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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Vydané v:	Abdominal imaging Ročník 47; číslo 8; s. 2770 - 2782
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.08.2022 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
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