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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1382815
This article is part of the Research Topic Artificial Intelligence and Imaging for Oncology View all 17 articles

2.5D peritumoural radiomics predicts postoperative recurrence in stage Ⅰ lung adenocarcinoma

Provisionally accepted
Haimei Lan Haimei Lan 1*Chaosheng Wei Chaosheng Wei 1*Fengming Xu Fengming Xu 1Eqing Yang Eqing Yang 1*Dayu Lu Dayu Lu 2*Qing Feng Qing Feng 1*Tao Li Tao Li 1*
  • 1 Liuzhou Workers Hospital, Liuzhou, Guangxi Zhuang Region, China
  • 2 Longtan Hospital of Guangxi Zhuang Autonomous Region, Longtan, China

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

    Objective：Radiomics can non-invasively predict the prognosis of a tumour by applying advanced imaging feature algorithms.The aim of this study was to predict the chance of postoperative recurrence by modelling tumour radiomics and peritumour radiomics and clinical features in patients with stage Ⅰ lung adenocarcinoma(LUAD). Materials and methods：Retrospective analysis of 190 patients with postoperative pathologically confirmed stage Ⅰ LUAD from centre 1, who were divided into training cohort and internal validation cohort, with centre 2 added as external validation cohort. To develop a combined radiation-clinical omics model nomogram incorporating clinical features based on images from low-dose lung cancer screening CT plain for predicting postoperative recurrence and to evaluate the performance of the nomogram in the training cohort, internal validation cohort and external validation cohort. Results：A total of 190 patients were included in the model in centre 1 and randomised into a training cohort of 133 and an internal validation cohort of 57 in a ratio of 7:3, and 39 were included in centre 2 as an external validation cohort. In the training cohort (AUC=0.865, 95% CI 0.824-0.906), internal validation cohort (AUC=0.902, 95% CI 0.851-0.953) and external validation cohort (AUC=0.830,95% CI 0.751-0.908), the combined radiation-clinical omics model had a good predictive ability. The combined model performed significantly better than the conventional single-modality models (clinical model, radiomic model), and the calibration curve and decision curve analysis(DCA) showed high accuracy and clinical utility of the nomogram. Conclusion：The combined preoperative radiation-clinical omics model provides good predictive value for postoperative recurrence in stage ⅠLUAD and combines the model's superiority in both internal and external validation cohorts, demonstrating its potential to aid in postoperative treatment strategies.

    Keywords: Radiomics, Lung Adenocarcinoma, Postoperative recurrence, nomogram, Peritumoral regions

    Received: 06 Feb 2024; Accepted: 06 Aug 2024.

    Copyright: © 2024 Lan, Wei, Xu, Yang, Lu, Feng and Li. 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:
    Haimei Lan, Liuzhou Workers Hospital, Liuzhou, Guangxi Zhuang Region, China
    Chaosheng Wei, Liuzhou Workers Hospital, Liuzhou, Guangxi Zhuang Region, China
    Eqing Yang, Liuzhou Workers Hospital, Liuzhou, Guangxi Zhuang Region, China
    Dayu Lu, Longtan Hospital of Guangxi Zhuang Autonomous Region, Longtan, 545005, China
    Qing Feng, Liuzhou Workers Hospital, Liuzhou, Guangxi Zhuang Region, China
    Tao Li, Liuzhou Workers Hospital, Liuzhou, Guangxi Zhuang Region, 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.