Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma DOI Creative Commons
Xiangyun Li, Xiaoqun Yang, Xianwei Yang

et al.

Technology in Cancer Research & Treatment, Journal Year: 2024, Volume and Issue: 23

Published: Jan. 1, 2024

Clear cell renal carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, treatment strategies ccRCC. This study aims to create model predict OS ccRCC patients. In this study, data from patients the TCGA database were used as training set, clinical serving validation set. Pathological features extracted H&E-stained slides using PyRadiomics, was constructed non-negative matrix factorization (NMF) algorithm. The model's predictive performance assessed through Kaplan-Meier (KM) curves Cox regression analysis. Additionally, differential gene expression, ontology (GO) enrichment analysis, immune infiltration, mutational conducted investigate underlying biological mechanisms. A total of 368 patients, comprising two subtypes (Cluster 1 Cluster 2) successfully NMF KM revealed that 2 associated worse OS. 76 genes identified between subtypes, primarily involving extracellular organization structure. Immune-related genes, including CTLA4, CD80, TIGIT, expressed 2, while VHL PBRM1 along mutations PI3K-Akt, HIF-1, MAPK signaling pathways, exhibited mutation rates exceeding 40% both subtypes. learning-based effectively predicts differentiates critical roles immune-related CTLA4 pathways offer new insights further research on molecular mechanisms, diagnosis,

Language: Английский

Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review DOI Open Access
Filippo Lococo, Galal Ghaly, Marco Chiappetta

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(10), P. 1832 - 1832

Published: May 10, 2024

Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques deep imaging data analysis. This could potentially improve precision efficiency in facilitating decisions. Furthermore, there are suggesting potential application AI-based models predicting terms survival rates disease progression integrating clinical, molecular data. In present narrative review, we will analyze preliminary studies reporting on how predict responses to various modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, immunotherapy. There is robust evidence that also plays a crucial role likelihood tumor recurrence after surgery pattern failure, which significant implications for tailoring adjuvant treatments. The successful implementation assessment requires integration sources, molecular, Machine (ML) (DL) enable these generate predictions, allowing precise individualized approach patient care. However, challenges relating quality, interpretability, ability generalize need be addressed. Collaboration among clinicians, scientists, regulators critical responsible maximizing its benefits providing more Continued research, validation, collaboration essential fully exploit outcomes. Herein, have summarized state art applications prognosis, order provide readers large comprehensive overview this challenging issue.

Language: Английский

Citations

12
A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer DOI

Seyed Masoud HaghighiKian,

Ahmad Shirinzadeh-Dastgiri,

Mohammad Vakili-Ojarood

et al.

Indian Journal of Surgical Oncology, Journal Year: 2024, Volume and Issue: 16(1), P. 257 - 278

Published: Sept. 5, 2024

Language: Английский

Citations

7
Technology and Future of Multi-Cancer Early Detection DOI Creative Commons
Danny A. Milner, Jochen K. Lennerz

Life, Journal Year: 2024, Volume and Issue: 14(7), P. 833 - 833

Published: June 29, 2024

Cancer remains a significant global health challenge due to its high morbidity and mortality rates. Early detection is essential for improving patient outcomes, yet current diagnostic methods lack the sensitivity specificity needed identifying early-stage cancers. Here, we explore potential of multi-omics approaches, which integrate genomic, transcriptomic, proteomic, metabolomic data, enhance early cancer detection. We highlight challenges benefits data integration from these diverse sources discuss successful examples applications in other fields. By leveraging advanced technologies, can significantly improve diagnostics, leading better outcomes more personalized care. underscore transformative approaches revolutionizing need continued research clinical integration.

Language: Английский

Citations

6
Transitioning to a Personalized Approach in Molecularly Subtyped Small-Cell Lung Cancer (SCLC) DOI Open Access
Anna Grenda, Paweł Krawczyk,

Adrian Obara

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(8), P. 4208 - 4208

Published: April 10, 2024

Lung cancer has become a major public health concern, standing as the leading cause of cancer-related deaths worldwide. Among its subtypes, small-cell lung (SCLC) is characterized by aggressive and rapid growth, poor differentiation, neuroendocrine features. Typically, SCLC diagnosed at an advanced stage (extensive disease, ED-SCLC), with distant metastases, strongly associated tobacco smoking prognosis. Recent clinical trials, such CASPIAN IMpower133, have demonstrated promising outcomes incorporation immune checkpoint inhibitors in first-line chemotherapy, to prolonged progression-free survival overall patients ED-SCLC compared standard chemotherapy. Other studies emphasized potential for future development molecularly targeted therapies patients, including IGF-1R, DLL3, BCL-2, MYC, or PARP. The molecular subdivision based on transcriptomic immunohistochemical analyses represents significant advancement both diagnostic approaches patients. Specific pathways are activated within distinct transcriptome subtypes SCLC, offering personalized treatment strategies, immunotherapies. Such tailored hold promise significantly improving

