방사선의학 웹진

바로가기  >

Abstract
Objectives: We aimed to find the best machine learning (ML) model using 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for evaluating metastatic mediastinal lymph nodes (MedLNs) in non-small cell lung cancer, and compare the diagnostic results with those of nuclear medicine physicians.

Methods: A total of 1329 MedLNs were reviewed. Boosted decision tree, logistic regression, support vector machine, neural network, and decision forest models were compared. The diagnostic performance of the best ML model was compared with that of physicians. The ML method was divided into ML with quantitative variables only (MLq) and adding clinical information (MLc). We performed an analysis based on the 18F-FDG-avidity of the MedLNs.

Results: The boosted decision tree model obtained higher sensitivity and negative predictive values but lower specificity and positive predictive values than the physicians. There was no significant difference between the accuracy of the physicians and MLq (79.8% vs. 76.8%, p = 0.067). The accuracy of MLc was significantly higher than that of the physicians (81.0% vs. 76.8%, p = 0.009). In MedLNs with low 18F-FDG-avidity, ML had significantly higher accuracy than the physicians (70.0% vs. 63.3%, p = 0.018).

Conclusion: Although there was no significant difference in accuracy between the MLq and physicians, the diagnostic performance of MLc was better than that of MLq or of the physicians. The ML method appeared to be useful for evaluating low metabolic MedLNs. Therefore, adding clinical information to the quantitative variables from 18F-FDG PET/CT can improve the diagnostic results of ML.

Key points: • Machine learning using two-class boosted decision tree model revealed the highest value of area under curve, and it showed higher sensitivity and negative predictive values but lower specificity and positive predictive values than nuclear medicine physicians. • The diagnostic results from machine learning method after adding clinical information to the quantitative variables improved accuracy significantly than nuclear medicine physicians. • Machine learning could improve the diagnostic significance of metastatic mediastinal lymph nodes, especially in mediastinal lymph nodes with low 18F-FDG-avidity.

그림설명. 전문의 보다 부스트된 의사결정트리 기계학습모델이 종격동 전이림프질 진단에 더 우수함.

Affiliations

Jang Yoo  1   2 , Miju Cheon  1 , Yong Jin Park  2 , Seung Hyup Hyun  2 , Jae Ill Zo  3 , Sang-Won Um  4 , Hong-Hee Won  5 , Kyung-Han Lee  2 , Byung-Tae Kim  2 , Joon Young Choi  6
1 Department of Nuclear Medicine, Veterans Health Service Medical Center, Seoul, South Korea.
2 Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
3 Department of Thoracic Surgery and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
4 Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
5 Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
6 Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea. jynm.choi@samsung.com.