Abstract
Objectives: To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features.Methods: This retrospective study included 335 lung adenocarcinoma patients who were randomly divided into a primary cohort (268 patients; 90 ALK-rearranged; and 178 ALK wild-type) and a test cohort (67 patients; 22 ALK-rearranged; and 45 ALK wild-type). One thousand two hundred and eighteen quantitative radiomic features were extracted from the semi-automatically delineated volume of interest (VOI) of the entire tumor using both the original and the pre-processed non-enhanced CT images. Twelve conventional CT features and seven clinical features were also collected. Normalized features were selected using a sequential of the F-test-based method, the density-based spatial clustering of applications with noise (DBSCAN) method, and the recursive feature elimination (RFE) method. Selected features were then used to build three predictive models (radiomic, radiological, and integrated models) for the ALK-rearranged phenotype by a soft voting classifier. Models were evaluated in the test cohort using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity, and the performances of three models were compared using the DeLong test.Results: Our results showed that the addition of clinical information and conventional CT features significantly enhanced the validation performance of the radiomic model in the primary cohort (AUC = 0.83–0.88, P = 0.01), but not in the test cohort (AUC = 0.80–0.88, P = 0.29). The majority of radiomic features associated with ALK mutations reflected information around and within the high-intensity voxels of lesions. The presence of the cavity and left lower lobe location were new imaging phenotypic patterns in association with ALK-rearranged tumors. Current smoking was strongly correlated with non-ALK-mutated lung adenocarcinoma.Conclusions: Our study demonstrates that radiomics-derived machine learning models can potentially serve as a non-invasive tool to identify ALK mutation of lung adenocarcinoma.
Highlights
Non-small cell lung cancer (NSCLC), especially lung adenocarcinoma, is the leading cause of cancer-related deaths worldwide [1, 2]
There was a higher percentage of central tumors in the anaplastic lymphoma kinase (ALK)+ group than in the ALK– group (P = 0.008), the peripheral lesions were more common within each group
We developed an integrated model that combined radiomic features, clinical data and conventional Computed tomography (CT) features (AUC = 0.88, accuracy = 0.79, sensitivity = 0.82, and specificity = 0.78 in the independent test cohort) for differentiating ALK mutations in lung adenocarcinoma patients
Summary
Non-small cell lung cancer (NSCLC), especially lung adenocarcinoma, is the leading cause of cancer-related deaths worldwide [1, 2]. The positivity rate of ALK is similar in the Asian population with NSCLC (4.9%) and is higher in those with lung adenocarcinomas (6.03%) [7]. Traditional molecular tests for detecting ALK rearrangements including fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC) are limited in the detection of genetic mutations and monitoring of therapeutic effects. Recent studies have reported a 30–87.5% intra-tumoural genetic heterogeneity rate for ALK fusions in NSCLCs, which challenges the accuracy of traditional ALK fusion tests based on tissues from a routine biopsy procedure [8,9,10]. Given the low occurrence of ALK mutations among NSCLCs, the purchasing of the devices and antibodies required for such molecular tests were cost-inefficient for both hospitals and patients. A non-invasive, convenient, and more reliable procedure for detecting ALK mutations is necessary
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