Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma DOI Creative Commons
Xiangyun Li, Xiaoqun Yang, Xianwei Yang

et al.

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

Published: Jan. 1, 2024

Clear cell renal carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, treatment strategies ccRCC. This study aims to create model predict OS ccRCC patients. In this study, data from patients the TCGA database were used as training set, clinical serving validation set. Pathological features extracted H&E-stained slides using PyRadiomics, was constructed non-negative matrix factorization (NMF) algorithm. The model's predictive performance assessed through Kaplan-Meier (KM) curves Cox regression analysis. Additionally, differential gene expression, ontology (GO) enrichment analysis, immune infiltration, mutational conducted investigate underlying biological mechanisms. A total of 368 patients, comprising two subtypes (Cluster 1 Cluster 2) successfully NMF KM revealed that 2 associated worse OS. 76 genes identified between subtypes, primarily involving extracellular organization structure. Immune-related genes, including CTLA4, CD80, TIGIT, expressed 2, while VHL PBRM1 along mutations PI3K-Akt, HIF-1, MAPK signaling pathways, exhibited mutation rates exceeding 40% both subtypes. learning-based effectively predicts differentiates critical roles immune-related CTLA4 pathways offer new insights further research on molecular mechanisms, diagnosis,

Language: Английский

Artificial intelligence in otorhinolaryngology: current trends and application areas DOI Creative Commons
Emre Demir, Burak Numan Uğurlu, Gülay Aktar Uğurlu

et al.

European Archives of Oto-Rhino-Laryngology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Abstract Purpose This study aims to perform a bibliometric analysis of scientific research on the use artificial intelligence (AI) in field Otorhinolaryngology (ORL), with specific focus identifying emerging AI trend topics within this discipline. Methods A total 498 articles ORL, published between 1982 and 2024, were retrieved from Web Science database. Various techniques, including keyword factor analysis, applied analyze data. Results The most prolific journal was European Archives Oto-Rhino-Laryngology ( n = 67). USA 200) China 61) productive countries AI-related ORL research. institutions Harvard University / Medical School 71). leading authors Lechien JR. 18) Rameau A. 17). frequently used keywords cochlear implant, head neck cancer, magnetic resonance imaging (MRI), hearing loss, patient education, diagnosis, radiomics, surgery, aids, laryngology ve otitis media. Recent trends otorhinolaryngology reflect dynamic focus, progressing hearing-related technologies such as aids implants earlier years, diagnostic innovations like audiometry, psychoacoustics, narrow band imaging. emphasis has recently shifted toward advanced applications MRI, computed tomography (CT) for conditions chronic rhinosinusitis, laryngology, Additionally, increasing attention been given quality life, prognosis, underscoring holistic approach treatment otorhinolaryngology. Conclusion significantly impacted especially therapeutic planning. With advancements MRI CT-based technologies, proven enhance disease detection management. future suggests promising path improving clinical decision-making, care, healthcare efficiency.

Language: Английский

Citations

1
Uncovering the Potential of Pathomics: Prognostic Prediction and Mechanistic Investigation of Pancreatic Cancer DOI
Rixin Su, Xiaohong Zhao, Fabiao Zhang

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Face and Neck Pilomatricoma Excision Using an Endoscope‐Assisted Hairline Approach DOI Creative Commons
Ken Woo, Dong Kun Lee, Seung Hoon Woo

et al.

OTO Open, Journal Year: 2025, Volume and Issue: 9(2)

Published: April 1, 2025

Abstract Objective Traditional transcutaneous approaches for pilomatricoma excision in the face and neck are effective but often leave conspicuous scars that compromise cosmetic outcomes. We aimed to evaluate a refined endoscope‐assisted hairline approach uses concealed scalp incision enhanced endoscopic visualization improve esthetic results while maintaining surgical efficacy. Study Design Prospective observational study. Setting Dankook University School of Medicine, Korea. Methods Fifty patients with benign pilomatricomas were prospectively enrolled allocated into two groups. Group A (n = 25) underwent approach, whereas B received conventional approach. Clinical data including operative time postoperative complications recorded. Cosmetic outcomes objectively evaluated using standardized photographic documentation patient satisfaction scores collected at 3 12 months postoperatively. Results The mean was significantly longer compared ( P < .001), reflecting technical intricacies No significant differences observed between groups hospital stay or overall complication rates. Importantly, higher objective assessments consistently demonstrating reduced scar visibility superior preservation skin integrity. Conclusion is safe highly technique cosmetically sensitive facial regions. This innovative method offers improvements without compromising safety, representing distinct advance over methods.

Language: Английский

Citations

0
Impact of oral flora in tongue coating and saliva on oral cancer risk and the regulatory role of Interleukin-8 DOI
Xuemin Wang,

Xiaona Song,

Jiping Gao

et al.

Cytokine, Journal Year: 2024, Volume and Issue: 185, P. 156821 - 156821

Published: Dec. 3, 2024

Language: Английский

Citations

1
Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma DOI Creative Commons
Xiangyun Li, Xiaoqun Yang, Xianwei Yang

et al.

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

Published: Jan. 1, 2024

Clear cell renal carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, treatment strategies ccRCC. This study aims to create model predict OS ccRCC patients. In this study, data from patients the TCGA database were used as training set, clinical serving validation set. Pathological features extracted H&E-stained slides using PyRadiomics, was constructed non-negative matrix factorization (NMF) algorithm. The model's predictive performance assessed through Kaplan-Meier (KM) curves Cox regression analysis. Additionally, differential gene expression, ontology (GO) enrichment analysis, immune infiltration, mutational conducted investigate underlying biological mechanisms. A total of 368 patients, comprising two subtypes (Cluster 1 Cluster 2) successfully NMF KM revealed that 2 associated worse OS. 76 genes identified between subtypes, primarily involving extracellular organization structure. Immune-related genes, including CTLA4, CD80, TIGIT, expressed 2, while VHL PBRM1 along mutations PI3K-Akt, HIF-1, MAPK signaling pathways, exhibited mutation rates exceeding 40% both subtypes. learning-based effectively predicts differentiates critical roles immune-related CTLA4 pathways offer new insights further research on molecular mechanisms, diagnosis,

Language: Английский

Citations

0