A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.
Elizabeth Jane SuttonNatsuko OnishiDuc A FehrBrittany Z DashevskyMeredith SadinskiKatja PinkerDanny F MartinezEdi BrogiLior BraunsteinPedram RazaviMahmoud El-TamerVirgilio SacchiniJoseph O DeasyElizabeth A MorrisHarini VeeraraghavanPublished in: Breast cancer research : BCR (2020)
This study validated a radiomics classifier combining radiomics with molecular subtypes that accurately classifies pCR on MRI post-NAC.
Keyphrases
- neoadjuvant chemotherapy
- contrast enhanced
- locally advanced
- machine learning
- magnetic resonance imaging
- lymph node
- sentinel lymph node
- lymph node metastasis
- diffusion weighted imaging
- magnetic resonance
- computed tomography
- rectal cancer
- artificial intelligence
- squamous cell carcinoma
- radiation therapy
- single molecule
- young adults
- early stage
- breast cancer risk