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. 2024 Oct;15(28):2061-2072.
doi: 10.1111/1759-7714.15428. Epub 2024 Aug 29.

Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population

Jiaxuan Wu  1   2   3 , Ruicen Li  4 , Jiadi Gan  1   2   3 , Qian Zheng  5 , Guoqing Wang  6 , Wenjuan Tao  7 , Ming Yang  8   9 , Wenyu Li  4 , Guiyi Ji  4 , Weimin Li  1   2   3   10   11   12
Affiliations

Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population

Jiaxuan Wu et al. Thorac Cancer. 2024 Oct.

Abstract

Background: With the rapid increase of chest computed tomography (CT) images, the workload faced by radiologists has increased dramatically. It is undeniable that the use of artificial intelligence (AI) image-assisted diagnosis system in clinical treatment is a major trend in medical development. Therefore, in order to explore the value and diagnostic accuracy of the current AI system in clinical application, we aim to compare the detection and differentiation of benign and malignant pulmonary nodules between AI system and physicians, so as to provide a theoretical basis for clinical application.

Methods: Our study encompassed a cohort of 23 336 patients who underwent chest low-dose spiral CT screening for lung cancer at the Health Management Center of West China Hospital. We conducted a comparative analysis between AI-assisted reading and manual interpretation, focusing on the detection and differentiation of benign and malignant pulmonary nodules.

Results: The AI-assisted reading exhibited a significantly higher screening positive rate and probability of diagnosing malignant pulmonary nodules compared with manual interpretation (p < 0.001). Moreover, AI scanning demonstrated a markedly superior detection rate of malignant pulmonary nodules compared with manual scanning (97.2% vs. 86.4%, p < 0.001). Additionally, the lung cancer detection rate was substantially higher in the AI reading group compared with the manual reading group (98.9% vs. 90.3%, p < 0.001).

Conclusions: Our findings underscore the superior screening positive rate and lung cancer detection rate achieved through AI-assisted reading compared with manual interpretation. Thus, AI exhibits considerable potential as an adjunctive tool in lung cancer screening within clinical practice settings.

Keywords: artificial intelligence; early diagnosis; lung cancer; pulmonary nodule; screening.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Chest computed tomography (CT) imaging‐aided diagnosis system. Digital imaging and communications in medicine image files of low‐dose thin‐slice chest CT of subjects in this study were imported into the chest CT image‐assisted diagnosis system V7.3.0 provided by Hangzhou Yitu Medical Technology Co., Ltd. (a) The interface of the lung window. (b) The interface of the mediastinal window. (c) 3D image of lung nodules.
FIGURE 2
FIGURE 2
The confusion matrix of our model for Lung‐RADS risk rating in two test datasets with different slice thickness of computed tomography images: Slice thickness ≤2 mm (a) and slice thickness ≥5 mm (b).
FIGURE 3
FIGURE 3
The confusion matrix of our model for pulmonary nodules classification in test dataset (a, solid; b, ground glass; c, part‐solid; and d, calcification).
FIGURE 4
FIGURE 4
Cumulative number of lung cancers. The number of lung cancer diagnoses has increased over time.
FIGURE 5
FIGURE 5
The patient, a 60‐year‐old male, presented with a chest computed tomography showing a nodule in the right upper posterior lung (a). The nodules were histopathologically confirmed as adenocarcinoma (HE ×100, [b]; HE ×400, [c]).
See this image and copyright information in PMC

References

    1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–249. - PubMed
    1. Wei W, Zeng H, Zheng R, Zhang S, An L, Chen R, et al. Cancer registration in China and its role in cancer prevention and control. Lancet Oncol. 2020;21:e342–e349. - PubMed
    1. Zheng RS, Chen R, Han BF, Wang SM, Li L, Sun KX, et al. Cancer incidence and mortality in China, 2022. Chinese J Oncol. 2024;46:221–231. - PubMed
    1. Jaklitsch MT, Jacobson FL, Austin JH, et al. The American Association for Thoracic Surgery guidelines for lung cancer screening using low‐dose computed tomography scans for lung cancer survivors and other high‐risk groups. J Thorac Cardiovasc Surg. 2012;144:33–38. - PubMed
    1. Naidich DP, Bankier AA, MacMahon H, Schaefer‐Prokop CM, Pistolesi M, Goo JM, et al. Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society. Radiology. 2013;266:304–317. - PubMed

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