Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study

Purpose To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation. Methods Consecutive patients with locally advanced rectal can...

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Veröffentlicht in:	Abdominal imaging Jg. 47; H. 8; S. 2770 - 2782
Hauptverfasser:	Horvat, Natally, Veeraraghavan, Harini, Nahas, Caio S. R., Bates, ‬David D. B., Ferreira, Felipe R., Zheng, Junting, Capanu, Marinela, Fuqua, James L., Fernandes, Maria Clara, Sosa, Ramon E., Jayaprakasam, Vetri Sudar, Cerri, Giovanni G., Nahas, Sergio C., Petkovska, Iva
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.08.2022 Springer Nature B.V
Schlagworte:	Artificial Intelligence Brazil Cancer Cancer therapies Chemoradiotherapy Classifiers Colorectal cancer Datasets Female Females Gastroenterology Hepatology Hollow Organ GI Humans Imaging Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Male Medicine Medicine & Public Health Middle Aged Patients Radiologists Radiology Radiomics Rectal Neoplasms - diagnostic imaging Rectal Neoplasms - pathology Rectal Neoplasms - therapy Rectum Retrospective Studies Texture Treatment Outcome Brazil Neoadjuvant therapy Watchful waiting Rectal cancer Magnetic resonance imaging Artificial intelligence
ISSN:	2366-0058, 2366-004X, 2366-0058
Online-Zugang:	Volltext
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