Dataset Information

The Impact of Extended Delayed Surgery for Indolent Lung Cancer or Part-solid Ground Glass Nodules.

ABSTRACT:

Background

During the COVID-19 pandemic, patients with lung cancer may experience treatment delays. The objective of this study was to evaluate the impact of extended treatment delays on survival among patients with stage I typical bronchopulmonary carcinoid (BC), lepidic predominant adenocarcinoma (LPA) or invasive adenocarcinoma with a lepidic component (ADL).

Methods

Using National Cancer Data Base data (2004-2015), multivariable Cox regression analysis with penalized smoothing splines was performed to examine the association between treatment delay and all-cause mortality for stage I BC, LPA and ADL. Propensity score-matched analyses compared the overall survival in patients who received "early" versus "delayed" surgery (i.e. 0-30 versus 90-120 days following diagnosis) across the different histologic subtypes.

Results

During the study period, patients with stage I BC (n=4,947), LPA (n=5,340) and ADL (n=6,816) underwent surgery. Cox regression analysis of these cohorts showed a gradual steady increase in the hazard ratio the longer treatment is delayed. However, in propensity score-matched analyses which created cohorts of patients who underwent early and delayed surgery that were well-balanced in patient characteristics, no significant differences in 5-year survival were found between early and delayed surgery for stage I BC (87% [95% CI:77-93] vs 89% [95% CI:80-94]), stage I LPA (73% [95% CI:64-80] vs 77% [95% CI:68-83]) and stage I ADL (71% [95% CI:64-76] vs 69% [95% CI:60-76]).

Conclusions

During the COVID-19 pandemic, for early-stage indolent lung tumors and part-solid ground glass lung nodules, a delay of surgery by 3-4 months following diagnosis can be considered.

SUBMITTER: Mayne NR

PROVIDER: S-EPMC8604629 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivesTo investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).MethodsThis study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic-radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed.ResultsFive radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic-radiomics model (AUC 0.77; 95% CI, 0.69-0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62-0.81) and Rad-score (AUC 0.72; 95% CI, 0.63-0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model.ConclusionsThe proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs.Key points• CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic-radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.