scholarly journals Patient-provider discussion of physical activity among early stage lung cancer survivors

2014 ◽  
Vol 24 (3) ◽  
pp. 359-362 ◽  
Author(s):  
Errol J. Philip ◽  
Elliot J. Coups ◽  
Marc B. Feinstein ◽  
Bernard J. Park ◽  
Donna J. Wilson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Lung Cancer ◽  
Cancer Survivors ◽  
Early Stage ◽  
Early Stage Lung Cancer ◽  
Stage Lung Cancer

scholarly journals Physical activity preferences of early-stage lung cancer survivors

2013 ◽  
Vol 22 (2) ◽  
pp. 495-502 ◽  
Author(s):  
Errol J. Philip ◽  
Elliot J. Coups ◽  
Marc B. Feinstein ◽  
Bernard J. Park ◽  
Donna J. Wilson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Lung Cancer ◽  
Cancer Survivors ◽  
Early Stage ◽  
Early Stage Lung Cancer ◽  
Activity Preferences ◽  
Stage Lung Cancer

scholarly journals P04.10 Relapsed or Metastatic Organotropism in Early Stage Lung Cancer after Radical Surgery

2021 ◽  
Vol 16 (3) ◽  
pp. S264-S265
Author(s):  
F. Xu ◽  
L. Yang ◽  
C. Liu ◽  
J. Ying ◽  
Y. Wang
Keyword(s):  
Lung Cancer ◽  
Radical Surgery ◽  
Early Stage ◽  
Early Stage Lung Cancer ◽  
Stage Lung Cancer

scholarly journals Structural and functional radiomics for lung cancer

2021 ◽  
Author(s):  
Guangyao Wu ◽  
Arthur Jochems ◽  
Turkey Refaee ◽  
Abdalla Ibrahim ◽  
Chenggong Yan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Treatment Strategy ◽  
Early Stage ◽  
Quality Data ◽  
Clinical Implementation ◽  
Early Stage Lung Cancer ◽  
Treatment Response Evaluation ◽  
Advanced Stage Lung Cancer ◽  
Stage Lung Cancer

Abstract Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”

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