Skip to main content

ORIGINAL RESEARCH article

Front. Oncol.
Sec. Thoracic Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1482965
This article is part of the Research Topic Deep Learning for Medical Imaging Applications View all 6 articles

A CT-based deep learning model for preoperative prediction of spread through air spaces in clinical stage I lung adenocarcinoma

Provisionally accepted
Xiaoling Ma Xiaoling Ma 1Weiheng He Weiheng He 1*Chong Chen Chong Chen 2*Fengmei Tan Fengmei Tan 1*Jun Chen Jun Chen 3*Lili Yang Lili Yang 1Dazhi Chen Dazhi Chen 1*Liming Xia Liming Xia 2*
  • 1 People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, China
  • 2 Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  • 3 Bayer Healthcare RAD, Wuhan, China

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

    A CT-based deep learning model for preoperative prediction of spread through air spaces in clinical stage I lung adenocarcinoma Objective: To develop and validate a deep learning signature for noninvasive prediction of spread through air spaces (STAS) in clinical stage I lung adenocarcinoma and compare its predictive performance with conventional clinical-semantic model. Methods: A total of 513 patients with pathologically-confirmed stage I lung adenocarcinoma were retrospectively enrolled and were divided into training cohort (n = 386) and independent validation cohort (n = 127) according to different center. Clinicopathological data were collected and CT semantic features were evaluated. Multivariate logistic regression analyses were conducted to construct a clinical-semantic model predictive of STAS. The Swin Transformer architecture was adopted to develop a deep learning signature predictive of STAS. Model performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive and negative predictive value, and calibration curve. AUC comparisons were performed by the DeLong test. Results: The proposed deep learning signature achieved an AUC of 0.869 (95% CI: 0.831, 0.901) in training cohort and 0.837 (95% CI: 0.831, 0.901) in validation cohort, surpassing clinical-semantic model both in training and validation cohort (all P＜0.01). Calibration curves demonstrated good agreement between STAS predicted probabilities using deep learning signature and actual observed probabilities in both cohorts. The inclusion of all clinical-semantic risk predictors failed to show an incremental value with respect to deep learning signature. Conclusions: The proposed deep learning signature based on Swin Transformer achieved a promising performance in predicting STAS in clinical stage I lung adenocarcinoma, thereby offering information in directing surgical strategy and facilitating adjuvant therapeutic scheduling.

    Keywords: deep learning, Lung Adenocarcinoma, spread though air space, Computer tomography, prediction

    Received: 19 Aug 2024; Accepted: 17 Dec 2024.

    Copyright: © 2024 Ma, He, Chen, Tan, Chen, Yang, Chen and Xia. 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:
    Weiheng He, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, China
    Chong Chen, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
    Fengmei Tan, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, China
    Jun Chen, Bayer Healthcare RAD, Wuhan, China
    Dazhi Chen, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, China
    Liming Xia, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 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.