Individualized nomogram for predicting ALK rearrangement status in lung adenocarcinoma patients
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Individualized nomogram for predicting ALK rearrangement status in lung adenocarcinoma patients Lan Song 1 & Zhenchen Zhu 1,2 & Huanwen Wu 3 & Wei Han 4 & Xin Cheng 5 & Ji Li 3 & Huayang Du 1 & Jing Lei 1 & Xin Sui 1 & Wei Song 1 & Zheng-yu Jin 1 Received: 18 May 2020 / Revised: 2 August 2020 / Accepted: 21 September 2020 # European Society of Radiology 2020
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 ALKnegative 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.
Lan Song and Zhenchen Zhu contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-07331-5) contains supplementary material, which is available to authorized users. * Wei Song [email protected] * Zheng-yu Jin [email protected] 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 10
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