the frontline of medical care. Development of CAD systems that encompasses
detection and diagnosis would be more desirable to patients and referring physicians. The
purpose of this project is to develop CAD systems for asbestos-related diseases. These systems
may enable early detection and treatment for asbestos-related lung cancers, mesothelioma andasbestosis by using thin-section CT images with slice thickness of 1mm. The goal of theresearch and development of this project is to reach a level at which the effectiveness of CAD
technologies can be reasonably proven and clinical tested for ensuring that the practicalapplication can be carried out.
The strategic plan of our research project consists of the four key pillars. The outlines ofthese achievements are as follows.
(1) Constructing a database with volumetric CT images and follow-up volumetric CT images ofasbestos-related diseases:!

In order to construct a large-scale database of volumetric CT images and their diagnosticresults, we constructed a digital diagnostic environment system which integrated theproposed anonymization system to protect the personal information of DICOM Images.
The data set of about 4700 cases were collected from four medical facilities with ethics
committee approval.

(2) Development of three-dimensional image analysis of asbestos-related diseases by using
volumetric CT images:!

Recent advances in the technology for multi-detector computed tomography (MDCT) have4

led to an increase in the number of examinations performed for diagnosis and treatment
planning. The distribution of the radiation dose to the patient can vary considerably
depending on the scan parameters employed for the CT examination. We developed a coretechnology for analysing radiation dose and scan parameters using MDCT. In addition, weproposed a model-dependent method to determine the modulation transfer function (MTF)
in the transversal plane of CT images.
!

One of the most important steps of CAD is the segmentation of the lung anatomies onthoracic CT datasets. When detecting or analyzing asbestos-related lung cancer andmesothelioma, the existence of diseases often deteriorate the performance of lung structure
segmentation. Especially, the segmentation process of lung lobes is influenced by diseasesof parenchyma. We improved automatic segmentation method of pulmonary lobes against
normal datasets and various respiratory disease datasets.

(3) Development of CAD technologies for detecting asbestos-related lung cancer andmesothelioma at early disease stage:!

We proposed a computer-aided prognosis prediction scheme that utilized quantitatively thederived image information to predict patient recurrent-free survival for asbestos-related
lung cancers. We demonstrated the potential usefulness of the scheme which can provide aquantitative risk score that is strongly correlated with prognostic factors.

!

In CT screening, comparative reading of present and past CT images is effective forevaluation of asbestos-related diseases. We developed a comparative reading algorithm forvolumetric CT images and evaluated its performance. We also developed computerized
detection schemes based on quantitative analysis of various suspected shadows including
asbestos-related lung cancer, mesothelioma, and asbestosis.

(4) Development of a CAD prototype system and its clinical application:!

We implemented the GUI and integrated the detection algorithms of asbestos-relateddiseases as a prototype CAD system. We have been evaluating the prototype computer
aided detection system.
We continuously strive to improve the performance of the developed CAD system that meetthe needs of clinical practice. Our achievements encourage us to promote the research anddevelop the core technologies of CAD systems for early diagnosis and treatment ofasbestos-related diseases.	 

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