scholarly journals OA05.09 Volume Doubling Time and Radiomic Features Predict Tumor Behavior of Screen-Detected Lung Cancers in the National Lung Screening Trial (NLST)

2021 ◽  
Vol 16 (1) ◽  
pp. S7
Author(s):  
J. Perez-Morales ◽  
I. Tunali ◽  
H. Lu ◽  
W. Mu ◽  
Y. Balagurunathan ◽  
...  
2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Matthew T Warkentin ◽  
Martin C Tammemägi ◽  
Matthew T Freedman ◽  
Lawrence R Ragard ◽  
William G Hocking ◽  
...  

Abstract Background A small proportion of non–small cell lung cancers (NSCLCs) have been observed to spread to distant lymph nodes (N3) or metastasize (M1) or both, while the primary tumor is small (≤3 cm, T1). These small aggressive NSCLCs (SA-NSLSC) are important as they are clinically significant, may identify unique biologic pathways, and warrant aggressive follow-up and treatment. This study identifies factors associated with SA-NSCLC and attempts to validate a previous finding that women with a family history of lung cancer are at particularly elevated risk of SA-NSCLC. Methods This study used a case–case design within the National Cancer Institute’s National Lung Screening Trial (NLST) cohort. Case patients and “control” patients were selected based on TNM staging parameters. Case patients (n = 64) had T1 NSCLCs that were N3 or M1 or both, while “control” patients (n = 206) had T2 or T3, N0 to N2, and M0 NSCLCs. Univariate and multivariable logistic regression were used to identify factors associated with SA-NSCLC. Results In bootstrap bias–corrected multivariable logistic regression models, small aggressive adenocarcinomas were associated with a positive history of emphysema (odds ratio [OR] = 5.15, 95% confidence interval [CI] = 1.63 to 23.00) and the interaction of female sex and a positive family history of lung cancer (OR = 6.55, 95% CI = 1.06 to 50.80). Conclusions Emphysema may play a role in early lung cancer progression. Females with a family history of lung cancer are at increased risk of having small aggressive lung adenocarcinomas. These results validate previous findings and encourage research on the role of female hormones interacting with family history and genetic factors in lung carcinogenesis and progression.


2017 ◽  
Vol 208 (5) ◽  
pp. 1011-1021 ◽  
Author(s):  
Rowena Yip ◽  
Claudia I. Henschke ◽  
Dong Ming Xu ◽  
Kunwei Li ◽  
Artit Jirapatnakul ◽  
...  

2012 ◽  
Vol 4 (3) ◽  
pp. 513-516 ◽  
Author(s):  
MAKI KANASHIKI ◽  
TAKUJI TOMIZAWA ◽  
IWAO YAMAGUCHI ◽  
KOICHI KURISHIMA ◽  
NOBUYUKI HIZAWA ◽  
...  

2018 ◽  
Vol 51 (4) ◽  
pp. 1702183
Author(s):  
Onno M. Mets ◽  
Kaman Chung ◽  
Pieter Zanen ◽  
Ernst T. Scholten ◽  
Wouter B. Veldhuis ◽  
...  

Current pulmonary nodule management guidelines are based on nodule volume doubling time, which assumes exponential growth behaviour. However, this is a theory that has never been validated in vivo in the routine-care target population. This study evaluates growth patterns of untreated solid and subsolid lung cancers of various histologies in a non-screening setting.Growth behaviour of pathology-proven lung cancers from two academic centres that were imaged at least three times before diagnosis (n=60) was analysed using dedicated software. Random-intercept random-slope mixed-models analysis was applied to test which growth pattern most accurately described lung cancer growth. Individual growth curves were plotted per pathology subgroup and nodule type.We confirmed that growth in both subsolid and solid lung cancers is best explained by an exponential model. However, subsolid lesions generally progress slower than solid ones. Baseline lesion volume was not related to growth, indicating that smaller lesions do not grow slower compared to larger ones.By showing that lung cancer conforms to exponential growth we provide the first experimental basis in the routine-care setting for the assumption made in volume doubling time analysis.


PLoS ONE ◽  
2016 ◽  
Vol 11 (8) ◽  
pp. e0159880 ◽  
Author(s):  
Matthew B. Schabath ◽  
Pierre P. Massion ◽  
Zachary J. Thompson ◽  
Steven A. Eschrich ◽  
Yoganand Balagurunathan ◽  
...  

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