Tumor biology and immune infiltration define primary liver cancer subsets linked to overall survival after immunotherapy DOI Creative Commons

Anuradha Budhu,

Erica C. Pehrsson, Aiwu Ruth He

et al.

Cell Reports Medicine, Journal Year: 2023, Volume and Issue: 4(6), P. 101052 - 101052

Published: May 23, 2023

Primary liver cancer is a rising cause of deaths in the US. Although immunotherapy with immune checkpoint inhibitors induces potent response subset patients, rates vary among individuals. Predicting which patients will respond to great interest field. In retrospective arm National Cancer Institute Cancers Liver: Accelerating Research Immunotherapy by Transdisciplinary Network (NCI-CLARITY) study, we use archived formalin-fixed, paraffin-embedded samples profile transcriptome and genomic alterations 86 hepatocellular carcinoma cholangiocarcinoma prior following inhibitor treatment. Using supervised unsupervised approaches, identify stable molecular subtypes linked overall survival distinguished two axes aggressive tumor biology microenvironmental features. Moreover, responses treatment differ between subtypes. Thus, heterogeneous may be stratified status indicative inhibitors.

Language: Английский

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

et al.

Nature Cancer, Journal Year: 2022, Volume and Issue: 3(9), P. 1026 - 1038

Published: Sept. 22, 2022

Language: Английский

Citations

251
A researcher’s guide to preclinical mouse NASH models DOI
Suchira Gallage, Jose Efren Barragan Avila, Pierluigi Ramadori

et al.

Nature Metabolism, Journal Year: 2022, Volume and Issue: 4(12), P. 1632 - 1649

Published: Dec. 20, 2022

Language: Английский

Citations

107
Artificial intelligence to identify genetic alterations in conventional histopathology DOI Creative Commons
Didem Çifçi, Sebastian Foersch, Jakob Nikolas Kather

et al.

The Journal of Pathology, Journal Year: 2022, Volume and Issue: 257(4), P. 430 - 444

Published: March 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.

Language: Английский

Citations

88
Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023 DOI Creative Commons
Harold Bays, Angela Fitch, Suzanne Cuda

et al.

Obesity Pillars, Journal Year: 2023, Volume and Issue: 6, P. 100065 - 100065

Published: April 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.

Language: Английский

Citations

69
Emerging role of molecular diagnosis and personalized therapy for hepatocellular carcinoma DOI Creative Commons
Ming-Da Wang,

Yong‐Kang Diao,

Lan‐Qing Yao

et al.

iLiver, Journal Year: 2024, Volume and Issue: 3(1), P. 100083 - 100083

Published: Feb. 9, 2024

Hepatocellular carcinoma (HCC) is a prevalent malignancy worldwide, ranking as the sixth most common and third leading cause of cancer-related mortality. Late diagnosis, limited management options, its complex etiology contribute to poor prognosis high mortality rates. Recent advances in understanding molecular mechanisms HCC innovations high-throughput sequencing technologies have led development diagnostics personalized therapies for this challenging malignancy. This review provides comprehensive overview research on diagnosis individualized treatment HCC. We highlight key potential future directions discuss application next-generation identify characterize genetic epigenetic alterations patients. These may aid selection targeted therapies, prediction response, monitoring disease progression. Furthermore, we explore role liquid biopsy prediction, monitoring, focusing circulating tumor cells, DNA, extracellular vesicles. also evolving landscape therapy HCC, including against oncogenic signaling pathways, immune checkpoint inhibitors, tumor-agnostic innovative cell-based therapies. challenges opportunities that lie ahead quest improve patient outcomes through integration precision emphasize need multi-interdisciplinary collaboration, refinement predictive prognostic biomarkers, more effective combination strategies new area medicine.

Language: Английский

Citations

16
Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma DOI Creative Commons
Zhiyuan Bo,

Jiatao Song,

Qikuan He

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 173, P. 108337 - 108337

Published: March 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.

Language: Английский

Citations

16
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

et al.

Journal for ImmunoTherapy of Cancer, Journal Year: 2025, Volume and Issue: 13(1), P. e008876 - e008876

Published: Jan. 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.

Language: Английский

Citations

2
Development and validation of machine learning models for MASLD: based on multiple potential screening indicators DOI Creative Commons
Hao Chen, Jingjing Zhang,

Xueqin Chen

et al.

Frontiers in Endocrinology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 21, 2025

Background Multifaceted factors play a crucial role in the prevention and treatment of metabolic dysfunction-associated steatotic liver disease (MASLD). This study aimed to utilize multifaceted indicators construct MASLD risk prediction machine learning models explore core within these models. Methods were constructed based on seven algorithms using all variables, insulin-related demographic characteristics other indicators, respectively. Subsequently, partial dependence plot(PDP) method SHapley Additive exPlanations (SHAP) utilized explain roles important variables model filter out optimal for constructing model. Results Ranking feature importance Random Forest (RF) eXtreme Gradient Boosting (XGBoost) found that both homeostasis assessment insulin resistance (HOMA-IR) triglyceride glucose-waist circumference (TyG-WC) first second most variables. The with top 10 was superior previous PDP SHAP methods further screen best (including HOMA-IR, TyG-WC, age, aspartate aminotransferase (AST), ethnicity) model, mean area under curve value 0.960. Conclusions HOMA-IR TyG-WC are predicting risk. Ultimately, our AST, ethnicity.

Language: Английский

Citations

2
Adversarial attacks and adversarial robustness in computational pathology DOI Creative Commons
Narmin Ghaffari Laleh, Daniel Truhn, Gregory P. Veldhuizen

et al.

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Sept. 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

Language: Английский

Citations

62
Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions DOI
Rocı́o I.R. Macı́as, Vincenzo Cardinale, Timothy J. Kendall

et al.

Gut, Journal Year: 2022, Volume and Issue: unknown, P. gutjnl - 327099

Published: May 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.

Language: Английский

Citations

52