Language: Английский

Citations

4
Diagnostic accuracy of MRI-based radiomic features for EGFR mutation status in non-small cell lung cancer patients with brain metastases: a meta-analysis DOI Creative Commons

Yu-qin Long,

Rong Zhao, Xianfeng Du

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 14

Published: Jan. 6, 2025

This meta-analysis aims to evaluate the diagnostic accuracy of magnetic resonance imaging (MRI) based radiomic features for predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases. We systematically searched PubMed, Embase, Cochrane Library, Web Science, Scopus, Wanfang, and China National Knowledge Infrastructure (CNKI) studies published up April 30, 2024. included those that utilized MRI-based detect EGFR mutations NSCLC Sensitivity, specificity, positive negative likelihood ratios (PLR, NLR), area under curve (AUC) were calculated accuracy. Quality assessment was performed using quality prognostic 2 (QUADAS-2) tool. Meta-analysis conducted random-effects models. A total 13 involving 2,348 included. The pooled sensitivity specificity detecting 0.86 (95% CI: 0.74-0.93) 0.83 0.72-0.91), respectively. PLR NLR as 5.14 (3.09, 8.55) 0.17 (0.10, 0.31), Substantial heterogeneity observed, I² values exceeding 50% all parameters. AUC receiver operating characteristic analysis 0.91 0.88-0.93). Subgroup indicated deep learning models Asian showed higher compared their respective counterparts. demonstrate a high potential accurately metastases, particularly when advanced techniques employed. However, variability performance across different underscores need standardized protocols enhance reproducibility clinical utility. https://www.crd.york.ac.uk/prospero/, identifier CRD42024544131.

Language: Английский

Citations

0
AI-Driven Personalized Healthcare Solutions DOI

C. V. Suresh Babu,

Yenumala Bhargavi,

P. Radha Krishna

et al.

Advances in healthcare information systems and administration book series, Journal Year: 2025, Volume and Issue: unknown, P. 241 - 276

Published: Jan. 10, 2025

This study aims to explore the transformative potential of AI-driven personalized healthcare in enhancing patient outcomes and optimizing delivery. Utilizing a comprehensive literature review analysis current AI technologies, research identifies key areas such as data integration, machine learning algorithms, engagement strategies. The findings reveal that can significantly improve treatment accuracy, predict disease risks, foster adherence through tailored interventions. However, challenges related privacy, algorithmic bias, regulatory compliance must be addressed ensure equitable implementation. concludes while holds promise for revolutionizing healthcare, collaborative approach involving stakeholders is essential overcoming barriers maximizing benefits. implications this underscore need ongoing innovation ethical considerations deployment technologies settings.

Language: Английский

Citations

0
Short-term intra- and peri-tumoral spatiotemporal CT radiomics for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer DOI
Xiao Bao, Peng Qin, Dongliang Bian

et al.

European Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

Language: Английский

Citations

0
Participatory AI Considerations for Advancing Racial Health Equity DOI
Andrea G. Parker,

Laura Vardoulakis,

Jatin Alla

et al.

Published: April 24, 2025

Language: Английский

Citations

0
The Application of Artificial Intelligence in Lung Cancer Research DOI Creative Commons
Fang Lei

Cancer Control, Journal Year: 2024, Volume and Issue: 31

Published: Jan. 1, 2024

The advent of artificial intelligence in healthcare is transforming medical research and clinical practice, with significant advancements the areas oncology. This commentary explores pivotal role plays lung cancer research, offering insights into its current applications future potential.

Language: Английский

Citations

1
A single-cell perspective on immunotherapy for pancreatic cancer: from microenvironment analysis to therapeutic strategy innovation DOI Creative Commons
Rui Wang, Jie Liu, Bo Jiang

et al.

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Oct. 30, 2024

Pancreatic cancer remains one of the most lethal malignancies, with conventional treatment options providing limited efficacy. Recent advancements in immunotherapy have offered new hope, yet unique tumor microenvironment (TME) pancreatic poses significant challenges to its successful application. This review explores transformative impact single-cell technology on understanding and cancer. By enabling high-resolution analysis cellular heterogeneity within TME, approaches elucidated complex interplay between various immune cell populations. These insights led identification predictive biomarkers development innovative, personalized immunotherapeutic strategies. The discusses role dissecting intricate landscape cancer, highlighting discovery T exhaustion profiles macrophage polarization states that influence response. Moreover, it outlines potential data guiding selection drugs optimizing plans. also addresses prospects translating these single-cell-based innovations into clinical practice, emphasizing need for interdisciplinary research integration artificial intelligence overcome current limitations. Ultimately, underscores promise driving therapeutic strategy innovation improving patient outcomes battle against

Language: Английский

Citations

1