A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study.
Camelia Alexandra CoadăMiriam SantoroVladislav ZybinMarco Di StanislaoGiulia PaolaniCecilia ModolonStella Di CostanzoLucia GenovesiMarco TeseiAntonio De LeoGloria RavegniniDario de BiaseAlessio Giuseppe MorgantiLuigi LovatoPierandrea De IacoLidia StrigariAnna Myriam PerronePublished in: Cancers (2023)
Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.
Keyphrases
- endometrial cancer
- contrast enhanced
- machine learning
- lymph node metastasis
- free survival
- computed tomography
- magnetic resonance imaging
- patients undergoing
- image quality
- magnetic resonance
- artificial intelligence
- human health
- big data
- squamous cell carcinoma
- positron emission tomography
- type diabetes
- deep learning
- climate change
- glycemic control
- clinical evaluation