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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Thoracic Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1406166

Predicting Pathological Grade of Stage I Pulmonary Adenocarcinoma: A CT Radiomics Approach

Provisionally accepted
Xiao ni Huang Xiao ni Huang 1*Yang Xue Yang Xue 2*Bing Deng Bing Deng 3*Yuan liang Jiang Yuan liang Jiang 2*Wen cai Huang Wen cai Huang 1,2*Jun Chen Jun Chen 4*Jia ni Zou Jia ni Zou 2*Huibin Tan Huibin Tan 2
  • 1 Southern Medical University, Guangzhou, Guangdong, China
  • 2 Wuhan People's Liberation Army General Hospital, Wuhan, Hubei Province, China
  • 3 School of Medicine, Wuhan University of Science and Technology, Wuhan, Hebei Province, China
  • 4 Bayer China Ltd., Shanghai, China

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

    Objectives: To investigate the value of CT radiomics combined with radiological features in predicting pathological grade of stage I invasive pulmonary adenocarcinoma (IPA) based on the International Association for the Study of Lung Cancer (IASLC) new grading system. Methods: The preoperative CT images and clinical information of 294 patients with stage I IPA were retrospectively analyzed (159 training set; 69 validation set; 66 test set). Referring to the IASLC new grading system, patients were divided into a low/intermediate-grade group and a high-grade group. Radiomic features were selected by using the least absolute shrinkage and selection operator (LASSO), the logistic regression (LR) classifier was used to establish radiomics model (RM), clinical-radiological features model (CRM) and combined rad-score with radiological features model (CRRM), and visualized CRRM by nomogram. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the performance and fitness of models. Results: In the training set, RM, CRM, and CRRM achieved AUCs of 0.825 [95% CI (0.735-0.916)], 0.849 [95% CI (0.772-0.925)], and 0.888 [95% CI (0.819-0.957)],respectively. For the validation set, the AUCs were 0.879 [95% CI (0.734-1.000)], 0.888 [95% CI (0.794-0.982)], and 0.922 [95% CI (0.835-1.000)], and for the test set, the AUCs were 0.814 [95% CI (0.674-0.954)], 0.849 [95% CI (0.750-0.948)], and 0.860 [95% CI (0.755-0.964)] for RM, CRM, and CRRM, respectively.All three models performed well in predicting pathological grade, especially the combined model, showing CT radiomics combined with radiological features had the potential to distinguish the pathological grade of early-stage IPA.

    Keywords: Tomography, X-Ray Computed, Adenocarcinoma of lung, Neoplasm Grading, Logistic Models, Nomograms

    Received: 24 Mar 2024; Accepted: 05 Sep 2024.

    Copyright: © 2024 Huang, Xue, Deng, Jiang, Huang, Chen, Zou and Tan. 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:
    Xiao ni Huang, Southern Medical University, Guangzhou, 510515, Guangdong, China
    Yang Xue, Wuhan People's Liberation Army General Hospital, Wuhan, Hubei Province, China
    Bing Deng, School of Medicine, Wuhan University of Science and Technology, Wuhan, Hebei Province, China
    Yuan liang Jiang, Wuhan People's Liberation Army General Hospital, Wuhan, Hubei Province, China
    Wen cai Huang, Wuhan People's Liberation Army General Hospital, Wuhan, Hubei Province, China
    Jun Chen, Bayer China Ltd., Shanghai, China
    Jia ni Zou, Wuhan People's Liberation Army General Hospital, Wuhan, Hubei Province, China

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