Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.

Journal: BMC women's health
Published Date: Mar 19, 2024

Abstract

BACKGROUND: Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment.

Authors

  • Xue-Fang Zhang
    Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.
  • Hong-Yuan Wu
    Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.
  • Xu-Wei Liang
    Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.
  • Jia-Luo Chen
    Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.
  • Jianpeng Li
    Department of Cardiology, Taizhou Second People's Hospital, The Affiliated Taizhou Second People's Hospital of Yangzhou University, Taizhou, China.
  • Shihao Zhang
    Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China. shihao_zhang@yeah.net.
  • Zhigang Liu
    Cardiac Surgery, TEDA International Cardiovascular Hospital, Tianjin, China.

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