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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1346336
This article is part of the Research Topic Artificial Intelligence and Imaging for Oncology View all 19 articles

radiomics-based machine learning models for differentiating pathological subtypes in cervical cancer: A multicenter study

Provisionally accepted
Huiling Liu Huiling Liu 1,2Mi Lao Mi Lao 3Cheng Chang Cheng Chang 1Yalin ZHANG Yalin ZHANG 2Yong Yin Yong Yin 4Ruozheng Wang Ruozheng Wang 2*
  • 1 Xinjiang Medical University, Ürümqi, China
  • 2 Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumqi, China
  • 3 Binzhou People’s Hospital, Binzhou, Shandong Province, China
  • 4 Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong Province, China

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

    This study was designed to determine the diagnostic performance of fluorine-18-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography (PET) / computed tomography (CT) radiomics-based machine learning (ML) in the classification of cervical adenocarcinoma (AC) and squamous cell carcinoma (SCC).Methods: Pretreatment 18 F-FDG PET/CT data were retrospectively collected from patients who were diagnosed with locally advanced cervical cancer at two centers.Radiomics features were extracted and selected by the Pearson correlation coefficient and least absolute shrinkage and selection operator regression analysis. Six ML algorithms were then applied to establish models, and the best-performing classifier was selected based on accuracy, sensitivity, specificity, and area under the curve (AUC). The performance of different model was assessed and compared using the DeLong test. Radiomics model for cervical cancer 2 Results: A total of 227 patients with locally advanced cervical cancer were enrolled in this study (N=136 for the training cohort, N=59 for the internal validation cohort, and N=32 for the external validation cohort). The PET radiomics model constructed based on the lightGBM algorithm had an accuracy of 0.915 and an AUC of 0.851 (95% confidence interval [CI], 0.715-0.986) in the internal validation cohort, which were higher than those of the CT radiomics model (accuracy: 0.661; AUC: 0.513 [95% CI, 0.339-0.688]). The DeLong test revealed no significant difference in AUC between the combined radiomics model and the PET radiomics model in either the training cohort (z=0.940, P=0.347) or the internal validation cohort (z=0.285, P=0.776). In the external validation cohort, the lightGBM-based PET radiomics model achieved good discrimination between SCC and AC (AUC = 0.730).The lightGBM-based PET radiomics model had great potential to predict the fine histological subtypes of locally advanced cervical cancer and might serve as a promising noninvasive approach for the diagnosis and management of locally advanced cervical cancer.

    Keywords: Locally advanced cervical cancer, Positron emission tomography, PET, Radiomics, adenocarcinoma, AC, squamous cell carcinoma, SCC

    Received: 29 Nov 2023; Accepted: 27 Aug 2024.

    Copyright: © 2024 Liu, Lao, Chang, ZHANG, Yin and Wang. 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: Ruozheng Wang, Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumqi, 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.