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Staging of esophageal cancer using PET/MRI: a systematic review with head-to-head comparison.
BMC Med Imaging
January 2025
Department of Pathology, Dr. Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Purpose: To evaluate the staging performance of positron emission tomography/magnetic resonance imaging (PET/MRI) for confirmed esophageal cancer based on the TNM classification system as well as compare it to other alternative modalities (e.g., endoscopic ultrasonography (EUS), computed tomography (CT), MRI, and PET/CT) in a full head-to-head manner.
View Article and Find Full Text PDFDecoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.
Nat Cancer
January 2025
Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany.
Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-derived (AID) markers for clinical decision support. We used xAI to decode the outcome of 15,726 patients across 38 solid cancer entities based on 350 markers, including clinical records, image-derived body compositions, and mutational tumor profiles.
View Article and Find Full Text PDFToward a biological definition of neuronal and glial synucleinopathies.
Nat Med
January 2025
Department of Neurology & Neurological Sciences, Stanford Movement Disorders Center, Stanford University, Stanford, CA, USA.
Cerebral accumulation of alpha-synuclein (αSyn) aggregates is the hallmark event in a group of neurodegenerative diseases-collectively called synucleinopathies-which include Parkinson's disease, dementia with Lewy bodies and multiple system atrophy. Currently, these are diagnosed by their clinical symptoms and definitively confirmed postmortem by the presence of αSyn deposits in the brain. Here, we summarize the drawbacks of the current clinical definition of synucleinopathies and outline the rationale for moving toward an earlier, biology-anchored definition of these disorders, with or without the presence of clinical symptoms.
View Article and Find Full Text PDFAccurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI) advances, particularly convolutional neural networks (CNNs), have significantly improved diagnostic capabilities and accuracy by enabling the automated analysis of medical images.
View Article and Find Full Text PDFA deep learning based model for diabetic retinopathy grading.
Sci Rep
January 2025
Department of Networks and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan.
Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy.
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