Does the hepatologist still need to rely on aminotransferases in clinical practice? A reappraisal of the role of a classic biomarker in the diagnosis and clinical management of chronic liver diseases DOI Creative Commons
Patrizia Burra, Calogero Cammà,

Pietro Invernizzi

и другие.

Annals of Hepatology, Год журнала: 2025, Номер unknown, С. 101900 - 101900

Опубликована: Март 1, 2025

Aminotransferases, particularly alanine aminotransferase (ALT), are commonly used in the detection, diagnosis, and management of chronic liver diseases. ALT, a sensitive cost-effective marker injury, remains pivotal predicting clinical outcomes guiding interventions several diseases including metabolic dysfunction-associated steatotic disease, viral hepatitis. This study aims to explore evolving role ALT as biomarker. A comprehensive review evidence was conducted, focusing on studies evaluating thresholds, diagnostic accuracy, integration with non-invasive assessment tools. Special emphasis given novel approaches, artificial intelligence-driven algorithms. Expert opinions from hepatology care perspectives were considered assess practical implications refining ALT-based strategies. levels influenced by diverse factors such age, gender, risks, challenging use specific thresholds biomarker disease prognosis. Emerging suggests redefining ranges enhance sensitivity accuracy detecting abnormalities. The advanced tools, intelligence, patient assessments can optimize early detection thus reducing underdiagnosis, asymptomatic or vulnerable populations. work highlights urgency tailor approaches primary specialized care, ensuring timely targeted intervention effectively address global burden

Язык: Английский

Artificial intelligence in histopathology: enhancing cancer research and clinical oncology DOI
Artem Shmatko, Narmin Ghaffari Laleh, Moritz Gerstung

и другие.

Nature Cancer, Год журнала: 2022, Номер 3(9), С. 1026 - 1038

Опубликована: Сен. 22, 2022

Язык: Английский

Процитировано

263
A researcher’s guide to preclinical mouse NASH models DOI
Suchira Gallage, Jose Efren Barragan Avila, Pierluigi Ramadori

и другие.

Nature Metabolism, Год журнала: 2022, Номер 4(12), С. 1632 - 1649

Опубликована: Дек. 20, 2022

Язык: Английский

Процитировано

117
Artificial intelligence to identify genetic alterations in conventional histopathology DOI Creative Commons
Didem Çifçi, Sebastian Foersch, Jakob Nikolas Kather

и другие.

The Journal of Pathology, Год журнала: 2022, Номер 257(4), С. 430 - 444

Опубликована: Март 28, 2022

Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many types, a limited set tests is currently part standard diagnostic workflows. However, universal testing for all alterations, especially rare ones, by cost and availability assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict probability specific genetic directly from conventional hematoxylin eosin (H&E) tissue slides. Although these are less accurate than gold (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools reduce workload analyses. this systematic literature review, we summarize state art predicting H&E using AI. We found AI perform reasonably well across although few algorithms been broadly validated. addition, FGFR, IDH, PIK3CA, BRAF, TP53, DNA repair pathways predictable while other rarely investigated were only poorly predictable. Finally, discuss next steps implementation AI-based surrogate © 2022 The Authors. Journal Pathology published John Wiley & Sons Ltd behalf Pathological Society Great Britain Ireland.

Язык: Английский

Процитировано

90
Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023 DOI Creative Commons
Harold Bays, Angela Fitch, Suzanne Cuda

и другие.

Obesity Pillars, Год журнала: 2023, Номер 6, С. 100065 - 100065

Опубликована: Апрель 20, 2023

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides clinicians an overview of Artificial Intelligence, focused on the management patients with obesity.

Язык: Английский

Процитировано

71
Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma DOI Creative Commons
Zhiyuan Bo,

Jiatao Song,

Qikuan He

и другие.

Computers in Biology and Medicine, Год журнала: 2024, Номер 173, С. 108337 - 108337

Опубликована: Март 24, 2024

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In past decade, artificial intelligence (AI) technology has undergone rapid development in field clinical medicine, bringing advantages efficient data processing accurate model construction. Promisingly, AI-based radiomics played increasingly important role decision-making HCC patients, providing new technical guarantees for prediction, diagnosis, prognostication. this review, we evaluated current landscape AI management HCC, including its individual treatment, survival Furthermore, discussed remaining challenges future perspectives regarding application HCC.

Язык: Английский

Процитировано

20
Advances in the understanding and therapeutic manipulation of cancer immune responsiveness: a Society for Immunotherapy of Cancer (SITC) review DOI Creative Commons

Alessandra Cesano,

Ryan C. Augustin, Luigi Barrea

и другие.

