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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1427743
This article is part of the Research Topic Quantitative Imaging: Revolutionizing Cancer Management with biological sensitivity, specificity, and AI integration View all 18 articles

Evaluating Peritumoral and Intratumoral Radiomics Signatures for Predicting Lymph Node Metastasis in Surgically Resectable Non-Small Cell Lung Cancer

Provisionally accepted
Xu Ran Xu Ran 1Wang Kaiyu Wang Kaiyu 1Bo Peng Bo Peng 1Zhou Xiang Zhou Xiang 1Wang Chenghao Wang Chenghao 1Lu Tong Lu Tong 2Jiaxin Shi Jiaxin Shi 1Zhao Jiaying Zhao Jiaying 1Zhang Linyou Zhang Linyou 1*
  • 1 Department of Thoracic Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China
  • 2 Department of Thoracic Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

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

    Background: Whether lymph node metastasis in non-small cell lung cancer is critical to clinical decision-making. This study was to develop a non-invasive predictive model for preoperative assessing lymph node metastasis in patients with non-small cell lung cancer (NSCLC) using radiomic features from chest CT images.In this retrospective study, 247 patients with resectable nonsmall cell lung cancer (NSCLC) were enrolled. These individuals underwent preoperative chest CT scans that identified lung nodules, followed by lobectomies and either lymph node sampling or dissection. We extracted both intratumoral and peritumoral radiomic features from the CT images, which were used as covariates to predict the lymph node metastasis status. By using ROC curves, Delong tests, Calibration curve, and DCA curves, intra-tumoral-peri-tumoral model performance were compared with models using only intratumoral features or clinical information.Finally, we constructed a model that combined clinical information and radiomic features to increase clinical applicability.Results: This study enrolled 247 patients (117 male and 130 females). In terms of predicting lymph node metastasis, the intra-tumoral-peri-tumoral model (0.953, 95%CI 0.9272-0.9792) has a higher AUC compared to the intratumoral radiomics model (0.898, 95%CI 0.8553-0.9402) and the clinical model (0.818, 95%CI 0.7653-0.8709). The DeLong test shows that the performance of the Intratumoral and Peritumoral radiomics models is superior to that of the Intratumoral or clinical feature model (p <0.001). In addition, to increase the clinical applicability of the model, we combined the intratumoral-peritumoral model and clinical information to construct a nomogram.Nomograms still have good predictive performance.The radiomics-based model incorporating both peritumoral and intratumoral features from CT images can more accurately predict lymph node metastasis in NSCLC than traditional methods.

    Keywords: Peritumoral, Intratumoral, Radiomics, lymph node metastasis, Non-small cell lung cancer

    Received: 04 May 2024; Accepted: 18 Sep 2024.

    Copyright: © 2024 Ran, Kaiyu, Peng, Xiang, Chenghao, Tong, Shi, Jiaying and Linyou. 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: Zhang Linyou, Department of Thoracic Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.