Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer.

Giuseppe FilittoFrancesca Coppola Nico Curti Enrico GiampieriDaniele Dall'OlioAlessandra MerlottiArrigo CattabrigaMaria Adriana CocozzaMakoto Taninokuchi TomassoniDaniel RemondiniLuisa PierottiLidia Strigari Dajana CuicchiAlessandra GuidoKarim RihawiAntonietta D'ErricoFrancesca Di FabioGilberto PoggioliAlessio Giuseppe MorgantiLuigi Ricciardiello Rita GolfieriGastone Castellani

Published in: Cancers (2022)

The results obtained regarding TRG prediction using the proposed fully automated pipeline prove its possible usage as a viable decision support system for radiologists in clinical practice.

Keyphrases

rectal cancer
locally advanced
end stage renal disease
clinical practice
machine learning
deep learning
high throughput
ejection fraction
newly diagnosed
chronic kidney disease
squamous cell carcinoma
artificial intelligence
peritoneal dialysis
lymph node
patient reported outcomes