Individualized nomogram for predicting ALK rearrangement status in lung adenocarcinoma patients

• Published in: European radiology 2021-04, Vol.31 (4), p.2034-2047
• Main Authors: Song, Lan, Zhu, Zhenchen, Wu, Huanwen, Han, Wei, Cheng, Xin, Li, Ji, Du, Huayang, Lei, Jing, Sui, Xin, Song, Wei, Jin, Zheng-yu
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Adenocarcinoma - diagnostic imaging; Adenocarcinoma - genetics; Adenocarcinoma of Lung - diagnostic imaging; Adenocarcinoma of Lung - genetics; Anaplastic Lymphoma Kinase - genetics; Chest; Computed tomography; Diagnostic Radiology; Enlargement; Gene Rearrangement; Growth patterns; Histology; Humans; Imaging; Internal Medicine; Interventional Radiology; Kinases; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - genetics; Lungs; Lymph nodes; Lymphoma; Medical imaging; Medicine; Medicine & Public Health; Mutation; Neuroradiology; Nomograms; Positron Emission Tomography Computed Tomography; Prediction models; Protein-tyrosine kinase; Radiology; Receptor Protein-Tyrosine Kinases - genetics; Regression analysis; Retrospective Studies; Sensitivity; Smoking; Statistical analysis; Training; Ultrasound
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-020-07331-5

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.