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Individualized nomogram for predicting ALK rearrangement status in lung adenocarcinoma patients

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Abstract

Objectives

To develop a nomogram to identify anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients using clinical, CT, PET/CT, and histopathological features.

Methods

This retrospective study included 399 lung adenocarcinoma patients (129 ALK-rearranged patients and 270 ALK-negative patients) that were randomly divided into a training cohort and an internal validation cohort (4:1 ratio). Clinical factors, radiologist-defined CT features, maximum standard uptake values (SUVmax), and histopathological features were used to construct predictive models with stepwise backward-selection multivariate logistic regression (MLR). The models were then evaluated using the AUC. The integrated model was compared to the clinico-radiological model using the DeLong test to evaluate the role of histopathological features. An associated individualized nomogram was established.

Results

The integrated model reached an AUC of 0.918 (95% CI, 0.886–0.950), sensitivity of 0.774, and specificity of 0.934 in the training cohort and an AUC of 0.857 (95% CI, 0.777–0.937), sensitivity of 0.739, and specificity of 0.810 in the validation cohort. The MLR analysis showed that younger age, never smoker, lymph node enlargement, the presence of cavity, high SUVmax, solid or micropapillary predominant histology subtype, and local invasiveness were strong and independent predictors of ALK rearrangements. The nomogram calculated the risk of harboring ALK mutation for lung adenocarcinoma patients and exhibited a good generalization ability.

Conclusion

Our study demonstrates that histopathological features added value to the imaging characteristics-based model. The nomogram with clinical, imaging, and histopathological features can serve as a supplementary non-invasive tool to evaluate the probability of ALK rearrangement in lung adenocarcinoma.

Key Points

• The developed nomogram can accurately predict the probability of lung adenocarcinoma harboring ALK-fused gene.

• Pathological analysis is important to predict ALK rearrangement in lung adenocarcinoma.

• Lung adenocarcinoma with lepidic predominant growth pattern and TTF-1 negativity is unlikely to have ALK rearrangement.

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Abbreviations

AIS:

Adenocarcinoma in situ

APA:

Acinar predominant adenocarcinoma

AUC:

Area under the curve

CI:

Confidence interval

CT:

Computed tomography

DICOM:

Digital imaging and communications in medicine

EML4:

Echinoderm microtubule associated protein-like 4

GGO:

Ground-glass opacity

IAC:

Invasive adenocarcinoma

IMA:

Invasive mucinous adenocarcinoma

LPA:

Lepidic predominant adenocarcinoma

MIA:

Minimally invasive adenocarcinoma

MLR:

Multivariate logistic regression

MPA:

Micropapillary predominant adenocarcinoma

NCCN:

National Comprehensive Cancer Network

NSCLC:

Non-small-cell lung cancer

PPA:

Papillary predominant adenocarcinoma

ROC:

Receiver operating characteristic

SPA:

Solid predominant adenocarcinoma

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Funding

This study has received funding from the Beijing Science and Technology Planning Project (Z201100005620008) and the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019PT320008 and 2018PT32003).

Author information

Author notes

  1. Lan Song and Zhenchen Zhu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China

    Lan Song, Zhenchen Zhu, Huayang Du, Jing Lei, Xin Sui, Wei Song & Zheng-yu Jin

  2. 4+4 MD Program, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Zhenchen Zhu

  3. Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Huanwen Wu & Ji Li

  4. Department of Epidemiology and Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China

    Wei Han

  5. Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Xin Cheng

Authors
  1. Lan Song
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  2. Zhenchen Zhu
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  10. Wei Song
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Corresponding authors

Correspondence to Wei Song or Zheng-yu Jin.

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Guarantor

The scientific guarantor of this publication is Zheng-yu Jin.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Dr. Wei Han (one of the authors) kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board of our institution due to the retrospective nature of the study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Song, L., Zhu, Z., Wu, H. et al. Individualized nomogram for predicting ALK rearrangement status in lung adenocarcinoma patients. Eur Radiol 31, 2034–2047 (2021). https://doi.org/10.1007/s00330-020-07331-5

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  • DOI: https://doi.org/10.1007/s00330-020-07331-5

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