Skip to main content

SYSTEMATIC REVIEW article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1381217
This article is part of the Research Topic Towards Precision Oncology: Assessing the Role of Radiomics and Artificial Intelligence View all 12 articles

Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis

Provisionally accepted
Lili Shi Lili Shi 1Jinli Zhao Jinli Zhao 2*Zhichao Wei Zhichao Wei 3*Huiqun Wu Huiqun Wu 3*Meihong Sheng Meihong Sheng 4*
  • 1 Medical School, Nantong University, Nantong, Jiangsu Province, China
  • 2 Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
  • 3 Nantong University, Nantong, Jiangsu Province, China
  • 4 The Second Affiliated Hospital of Nantong University and Nantong First People’s Hospital, Nantong, China

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

    The aim of this study was to systematically review the studies on radiomics models in distinguishing between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and evaluate the classification performance of radiomics models using images from various imaging techniques. Materials and methods: PubMed, Embase and Web of Science Core Collection were utilized to search for radiomics studies that differentiate between LUAD and LUSC. The assessment of the quality of studies included utilized the improved Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS). Meta-analysis was conducted to assess the classification performance of radiomics models using various imaging techniques. Results: The qualitative analysis included 40 studies, while the quantitative synthesis included 21 studies. Median RQS for 40 studies was 12 (range -5~19). Sixteen studies were deemed to have a low risk of bias and low concerns regarding applicability. The radiomics model based on CT images had a pooled sensitivity of 0.78 (95%CI: 0.71~0.83), specificity of 0.85 (95%CI:0.73~0.92), and the area under summary receiver operating characteristic curve (SROC-AUC) of 0.86 (95%CI:0 .82~0.89). As for PET images, the pooled sensitivity was 0.80 (95%CI: 0.61~0.91), specificity was 0.77 (95%CI: 0.60~0.88), and the SROC-AUC was 0.85 (95%CI: 0.82~0.88). PET/CT images had a pooled sensitivity of 0.87 (95%CI: 0.72~0.94), specificity of 0.88 (95%CI: 0.80~0.93), and an SROC-AUC of 0.93 (95%CI: 0.91~0.95). MRI images had a pooled sensitivity of 0.73 (95%CI: 0.61~0.82), specificity of 0.80 (95%CI: 0.65~0.90), and an SROC-AUC of 0.79 (95%CI: 0.75~0.82).Radiomics models demonstrate potential in distinguishing between LUAD and LUSC. Nevertheless, it is crucial to conduct a well-designed and powered prospective radiomics studies to establish their credibility in clinical application.

    Keywords: Radiomics, Lung Adenocarcinoma, Lung squamous cell carcinoma, Texture Analysis, computed tomography, positron emission tomography, Magnetic Resonance Imaging

    Received: 09 Feb 2024; Accepted: 05 Sep 2024.

    Copyright: © 2024 Shi, Zhao, Wei, Wu and Sheng. 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:
    Jinli Zhao, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu Province, China
    Zhichao Wei, Nantong University, Nantong, 226019, Jiangsu Province, China
    Huiqun Wu, Nantong University, Nantong, 226019, Jiangsu Province, China
    Meihong Sheng, The Second Affiliated Hospital of Nantong University and Nantong First People’s Hospital, Nantong, 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.