Dataset Information

Timing, Sites, and Correlates of Lung Cancer Recurrence.

ABSTRACT:

Introduction

Understanding temporal and anatomic patterns of lung cancer recurrence could guide disease management and monitoring. However, these data are not available in population-based datasets and are not routinely recorded in clinical trials.

Materials and methods

We identified cases of stage 1 to 3 lung cancer diagnosed January 1, 2000, to December 31, 2017, in the tumor registry of a National Cancer Institute-designated comprehensive cancer center. For cases with documented disease recurrence, we recorded anatomic site(s) and timing. We estimated time to recurrence using Kaplan-Meier methods. Associations between case characteristics and recurrence features were assessed using univariable and multivariable logistic regression models and Cox regression models.

Results

A total of 1619 cases of stage 1 to 3 lung cancer from 1549 patients were included in the analysis. Of these, 466 (30%) patients developed recurrent lung cancer. The most common type of first recurrence was distant disease, most commonly central nervous system (CNS) (37%). In multivariable analyses, race (P = .02) and primary treatment modality (P < .001) correlated with recurrent disease, whereas tumor histology (P = .004) and primary treatment modality (P < .001) were associated specifically with distant recurrence. Patient age (P = .05) and initial TNM stage (P = .001) correlated with timing of recurrence.

Conclusion

In this single-center series of stage 1 to 3 lung cancer, recurrent disease was associated with race, histology, and treatment modality, and most commonly occurred in the CNS. Modulation of clinical and radiographic disease monitoring according to recurrence risk, timing, and site may offer a means to identify future lung cancer when it remains asymptomatic and highly treatable.

SUBMITTER: Karacz CM

PROVIDER: S-EPMC7061059 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

Timing, Sites, and Correlates of Lung Cancer Recurrence.

Karacz Chelsea M CM Yan Jingsheng J Zhu Hong H Gerber David E DE

Clinical lung cancer 20191220 2

<h4>Introduction</h4>Understanding temporal and anatomic patterns of lung cancer recurrence could guide disease management and monitoring. However, these data are not available in population-based datasets and are not routinely recorded in clinical trials.<h4>Materials and methods</h4>We identified cases of stage 1 to 3 lung cancer diagnosed January 1, 2000, to December 31, 2017, in the tumor registry of a National Cancer Institute-designated comprehensive cancer center. For cases with documente ...[more]

PMID: 31932216

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Project description:Breast cancer has a long natural history. Established and emerging biologic markers address overall risk but not necessarily timing of recurrence. 346 adjuvant naïve breast cancer cases from Guy's Hospital with 23 years minimum follow-up and archival blocks were recut and reassessed for hormone-receptors (HR), HER2-receptor and grade. Disease-specific survival (DSS) was analyzed by recursive partitioning. To validate insights from this analysis, gene-signatures (proliferative and HR-negative) were evaluated for their ability to predict early versus late metastatic risk in 683 node-negative, adjuvant naïve breast cancers annotated with expression microarray data. Risk partitioning showed that adjuvant naïve node-negative outcome risk was primarily partitioned by tumor receptor status and grade but not tumor size. HR-positive and HER2-negative (HRpos) risk was partitioned by tumor grade; low grade cases have very low early risk but a 20% fall-off in DSS 10 or more years after diagnosis. Higher grade HRpos cases have risk over >20 years. Triple-negative (Tneg) and HER2-positive (HER2pos) cases DSS events occurred primarily within the first 5 years. Among node-positive cases, only low grade conferred late risk, suggesting that proliferative gene signatures that identify proliferation would be important for predicting early but not late recurrence. Using pooled data from four publicly available data sets for node-negative tumors annotated with gene expression and outcome data, we evaluated four prognostic gene signatures: two proliferation-based and two immune function-based. Tumor proliferative capacity predicted early but not late metastatic risk for HRpos cases. The immune function or HRneg specific signatures predicted only early metastatic risk in Tneg and HER2pos cases. Breast cancer prognostic signatures need to inform both risk and timing of metastatic events and may best be applied within subsets. Current signatures predict for outcome risk within 5 years of diagnosis. Predictors of late risk for HR positive disease are needed.

Project description:Background:This study developed, validated, and disseminated a generalizable informatics algorithm for detecting breast cancer recurrence and timing using a gold standard measure of recurrence coupled with data derived from a readily available common data model that pools health insurance claims and electronic health records data. Methods:The algorithm has two parts: to detect the presence of recurrence and to estimate the timing of recurrence. The primary data source was the Cancer Research Network Virtual Data Warehouse (VDW). Sixteen potential indicators of recurrence were considered for model development. The final recurrence detection and timing models were determined, respectively, by maximizing the area under the ROC curve (AUROC) and minimizing average absolute error. Detection and timing algorithms were validated using VDW data in comparison with a gold standard recurrence capture from a third site in which recurrences were validated through chart review. Performance of this algorithm, stratified by stage at diagnosis, was compared with other published algorithms. All statistical tests were two-sided. Results:Detection model AUROCs were 0.939 (95% confidence interval [CI] = 0.917 to 0.955) in the training data set (n?=?3370) and 0.956 (95% CI?=?0.944 to 0.971) and 0.900 (95% CI?=?0.872 to 0.928), respectively, in the two validation data sets (n?=?3370 and 3961, respectively). Timing models yielded average absolute prediction errors of 12.6% (95% CI?=?10.5% to 14.5%) in the training data and 11.7% (95% CI?=?9.9% to 13.5%) and 10.8% (95% CI?=?9.6% to 12.2%) in the validation data sets, respectively, and were statistically significantly lower by 12.6% (95% CI?=?8.8% to 16.5%, P < .001) than those estimated using previously reported timing algorithms. Similar covariates were included in both detection and timing algorithms but differed substantially from previous studies. Conclusions:Valid and reliable detection of recurrence using data derived from electronic medical records and insurance claims is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for breast cancer patients and those who develop recurrence.

Dataset Information

Timing, Sites, and Correlates of Lung Cancer Recurrence.

Introduction

Materials and methods

Results

Conclusion

Publications

Timing, Sites, and Correlates of Lung Cancer Recurrence.

Similar Datasets

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