Literature DB >> 26712103

Association Between Computed Tomographic Features and Kirsten Rat Sarcoma Viral Oncogene Mutations in Patients With Stage I Lung Adenocarcinoma and Their Prognostic Value.

Hua Wang1, Matthew B Schabath2, Ying Liu1, Olya Stringfield3, Yoganand Balagurunathan3, John J Heine3, Steven A Eschrich4, Zhaoxiang Ye5, Robert J Gillies6.   

Abstract

BACKGROUND: We investigated the association between computed tomographic (CT) features and Kirsten rat sarcoma viral oncogene (KRAS) mutations in patients with stage I lung adenocarcinoma and their prognostic value. PATIENTS AND METHODS: A total of 79 patients with pathologic stage I lung adenocarcinoma, available KRAS mutational status, preoperative CT images available, and survival data were included in the present study. Seven CT features, including spiculation, concavity, ground-glass opacity, bubble-like lucency, air bronchogram, pleural retraction, and pleural attachment, were evaluated. The association among the clinical characteristics, CT features, and mutational status was analyzed using Student's t test, the χ(2) test or Fisher's exact test, and logistic regression. The association among CT features, mutational status, and overall survival was analyzed using Kaplan-Meier survival curves with the log-rank test and Cox proportional hazard regression.
RESULTS: The prevalence of KRAS mutations was 41.77%. Spiculation was significantly associated with the presence of KRAS mutations (odds ratio, 2.99; 95% confidence interval [CI], 1.16-7.68). Although KRAS mutational status was not significantly associated with overall survival, the presence of pleural attachment was associated with an increased risk of death (hazard ratio, 2.46; 95% CI, 1.09-5.53). When analyzing KRAS mutational status and pleural attachment combined, patients with wild-type KRAS and no pleural attachment had significantly better survival than did those with wild-type KRAS and pleural attachment (P = .014).
CONCLUSION: These data suggest that spiculation is associated with KRAS mutations and pleural attachment is associated with overall survival in patients with stage I lung adenocarcinoma. Combining the analysis of KRAS mutational status and CT features could better predict survival.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computed tomography; Imaging; KRAS; Lung neoplasms; Prognosis

Mesh:

Substances:

Year:  2015        PMID: 26712103      PMCID: PMC4887405          DOI: 10.1016/j.cllc.2015.11.002

Source DB:  PubMed          Journal:  Clin Lung Cancer        ISSN: 1525-7304            Impact factor:   4.785


  40 in total

1.  Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation.

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2.  Predictive CT findings of malignancy in ground-glass nodules on thin-section chest CT: the effects on radiologist performance.

Authors:  Hyun Ju Lee; Jin Mo Goo; Chang Hyun Lee; Chang Min Park; Kwang Gi Kim; Eun-Ah Park; Ho Yun Lee
Journal:  Eur Radiol       Date:  2008-10-17       Impact factor: 5.315

3.  Prognostic significance of high-resolution CT findings in small peripheral adenocarcinoma of the lung: a retrospective study on 64 patients.

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4.  Cytomorphologic features of advanced lung adenocarcinomas tested for EGFR and KRAS mutations: a retrospective review of 50 cases.

Authors:  Jonathan D Marotti; Mary C Schwab; Nancy J McNulty; James R Rigas; Peter A DeLong; Vincent A Memoli; Gregory J Tsongalis; Vijayalakshmi Padmanabhan
Journal:  Diagn Cytopathol       Date:  2011-06-16       Impact factor: 1.582

5.  Phosphoinositide-3-kinase catalytic alpha and KRAS mutations are important predictors of resistance to therapy with epidermal growth factor receptor tyrosine kinase inhibitors in patients with advanced non-small cell lung cancer.

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6.  Prognostic value of specific KRAS mutations in lung adenocarcinomas.

Authors:  J M Siegfried; A T Gillespie; R Mera; T J Casey; P Keohavong; J R Testa; J D Hunt
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  1997-10       Impact factor: 4.254

Review 7.  Are RAS mutations predictive markers of resistance to standard chemotherapy?

Authors:  Yohann Loriot; Pierre Mordant; Eric Deutsch; Ken André Olaussen; Jean-Charles Soria
Journal:  Nat Rev Clin Oncol       Date:  2009-07-14       Impact factor: 66.675

8.  Prognostic value of KRAS mutations and Ki-67 expression in stage I lung adenocarcinomas.

Authors:  Tetsukan Woo; Koji Okudela; Takuya Yazawa; Nobuyuki Wada; Nobuo Ogawa; Naoki Ishiwa; Michihiko Tajiri; Yasushi Rino; Hitoshi Kitamura; Munetaka Masuda
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9.  KRAS mutation as the biomarker of response to chemotherapy and EGFR-TKIs in patients with advanced non-small cell lung cancer: clues for its potential use in second-line therapy decision making.

Authors:  Alma D Campos-Parra; Carlos Zuloaga; María Eugenia Vazquez Manríquez; Alejandro Avilés; Jose Borbolla-Escoboza; Andrés Cardona; Abelardo Meneses; Oscar Arrieta
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10.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

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Journal:  Eur Radiol       Date:  2017-08-07       Impact factor: 5.315

Review 2.  Pulmonary ground-glass opacity: computed tomography features, histopathology and molecular pathology.

Authors:  Jian-Wei Gao; Stefania Rizzo; Li-Hong Ma; Xiang-Yu Qiu; Arne Warth; Nobuhiko Seki; Mizue Hasegawa; Jia-Wei Zou; Qian Li; Marco Femia; Tang-Feng Lv; Yong Song
Journal:  Transl Lung Cancer Res       Date:  2017-02

Review 3.  A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers.

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4.  Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer.

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5.  Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients.

Authors:  Ilke Tunali; Olya Stringfield; Albert Guvenis; Hua Wang; Ying Liu; Yoganand Balagurunathan; Philippe Lambin; Robert J Gillies; Matthew B Schabath
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6.  Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer.

Authors:  Stephen S F Yip; Ying Liu; Chintan Parmar; Qian Li; Shichang Liu; Fangyuan Qu; Zhaoxiang Ye; Robert J Gillies; Hugo J W L Aerts
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7.  Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial.

Authors:  Dmitry Cherezov; Samuel H Hawkins; Dmitry B Goldgof; Lawrence O Hall; Ying Liu; Qian Li; Yoganand Balagurunathan; Robert J Gillies; Matthew B Schabath
Journal:  Cancer Med       Date:  2018-12-01       Impact factor: 4.452

8.  Analysis of the clinicopathological characteristics, genetic phenotypes, and prognostic of pure mucinous adenocarcinoma.

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9.  Computer-Aided Nodule Assessment and Risk Yield (CANARY) may facilitate non-invasive prediction of EGFR mutation status in lung adenocarcinomas.

Authors:  Ryan Clay; Benjamin R Kipp; Sarah Jenkins; Ron A Karwoski; Fabien Maldonado; Srinivasan Rajagopalan; Jesse S Voss; Brian J Bartholmai; Marie Christine Aubry; Tobias Peikert
Journal:  Sci Rep       Date:  2017-12-15       Impact factor: 4.379

10.  Dual-energy spectral CT characteristics in surgically resected lung adenocarcinoma: comparison between Kirsten rat sarcoma viral oncogene mutations and epidermal growth factor receptor mutations.

Authors:  Meng Li; Li Zhang; Wei Tang; Jian-Chun Duan; Yu-Jing Jin; Lin-Lin Qi; Ning Wu
Journal:  Cancer Imaging       Date:  2019-11-29       Impact factor: 3.909

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