Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study.
Natally HorvatHarini VeeraraghavanCaio S R NahasDavid D B BatesFelipe R FerreiraJunting ZhengMarinela CapanuJames L FuquaMaria Clara FernandesRamon E SosaVetri Sudar JayaprakasamGiovanni G CerriSergio C NahasIva PetkovskaPublished in: Abdominal radiology (New York) (2022)
We developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC.
Keyphrases
- artificial intelligence
- rectal cancer
- magnetic resonance imaging
- locally advanced
- machine learning
- big data
- deep learning
- contrast enhanced
- systematic review
- computed tomography
- lymph node
- squamous cell carcinoma
- lymph node metastasis
- radiation therapy
- combination therapy
- magnetic resonance
- replacement therapy
- diffusion weighted imaging