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  • [Cancer Imaging.] Enhancing prognosis prediction using pre-treatment nodal SUVmax and HPV status in cervical squamous cell carcinoma.

    경북의대 / 홍채문, 정건오*, 정신영*

  • 출처
    Cancer Imaging.
  • 등재일
    2019 Jun 24
  • 저널이슈번호
    19(1):43. doi: 10.1186/s40644-019-0226-4.
  • 내용

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    Abstract
    BACKGROUND:
    This study was to evaluate the prognostic value of metabolic parameters on F-18-FDG PET/CT and the status of human papillomavirus (HPV) infection and known prognostic variables for predicting tumor recurrence and investigating a prognostic model in patients with locally advanced cervical cancer treated with concurrent chemoradiotherapy (CCRT).

    METHODS:
    A total of 129 patients with cervical squamous cell carcinoma who underwent initial CCRT were eligible for this study. Univariate and multivariate analyses were performed using traditional prognostic factors, metabolic parameters, and HPV infection. Classification and regression decision tree (CART) was used to establish new classification.

    RESULTS:
    Among 129 patients, 29 patients (22.5%) had recurrence after a median follow-up of 60 months (range, 3-125 months). Tumor size, para-aortic lymph node metastasis, nodal SUVmax, and HPV infection status were identified as independent prognostic factors by multivariate analysis. The CART analysis classified the patients into three groups. The first node was nodal SUVmax, and HPV status was the second node for patients with nodal SUVmax ≤7.49; Group A (nodal SUVmax ≤7.49 and HPV positive, HR 1.0), Group B (nodal SUVmax ≤7.49 and HPV negative, HR 3.56), and Group C (nodal SUVmax > 7.49, HR 10.13). Disease-free survival was significantly different among the three groups (p < 0.001).

    CONCLUSION:
    The nodal SUVmax on F-18 FDG PET/CT and HPV infection status before CCRT are powerful independent prognostic factors for the prediction of disease-free survival in patients with cervical squamous cell carcinoma who underwent initial CCRT. We also suggest a simple prognosis prediction model using pre-treatment FDG PET/CT and HPV genotyping; however, it needs further validation in an independent dataset.

     


    Author information

    Hong CM1,2, Park SH3,4, Chong GO5,6, Lee YH7,8, Jeong JH1,9, Lee SW1,9, Lee J1,2, Ahn BC1,2, Jeong SY10,11.
    1
    Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
    2
    Department of Nuclear Medicine, Kyungpook National University Hospital, 130 Dongdeok-ro, Jung-gu, Daegu, 41944, Republic of Korea.
    3
    Department of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
    4
    Department of Radiation Oncology, Kyungpook National University Chilgok Hospital, 807 Hoguk-ro, Buk-gu, Daegu, 41404, Republic of Korea.
    5
    Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. gochong@knu.ac.kr.
    6
    Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University Chilgok Hospital, 807, Hoguk-ro, Buk-gu, Daegu, 41404, Republic of Korea. gochong@knu.ac.kr.
    7
    Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
    8
    Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University Chilgok Hospital, 807, Hoguk-ro, Buk-gu, Daegu, 41404, Republic of Korea.
    9
    Department of Nuclear Medicine, School of Medicine, Kyungpook National University Chilgok Hospital, 807, Hoguk-ro, Buk-gu, Daegu, 41404, Republic of Korea.
    10
    Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. syjeong@knu.ac.kr.
    11
    Department of Nuclear Medicine, School of Medicine, Kyungpook National University Chilgok Hospital, 807, Hoguk-ro, Buk-gu, Daegu, 41404, Republic of Korea. syjeong@knu.ac.kr.

  • 키워드
    Cervical cancer; Classification and regression tree; Concurrent chemoradiotherapy; FDG PET/CT; Human papilloma virus; Lymph node; Prognosis prediction
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