Heart transplantation is the ultimate treatment for end-stage heart failure, significantly extending life expectancy. However, long-term outcomes are limited by cardiac allograft vasculopathy (CAV) and allograft rejection (AR) despite advances in immunosuppression [1–3]. Endomyocardial biopsy (EMB) and invasive coronary angiography (ICA) remain the gold standard for evaluating AR and CAV, respectively [4]. However, both are invasive, carry procedural risks, and exhibit limitations in sensitivity…
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From AI breakthroughs to imaging trends, we serve up real-time radiology insights.
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Differentiating large-duct pancreatic ductal adenocarcinoma from malignant intraductal papillary mucinous neoplasm: MRI characteristics and diagnostic implications
Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic malignancy and is characterized by aggressive behavior and a poor prognosis, with a 5-year overall survival rate of < 10% [1,2]. Large-duct PDAC (LD-PDAC) is a recently recognized morphologic variant of PDAC and is characterized by dilated glandular structures (i.e., large cancer glands) that comprise more than 50% of the tumor [3–5].
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Concurrent driver mutations induce distinct tumour morphologies on radiological imaging
Most radiogenomic studies examine single driver mutations in isolation, despite evidence that co-occurring driver alterations are common and functionally interacting in cancer. Whether concurrent mutations produce distinct imaging phenotypes or simply average single-mutation effects remains unknown.
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Normal-resolution vs. super-resolution deep learning reconstruction for diagnosis of functionally significant coronary stenosis using cardiac CT
Super-resolution deep learning reconstruction (SR-DLR) has been developed to reduce image noise and enhance spatial resolution beyond that of normal-resolution deep learning reconstruction (NR-DLR).
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Beyond Pattern Recognition: A Gödelian Limit on Self-Validation in Radiological AI
Artificial intelligence (AI) systems increasingly match or exceed human performance in narrowly defined radiological tasks, accelerating their clinical deployment. Yet these advances obscure a fundamental limitation: AI systems cannot independently determine when their outputs are valid, clinically appropriate, or ethically actionable in real-world practice. We argue that this limitation is structural rather than temporary. Using Gödel’s incompleteness theorem as a clarifying metaphor—not a form…
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Non-invasive prediction of Ki-67 expression in gastric cancer using AI-based dual-energy CT: a multicenter study
To develop and validate a machine learning model based on quantitative parameters of dual-energy CT (DECT) virtual monoenergetic images (VMIs) for the noninvasive preoperative prediction of Ki-67 expression status in gastric cancer
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Multicancer Tests: Has the Reality Lived Up to the Hype?
Dr Jason L. Oke explores the latest data on multicancer tests and highlights what we actually know, and what we still don’t know. Medscape Oncology