Leena R David, Wiam Elshami, Olanrewaju Lawal, Nitika Panakkal, Visakh T, Mohamed Abuzaid, Andrew England, Mark McEntee, T Somasundaram, Suresh Sukumar, Sneha Ravichandran, Meliz Yuvali
Evolving radiographic practice: Identifying possible skill requirements for future radiographers practicing in the United Arab Emirates (UAE)
Journal of medical imaging and radiation sciences
study aimed assess tumor regression grade trg patients rectal cancer neoadjuvant chemoradiotherapy ncrt machine learningbased radiomics analysis using baseline t2weighted magnetic resonance mr images total 148 patients locally advanced rectal cancert24 n underwent mr imaging baseline chemoradiotherapy january 2010 may 2021 included region interest tumor mass drawn radiologist oblique axial t2weighted images main features selected using principal component analysis dimension reduction among 116 radiomics three clinical features among eight learning models used prediction model development model showing best performance selected treatment responses classified either good poor based mrassessed trg mrtrg pathologic trg ptrg model performance assessed using area receiver operating curve auroc classify response group approximately 49 patients good response gr group based mrtrg 73148 269 based ptrg 28104 aucs clinical data radiomics models combined radiomics clinical data model predicting mrtrg 080 95 confidence interval ci 073 087 074 95 ci 066 081 07595 ci 068 082 predicting ptrg 062 95 ci 052 071 074 95 ci 065 082 079 95 ci 071 087 radiomics combined clinical data model using baseline t2weighted mr images demonstrated feasible diagnostic performance predicting mrassessed pathologic treatment response patients rectal cancer ncrt