Liquid biopsy for diagnostic and prognostic evaluation of melanoma

Frontiers in Cell and Developmental Biology, Journal Year: 2024, Volume and Issue: 12

Published: Aug. 2, 2024

Melanoma is the most aggressive form of skin cancer, and majority cases are associated with chronic or intermittent sun exposure. The incidence melanoma has grown exponentially over last 50 years, especially in populations fairer skin, at lower altitudes geriatric populations. gold standard for diagnosis performing an excisional biopsy full resection incisional tissue biopsy. However, due to their invasiveness, conventional techniques not suitable continuous disease monitoring. Utilization liquid represent substantial promise early detection melanoma. Through this procedure, tumor-specific components shed into circulation can be analyzed only but also treatment selection risk assessment. Additionally, significantly less invasive than offers a novel way monitor response relapse, predicting metastasis.

Language: Английский

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Applications of Multimodal Artificial Intelligence in Non-Hodgkin Lymphoma B Cells

Biomedicines, Journal Year: 2024, Volume and Issue: 12(8), P. 1753 - 1753

Published: Aug. 5, 2024

Given advancements in large-scale data and AI, integrating multimodal artificial intelligence into cancer research can enhance our understanding of tumor behavior by simultaneously processing diverse biomedical types. In this review, we explore the potential AI comprehending B-cell non-Hodgkin lymphomas (B-NHLs). (B-NHLs) represent a particular challenge oncology due to heterogeneity intricate ecosystem which tumors develop. These complexities complicate diagnosis, prognosis, therapy response, emphasizing need use sophisticated approaches personalized treatment strategies for better patient outcomes. Therefore, be leveraged synthesize critical information from available such as clinical record, imaging, pathology omics data, picture whole tumor. first define various types modalities, frameworks, several applications precision medicine. Then, provide examples its usage B-NHLs, analyzing complexity ecosystem, identifying immune biomarkers, optimizing strategy, applications. Lastly, address limitations future directions highlighting overcome these challenges practice application healthcare.

Language: Английский

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Liquid biopsy approaches and immunotherapy in colorectal cancer for precision medicine: Are we there yet?

Frontiers in Oncology, Journal Year: 2023, Volume and Issue: 12

Published: Jan. 6, 2023

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths globally, with nearly half patients detected in advanced stages. This due to fact that symptoms associated CRC often do not appear until has reached an stage. suggests a slow progression, making it curable and preventive if its early Therefore, there urgent clinical need improve detection personalize therapy for this cancer. Recently, liquid biopsy as non-invasive or nominally invasive approach attracted considerable interest real-time disease monitoring capability through repeated sample analysis. Several studies have revealed potential application real setting using circulating RNA/miRNA, tumor cells (CTCs), exosomes, etc. However, Liquid still remains challenge since are currently no promising results high specificity might be employed optimal circulatory biomarkers. review, we conferred plausible role less explored components like mitochondrial DNA (mtDNA), organoid model CTCs, cancer-associated fibroblasts (cCAFs); which may allow researchers develop improved strategies unravel unfulfilled requirements patients. Moreover, also discussed immunotherapy approaches prognosis MSI (Microsatellite Instability) neoantigens immune microenvironment (TME) detail.

Language: Английский

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Applications of AI in Cancer Detection — A Review of the Specific Ways in which AI Is Being Used to Detect and Diagnose Various Types of Cancer

Published: Jan. 3, 2025

The mixing of superior deep learning strategies has profoundly impacted the sector sickness identification, promising sizable advancements in diagnostic accuracy and performance. This paper explores utilization multi-scale convolutional layers, interest mechanisms, switch learning, generative adversarial networks (GANs), self-supervised healthcare domain. These techniques collectively beautify capability neural (CNNs) to discover diagnose diseases from medical pix with extraordinary precision. Multi-scale layers allow models capture features at numerous scales, improving sensitivity specificity disease detection, mainly situations like most cancers. Attention mechanisms similarly refine this process by allowing focus on applicable components an picture, mirroring meticulous examination professionals. Transfer leveraging training fashions, extensively reduces reliance tremendous, categorized datasets, thereby expediting development enhancing version accuracy. approach shown outstanding success throughout distinctive imaging modalities, X-rays CT scans, adaptability robustness models. GANs contribute via producing artificial records augment schooling addressing challenge limited data availability model performance, specifically uncommon scenarios. Self-supervised which trains fashions unlabeled proxy duties, demonstrated comparable performance absolutely supervised while requiring fewer samples, therefore lowering need for luxurious time-eating annotation. Innovations those areas have not only improved technical identification but also opened new avenues his or her application. Future research should explore multimodal mixes various assets, including genomic information digital health data, imparting a more complete perspective. implementation federated guarantees privacy decentralized assets. Explainable AI (XAI) enhance interpretability, fostering extra consider popularity amongst Moreover, integration wearable devices continuous fitness tracking improvement real-time adaptive hold tremendous promise revolutionizing patient care control. comprehensive method methodologies disorder underscores transformative potential healthcare. With aid modern-day demanding exploring progressive answers, we can pave way greater accuracy, efficiency, personalized systems, end results advancing current exercise.

