The Value of Artificial Intelligence Film Reading System Based on Deep Learning in the Diagnosis of Non-Small-Cell Lung Cancer and the Significance of Efficacy Monitoring: A Retrospective, Clinical, Nonrandomized, Controlled Study

Abstract

Objective. To explore the value of artificial intelligence (AI) film reading system based on deep learning in the diagnosis of non-small-cell lung cancer (NSCLC) and the significance of curative effect monitoring. Methods. We retrospectively selected 104 suspected NSCLC cases from the self-built chest CT pulmonary nodule database in our hospital, and all of them were confirmed by pathological examination. The lung CT images of the selected patients were introduced into the AI reading system of pulmonary nodules, and the recording software automatically identified the nodules, and the results were compared with the results of the original image report. The nodules detected by the AI software and film readers were evaluated by two chest experts and recorded their size and characteristics. Comparison of calculation sensitivity, false positive rate evaluation of the NSCLC software, and physician’s efficiency of nodule detection whether there was a significant difference between the two groups. Results. The sensitivity, specificity, accuracy, positive predictive rate, and false positive rate of NSCLC diagnosed by radiologists were 72.94% (62/85), 92.06% (58/63), 81.08% (62+58/148), 92.53% (62/67), and 7.93% (5/63), respectively. The sensitivity, specificity, accuracy, positive prediction rate, and false positive rate of AI film reading system in the diagnosis of NSCLC were 94.12% (80/85), 77.77% (49/63), 87.161% ( 80 + 49 /148), 85.11% (80/94), and 22.22% (14/63), respectively. Compared with radiologists, the sensitivity and false positive rate of artificial intelligence film reading system in the diagnosis of NSCLC were higher ( P < 0.05 ). The sensitivity, specificity, accuracy, positive prediction rate, and negative prediction rate of artificial intelligence film reading system in evaluating the efficacy of patients with NSCLC were 87.50% (63/72), 69.23% (9/13), 84.70% ( 63 + 9 )/85, 94.02% (63/67), and 50% (9/18), respectively. Conclusion. The AI film reading system based on deep learning has higher sensitivity for the diagnosis of NSCLC than radiologists and can be used as an auxiliary detection tool for doctors to screen for NSCLC, but its false positive rate is relatively high. Attention should be paid to identification. Meanwhile, the AI film reading system based on deep learning also has a certain guiding significance for the diagnosis and treatment monitoring of NSCLC.

Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis

“…The included studies were published between 2019 and 2022. Out of the six included studies, two were conducted in the USA [ 37 , 38 ], two were conducted in China [ 39 , 40 ], one was conducted in the UK [ 41 ], and one in Turkey [ 42 ]. Five studies used an external dataset for validation [ 37 , 38 , 40 , 41 , 42 ], while one provided diagnostic performance using cross-validation on the same dataset of training [ 39 ].…”

“…Out of the six included studies, two were conducted in the USA [ 37 , 38 ], two were conducted in China [ 39 , 40 ], one was conducted in the UK [ 41 ], and one in Turkey [ 42 ]. Five studies used an external dataset for validation [ 37 , 38 , 40 , 41 , 42 ], while one provided diagnostic performance using cross-validation on the same dataset of training [ 39 ]. All six studies used convolutional neural networks as the main artificial intelligence tool and considered histopathological diagnosis as a reference standard for confirming malignant nodules.…”

“… Forest plot of the pooled sensitivity of DL in the detection and classification of lung cancer [ 37 , 38 , 39 , 40 , 41 , 42 ]. …”

The role of artificial intelligence in the management of lung cancer: a narrative review

“…They found that doctors can use the AI film reading system based on deep learning as a secondary detection tool to screen for NSCLC; however, it has a greater false positive rate than radiologists for diagnosing the disease. In the meantime, the deep learning-based AI film reading system also has some guiding value for NSCLC diagnosis and therapy monitoring [63]. The diagnosis of lung lesions by AI is steel under question as it has a high rate of false positive diagnosis.…”

Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI

“…Unlike radiomics, which relies on predefined artificial features, deep learning algorithms can extract more abstract high-dimensional features in a more automatic way that is not susceptible to subjective influence ( 22 ). Therefore, such algorithms have been widely used in the automatic recognition, segmentation, and classification of lung cancer, breast cancer, rectal cancer, and other tumors ( 23 – 25 ). In this study, we trained a convolutional neural network (CNN) classifier based on an integration of two-dimensional (2D) multimodal MR images and three-dimensional (3D) shape-based radiomics features to perform preoperative prediction of mitotic index in GIST.…”