Topological radiogenomics based on persistent lifetime images for identification of epidermal growth factor receptor mutation in patients with non-small cell lung tumors

Takumi, Kodama and Hidetaka, Arimura and Tomoki, Tokuda and Kentaro, Tanaka and Hidetake, Yabuuchi and Nadia Fareeda, Muhammad Gowdh and Liam, Chong Kin and Chai, Chee Shee and Ng, Kwan Hoong (2025) Topological radiogenomics based on persistent lifetime images for identification of epidermal growth factor receptor mutation in patients with non-small cell lung tumors. Computers in Biology and Medicine, 185. pp. 1-14. ISSN 0010-4825

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Abstract

We hypothesized that persistent lifetime (PLT) images could represent tumor imaging traits, locations, and persistent contrasts of topological components (connected and hole components) corresponding to gene mutations such as epidermal growth factor receptor (EGFR) mutant signs. We aimed to develop a topological radiogenomic approach using PLT images to identify EGFR mutation-positive patients with non-small cell lung cancer (NSCLC). The PLT image was newly proposed to visualize the locations and persistent contrasts of the topological components for a sequence of binary images with consecutive thresholding of an original computed tomography (CT) image. This study employed 226 NSCLC patients (94 mutant and 132 wildtype patients) with pretreatment contrast-enhanced CT images obtained from four datasets from different countries for training and testing prediction models. Two-dimensional (2D) and three-dimensional (3D) PLT images were assumed to characterize specific imaging traits (e.g., air bronchogram sign, cavitation, and ground glass nodule) of EGFR-mutant tumors. Seven types of machine learning classification models were constructed to predict EGFR mutations with significant features selected from 2D-PLT, 3D-PLT, and conventional radiogenomic features. Among the means and standard deviations of the test areas under the receiver operating characteristic curves (AUCs) of all radiogenomic approaches in a four-fold cross-validation test, the 2D-PLT features showed the highest AUC with the lowest standard deviation of 0.927 0.08. The best radiogenomic approaches with the highest AUC were the random forest model trained with the Betti number (BN) map features (AUC = 0.984) in the internal test and the adapting boosting model trained with the BN map features (AUC = 0.717) in the external test. PLT features can be used as radiogenomic imaging biomarkers for the identification of EGFR mutation status in patients with NSCLC.

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