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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1417753
This article is part of the Research Topic Advancing Cancer Imaging Technologies: Bridging the Gap from Research to Clinical Practice View all articles

AI-Enhanced Diagnostic Model for Pulmonary Nodule Classification

Provisionally accepted
Jifei Chen Jifei Chen 1Moyu Ming # Moyu Ming # 2Shuangping Huang Shuangping Huang 2Xuan Wei Xuan Wei 2Jinyan Wu Jinyan Wu 2Sufang Zhou Sufang Zhou 2Zhougui Ling Zhougui Ling 2*
  • 1 School of Basic Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Region, China
  • 2 Department of Pulmonary and Critical Care Medicine, the Fourth Affiliated Hospital of Guangxi Medical University,, Liuzhou, China

The final, formatted version of the article will be published soon.

    Background. The identification of benign and malignant pulmonary nodules (BPN and MPN) can significantly reduce mortality.However, a reliable and validated diagnostic model for clinical decision-making is still lacking.Methods. Enzyme-linked immunosorbent assay and electro chemiluminescent immunoassay were utilized to determine the serum concentrations of 7AABs (p53, GAGE7, PGP9.5, CAGE, MAGEA1, SOX2, GBU4-5), and 4TTMs (CYFR21, CEA, NSE and SCC) in 260 participants (72 BPNs and 188 early-stage MPNs), respectively. The malignancy probability was calculated using Artificial intelligence pulmonary nodule auxiliary diagnosis system, or Mayo model. Along with age, sex, smoking history and nodule size, 18 variables were enrolled for model development. Baseline comparation, univariate ROC analysis, variable correlation analysis, lasso regression, univariate and stepwise logistic regression, and decision curve analysis (DCA) was used to reduce and screen variables. A nomogram and DCA were built for model construction and clinical use. Training (60%) and validation (40%) cohorts were used to for model validation.Results. Age, CYFRA21_1, AI, PGP9.5, GAGE7, and GBU4_5 was screened out from 18 variables and utilized to establish the regression model for identifying BPN and early-stage MPN, as well as nomogram and DCA for clinical practical use. The AUC of the nomogram in the training and validation cohorts were 0.884 and 0.820, respectively. Moreover, the calibration curve showed high coherence between the predicted and actual probability.This diagnostic model and DCA could provide evidence for upgrading or maintaining the current clinical decision based on malignancy probability stratification. It enables low and moderate risk or ambiguous patients to benefit from more precise clinical decision stratification, more timely detection of malignant nodules, and early treatment.

    Keywords: Pulmonary nodule, Diagnostic model, nomogram, DCA, lung cancer

    Received: 15 Apr 2024; Accepted: 29 Jul 2024.

    Copyright: © 2024 Chen, Ming #, Huang, Wei, Wu, Zhou and Ling. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Zhougui Ling, Department of Pulmonary and Critical Care Medicine, the Fourth Affiliated Hospital of Guangxi Medical University,, Liuzhou, China

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