Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy - a review DOI Creative Commons

Marek Zdrenka,

Adam Kowalewski, Navid Ahmadi

et al.

Biomolecules and Biomedicine, Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 29, 2023

Anti-programmed cell death ligand 1 (Anti-PD-L1) immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available form of "kits", facilitating testing for expression. These kits comprise antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and size tissue specimens assessed, can lead to status misclassification, potentially hindering initiation therapy. Therefore, development more accurate predictive biomarkers distinguish between responders non-responders prior anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods detection, deepening our understanding impact additional biomarkers. In article, we review potential solutions improve accuracy assessment order precisely anticipate patients' responses therapy, monitor disease progression predict clinical outcomes.

Language: Английский

A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics DOI Open Access
Hong-Yu Zhou,

Yizhou Yu,

Chengdi Wang

et al.

Nature Biomedical Engineering, Journal Year: 2023, Volume and Issue: 7(6), P. 743 - 755

Published: June 12, 2023

Language: Английский

Citations

133
Radiomics and artificial intelligence for precision medicine in lung cancer treatment DOI Creative Commons
Mitchell Chen, Susan J. Copley, Patrizia Viola

et al.

Seminars in Cancer Biology, Journal Year: 2023, Volume and Issue: 93, P. 97 - 113

Published: May 19, 2023

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at mesoscopic scale, phenotypic characteristics that are generally indiscernible to human eye but can be captured non-invasively on medical imaging as radiomic features, which form a high dimensional data space amenable machine learning. Radiomic features harnessed and used in an artificial intelligence paradigm risk stratify patients, predict for histological molecular findings, clinical outcome measures, thereby facilitating precision medicine improving patient care. Compared tissue sampling-driven approaches, radiomics-based methods superior being non-invasive, reproducible, cheaper, less susceptible intra-tumoral heterogeneity. This review focuses application radiomics, combined with intelligence, delivering lung treatment, discussion centered pioneering groundbreaking works, future research directions area.

Language: Английский

Citations

83
Advances in artificial intelligence to predict cancer immunotherapy efficacy DOI Creative Commons
Jindong Xie, Xiyuan Luo, Xinpei Deng

et al.

Frontiers in Immunology, Journal Year: 2023, Volume and Issue: 13

Published: Jan. 4, 2023

Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accurately screen individuals for immunotherapy sensitivity and forecast its efficacy. With application artificial intelligence (AI) in medical field recent years, an increasing number studies have indicated that efficacy can be better anticipated with help AI technology reach precision medicine. This article focuses on current prediction models based information from histopathological slides, imaging-omics, genomics, proteomics, reviews their research progress applications. Furthermore, we also discuss existing challenges encountered by as well future directions need improved, provide a point reference early implementation AI-assisted diagnosis treatment systems future.

Language: Английский

Citations

58
Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review DOI Open Access

Diny Dixon,

Hina Sattar,

Natalia Moros

et al.

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: May 9, 2024

This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, decision-making, is leveraged to predict optimize plans, enhance rates through analysis vast datasets, including electronic health records (EHRs), imaging, genetic data. The utilization machine learning (ML) deep (DL) techniques enables personalized medicine by facilitating early detection conditions, precision drug discovery, tailoring individual profiles. Ethical considerations, data privacy, bias, accountability, emerge vital responsible implementation AI healthcare. findings underscore potential revolutionizing clinical decision-making healthcare delivery, emphasizing necessity ethical guidelines continuous model validation ensure its safe effective use augmenting human judgment medical practice.

Language: Английский

Citations

46
A review of cancer data fusion methods based on deep learning DOI
Yuxin Zhao, Xiaobo Li, Changjun Zhou

et al.

Information Fusion, Journal Year: 2024, Volume and Issue: 108, P. 102361 - 102361

Published: March 20, 2024

Language: Английский

Citations

19
Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer DOI

Xiaomeng Yin,

Hu Liao,

Yun Hong

et al.

Seminars in Cancer Biology, Journal Year: 2022, Volume and Issue: 86, P. 146 - 159

Published: Aug. 11, 2022

Language: Английский

Citations

64
Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology DOI Creative Commons
Jun Shao, Jiechao Ma, Qin Zhang

et al.

Seminars in Cancer Biology, Journal Year: 2023, Volume and Issue: 91, P. 1 - 15

Published: Feb. 20, 2023

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation or visual inspection histopathology slides experienced pathologists in a clinical context. In past decade, advances artificial intelligence (AI) technologies have demonstrated remarkable potential assisting physicians with accurate diagnosis oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance stratification patients context precision therapy. Given that mutation is unaffordable time-consuming considerable number patients, predicting gene mutations based routine radiological scans whole-slide images tissue AI-based methods has become hot issue actual practice. this review, we synthesized general framework integration (MMI) intelligent diagnostics beyond standard techniques. Then summarized emerging applications prediction mutational profiles common cancers (lung, brain, breast, other tumor types) pertaining radiology histology imaging. Furthermore, concluded there truly exist multiple challenges way its real-world application medical field, including curation, feature fusion, model interpretability, practice regulations. Despite challenges, still prospect implementation highly decision-support tool aid oncologists future management.

Language: Английский

Citations

38
Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy DOI

Zhe Zhang,

Xiawei Wei

Seminars in Cancer Biology, Journal Year: 2023, Volume and Issue: 90, P. 57 - 72

Published: Feb. 14, 2023

Language: Английский

Citations

25
Concepts and applications of digital twins in healthcare and medicine DOI Creative Commons
Kang Zhang, Hong-Yu Zhou, Daniel T. Baptista‐Hon

et al.

Patterns, Journal Year: 2024, Volume and Issue: 5(8), P. 101028 - 101028

Published: Aug. 1, 2024

The digital twin (DT) is a concept widely used in industry to create replicas of physical objects or systems. dynamic, bi-directional link between the entity and its counterpart enables real-time update entity. It can predict perturbations related object's function. obvious applications DTs healthcare medicine are extremely attractive prospects that have potential revolutionize patient diagnosis treatment. However, challenges including technical obstacles, biological heterogeneity, ethical considerations make it difficult achieve desired goal. Advances multi-modal deep learning methods, embodied AI agents, metaverse may mitigate some difficulties. Here, we discuss basic concepts underlying DTs, requirements for implementing medicine, their current uses. We also provide our perspective on five hallmarks DT system advance research this field.

Language: Английский

Citations

16
Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC DOI Open Access
Oraianthi Fiste, Ioannis Gkiozos, Andriani Charpidou

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(4), P. 831 - 831

Published: Feb. 19, 2024

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite public health interventions including tobacco-free campaigns, screening early detection methods, recent therapeutic advances, ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains unmet need for robust standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents computer-based science concerned with large datasets complex problem-solving. Its concept brought a paradigm shift oncology considering its immense potential improved diagnosis, treatment guidance, prognosis. In this review, we present current state AI-driven applications management, particular focus radiomics pathomics, critically discuss both existing limitations future directions field. The thoracic community should not be discouraged by likely long road AI implementation into daily practice, as transformative impact personalized approaches undeniable.

Language: Английский

Citations

14