Advances in Genomic Data and Biomarkers: Revolutionizing NSCLC Diagnosis and Treatment

Cancers, Journal Year: 2023, Volume and Issue: 15(13), P. 3474 - 3474

Published: July 3, 2023

Non-small cell lung cancer (NSCLC) is a significant public health concern with high mortality rates. Recent advancements in genomic data, bioinformatics tools, and the utilization of biomarkers have improved possibilities for early diagnosis, effective treatment, follow-up NSCLC. Biomarkers play crucial role precision medicine by providing measurable indicators disease characteristics, enabling tailored treatment strategies. The integration big data artificial intelligence (AI) further enhances potential personalized through advanced biomarker analysis. However, challenges remain impact new on efficacy due to limited evidence. Data analysis, interpretation, adoption approaches clinical practice pose additional emphasize technologies such as analysis (AI), which enhance Despite these obstacles, into has shown promising results NSCLC, improving patient outcomes targeted therapies. Continued research discovery, utilization, evidence generation are necessary overcome medicine. Addressing obstacles will contribute continued improvement non-small cancer.

Language: Английский

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Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

Bioengineering, Journal Year: 2023, Volume and Issue: 10(3), P. 320 - 320

Published: March 2, 2023

Recently, deep learning and the Internet of Things (IoT) have been widely used in healthcare monitoring system for decision making. Disease prediction is one emerging applications current practices. In method described this paper, lung cancer implemented using IoT, which a challenging task computer-aided diagnosis (CAD). Because dangerous medical disease that must be identified at higher detection rate, disease-related information obtained from IoT devices transmitted to server. The data are then processed classified into two categories, benign malignant, multi-layer CNN (ML-CNN) model. addition, particle swarm optimization improve ability (loss accuracy). This step uses (CT scan sensor information) based on Medical (IoMT). For purpose, image IoMT sensors gathered, classification actions taken. performance proposed technique compared with well-known existing methods, such as Support Vector Machine (SVM), probabilistic neural network (PNN), conventional CNN, terms accuracy, precision, sensitivity, specificity, F-score, computation time. datasets were tested evaluate performance: Lung Image Database Consortium (LIDC) Linear Imaging Self-Scanning Sensor (LISS) datasets. Compared alternative trial outcomes showed suggested has potential help radiologist make an accurate efficient early diagnosis. ML-CNN was analyzed Python, where accuracy (2.5-10.5%) high when number instances, precision (2.3-9.5%) sensitivity (2.4-12.5%) several F-score (2-30%) cases, error rate (0.7-11.5%) low time (170 ms 400 ms) how many cases computed work, including previous known methods. architecture shows outperforms works.

Language: Английский

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Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review

Cancers, Journal Year: 2023, Volume and Issue: 15(15), P. 3981 - 3981

Published: Aug. 5, 2023

Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. accurate diagnosis based on distinct histological patterns combined molecular data for personalized treatment. Precise lung classification from single H&E slide can be challenging pathologist, requiring most time additional histochemical and special immunohistochemical stains final pathology report. According to WHO, small biopsy cytology specimens are available materials about 70% patients advanced-stage unresectable disease. Thus, limited diagnostic material necessitates its optimal management processing completion predictive testing according published guidelines. During new era Digital Pathology, Deep Learning offers potential interpretation assist pathologists’ routine practice. Herein, we systematically review current Artificial Intelligence-based approaches using cytological images cancer. Most literature centered distinction between adenocarcinoma, squamous cell carcinoma, reflecting realistic pathologist’s routine. Furthermore, several studies developed algorithms adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, PD-L1 expression estimation.

Language: Английский

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Advancing Oncology Diagnostics: AI-Enabled Early Detection of Lung Cancer Through Hybrid Histological Image Analysis

