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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Thoracic Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1448333

CT Morphological Features and Histogram Parameters to Predict Micropapillary or Solid Components in Stage IA Lung Adenocarcinoma

Provisionally accepted
Qin Chen Qin Chen Kaihe Lin Kaihe Lin Baoteng Zhang Baoteng Zhang Youqin Jiang Youqin Jiang Suying Wu Suying Wu Jiajun Lin Jiajun Lin *
  • Department of Radiology，The First Hospital of Putian City, Putian，Fujian, China

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

    Abstract: Objectives: This study aimed to construct prediction models based on computerized tomography (CT) signs, histogram and morphology features for the diagnosis of micropapillary or solid (MIP/SOL) components of stage IA lung adenocarcinoma (LUAC) and to evaluate the models' performance. Methods: This clinical retrospective study included image data of 376 patients with stage IA LUAC based on postoperative pathology, admitted to Putian First Hospital from January 2019 to June 2023. According to the presence of MIP/SOL components in postoperative pathology, patients were divided into MIP/SOL+ and MIP/SOL- groups. Cases with tumors ≤ 3 cm and ≤ 2 cm were separately analyzed. Each subgroup of patients was then randomly divided into a training set and a test set in a ratio of 7:3. The training set was used to build the prediction model, and the test set was used for internal validation. Results: For tumors ≤ 3 cm，ground-glass opacity (GGO) [odds ratio (OR) = 0.244; 95% confidence interval (CI): 0.103–0.569; p = 0.001], entropy (OR = 1.748; 95% CI: 1.213–2.577; p = 0.004), average CT value (OR = 1.002; 95% CI: 1.000–1.004; p = 0.002), and kurtosis (OR = 1.240; 95% CI: 1.023–1.513; p = 0.030) were independent predictors of MIP/SOL components of stage IA LUAC. The area under the ROC curve (AUC) of the nomogram prediction model for predicting MIP/SOL components was 0.816 (95% CI: 0.756–0.877) in the training set and 0.789 (95% CI: 0.689–0.889) in the test set. In contrast, for tumors ≤ 2 cm, kurtosis was no longer an independent predictor. The nomogram prediction model had an AUC of 0.811 (95% CI: 0.731–0.891) in the training set and 0.833 (95% CI: 0.733–0.932) in the test set. Conclusion: For tumors ≤ 3 cm and ≤ 2 cm, GGO, average CT value, and entropy were the same independent influencing factors in predicting MIP/SOL components of stage IA LUAC. The nomogram prediction models have potential diagnostic value for identifying MIP/SOL components of early-stage LUAC. Keywords: Lung adenocarcinoma; CT histogram; micropapillary components; solid components; prediction model; artificial intelligence

    Keywords: Lung Adenocarcinoma, CT histogram, micropapillary components, Solid components, Prediction model, artificial intelligence

    Received: 13 Jun 2024; Accepted: 11 Jul 2024.

    Copyright: © 2024 Chen, Lin, Zhang, Jiang, Wu and Lin. 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: Jiajun Lin, Department of Radiology，The First Hospital of Putian City, Putian，Fujian, China

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