Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study.
Ya-Jiao ZhangChao WuJinglong DuZhibo XiaoFurong LvYanbing LiuPublished in: Abdominal radiology (New York) (2023)
A DLRN based on intratumoral and peritumoral regions had the potential to predict and stratify recurrence risk factors for early-stage cervical cancers and enhance the value of individualized precision treatment.
Keyphrases
- early stage
- deep learning
- risk factors
- lymph node metastasis
- contrast enhanced
- sentinel lymph node
- free survival
- magnetic resonance imaging
- squamous cell carcinoma
- magnetic resonance
- convolutional neural network
- computed tomography
- risk assessment
- radiation therapy
- lymph node
- neoadjuvant chemotherapy
- combination therapy
- human health
- childhood cancer