Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning.
Samira AbbaspourHamid AbdollahiHossein ArabalibeikMaedeh BarahmanAmir Mohammad ArefpourPedram FadaviMohammadreza AySeied Rabi MahdaviPublished in: Abdominal radiology (New York) (2022)
This study demonstrated that the EUS-based radiomics model could serve as pretreatment biomarkers in predicting pathologic features of rectal cancer. The wavelet filter and machine learning methods (LR and SVM) had good results on the EUS images of rectal cancer.
Keyphrases
- locally advanced
- rectal cancer
- neoadjuvant chemotherapy
- machine learning
- phase ii study
- lymph node metastasis
- convolutional neural network
- squamous cell carcinoma
- deep learning
- magnetic resonance imaging
- radiation therapy
- fine needle aspiration
- contrast enhanced
- optical coherence tomography
- ultrasound guided
- artificial intelligence
- clinical trial
- computed tomography
- magnetic resonance
- lymph node
- open label