scholarly journals Surgery in high-volume hospitals not commission on cancer accreditation leads to increased cancer-specific survival for early-stage lung cancer

2015 ◽  
Vol 210 (4) ◽  
pp. 643-647 ◽  
Author(s):  
Elizabeth A. David ◽  
David T. Cooke ◽  
Yingjia Chen ◽  
Andrew Perry ◽  
Robert J. Canter ◽  
...  
2016 ◽  
Vol 30 (8) ◽  
pp. 912-917 ◽  
Author(s):  
Thomas Klikovits ◽  
Christopher Lambers ◽  
Bahil Ghanim ◽  
Balazs Dome ◽  
Gabriella Murakoezy ◽  
...  

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 7571-7571 ◽  
Author(s):  
Alissa S. Marr ◽  
Valerie Shostrom ◽  
K. M Islam ◽  
Apar Kishor Ganti

7571 Background: Although the majority of lung cancer patients are over the age of 65, there are limited data on outcomes of treatment options for early stage lung cancer in older patients. Methods: Treatment and outcome data of stage I and II non-small cell lung cancer (NSCLC) patients were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Treatment modalities included no treatment, surgery, radiation, and a combination of surgery and radiation. Patients were divided based on age groups into <65, 65-75, and >75 years old. Multivariate logistic regression was used to compare the likelihood of survival in the three age groups while controlling for gender and race. Results: A total of 10,763 patients diagnosed with stage I and II NSCLC between 1988 and 2007 within the SEER database were analyzed. The age distribution was as follows: <65 (n=3558), 65-75 years (n= 4454), >75 years (n=2751). Patients <65 years of age were more likely than those >75 years of age to be treated with surgery (72.5% vs. 53.5%, respectively; p = <0.0001). Patients >75 years of age were more often treated with radiation alone (23%) or no treatment (18.2%) as compared to those patients <65 (9% and 4.9%, respectively; p = <0.0001). Patients <65 years of age with stage I lung cancer had a statistically significant improved lung cancer-specific 5-year survival with surgery alone as compared to those 65-75 years and >75 years. Lung cancer specific mortality at 5 years was 19%, 26% and 30%, respectively; p= <0.0001. Similar results were seen in stage II patients. When stage I patients received radiation therapy, lung cancer-specific deaths at 5 years were not different between the three groups (66% vs. 63% vs. 66%, respectively; p=0.1263). The 5-year lung cancer- related mortality was lower in younger patients who received no treatment (51% in <65, 56% in 65-75, and 57% in >75 years old; p=0.006). Conclusions: Older patients treated surgically for stages I and II NSCLC have a lower lung cancer-specific survival when compared to younger patients. In contrast, there is no difference in lung cancer-specific survival for patients treated with radiation therapy. Hence, careful selection of older patients for surgical therapy of early stage NSCLC is warranted.


2021 ◽  
Vol 16 (3) ◽  
pp. S264-S265
Author(s):  
F. Xu ◽  
L. Yang ◽  
C. Liu ◽  
J. Ying ◽  
Y. Wang

Author(s):  
Guangyao Wu ◽  
Arthur Jochems ◽  
Turkey Refaee ◽  
Abdalla Ibrahim ◽  
Chenggong Yan ◽  
...  

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.”


2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Seijiro Sato ◽  
Masaya Nakamura ◽  
Yuki Shimizu ◽  
Tatsuya Goto ◽  
Terumoto Koike ◽  
...  

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