A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification

• Published in: British journal of radiology Vol. 94; no. 1123; p. 20210222
• Main Authors: Gürsoy Çoruh, Ayşegül, Yenigün, Bülent, Uzun, Çağlar, Kahya, Yusuf, Büyükceran, Emre Utkan, Elhan, Atilla, Orhan, Kaan, Kayı Cangır, Ayten
• Format: Journal Article
• Language: English
• Published: England The British Institute of Radiology 01.07.2021
• Subjects: Adult; Aged; Aged, 80 and over; Contrast Media; Deep Learning; Female; Humans; Imaging, Three-Dimensional; Iohexol; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Male; Middle Aged; Observation; Radiographic Image Interpretation, Computer-Assisted; Tomography, X-Ray Computed - methods
• ISSN: 0007-1285; 1748-880X; 1748-880X
• DOI: 10.1259/bjr.20210222

To compare the diagnostic performance of a newly developed artificial intelligence (AI) algorithm derived from the fusion of convolution neural networks (CNN) versus human observers in the estimation of malignancy risk in pulmonary nodules. The study population consists of 158 nodules from 158 patients. All nodules (81 benign and 77 malignant) were determined to be malignant or benign by a radiologist based on pathologic assessment and/or follow-up imaging. Two radiologists and an AI platform analyzed the nodules based on the Lung-RADS classification. The two observers also noted the size, location, and morphologic features of the nodules. An intraclass correlation coefficient was calculated for both observers and the AI; ROC curve analysis was performed to determine diagnostic performances. Nodule size, presence of spiculation, and presence of fat were significantly different between the malignant and benign nodules ( < 0.001, for all three). Eighteen (11.3%) nodules were not detected and analyzed by the AI. Observer 1, observer 2, and the AI had an AUC of 0.917 ± 0.023, 0.870 ± 0.033, and 0.790 ± 0.037 in the ROC analysis of malignity probability, respectively. The observers were in almost perfect agreement for localization, nodule size, and lung-RADS classification [κ (95% CI)=0.984 (0.961-1.000), 0.978 (0.970-0.984), and 0.924 (0.878-0.970), respectively]. The performance of the fusion AI algorithm in estimating the risk of malignancy was slightly lower than the performance of the observers. Fusion AI algorithms might be applied in an assisting role, especially for inexperienced radiologists. In this study, we proposed a fusion model using four state-of-art object detectors for lung nodule detection and discrimination. The use of fusion of deep learning neural networks might be used in a supportive role for radiologists when interpreting lung nodule discrimination.