Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Deep Learning–based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports
48
Zitationen
14
Autoren
2022
Jahr
Abstract
The developed deep NLP model reached the performance level of medical students but not radiologists in curating oncologic outcomes from radiology FTOR.<b>Keywords:</b> Neural Networks, Computer Applications-Detection/Diagnosis, Oncology, Research Design, Staging, Tumor Response, Comparative Studies, Decision Analysis, Experimental Investigations, Observer Performance, Outcomes Analysis <i>Supplemental material is available for this article.</i> © RSNA, 2022.
Ähnliche Arbeiten
New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)
2008 · 28.817 Zit.
TNM Classification of Malignant Tumours
1987 · 16.123 Zit.
A survey on deep learning in medical image analysis
2017 · 13.514 Zit.
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
2011 · 10.745 Zit.
The American Joint Committee on Cancer: the 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM
2010 · 9.103 Zit.