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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1414609

The Deep Learning Radiomics Nomogram Helps to Evaluate the Lymph Node Status in Cervical Adenocarcinoma/Adenosquamous Carcinoma

Provisionally accepted
Mei Ling Xiao Mei Ling Xiao 1,2Le Fu Le Fu 3Ting Qian Ting Qian 4Yan Wei Yan Wei 5Feng Hua Ma Feng Hua Ma 6Yong Ai Li Yong Ai Li 2Jie Jun Cheng Jie Jun Cheng 3Zhao Xia Qian Zhao Xia Qian 4Guo Fu Zhang Guo Fu Zhang 6Jin Wei Qiang Jin Wei Qiang 2*
  • 1 Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
  • 2 Jinshan Hospital, Shanghai, China
  • 3 Shanghai First Maternity and Infant Hospital, Shanghai, Shanghai Municipality, China
  • 4 International Peace Maternity and Child Health Hospital, Shanghai, Shanghai Municipality, China
  • 5 Zhejiang University of Technology, Hangzhou, Zhejiang Province, China
  • 6 Obstetrics and Gynecology Hospital, Fudan University, Shanghai, Shanghai Municipality, China

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

    The accurate assessment of lymph node metastasis (LNM) can facilitate clinical decision-making on radiotherapy or radical hysterectomy (RH) in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC). This study aims to develop a deep learning radiomics nomogram (DLRN) to preoperatively evaluate LNM in cervical AC/ASC.A total of 652 patients from multi-center were enrolled and randomly allocated into primary, internal and external validation cohorts. The radiomics features were extracted from axial T2-weighted imaging (T2WI), diffusionweighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI). The DL features from T2WI, DWI and CE-T1WI were exported from Resnet 34 which was pretrained by 14 million natural images of ImageNet dataset. The radscore (RS) and DL score (DLS) were independently obtained after repeatability test, Pearson correlation coefficient (PCC), minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) algorithm performed on the radiomics and DL feature sets. The DLRN was then developed by integrating the RS, DLS and independent clinicopathological factors for evaluating the LNM in cervical AC/ASC. Results The nomogram of DLRN integrated FIGO stage, menopause, RS and DLS achieved AUCs of 0.79 (95% CI, 0.74-0.83), 0.87 (95% CI, 0.81-0.92) and 0.86 (95% CI, 0.79-0.91) in the primary, internal and external validation cohorts. Compared with RS, DLS, and clinical model, DLRN had a significant higher AUC for evaluating LNM (all P < 0.005). Conclusions The nomogram of DLRN can accurately evaluate LNM in cervical AC/ASC.

    Keywords: Magnetic Resonance Imaging, Cervical adenosquamous carcinoma, cervical adenocarcinoma, Radiomics, deep learning, lymph node metastasis

    Received: 15 Apr 2024; Accepted: 20 Nov 2024.

    Copyright: © 2024 Xiao, Fu, Qian, Wei, Ma, Li, Cheng, Qian, Zhang and Qiang. 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: Jin Wei Qiang, Jinshan Hospital, Shanghai, China

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