Literature DB >> 33707479

Value of CT features for predicting EGFR mutations and ALK positivity in patients with lung adenocarcinoma.

Xiaoyu Han1,2, Jun Fan3, Yumin Li1,2, Yukun Cao1,2, Jin Gu1,2, Xi Jia1,2, Yuhui Wang4,5, Heshui Shi6,7.   

Abstract

The aim of this study was to identify the relationships of epidermal growth factor receptor (EGFR) mutations and anaplastic large-cell lymphoma kinase (ALK) status with CT characteristics in adenocarcinoma using the largest patient cohort to date. In this study, preoperative chest CT findings prior to treatment were retrospectively evaluated in 827 surgically resected lung adenocarcinomas. All patients were tested for EGFR mutations and ALK status. EGFR mutations were found in 489 (59.1%) patients, and ALK positivity was found in 57 (7.0%). By logistic regression, the most significant independent prognostic factors of EGFR effective mutations were female sex, nonsmoker status, GGO air bronchograms and pleural retraction. For EGFR mutation prediction, receiver operating characteristic (ROC) curves yielded areas under the curve (AUCs) of 0.682 and 0.758 for clinical only or combined CT features, respectively, with a significant difference (p < 0.001). Furthermore, the exon 21 mutation rate in GGO was significantly higher than the exon 19 mutation rate(p = 0.029). The most significant independent prognostic factors of ALK positivity were age, solid-predominant-subtype tumours, mucinous lung adenocarcinoma, solid tumours and no air bronchograms on CT. ROC curve analysis showed that for predicting ALK positivity, the use of clinical variables combined with CT features (AUC = 0.739) was superior to the use of clinical variables alone (AUC = 0.657), with a significant difference (p = 0.0082). The use of CT features for patients may allow analyses of tumours and more accurately predict patient populations who will benefit from therapies targeting treatment.

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Year:  2021        PMID: 33707479      PMCID: PMC7952563          DOI: 10.1038/s41598-021-83646-7

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  38 in total

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4.  Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology.

Authors:  Wenting Tu; Guangyuan Sun; Li Fan; Yun Wang; Yi Xia; Yu Guan; Qiong Li; Di Zhang; Shiyuan Liu; Zhaobin Li
Journal:  Lung Cancer       Date:  2019-03-26       Impact factor: 5.705

5.  Comparative analysis of clinicoradiologic characteristics of lung adenocarcinomas with ALK rearrangements or EGFR mutations.

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Journal:  Eur Radiol       Date:  2015-01-11       Impact factor: 5.315

6.  EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy.

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7.  Are there imaging characteristics associated with lung adenocarcinomas harboring ALK rearrangements?

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Journal:  Lung Cancer       Date:  2014-09-17       Impact factor: 5.705

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9.  Comparison of Clinicopathological Features and Prognosis between ALK Rearrangements and EGFR Mutations in Surgically Resected Early-stage Lung Adenocarcinoma.

Authors:  Pupu Li; Qiongqiong Gao; Xiangli Jiang; Zhongli Zhan; Qingna Yan; Zhaona Li; Chun Huang
Journal:  J Cancer       Date:  2019-01-01       Impact factor: 4.207

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  5 in total

1.  A commentary on the "Clinical and CT patterns to predict EGFR mutation in patients with non-small cell lung cancer: A systematic literature review and meta-analysis".

Authors:  Ali Akhavi Milani
Journal:  Eur J Radiol Open       Date:  2022-02-19

2.  Deep learning analysis to predict EGFR mutation status in lung adenocarcinoma manifesting as pure ground-glass opacity nodules on CT.

Authors:  Hyun Jung Yoon; Jieun Choi; Eunjin Kim; Sang-Won Um; Noeul Kang; Wook Kim; Geena Kim; Hyunjin Park; Ho Yun Lee
Journal:  Front Oncol       Date:  2022-09-02       Impact factor: 5.738

Review 3.  Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients.

Authors:  Lanlan Liu; Xianzhi Xiong
Journal:  Curr Oncol       Date:  2021-12-24       Impact factor: 3.677

Review 4.  Molecular typing of lung adenocarcinoma with computed tomography and CT image-based radiomics: a narrative review of research progress and prospects.

Authors:  Jing-Wen Ma; Meng Li
Journal:  Transl Cancer Res       Date:  2021-09       Impact factor: 1.241

Review 5.  Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges.

Authors:  Francisco Silva; Tania Pereira; Inês Neves; Joana Morgado; Cláudia Freitas; Mafalda Malafaia; Joana Sousa; João Fonseca; Eduardo Negrão; Beatriz Flor de Lima; Miguel Correia da Silva; António J Madureira; Isabel Ramos; José Luis Costa; Venceslau Hespanhol; António Cunha; Hélder P Oliveira
Journal:  J Pers Med       Date:  2022-03-16
  5 in total

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