Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study

BMC Cancer, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 22, 2025

Language: Английский

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A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer

Cancers, Journal Year: 2025, Volume and Issue: 17(5), P. 882 - 882

Published: March 4, 2025

According to data from the World Health Organization (WHO), lung cancer is becoming a global epidemic. It particularly high in list of leading causes death not only developed countries, but also worldwide; furthermore, it holds place terms cancer-related mortality. Nevertheless, many breakthroughs have been made last two decades regarding its management, with one most prominent being implementation artificial intelligence (AI) various aspects disease management. We included 473 papers this thorough review, which published during 5-10 years, order describe these breakthroughs. In screening programs, AI capable detecting suspicious nodules different imaging modalities-such as chest X-rays, computed tomography (CT), and positron emission (PET) scans-but discriminating between benign malignant well, success rates comparable or even better than those experienced radiologists. Furthermore, seems be able recognize biomarkers that appear patients who may develop cancer, years before event. Moreover, can assist pathologists cytologists recognizing type tumor, well specific histologic genetic markers play key role treating disease. Finally, treatment field, guide development personalized options for patients, possibly improving their prognosis.

Language: Английский

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Evolutionary induced survival trees for medical prognosis assessment

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112674 - 112674

Published: Jan. 1, 2025

Language: Английский

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Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer

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

Published: Jan. 6, 2025

Programmed cell death (PCD) is closely related to the occurrence, development, and treatment of breast cancer. The aim this study was investigate association between various programmed patterns prognosis cancer (BRCA) patients. levels 19 different deaths in were assessed by ssGSEA analysis, these PCD scores summed obtain PCDS for each sample. relationship with immune as well metabolism-related pathways explored. PCD-associated subtypes obtained unsupervised consensus clustering differentially expressed genes analyzed. prognostic signature (PCDRS) constructed best combination 101 machine learning algorithm combinations, C-index PCDRS compared 30 published signatures. In addition, we analyzed relation therapeutic responses. distribution cells explored single-cell analysis spatial transcriptome analysis. Potential drugs targeting key Cmap. Finally, expression clinical tissues verified RT-PCR. showed higher normal. Different groups significant differences pathways. PCDRS, consisting seven genes, robust predictive ability over other signatures datasets. high group had a poorer strongly associated cancer-promoting tumor microenvironment. low exhibited anti-cancer immunity responded better checkpoint inhibitors chemotherapy-related drugs. Clofibrate imatinib could serve potential small-molecule complexes SLC7A5 BCL2A1, respectively. mRNA upregulated tissues. can be used biomarker assess response BRCA patients, which offers novel insights monitoring personalization

Language: Английский

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Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model

Ecotoxicology and Environmental Safety, Journal Year: 2025, Volume and Issue: 292, P. 117945 - 117945

Published: Feb. 22, 2025

Language: Английский

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An Adaptive Dendritic Neural Model for Lung Cancer Prediction

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: March 3, 2025

Lung cancer is a leading cause of cancer-related deaths, often diagnosed late due to its aggressive nature. This study presents novel Adaptive Dendritic Neural Model (ADNM) enhance diagnostic accuracy in high-dimensional healthcare data. Utilizing hyperparameter optimization and activation mechanisms, ADNM improves scalability feature selection for multi-class lung prediction. Using Kaggle dataset, Particle Swarm Optimization (PSO) selected features, while bootstrap assessed performance. achieved 98.39% accuracy, 99% AUC, Cohen's kappa 96.95%, with rapid convergence via the Adam optimizer, demonstrating potential improving early diagnosis personalized treatment oncology.

Language: Английский

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Artificial intelligence-assisted machine learning models for predicting lung cancer survival

Asia-Pacific Journal of Oncology Nursing, Journal Year: 2025, Volume and Issue: unknown, P. 100680 - 100680

Published: March 1, 2025

This study aimed to evaluate the feasibility of large language model-Advanced Data Analysis (ADA) in developing and implementing machine learning models predict survival outcomes for lung cancer patients, with a focus on its implications nursing practice. A retrospective design was employed using dataset patients. included sociodemographic, clinical, treatment-specific, comorbidity variables. Large model-ADA used build three models. Model performance validated, results were presented calibration plots. Of 737 rate this cohort 73.3%, mean age 59.32 years. Calibration plots indicated robust model reliability across all The Random Forest demonstrated highest predictive accuracy among Most critical features identified preoperative white blood cells (2.2%), function Forced Expiratory Volume one second (2.1%), arterial oxygen saturation (1.9%), partial pressure (1.7%), albumin (1.6%), preparation time (1.5%), at admission carbon dioxide hospital stay days postoperative total thoracic tube drainage (1.4%). effectively facilitates development prediction, enabling non-technical health care professionals harness power advanced analytics. findings underscore importance factors predicting outcomes, while also highlighting need external validation diverse settings.

Language: Английский

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Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study

Cancer Medicine, Journal Year: 2025, Volume and Issue: 14(5)

Published: March 1, 2025

Gallbladder polyps (GBPs) are increasingly prevalent, with the majority being benign; however, neoplastic carry a risk of malignant transformation, highlighting importance accurate differentiation. This study aimed to develop and validate interpretable machine learning (ML) models accurately predict GBPs in retrospective cohort, identifying key features providing model explanations using Shapley additive (SHAP) method. A total 924 patients who underwent cholecystectomy between January 2013 December 2023 at Qilu Hospital Shandong University were included. The patient characteristics, laboratory results, preoperative ultrasound findings, postoperative pathological results collected. dataset was randomly split, 80% used for training remaining 20% testing. employed nine ML algorithms construct predictive models. Subsequently, performance evaluated compared several metrics, including area under receiver operating characteristic curve (AUC). Feature ranked, interpretability enhanced by SHAP K-nearest neighbors, C5.0 decision tree algorithm, gradient boosting showed highest performance, efficacy polyps. method revealed top five predictors according ranking. polyp size recognized as most important predictor variable, indicating that lesions ≥ 18 mm should prompt heightened clinical surveillance timely intervention. Our GBP patients, guidance treatment planning resource allocation. model's transparency fosters trust understanding, empowering physicians confidently use its predictions improved care.

Language: Английский

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Machine learning models for predicting missing gender in cancer data

International Journal of Data Science and Analytics, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Language: Английский

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Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7

Published: July 3, 2024

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. proves valuable in guiding treatment decisions, optimizing resource allocation, interventions precision medicine. The wide range diseases, existence various variants within same disease, reliance on available data necessitate disease-specific computational survival predictors. widespread adoption artificial intelligence (AI) methods crafting predictors has undoubtedly revolutionized this field. However, ever-increasing demand for more sophisticated effective models necessitates continued creation innovative advancements. To catalyze these advancements, it is crucial bring existing knowledge insights into a centralized platform. paper hand thoroughly examines 23 review studies provides concise overview their scope limitations. Focusing comprehensive set 90 most recent across 44 diverse delves types that are used development This exhaustive analysis encompasses utilized modalities along with detailed subsets features, feature engineering methods, specific statistical, machine deep learning approaches have been employed. It also about sources, open-source predictors, frameworks.

Language: Английский

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