DOI QR코드

DOI QR Code

Studies and Real-World Experience Regarding the Clinical Application of Artificial Intelligence Software for Lung Nodule Detection

폐결절 검출 인공지능 소프트웨어의 임상적 활용에 관한 연구와 실제 사용 경험

  • Junghoon Kim (Department of Radiology, Seoul National University Bundang Hospital)
  • 김정훈 (분당서울대학교병원 영상의학과)
  • Received : 2024.03.28
  • Accepted : 2024.07.24
  • Published : 2024.07.01

Abstract

This article discusses studies and real-world experiences related to the clinical application of artificial intelligence-based computer-aided detection (AI-CAD) software (LuCAS-plus, Monitor Corporation) in detecting pulmonary nodules. During clinical trials for lung cancer screening, AI-CAD exhibited performance comparable to that of medical professionals in terms of sensitivity and specificity. Studies revealed that applying AI-CAD for diagnosing pulmonary metastases led to high detection rates. The use of a nodule matching algorithm in diagnosing pulmonary metastases significantly reduced false non-metastasis results. In clinical settings, implementing AI-CAD enhanced the efficiency of pulmonary nodule detection, saving time and effort during CT reading. Overall, AI-CAD is expected to offer substantial support for lung cancer screening and the interpretation of chest CT scans for malignant tumor surveillance.

본 종설에서는 모니터코퍼레이션(주)의 폐결절 검출을 위한 인공지능 기반 컴퓨터 보조 병변 검출(artificial intelligence-based computer-aided detection; 이하 AI-CAD) 소프트웨어 LuCAS-plus의 임상적 활용에 관한 연구와 실제 사용 경험을 기술하였다. AI-CAD의 폐암 검진에 대한 임상시험에서는 민감도와 특이도 측면에서 의료진의 판단과 비슷한 수준의 성능을 보였으며, 악성 종양의 폐전이 진단에 적용한 실증연구에서도 높은 검출 성능을 보여주었다. 또한 폐전이 진단에서 AI-CAD와 결절 매칭 알고리즘을 함께 사용할 경우 위양성 결과를 유의하게 감소시킬 수 있었다. 실제 판독에서도 AI-CAD를 적용함으로써 흉부 CT 판독의 정확성을 향상시키고, 판독에 드는 시간과 노력을 절약할 수 있었다. 종합하면, 폐결절 검출 AI-CAD는 폐암 검진과 악성 종양 경과 관찰 흉부 CT 판독에 유의한 도움을 줄 것으로 기대된다.

Keywords

References

  1. National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395-409
  2. de Koning HJ, van der Aalst CM, de Jong PA, Scholten ET, Nackaerts K, Heuvelmans MA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med 2020;382:503-513
  3. Chen H, Huang S, Zeng Q, Zhang M, Ni Z, Li X, et al. A retrospective study analyzing missed diagnosis of lung metastases at their early stages on computed tomography. J Thorac Dis 2019;11:3360-3368
  4. Committee for Scientific Affairs, The Japanese Association for Thoracic Surgery, Shimizu H, Endo S, Natsugoe S, Doki Y, et al. Thoracic and cardiovascular surgery in Japan in 2016: annual report by the Japanese Association for Thoracic Surgery. Gen Thorac Cardiovasc Surg 2019;67:377-411
  5. Kanzaki R, Fukui E, Kanou T, Ose N, Funaki S, Minami M, et al. Preoperative evaluation and indications for pulmonary metastasectomy. J Thorac Dis 2021;13:2590-2602
  6. Petrella F, Diotti C, Rimessi A, Spaggiari L. Pulmonary metastasectomy: an overview. J Thorac Dis 2017;9(Suppl 12):S1291-S1298
  7. da Silva GLF, Valente TLA, Silva AC, de Paiva AC, Gattass M. Convolutional neural network-based PSO for lung nodule false positive reduction on CT images. Comput Methods Programs Biomed 2018;162:109-118
  8. Dou Q, Chen H, Yu L, Qin J, Heng PA. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng 2017;64:1558-1567
  9. Li W, Cao P, Zhao D, Wang J. Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput Math Methods Med 2016;2016:6215085
  10. Murphy A, Skalski M, Gaillard F. The utilisation of convolutional neural networks in detecting pulmonary nodules: a review. Br J Radiol 2018;91:20180028
  11. Li L, Liu Z, Huang H, Lin M, Luo D. Evaluating the performance of a deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing lung nodules: comparison with the performance of double reading by radiologists. Thorac Cancer 2019;10:183-192
  12. Yoon SH, Oh DY, Kim HJ, Jang S, Kim M, Kim J, et al. Usefulness of longitudinal nodule-matching algorithm in computer-aided diagnosis of new pulmonary metastases on cancer surveillance CT scans. Quant Imaging Med Surg 2024;14:1493-1506