Deep learning with deep convolutional neural network using FDG-PET/CT for malignant pleural mesothelioma diagnosis
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Kazuhiro Kitajima1, Hidetoshi Matsuo2, Atsushi Kono2, Kozo Kuribayashi3, Takashi Kijima3, Masaki Hashimoto4, Seiki Hasegawa4, Takamichi Murakami2 and Koichiro Yamakado1
1 Department of Radiology, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan
2 Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
3 Division of Respiratory Medicine, Department of Internal Medicine, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan
4 Department of Thoracic Surgery, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan
|Kazuhiro Kitajima,||email:||[email protected]|
Keywords: mesothelioma; artificial intelligence; deep learning; FDG (fluorodeoxyglucose); PET-CT (positron emission tomography-computed tomography)
Received: March 20, 2021 Accepted: May 14, 2021 Published: June 08, 2021
Objectives: This study analyzed an artificial intelligence (AI) deep learning method with a three-dimensional deep convolutional neural network (3D DCNN) in regard to diagnostic accuracy to differentiate malignant pleural mesothelioma (MPM) from benign pleural disease using FDG-PET/CT results.
Results: For protocol A, the area under the ROC curve (AUC)/sensitivity/specificity/accuracy values were 0.825/77.9% (81/104)/76.4% (55/72)/77.3% (136/176), while those for protocol B were 0.854/80.8% (84/104)/77.8% (56/72)/79.5% (140/176), for protocol C were 0.881/85.6% (89/104)/75.0% (54/72)/81.3% (143/176), and for protocol D were 0.896/88.5% (92/104)/73.6% (53/72)/82.4% (145/176). Protocol D showed significantly better diagnostic performance as compared to A, B, and C in ROC analysis (p = 0.031, p = 0.0020, p = 0.041, respectively).
Materials and Methods: Eight hundred seventy-five consecutive patients with histologically proven or suspected MPM, shown by history, physical examination findings, and chest CT results, who underwent FDG-PET/CT examinations between 2007 and 2017 were investigated in a retrospective manner. There were 525 patients (314 MPM, 211 benign pleural disease) in the deep learning training set, 174 (102 MPM, 72 benign pleural disease) in the validation set, and 176 (104 MPM, 72 benign pleural disease) in the test set. Using AI with PET/CT alone (protocol A), human visual reading (protocol B), a quantitative method that incorporated maximum standardized uptake value (SUVmax) (protocol C), and a combination of PET/CT, SUVmax, gender, and age (protocol D), obtained data were subjected to ROC curve analyses.
Conclusions: Deep learning with 3D DCNN in combination with FDG-PET/CT imaging results as well as clinical features comprise a novel potential tool shows flexibility for differential diagnosis of MPM.
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