<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>There are numerous imaging methods for clinical screening and diagnosis of solid lung nodules, but they all have certain limitations. This paper selected patients with lung nodules in the People’s Hospital of Province X as an example. C.T. scans were performed on the patients with lung nodules to obtain their imaging histologic features. Then, based on the generative adversarial network in deep learning and using self-supervised learning to optimize the generative negative discriminator, a semi-supervised GAN model was established for the identification and predictive diagnosis of benign and malignant solid lung nodules. A regression analysis model was constructed and data analysis was performed to identify the independent risk factors related to the malignancy of solid lung nodules. The results showed that the patient’s tumor diameter became the most significant independent risk factor for the benign-malignant nature of lung nodules, with an OR of 3.421, which showed a significant difference at the 1% level. The I.C.C. value of each feature of solid lung nodules was more critical than 0.85 in the impactomics feature screening, and the A.U.C. value of benign and malignant prediction diagnosis of solid lung nodules using semi-supervised GAN model reached 0.98. Combining CT impactomics with deep learning can improve the differential prediction of benign and malignant diagnosis of solid lung nodules, which can provide high value for the clinical workers to treat solid lung nodules. The combination of C.T. impactomics and deep learning can improve the differential diagnosis of benign and malignant solid lung nodules and provide high value for clinical workers to treat solid lung nodules.</p>
</abstract>

There are numerous imaging methods for clinical screening and diagnosis of solid lung nodules, but they all have certain limitations. This paper selected patients with lung nodules in the People’s Hospital of Province X as an example. C.T. scans were performed on the patients with lung nodules to obtain their imaging histologic features. Then, based on the generative adversarial network in deep learning and using self-supervised learning to optimize the generative negative discriminator, a semi-supervised GAN model was established for the identification and predictive diagnosis of benign and malignant solid lung nodules. A regression analysis model was constructed and data analysis was performed to identify the independent risk factors related to the malignancy of solid lung nodules. The results showed that the patient’s tumor diameter became the most significant independent risk factor for the benign-malignant nature of lung nodules, with an OR of 3.421, which showed a significant difference at the 1% level. The I.C.C. value of each feature of solid lung nodules was more critical than 0.85 in the impactomics feature screening, and the A.U.C. value of benign and malignant prediction diagnosis of solid lung nodules using semi-supervised GAN model reached 0.98. Combining CT impactomics with deep learning can improve the differential prediction of benign and malignant diagnosis of solid lung nodules, which can provide high value for the clinical workers to treat solid lung nodules. The combination of C.T. impactomics and deep learning can improve the differential diagnosis of benign and malignant solid lung nodules and provide high value for clinical workers to treat solid lung nodules.

Differential diagnostic value of benign and malignant solid lung nodules based on deep learning

School of Health Science and Engineering University of Shanghai for Science and Technology

Department of Thoracic Surgery, Shanghai Changzheng Hospital

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

There are numerous imaging methods for clinical screening and diagnosis of solid lung nodules, but they all have certain limitations. This paper selected...

Differential diagnostic value of benign and malignant solid lung nodules based on deep learning

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 02 sty 2024

Przyjęty: 08 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0321

Słowa kluczowe
Self-supervised learning, Generative adversarial network, Discriminator, Regression analysis model, Solid lung nodule

© 2024 Xiaoyu Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Differential diagnostic value of benign and malignant solid lung nodules based on deep learning

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 02 sty 2024

Przyjęty: 08 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0321

Słowa kluczoweSelf-supervised learning, Generative adversarial network, Discriminator, Regression analysis model, Solid lung nodule

© 2024 Xiaoyu Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Self-supervised learning, Generative adversarial network, Discriminator, Regression analysis model, Solid lung nodule