판정행렬을 기반한 일체형 PET-MRI의 폐암 진단 유용성 평가

• 영문명: Evaluation of Usefulness for Diagnosis of Lung Cancer on Integrated PET-MRI Using Decision Matrix
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 김정수(Jung-Soo Kim) 양현진(Hyun-Jin Yang) 김유미(Yoo-Mi Kim) 권형진(Hyeong-Jin Kwon) 박찬록(Chanrok Park)
• 간행물 정보: 『방사선기술과학』방사선기술과학 제44권 제6호, 635~643쪽, 전체 9쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2021.12.30

영문 초록

The results of empirical researches on the diagnosis of lung cancer are insufficient, so it is limited to objectively judge the clinical possibility and utilization according to the accuracy of diagnosis. Thus, this study retrospectively analyzed the lung cancer diagnostic performance of PET-MRI (Positron Emission Tomography-Magnetic Resonance Imaging) by using the decision matrix. This study selected and experimented total 165 patients who received both hematological CEA (Carcinoembryonic Antigen) test and hybrid PET-MRI (18F-FDG, 5.18 MBq/㎏ / Body TIM coil. VIVE-Dixon). After setting up the result of CEA (positive:>4 ㎍/ℓ. negative:<2.5㎍/ℓ) as golden data, the lung cancer was found in the image of PET-MRI, and then the SUVmax (positive:>4, negative:<1.5) was measured, and then evaluated the correlation and significance of results of relative diagnostic performance of PET-MRI compared to CEA through the statistical verification (t-test, P>0.05). Through this, the PET-MRI was analyzed as 96.29% of sensitivity, 95.23% of specificity, 3.70% of false negative rate, 4.76% of false positive rate, and 95.75% of accuracy. The false negative rate was 1.06% lower than the false positive rate. The PET-MRI that significant accuracy of diagnosis through high sensitivity and specificity, and low false negative rate and false positive rate of lung cancer, could acquire the fusion image of specialized soft tissue by combining the radio-pharmaceuticals with various sequences, so its clinical value and usefulness are regarded as latently sufficient.

목차

Ⅰ. 서론

Ⅱ. 대상 및 방법

Ⅲ. 결과

Ⅳ. 고찰

Ⅴ. 결론

키워드

폐암 암배아항원 전자방출단층촬영-자기공명영상 판정행렬 정확도 Lung cancer CEA PET-MRI Decision matrix Accuracy