Journal for ImmunoTherapy of Cancer, Год журнала: 2025, Номер 13(1), С. e008876 - e008876

Опубликована: Янв. 1, 2025

Cancer immunotherapy-including immune checkpoint inhibition (ICI) and adoptive cell therapy (ACT)-has become a standard, potentially curative treatment for subset of advanced solid liquid tumors. However, most patients with cancer do not benefit from the rapidly evolving improvements in understanding principal mechanisms determining responsiveness (CIR); including patient-specific genetically determined acquired factors, as well intrinsic biology. Though CIR is multifactorial, fundamental concepts are emerging that should be considered design novel therapeutic strategies related clinical studies. Recent advancements approaches to address limitations current treatments discussed here, specific focus on ICI ACT.

Язык: Английский

Процитировано

3
Adversarial attacks and adversarial robustness in computational pathology DOI Creative Commons
Narmin Ghaffari Laleh, Daniel Truhn, Gregory Patrick Veldhuizen

и другие.

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Сен. 29, 2022

Abstract Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential quantify mitigate this risk before widespread clinical use. Here, we show that convolutional neural networks (CNNs) highly susceptible white- black-box attacks clinically relevant weakly-supervised classification tasks. Adversarially robust training dual batch normalization (DBN) possible mitigation strategies but require precise knowledge of the type attack used inference. We demonstrate vision transformers (ViTs) perform equally well compared CNNs at baseline, orders magnitude more At a mechanistic level, associated with latent representation categories ViTs CNNs. Our results line previous theoretical studies provide empirical evidence learners computational pathology. This implies large-scale rollout models should rely on rather than CNN-based classifiers inherent protection against perturbation input data, especially

Язык: Английский

Процитировано

66
Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions DOI
Rocı́o I.R. Macı́as, Vincenzo Cardinale, Timothy J. Kendall

и другие.

Gut, Год журнала: 2022, Номер unknown, С. gutjnl - 327099

Опубликована: Май 17, 2022

Cholangiocarcinoma (CCA) is a malignant tumour arising from the biliary system. In Europe, this frequently presents as sporadic cancer in patients without defined risk factors and usually diagnosed at advanced stages with consequent poor prognosis. Therefore, identification of biomarkers represents an utmost need for CCA. Numerous studies proposed wide spectrum tissue molecular levels. With present paper, multidisciplinary group experts within European Network Study discusses clinical role provides selection based on their current relevance potential applications framework Recent advances are by dividing diagnosis, prognosis therapy response. Limitations also identified, together specific promising areas (ie, artificial intelligence, patient-derived organoids, targeted therapy) where research should be focused to develop future biomarkers.

Язык: Английский

Процитировано

53
MRI‐Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges DOI Open Access
Tianyi Xia, Ben Y. Zhao, Binrong Li

и другие.

Journal of Magnetic Resonance Imaging, Год журнала: 2023, Номер 59(3), С. 767 - 783

Опубликована: Авг. 30, 2023

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and third leading cause of cancer‐related death worldwide. HCC exhibits strong inter‐tumor heterogeneity, with different biological characteristics closely associated prognosis. In addition, patients often distribute at stages require diverse treatment options each stage. Due to variability in tumor sensitivity therapies, determining optimal approach can be challenging for clinicians prior treatment. Artificial intelligence (AI) technology, including radiomics deep learning approaches, has emerged as a unique opportunity improve spectrum clinical care by predicting prognosis medical imaging field. The utilizes handcrafted features derived from specific mathematical formulas construct various machine‐learning models applications. terms approach, convolutional neural network are developed achieve high classification performance based on automatic feature extraction images. Magnetic resonance offers advantage superior tissue resolution functional information. This comprehensive evaluation plays vital role accurate assessment effective planning patients. Recent studies have applied approaches develop AI‐enabled accuracy prognosis, such microvascular invasion recurrence. Although demonstrated promising potential prediction performance, one biggest challenges, interpretability, hindered their implementation practice. future, continued research needed interpretability models, aspects domain knowledge, novel algorithms, multi‐dimension data sources. Overcoming these challenges would allow significantly impact provided patients, ultimately deployment use. Level Evidence 5 Technical Efficacy Stage 2

Язык: Английский

Процитировано

42
Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions DOI Open Access
Vlad Ratziu, Marcus Hompesch,

Mathieu Petitjean

и другие.

Journal of Hepatology, Год журнала: 2023, Номер 80(2), С. 335 - 351

Опубликована: Окт. 24, 2023

Язык: Английский

Процитировано

39