Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial intelligence application in routine pathology: ChatGPT-4.0 and Gemini 2.5 pro performance in detection of MMR-deficiency in colorectal cancer
0
Zitationen
10
Autoren
2026
Jahr
Abstract
This drop in sensitivity was attributed to the models' reluctance to confirm true protein loss, often categorizing the result as "Indeterminate" or "Inadequate". Crucially, the diagnostic certainty of the AI models increased dramatically only when a clear Internal Positive Control (IPC) was visible in the image, establishing the IPC as an indispensable feature for robust dMMR identification and differentiating true biological loss from technical artifacts.
Ähnliche Arbeiten
New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)
2008 · 28.834 Zit.
TNM Classification of Malignant Tumours
1987 · 16.123 Zit.
A survey on deep learning in medical image analysis
2017 · 13.528 Zit.
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
2011 · 10.749 Zit.
The American Joint Committee on Cancer: the 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM
2010 · 9.104 Zit.