Abstract
BACKGROUND: Distinguishing multiple primary lung cancer (MPLC) from intrapulmonary metastasis (IPM) is critical for their disparate treatment strategy and prognosis. This study aimed to establish a non-invasive model to make the differentiation pre-operatively. METHODS: We retrospectively studied 168 patients with multiple lung cancers (307 pairs of lesions) including 118 cases for modeling and internal validation, and 50 cases for independent external validation. Radiomic features on computed tomography (CT) were extracted to calculate the absolute deviation of paired lesions. Features were then selected by correlation coefficients and random forest classifier 5-fold cross-validation, based on which the lesion pair relation estimation (PRE) model was developed. A major voting strategy was used to decide diagnosis for cases with multiple pairs of lesions. Cases from another institute were included as the external validation set for the PRE model to compete with two experienced clinicians. RESULTS: Seven radiomic features were selected for the PRE model construction. With major voting strategy, the mean area under receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the training versus internal validation versus external validation cohort to distinguish MPLC were 0.983 versus 0.844 versus 0.793, 0.942 versus 0.846 versus 0.760, 0.905 versus 0.728 versus 0.727, and 0.962 versus 0.910 versus 0.769, respectively. AUCs of the two clinicians were 0.619 and 0.580. CONCLUSIONS: The CT radiomic feature-based lesion PRE model is potentially an accurate diagnostic tool for the differentiation of MPLC and IPM, which could help with clinical decision making.