Dataset Information

Anaplastic lymphoma kinase rearrangement in surgically resected stage IA lung adenocarcinoma.

ABSTRACT:

Background

It has not been determined if adjuvant chemotherapy would be helpful for completely resected early-stage lung adenocarcinoma even with unfavorable genetic markers. As the positive anaplastic lymphoma kinase (ALK) rearrangement is associated with aggressive clinical feature in lung adenocarcinoma, we evaluated the treatment outcomes of completely resected stage IA lung adenocarcinoma according to initial ALK status.

Methods

This is a retrospective cohort study including 309 patients with surgically resected stage IA lung adenocarcinoma from February 2010 to December 2013. Patients were screened for ALK rearrangement using immunohistochemistry. A positive ALK status was defined as an immunohistochemistry score of 2+ or more. Both disease-free survival (DFS) and the initial recurrence pattern were analyzed according to ALK status.

Results

Twenty-three (7.4%) patients had ALK-positive adenocarcinoma. During the median follow-up of 35.8 months, recurrence developed in 34 (11.0%) patients. The patients with ALK-positive tumor had significantly lower 5-year DFS rate (62.4%) compared to those with ALK-negative tumor (86.5%; P=0.038). The multivariable analysis showed that ALK rearrangement was associated with a higher risk of disease recurrence (adjusted hazard ratio =2.64; 95% confidence interval, 1.08-6.44). In addition, patient with ALK-positive tumor showed more frequent recurrence in regional lymph nodes compared with those with ALK-negative tumor (83.3% vs. 28.6%; P=0.031).

Conclusions

In patients with completely resected stage IA lung adenocarcinoma, ALK rearrangement was associated with unfavorable DFS and more frequent regional lymph node metastasis. Therefore, careful surveillance for recurrence should be performed in this subset of patients.

SUBMITTER: Shin SH

PROVIDER: S-EPMC6051845 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

Anaplastic lymphoma kinase rearrangement in surgically resected stage IA lung adenocarcinoma.

Shin Sun Hye SH Lee Hyun H Jeong Byeong-Ho BH Choi Yong Soo YS Shin Myung-Hee MH Kim Seonwoo S Han Joungho J Lee Kyung Soo KS Shim Young Mog YM Kwon O Jung OJ Kim Hojoong H

Journal of thoracic disease 20180601 6

<h4>Background</h4>It has not been determined if adjuvant chemotherapy would be helpful for completely resected early-stage lung adenocarcinoma even with unfavorable genetic markers. As the positive anaplastic lymphoma kinase (<i>ALK</i>) rearrangement is associated with aggressive clinical feature in lung adenocarcinoma, we evaluated the treatment outcomes of completely resected stage IA lung adenocarcinoma according to initial <i>ALK</i> status.<h4>Methods</h4>This is a retrospective cohort st ...[more]

PMID: 30069341

Similar Datasets

Project description:Purpose:To develop a radiogenomics classifier to assess anaplastic lymphoma kinase (ALK) gene rearrangement status in pretreated solid lung adenocarcinoma noninvasively. Materials and Methods:This study consisted of 140 consecutive pretreated solid lung adenocarcinoma patients with complete enhanced CT scans who were tested for both EGFR mutations and ALK status. Pre-contrast CT and standard post-contrast CT radiogenomics machine learning classifiers were designed as two separate classifiers. In each classifier, dataset was randomly split into training and independent testing group on a 7:3 ratio, accordingly subjected to a 5-fold cross-validation. After normalization, best feature subsets were selected by Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) or recursive feature elimination (RFE), whereupon a radiomics classifier was built with support vector machine (SVM). The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results:In classifier one, 98 cases were selected as training data set, 42 cases as independent testing data set. In classifier two, 87 cases were selected as training data set, 37 cases as independent testing data set. Both classifiers extracted 851 radiomics features. The top 25 pre-contrast features and top 19 post-contrast features were selected to build optimal ALK+ radiogenomics classifiers accordingly. The accuracies, AUCs, sensitivity, specificity, PPV, and NPV of pre-contrast CT classifier were 78.57%, 80.10% (CI: 0.6538-0.9222), 71.43%, 82.14%, 66.67%, and 85.19%, respectively. Those results of standard post-contrast CT classifier were 81.08%, 82.85% (CI: 0.6630-0.9567), 76.92%, 83.33%, 71.43%, and 86.96%. Conclusion:Solid lung adenocarcinoma ALK+ radiogenomics classifier of standard post-contrast CT radiomics biomarkers produced superior performance compared with that of pre-contrast one, suggesting that post-contrast CT radiomics should be recommended in the context of solid lung adenocarcinoma radiogenomics AI. Standard post-contrast CT machine learning radiogenomics classifier could help precisely identify solid adenocarcinoma ALK rearrangement status, which may act as a pragmatic and cost-efficient substitute for traditional invasive ALK status test.

Dataset Information

Anaplastic lymphoma kinase rearrangement in surgically resected stage IA lung adenocarcinoma.

Background

Methods

Results

Conclusions

Publications

Anaplastic lymphoma kinase rearrangement in surgically resected stage IA lung adenocarcinoma.

Similar Datasets

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