Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer DOI Creative Commons

Hongyu Wang,

Zhiqiang He, Jiang Xu

et al.

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

Published: Jan. 29, 2025

Background Cervical lymph node metastasis (LNM) is a significant factor that leads to poor prognosis in laryngeal cancer. Early-stage supraglottic cancer (SGLC) prone LNM. However, research on risk factors for predicting cervical LNM early-stage SGLC limited. This study seeks create and validate predictive model through the application of machine learning (ML) algorithms. Methods The training set internal validation data were extracted from Surveillance, Epidemiology, End Results (SEER) database. Data 78 patients collected Fujian Provincial Hospital independent external validation. We identified four variables associated with developed six ML models based these predict patients. In two cohorts, 167 (47.44%) 26 (33.33%) experienced LNM, respectively. Age, T stage, grade, tumor size as predictors All performed well, both validations, eXtreme Gradient Boosting (XGB) outperformed other models, AUC values 0.87 0.80, decision curve analysis demonstrated have excellent clinical applicability. Conclusions Our indicates combining algorithms can effectively diagnosed SGLC. first apply

Language: Английский

Improving health literacy using the power of digital communications to achieve better health outcomes for patients and practitioners DOI Creative Commons
Patrick Fitzpatrick

Frontiers in Digital Health, Journal Year: 2023, Volume and Issue: 5

Published: Nov. 17, 2023

Digital communication tools have demonstrated significant potential to improve health literacy which ultimately leads better outcomes. In this article, we examine the power of digital such as mobile apps, telemedicine and online information resources promote literacy. We outline evidence that facilitate patient education, self-management empowerment possibilities. addition, technology is optimising for improved clinical decision-making, treatment options among providers. also explore challenges limitations associated with literacy, including issues related access, reliability privacy. propose leveraging key engagement enhance across demographics leading transformation healthcare delivery driving outcomes all.

Language: Английский

Citations

96
Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment DOI Creative Commons
Sirvan Khalighi, Kartik Reddy, Abhishek Midya

et al.

npj Precision Oncology, Journal Year: 2024, Volume and Issue: 8(1)

Published: March 29, 2024

Abstract This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent significant global health issue. AI has brought transformative innovations to tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, treatment planning. Assessing its influence across all facets malignant management- diagnosis, prognosis, therapy- models outperform human evaluations terms accuracy specificity. Their ability discern molecular aspects from imaging may reduce reliance invasive diagnostics accelerate time diagnoses. The covers techniques, classical machine learning deep learning, highlighting current applications challenges. Promising directions future research include multimodal data integration, generative AI, large medical language models, precise delineation characterization, addressing racial gender disparities. Adaptive personalized strategies are also emphasized optimizing clinical outcomes. Ethical, legal, social implications discussed, advocating transparency fairness integration neuro-oncology providing holistic understanding impact patient care.

Language: Английский

Citations

62
Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues DOI Creative Commons
Muhammad Ali, Viviana Benfante,

Ghazal Basirinia

et al.

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(2), P. 59 - 59

Published: Feb. 15, 2025

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer and immunology, with object detection, feature extraction, classification, segmentation applications. Advancements in deep learning (DL) research have been a critical factor advancing computer techniques for mining. A significant improvement the accuracy of detection algorithms has achieved result emergence open-source software innovative neural network architectures. Automated now enables extraction quantifiable cellular spatial features from microscope images cells tissues, providing insights into organization various diseases. This review aims to examine latest AI DL mining microscopy images, aid biologists who less background knowledge machine (ML), incorporate ML models focus images.

Language: Английский

Citations

3
Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions DOI Creative Commons
David B. Olawade, Aanuoluwapo Clement David-Olawade, Temitope Adereni

et al.

Diseases, Journal Year: 2025, Volume and Issue: 13(1), P. 24 - 24

Published: Jan. 20, 2025

Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy radiation often result in significant side effects varied patient outcomes. Immunotherapy has emerged as promising alternative, harnessing the immune system to target cancer cells. However, complexity responses tumor heterogeneity challenges its effectiveness. Objective: This mini-narrative review explores role artificial intelligence [AI] enhancing efficacy immunotherapy, predicting responses, discovering novel therapeutic targets. Methods: A comprehensive literature was conducted, focusing on studies published between 2010 2024 that examined application AI immunotherapy. Databases such PubMed, Google Scholar, Web Science were utilized, articles selected based relevance topic. Results: significantly contributed identifying biomarkers predict immunotherapy by analyzing genomic, transcriptomic, proteomic data. It also optimizes combination therapies most effective treatment protocols. AI-driven predictive models help assess response guiding clinical decision-making minimizing effects. Additionally, facilitates discovery targets, neoantigens, enabling development personalized immunotherapies. Conclusions: holds immense potential transforming related data privacy, algorithm transparency, integration must be addressed. Overcoming these hurdles will likely make central component future offering more treatments.

