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REVIEW article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1491762

PET/CT Radiomics and Deep Learning in the Diagnosis of Benign and Malignant Pulmonary Nodules: Progress and Challenges

Provisionally accepted
Yan Sun Yan Sun Xinyu Ge Xinyu Ge Rong Niu Rong Niu Jianxiong Gao Jianxiong Gao Yunmei Shi Yunmei Shi Xiaoliang Shao Xiaoliang Shao Yuetao Wang Yuetao Wang Xiaonan Shao Xiaonan Shao *
  • the Third Affiliated Hospital of Soochow University, Changzhou, China

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

    Lung cancer is currently the leading cause of cancer-related deaths, and early diagnosis and screening can significantly reduce its mortality rate. Since some early-stage lung cancers lack obvious clinical symptoms and only present as pulmonary nodules (PNs) in imaging examinations, accurately determining the benign or malignant nature of PNs is crucial for improving patient survival rates. 18 F-FDG PET/CT is important in diagnosing PNs, but its specificity needs improvement. Radiomics can provide information beyond traditional visual assessment, overcoming its limitations by extracting high-throughput quantitative features from medical images. Radiomics features based on 18 F-FDG PET/CT and deep learning methods have shown great potential in the noninvasive diagnosis of PNs. This paper reviews the latest advancements in these methods and discusses their contributions to improving diagnostic accuracy and the challenges they face.

    Keywords: pulmonary nodules, Lung Neoplasms, PET/CT, Radiomics, deep learning

    Received: 06 Sep 2024; Accepted: 25 Oct 2024.

    Copyright: © 2024 Sun, Ge, Niu, Gao, Shi, Shao, Wang and Shao. 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: Xiaonan Shao, the Third Affiliated Hospital of Soochow University, Changzhou, 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.