Glioblastoma and radiotherapy: A multicenter AI study for Survival Predictions from MRI (GRASP study).
Alysha ChelliahDavid A WoodLiane S CanasHaris ShuaibStuart CurrieKavi FataniaRussell FroodChris Rowland-HillStefanie ThustStephen J WastlingSean TenantCatherine McBainKaren FowerakerMatthew WilliamsQiquan WangAndrei RomanCarmen DragosMark MacDonaldNorlinah Mohamed IbrahimChristian A LinaresAhmed BassiounyAysha LuisThomas YoungJuliet BrockEdward ChandyErica BeaumontTai-Chung LamLiam WelshJoanne LewisRyan Koshy MathewEric KerfootRichard BrownDaniel BeasleyJennifer GlendenningLucy BrazilAngela SwampillaiKeyoumars AshkanSébastien OurselinMarc ModatThomas C BoothPublished in: Neuro-oncology (2024)
A deep learning model using MRI images after radiotherapy reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.
Keyphrases
- deep learning
- early stage
- clinical trial
- magnetic resonance imaging
- end stage renal disease
- artificial intelligence
- contrast enhanced
- ejection fraction
- chronic kidney disease
- locally advanced
- radiation induced
- convolutional neural network
- computed tomography
- randomized controlled trial
- magnetic resonance
- machine learning
- diffusion weighted imaging
- open label
- free survival
- combination therapy
- phase ii
- rectal cancer
- smoking cessation