Language: Английский

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Artificial Intelligence for Early Breast Cancer Detection

AI in Precision Oncology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Language: Английский

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NETest® 2.0—A decade of innovation in neuroendocrine tumor diagnostics

Journal of Neuroendocrinology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 13, 2025

Abstract Gastroenteropancreatic neuroendocrine neoplasms (GEP‐NENs) are challenging to diagnose and manage. Because there is a critical need for reliable biomarker, we previously developed the NETest, liquid biopsy test that quantifies expression of 51 tumor (NET)‐specific genes in blood using real‐time PCR (NETest 1.0). In this study, have leveraged our well‐established laboratory approach (blood collection, RNA isolation, qPCR) with contemporary supervised machine learning methods expanded training testing sets improve discrimination calibration NETest algorithm 2.0). qPCR measurements RNA‐stabilized blood‐derived gene NET markers were used train two classifiers. The first classifier trained on 78 Controls 162 NETs, distinguishing NETs from controls; second, 134 stable disease samples, 61 progressive differentiated disease. all cases, 80% data was retained model training, while remaining 20% performance evaluation. predictive AI system assessed sensitivity, specificity, Area under Received Operating Characteristic curves (AUROC). highest validation independent sets. Validation Cohort #I consisted 277 patients 186 healthy controls United States, Latin America, Europe, Africa Asia, #II comprised 291 European Swiss Registry. A specificity cohort 147 gastrointestinal, pancreatic lung malignancies (non‐NETs) also evaluated. 2.0 Algorithm #1 (Random Forest/gene normalized ATG4B ) achieved an AUROC 0.91 (Validation #I), sensitivity 95% 81%. #II, 92% image‐positive detected. differentiating other 0.95; 90%. #2 ALG9 demonstrated 0.81 0.82 disease, specificities 81% 82%, respectively. Model not affected by gender, ethnicity or age. Substantial improvements both algorithms identified head‐to‐head comparisons 1.0 (diagnostic: p = 1.73 × 10 −9 ; prognostic: 1.02 −10 ). exhibited improved diagnostic prognostic capabilities over 1.0. assay malignancies. tool geographically diverse cohorts highlights their potential widespread clinical use.

Language: Английский

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Artificial Intelligence in Ovarian Cancer: A Systematic Review and Meta-Analysis of Predictive AI Models in Genomics, Radiomics, and Immunotherapy

AI, Journal Year: 2025, Volume and Issue: 6(4), P. 84 - 84

Published: April 18, 2025

Background/Objectives: Artificial intelligence (AI) is increasingly influencing oncological research by enabling precision medicine in ovarian cancer through enhanced prediction of therapy response and patient stratification. This systematic review meta-analysis was conducted to assess the performance AI-driven models across three key domains: genomics molecular profiling, radiomics-based imaging analysis, immunotherapy response. Methods: Relevant studies were identified a search multiple databases (2020–2025), adhering PRISMA guidelines. Results: Thirteen met inclusion criteria, involving over 10,000 patients encompassing diverse AI such as machine learning classifiers deep architectures. Pooled AUCs indicated strong predictive for genomics-based (0.78), (0.88), immunotherapy-based (0.77) models. Notably, radiogenomics-based integrating data yielded highest accuracy (AUC = 0.975), highlighting potential multi-modal approaches. Heterogeneity risk bias assessed, evidence certainty graded. Conclusions: Overall, demonstrated promise predicting therapeutic outcomes cancer, with radiomics integrated radiogenomics emerging leading strategies. Future efforts should prioritize explainability, prospective multi-center validation, integration immune spatial transcriptomic support clinical implementation individualized treatment Unlike earlier reviews, this study synthesizes broader range applications provides pooled metrics It examines methodological soundness selected highlights current gaps opportunities translation, offering comprehensive forward-looking perspective field.

Language: Английский

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Driving Knowledge to Action: Building a Better Future With Artificial Intelligence–Enabled Multidisciplinary Oncology

American Society of Clinical Oncology Educational Book, Journal Year: 2025, Volume and Issue: 45(3)

Published: May 2, 2025

Artificial intelligence (AI) is transforming multidisciplinary oncology at an unprecedented pace, redefining how clinicians detect, classify, and treat cancer. From earlier more accurate diagnoses to personalized treatment planning, AI's impact evident across radiology, pathology, radiation oncology, medical oncology. By leveraging vast diverse data—including imaging, genomic, clinical, real-world evidence—AI algorithms can uncover complex patterns, accelerate drug discovery, help identify optimal regimens for each patient. However, realizing the full potential of AI also necessitates addressing concerns regarding data quality, algorithmic bias, explainability, privacy, regulatory oversight—especially in low- middle-income countries (LMICs), where disparities cancer care are particularly pronounced. This study provides a comprehensive overview reshaping care, reviews its benefits challenges, outlines ethical policy implications line with ASCO's 2025 theme, Driving Knowledge Action. We offer concrete calls action clinicians, researchers, industry stakeholders, policymakers ensure that AI-driven, patient-centric accessible, equitable, sustainable worldwide.

Language: Английский

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Tracing the history of clinical practice of liquid biopsy: a bibliometric analysis

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: May 13, 2025

Introduction Liquid biopsy holds great promise in clinical diagnosis, treatment, and prognostic monitoring. This study reveals the development of liquid practice through a comprehensive bibliometric analysis. Methods A total 40 years research literature this field was included from Web Science Core Collection (WoSCC), analyzing evolving trends practice. We constructed co-occurrence networks for countries, institutions, authors, keywords, integrating citation analysis journal impact metrics to provide view landscape biopsy. Results The results show significant growth trend biopsy, with China United States being leading contributors. Institutions such as Harvard University California system play central role global collaboration network. Cancers has become primary publication outlet field, while highly cited journals like Clinical Cancer Research crucial advancing its development. Keyword that progressively expanded into applications, personalized evaluation. Discussion Overall, technology applications continue mature, is expected an even greater early treatment evaluation, cancer other diseases.

Language: Английский

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The impact of liquid biopsy in breast cancer: Redefining the landscape of non-invasive precision oncology.

The Journal of Liquid Biopsy, Journal Year: 2025, Volume and Issue: unknown, P. 100299 - 100299

Published: May 1, 2025

Language: Английский

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