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

BMC Cancer, Год журнала: 2025, Номер 25(1)

Опубликована: Янв. 22, 2025

Язык: 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, Год журнала: 2025, Номер 14

Опубликована: Янв. 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

Язык: Английский

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

Cancers, Год журнала: 2025, Номер 17(5), С. 882 - 882

Опубликована: Март 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.

Язык: Английский

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An Empowered Transfer Learning Model for Predictive Classification of Lung Cancer

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Май 21, 2025

ABSTRACT Lung cancer detection and treatment is processed using upgraded medical tools radiology experts from multiple data sources. The challenge in decision making dependent on understanding a given electronic health records or datasets. In this paper, we have proposed an improvised approach of lung classification based intensity driven RoI selection the Images Database Consortium Image Collection (LIDC-IDRI), Cancer Imaging Archive (CIA) technique developed label customization annotating vulnerable regions. optimized for higher dimensionality mapping RoIs. deploys feedback-based upgrading monitoring via transfer learning framework. trained dataset optimizer updated to customized models decision-making capabilities. deployed CoVNET framework has demonstrated accuracy 97.84% under 60:40 training testing-based model.

Язык: 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, Год журнала: 2024, Номер 7

Опубликована: Июль 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.

Язык: Английский

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Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam

BMC Medical Imaging, Год журнала: 2024, Номер 24(1)

Опубликована: Июль 19, 2024

Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitations such subjectivity constraints handling complex image features. This research paper proposes an integrated deep approach utilizing pre-trained models-VGG16, ResNet50, InceptionV3-combined within unified framework to improve diagnostic accuracy medical imaging. The method focuses lung cancer detection using images resized converted uniform format optimize performance ensure consistency across datasets. Our proposed model leverages the strengths of each network, achieving high degree feature extraction robustness by freezing early convolutional layers fine-tuning deeper layers. Additionally, techniques like SMOTE Gaussian Blur are applied address class imbalance, enhancing training underrepresented classes. model's was validated IQ-OTH/NCCD dataset, which collected from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases over period three months fall 2019. achieved 98.18%, precision recall rates notably all improvement highlights potential systems diagnostics, providing more accurate, reliable, efficient means disease detection.

Язык: Английский

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Evolutionary induced survival trees for medical prognosis assessment

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112674 - 112674

Опубликована: Янв. 1, 2025

Язык: 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, Год журнала: 2025, Номер 292, С. 117945 - 117945

Опубликована: Фев. 22, 2025

Язык: Английский

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An Adaptive Dendritic Neural Model for Lung Cancer Prediction

Computer Methods in Biomechanics & Biomedical Engineering, Год журнала: 2025, Номер unknown, С. 1 - 14

Опубликована: Март 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.

Язык: Английский

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

Asia-Pacific Journal of Oncology Nursing, Год журнала: 2025, Номер unknown, С. 100680 - 100680

Опубликована: Март 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.

Язык: Английский

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