Transitioning to a Personalized Approach in Molecularly Subtyped Small-Cell Lung Cancer (SCLC)

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

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Finding the missed millions: innovations to bring tuberculosis diagnosis closer to key populations

BMC Global and Public Health, Journal Year: 2024, Volume and Issue: 2(1)

Published: May 17, 2024

Abstract Current strategies to promptly, effectively, and equitably screen people with tuberculosis (TB) link them diagnosis care are insufficient; new approaches required find the millions of around world TB who missed each year. Interventions also need be designed considering how interact health facilities where appropriate should suitable for use in community. Here, historical, new, reemerging technologies that being utilised globally discussed, whilst highlighting we evaluate tests is just as important themselves.

Language: Английский

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The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

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

Published: Jan. 1, 2024

Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, Deep Learning oncology, explaining key concepts algorithms (like SVM, Naïve Bayes, CNN) a clear, accessible manner. It aims to make advancements understandable broad audience, focusing on their application diagnosing, classifying, predicting various types, thereby underlining AI's potential better outcomes. Moreover, we present tabular summary most significant advances from literature, offering time-saving resource for readers grasp each study's main contributions. The remarkable benefits AI-powered underscore advancing research clinical practice. is valuable researchers clinicians interested transformative implications care.

Language: Английский

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Unravelling the diagnostic pathology and molecular biomarkers in lung cancer

Breathe, Journal Year: 2024, Volume and Issue: 20(2), P. 230192 - 230192

Published: June 1, 2024

The progress in lung cancer treatment is closely interlinked with the diagnostic methods. There are four steps before commencing treatment: estimation of patient's performance status, assessment disease stage (tumour, node, metastasis), recognition histological subtype, and detection biomarkers. resection rate <30% >70% patients need systemic therapy, which individually adjusted. Accurate diagnosis very important it basis further molecular diagnosis. In many cases only small biopsy samples available rules for their defined this review. use immunochemistry at least thyroid transcription factor 1 (TTF1) p40 decisive distinction between adenocarcinoma squamous cell carcinoma. Molecular known driver mutations necessary introducing targeted therapy multiplex gene panel assays using next-generation sequencing recommended. Immunotherapy checkpoint inhibitors second promising method best results tumours high programmed death-ligand (PD-L1) expression on cells. Finally, determination a full tumour pattern will be possible artificial intelligence near future.

Language: Английский

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Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning

Cancer Imaging, Journal Year: 2024, Volume and Issue: 24(1)

Published: Sept. 30, 2024

Language: Английский

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Inteligencia Artificial en la detección del cáncer de pulmón

LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 16, 2025

El cáncer de pulmón en la actualidad se ha convertido patología oncológica diagnosticada con mayor frecuencia, y además figura como una las principales causas muerte. Esta enfermedad tiene tasa elevada mortalidad que relaciona falta síntomas etapas tempranas, lo ocasiona confirmación del diagnóstico suceda avanzadas, dando resultado opciones tratamiento disminuyan ocasiones estos pacientes no lleguen a tener curación. En el caso administre manera oportuna supervivencia 10 años es 88%. Con anteriormente mencionado buscado maneras mejorar detección temprana pulmón, entre estas mejoras menciona uso inteligencia artificial esta enfermedad. Se realizó revisión bibliográfica diversas bases datos científicas objetivo identificar sintetizar información relevante sobre mediante artificial. La conjunto tomografía computarizada dosis baja mejora tanto sensibilidad especificidad oportuno proporcionan un análisis más preciso reducir los falsos positivos negativos. Sin embargo, al ser nueva herramienta existe control regularizaciones adecuadas para este tipo tecnologías.

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Deep learning−based histopathological image analysis for lung cancer diagnosis

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 239 - 246

Published: Jan. 1, 2025

Language: Английский

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Deep Learning in Thoracic Oncology: Meta-Analytical Insights into Lung Nodule Early-Detection Technologies

Cancers, Journal Year: 2025, Volume and Issue: 17(4), P. 621 - 621

Published: Feb. 12, 2025

Background/Objectives: Detecting lung nodules on computed tomography (CT) images is critical for diagnosing thoracic cancers. Deep learning models, particularly convolutional neural networks (CNNs), show promise in automating this process. This systematic review and meta-analysis aim to evaluate the diagnostic accuracy of these focusing lesion-wise sensitivity as primary metric. Methods: A comprehensive literature search was conducted, identifying 48 studies published up 7 November 2023. The pooled performance assessed using a random-effects model, with key outcome. Factors influencing model performance, including participant demographics, dataset privacy, data splitting methods, were analyzed. Methodological rigor maintained through Checklist Artificial Intelligence Medical Imaging (CLAIM) Quality Assessment Diagnostic Accuracy Studies-2 (QUADAS-2) tools. Trial Registration: registered PROSPERO under CRD42023479887. Results: revealed 79% (95% CI: 72-86%) independent datasets 85% 83-88%) across all datasets. Variability associated characteristics study methodologies. Conclusions: While deep models demonstrate significant potential nodule detection, findings highlight need more diverse datasets, standardized evaluation protocols, interventional enhance generalizability clinical applicability. Further research necessary validate broader patient populations.

Language: Английский

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The Diagnostic Classification of the Pathological Image Using Computer Vision

Algorithms, Journal Year: 2025, Volume and Issue: 18(2), P. 96 - 96

Published: Feb. 8, 2025

Computer vision and artificial intelligence have revolutionized the field of pathological image analysis, enabling faster more accurate diagnostic classification. Deep learning architectures like convolutional neural networks (CNNs), shown superior performance in tasks such as classification, segmentation, object detection pathology. has significantly improved accuracy disease diagnosis healthcare. By leveraging advanced algorithms machine techniques, computer systems can analyze medical images with high precision, often matching or even surpassing human expert performance. In pathology, deep models been trained on large datasets annotated pathology to perform cancer diagnosis, grading, prognostication. While approaches show great promise challenges remain, including issues related model interpretability, reliability, generalization across diverse patient populations imaging settings.

Language: Английский

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Artificial Intelligence user interface preferences in radiology: A scoping review

Journal of medical imaging and radiation sciences, Journal Year: 2025, Volume and Issue: 56(3), P. 101866 - 101866

Published: Feb. 27, 2025

Modern forms of Artificial intelligence (AI) have developed in radiology over the past few years. With current workforce shortages, both and radiography professions, AI continues to prove its place supporting clinical processes. The aim scoping review was investigate existing literature on topic preference use artificial interfaces within a context. Using systematic approach, papers were chosen against an inclusion criterion addressing radiological user interface preferences be included review. Arksey O'Malley's Levac's framework used inform procedural steps for Four databases searched including MEDLINE Ovid, Scopus, Web Science Engineering Village. Reliability improved through involvement three researchers select criteria. Six identified fit criteria preferences. These varied methodologically two observational studies, simulated testing diagnostic accuracy study multi-case study. evaluated studies. Mixed responses obtained with some alignment heatmap image overlays highly detailed are linked higher amongst users. Limited exists lack research evaluating preference, either post or pre-deployment. mix methods studies indicated that there is not yet standardised method assessing tool design radiology, common System Usability Scale survey conjunction another method. There also response when considering preferred though simple, non-complicated designs suggested ideal by participants. Medical imaging essential acceptability technology into departments. This landscape setting. requirement more focussing end professional their explicit need further field, due outcome measures, clear findings regarding radiographers. dearth radiographers small sample sizes participants these identifies mindset shift required vendors alike.

Language: Английский

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