Skip to main content

ORIGINAL RESEARCH article

Front. Oncol.
Sec. Head and Neck Cancer
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1489973
This article is part of the Research Topic Early Diagnosis in Head and Neck Cancer: Advances, Techniques, and Challenges View all articles

Value of Radiomics and Deep Learning Feature Fusion Models Based on DCE-MRI in Distinguishing Sinonasal Squamous Cell Carcinoma from Lymphoma

Provisionally accepted
Ziwei Zhang Ziwei Zhang 1,2Duo Zhang Duo Zhang 2*Yunze Yang Yunze Yang 1,2Yang Liu Yang Liu 2*Jianjun Zhang Jianjun Zhang 2*
  • 1 Graduate School, Chengde Medical University, Chengde, China
  • 2 Baoding First Central Hospital, Baoding, Hebei Province, China

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

    Problem Sinonasal squamous cell carcinoma (SNSCC) and sinonasal lymphoma (SNL) lack distinct clinical manifestations and traditional imaging characteristics, complicating the accurate differentiation between these tumors and the selection of appropriate treatment strategies. Consequently, there is an urgent need for a method that can precisely distinguish between these tumors preoperatively to formulate suitable treatment plans for patients. Results: The feature fusion model of radiomics and DL has higher accuracy in distinguishing SNSCC from SNL than CML or DL alone. The ExtraTrees model based on DLR fusion features of DCE-T1WI had an AUC value of 0.995 in the training set and 0.939 in the validation set.The DLRN model based on the fusion of predictive scores had an AUC value of 0.995 in the training set and 0.911 in the validation set.The DLRN model based on the fusion of predictive scores had an AUC value of 0.995 in the training set and 0.911 in the validation set. Conclusion This study, by constructing a feature integration model combining radiomics and deep learning (DL), has demonstrated strong predictive capabilities in the preoperative non-invasive diagnosis of SNSCC and SNL, offering valuable information for tailoring personalized treatment plans for patients.

    Keywords: Sinonasal, Squamous cell carcinoma, Lymphoma, Radiomics, deep learning

    Received: 02 Sep 2024; Accepted: 05 Nov 2024.

    Copyright: © 2024 Zhang, Zhang, Yang, Liu and Zhang. 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:
    Duo Zhang, Baoding First Central Hospital, Baoding, 071000, Hebei Province, China
    Yang Liu, Baoding First Central Hospital, Baoding, 071000, Hebei Province, China
    Jianjun Zhang, Baoding First Central Hospital, Baoding, 071000, Hebei Province, 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.