Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study.
Miriam SantoroVladislav ZybinCamelia Alexandra CoadăGiulia MantovaniGiulia PaolaniMarco Di StanislaoCecilia ModolonStella Di CostanzoAndrei LeboviciGloria RavegniniAntonio De LeoMarco TeseiPietro PasquiniLuigi LovatoAlessio Giuseppe MorgantiMaria Abbondanza PantaleoPierandrea De IacoLidia StrigariAnna Myriam PerronePublished in: Cancers (2024)
CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.
Keyphrases
- machine learning
- computed tomography
- contrast enhanced
- deep learning
- human health
- magnetic resonance imaging
- positron emission tomography
- lymph node metastasis
- artificial intelligence
- convolutional neural network
- squamous cell carcinoma
- robot assisted
- big data
- magnetic resonance
- minimally invasive
- replacement therapy