Language: Английский

Citations

2
Advancing Patient-Centered Shared Decision-Making with AI Systems for Older Adult Cancer Patients DOI Open Access
Yuexing Hao, Zeyu Liu,

Robert N. Riter

et al.

Published: May 11, 2024

Shared decision making (SDM) plays a vital role in clinical practice guidelines, fostering enduring therapeutic communication and patient-clinician relationships. Previous research indicates that active patient participation decision-making improves satisfaction treatment outcomes. However, medical can be intricate multifaceted. To help make SDM more accessible, we designed patient-centered Artificial Intelligence (AI) system for older adult cancer patients who lack high health literacy to become involved the process improve comprehension toward We conducted pilot feasibility study through 12 preliminary interviews followed by 25 usability testing after development, with survivors clinicians. Results indicated promise AI system's ability enhance SDM, providing personalized healthcare experiences education patients. Clinician responses also provided useful suggestions SDM's new design opportunities mitigating errors improving efficiency.

Language: Английский

Citations

11
From Crypts to Cancer: A Holistic Perspective on Colorectal Carcinogenesis and Therapeutic Strategies DOI Open Access
Ehsan Gharib, Gilles A. Robichaud

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(17), P. 9463 - 9463

Published: Aug. 30, 2024

Colorectal cancer (CRC) represents a significant global health burden, with high incidence and mortality rates worldwide. Recent progress in research highlights the distinct clinical molecular characteristics of colon versus rectal cancers, underscoring tumor location's importance treatment approaches. This article provides comprehensive review our current understanding CRC epidemiology, risk factors, pathogenesis, management strategies. We also present intricate cellular architecture colonic crypts their roles intestinal homeostasis. carcinogenesis multistep processes are described, covering conventional adenoma-carcinoma sequence, alternative serrated pathways, influential Vogelstein model, which proposes sequential

Language: Английский

Citations

10
Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review DOI

Youssef Alaaeldin Ali Mohamed,

Bee Luan Khoo,

Mohd Shahrimie Mohd Asaari

et al.

International Journal of Medical Informatics, Journal Year: 2024, Volume and Issue: 193, P. 105689 - 105689

Published: Nov. 4, 2024

Language: Английский

Citations

9
Artificial Intelligence and Cancer Health Equity: Bridging the Divide or Widening the Gap DOI
Irene Dankwa‐Mullan, Kingsley Ndoh, Darlington Ahiale Akogo

et al.

Current Oncology Reports, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

Citations

1
Artificial intelligence-assisted point-of-care devices for lung cancer DOI
Xinyi Ng, Anis Salwa Mohd Khairuddin,

Hai Chuan Liu

et al.

Clinica Chimica Acta, Journal Year: 2025, Volume and Issue: unknown, P. 120191 - 120191

Published: Feb. 1, 2025

Language: Английский

Citations

1
AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis DOI Creative Commons
Esther Ugo Alum

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 13, 2025

Cancer remains a significant health issue, resulting in around 10 million deaths per year, particularly developing nations. Demographic changes, socio-economic variables, and lifestyle choices are responsible for the rise cancer cases. Despite potential to mitigate adverse effects of by early detection implementation prevention methods, several nations have limited screening facilities. In oncology, use artificial intelligence (AI) represents transformative advancement diagnosis, prognosis, treatment. The AI biomarker discovery improves precision medicine uncovering signatures that essential treatment diseases within vast diverse datasets. Deep learning machine diagnostics two examples technologies changing way biomarkers made finding patterns large datasets making new make it possible deliver accurate effective therapies. Existing gaps include data quality, algorithmic transparency, ethical concerns privacy, among others. methodologies with seeks transform improving patient survival rates through enhanced diagnosis targeted therapy. This commentary aims clarify how is identification novel optimal focused treatment, improved clinical outcomes, while also addressing certain obstacles issues related application oncology. Data from reputable scientific databases such as PubMed, Scopus, ScienceDirect were utilized.

Language: Английский

Citations

1