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 64396 - 64415

Published: Jan. 1, 2024

Against the backdrop of pervasive global challenge cancer, with particular emphasis on lung cancer (LC), this study centers its investigation critical realm early detection leveraging artificial intelligence (AI) within domain histological image analysis. Through fusion DenseNet201 color histogram techniques, a novel hybrid feature set emerges, engineered to elevate classification accuracy. The comprehensive evaluation encompasses eight diverse machine learning (ML) algorithms, spanning from K-Nearest Neighbors (KNN) Support Vector Machines (SVM), including notable contenders such as LightGBM (LGBM), CatBoost, XGBoost, decision trees (DT), random forests (RF), and multinomial naive Bayes (MultinomialNB). This rigorous examination illuminates distinguished model, achieving remarkable accuracy rate 99.683% LC25000 dataset. extension methodology breast detection, utilizing BreakHis dataset, yields commendable 94.808%. These findings underscore transformative potential AI in intricate landscape histopathological analysis, positioning it pivotal force advancing diagnostic capabilities. A meticulous comparative analysis not only underscores merits but also elucidates limitations existing applications medical imaging, thereby charting roadmap for future refinements clinical deployments. Consequently, continued research settings is advocated, ultimate aim fortifying diagnosis subsequently enhancing patient outcomes through judicious therapeutic interventions.

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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Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(9), P. 5809 - 5809

Published: May 8, 2023

In recent years, numerous explainable artificial intelligence (XAI) use cases have been developed, to solve real problems in industrial applications while maintaining the explainability level of used (AI) models judge their quality and potentially hold accountable if they become corrupted. Therefore, understanding state-of-the-art methods, pointing out issues, deriving future directions are important drive XAI research efficiently. This paper presents a systematic literature review local explanation techniques practical various sectors. We first establish need for response opaque AI survey different methods applications. The number studies is then examined with several factors, including industry sectors, models, data types, XAI-based usage purpose. also look at advantages disadvantages how well work settings. difficulties using covered, computing complexity trade-off between precision interpretability. Our findings demonstrate that can boost models’ transparency interpretability give insightful information about them. efficiency these procedures must be improved, ethical concerns application resolved. contributes increasing knowledge strategies offers guidance academics professionals who want

Language: Английский

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Interpretable synthetic signals for explainable one-class time-series classification

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 131, P. 107716 - 107716

Published: Jan. 3, 2024

Language: Английский

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A lightweight xAI approach to cervical cancer classification

Medical & Biological Engineering & Computing, Journal Year: 2024, Volume and Issue: 62(8), P. 2281 - 2304

Published: March 20, 2024

Cervical cancer is caused in the vast majority of cases by human papilloma virus (HPV) through sexual contact and requires a specific molecular-based analysis to be detected. As an HPV vaccine available, incidence cervical up ten times higher areas without adequate healthcare resources. In recent years, liquid cytology has been used overcome these shortcomings perform mass screening. addition, classifiers based on convolutional neural networks can developed help pathologists diagnose disease. However, systems always require final verification pathologist make diagnosis. For this reason, explainable AI techniques are required highlight most significant data professional, as it determine confidence results image for classification (allowing professional point out he/she thinks important cross-check them against those detected system order create incremental learning systems). work, 4-phase optimization process obtain custom deep-learning classifier distinguishing between 4 severity classes with liquid-cytology images. The obtains accuracy over 97% 100% 2 execution under 1 s (including report generation). Compared previous works, proposed better lower computational cost.

Language: Английский

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Multi-organ squamous cell carcinoma classification using feature interpretation technique for explainability

Journal of Applied Biomedicine, Journal Year: 2024, Volume and Issue: 44(2), P. 312 - 326

Published: April 1, 2024

Squamous cell carcinoma is the most common type of cancer that occurs in many organs human body. To detect carcinoma, pathologists observe tissue samples at multiple magnifications, which time-consuming and prone to inter- or intra-observer variability. The key challenge for automation squamous diagnosis extract features low (100x) magnification explain decision-making process healthcare professionals. existing literature used either machine learning deep models specific organs. In this work, we report on implementation an explainable diagnostic aid system any organ present a comparative analysis with state-of-the-art models. A classifier ensemble feature selection technique developed provide automatic distinguishing between positive negative cases based histopathological images. Moreover, AI techniques such as ELI5, LIME SHAP are introduced model provides interpretability prediction made by classifier. results show achieved accuracy 93.43% 96.66% public multi-centric private datasets, respectively. proposed CatBoost remarkable performance diagnosing multi-organ from images, even when various illumination variations were introduced.

Language: Английский

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ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological images

Computer Methods and Programs in Biomedicine, Journal Year: 2024, Volume and Issue: 251, P. 108207 - 108207

Published: May 4, 2024

Language: Английский

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