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Estimation of pathological subtypes in subsolid lung nodules using artificial intelligence.
Hu, Xiaoqin; Yang, Liu; Kang, Tong; Yu, Hanhua; Zhao, Tingkuan; Huang, Yuanyi; Kong, Yuefeng.
Afiliación
  • Hu X; Department of Radiology, The Fourth Hospital of Wuhan, Wuhan, China.
  • Yang L; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, China.
  • Kang T; Department of Radiology, The Fourth Hospital of Wuhan, Wuhan, China.
  • Yu H; Department of Radiology, The Fourth Hospital of Wuhan, Wuhan, China.
  • Zhao T; Department of Pathology, Jingzhou Central Hospital, The Second Clinical Medical College, Yangtze University, Jingzhou, China.
  • Huang Y; Department of Radiology, Jingzhou Central Hospital, The Second Clinical Medical College, Yangtze University, Jingzhou, China.
  • Kong Y; Department of Radiology, The Fourth Hospital of Wuhan, Wuhan, China.
Heliyon ; 10(15): e34863, 2024 Aug 15.
Article en En | MEDLINE | ID: mdl-39170291
ABSTRACT

Objective:

This study aimed to investigate the value of artificial intelligence (AI) for distinguishing pathological subtypes of invasive pulmonary adenocarcinomas in patients with subsolid nodules (SSNs). Materials and

methods:

This retrospective study included 110 consecutive patients with 120 SSNs. The qualitative and quantitative imaging characteristics of SSNs were extracted automatically using an artificially intelligent assessment system. Then, radiologists had to verify these characteristics again. We split all cases into two groups non-IA including 11 Atypical adenomatous hyperplasia (AAH) and 25 adenocarcinoma in situ (AIS) or IA including 7 minimally invasive adenocarcinoma (MIA) and 77 invasive adenocarcinoma (IAC). Variables that exhibited statistically significant differences between the non-IA and IA in the univariate analysis were included in the multivariate logistic regression analysis. Receiver operating characteristic (ROC) analyses were conducted to determine the cut-off values and their diagnostic performances.

Results:

Multivariate logistic regression analysis showed that the major diameter (odds ratio [OR] = 1.38; 95 % confidence interval [CI], 1.02-1.87; P = 0.036) and entropy of three-dimensional(3D) CT value (OR = 3.73, 95 % CI, 1.13-2.33, P = 0.031) were independent risk factors for adenocarcinomas. The cut-off values of the major diameter and the entropy of 3D CT value for the diagnosis of invasive adenocarcinoma were 15.5 mm and 5.17, respectively. To improve the classification performance, we fused the major diameter and the entropy of 3D CT value as a combined model, and the (AUC） of the model was 0.868 (sensitivity = 0.845, specificity = 0.806).

Conclusion:

The major diameter and entropy of 3D CT value can distinguish non-IA from IA. AI can improve performance in distinguishing pathological subtypes of invasive pulmonary adenocarcinomas in patients with SSNs.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido