Abstract
BackgroundThe objective of this study was to assess the value of quantitative radiomics features in discriminating second primary lung cancers (SPLCs) from pulmonary metastases (PMs).MethodsThis retrospective study enrolled 252 malignant pulmonary nodules with histopathologically confirmed SPLCs or PMs and randomly assigned them to a training or validation cohort. Clinical data were collected from the electronic medical records system. The imaging and radiomics features of each nodule were extracted from CT images.ResultsA rad-score was generated from the training cohort using the least absolute shrinkage and selection operator regression. A clinical and radiographic model was constructed using the clinical and imaging features selected by univariate and multivariate regression. A nomogram composed of clinical-radiographic factors and a rad-score were developed to validate the discriminative ability. The rad-scores differed significantly between the SPLC and PM groups. Sixteen radiomics features and four clinical-radiographic features were selected to build the final model to differentiate between SPLCs and PMs. The comprehensive clinical radiographic–radiomics model demonstrated good discriminative capacity with an area under the curve of the receiver operating characteristic curve of 0.9421 and 0.9041 in the respective training and validation cohorts. The decision curve analysis demonstrated that the comprehensive model showed a higher clinical value than the model without the rad-score.ConclusionThe proposed model based on clinical data, imaging features, and radiomics features could accurately discriminate SPLCs from PMs. The model thus has the potential to support clinicians in improving decision-making in a noninvasive manner.
Highlights
Over the last few decades, owing to advancements in cancer screening and treatment, the life expectancy of cancer survivors continues to improve
Of the 245 patients, 252 solid pulmonary lesions were pathologically diagnosed as malignant foci, including 97 primary lesions and 155 metastatic lesions
There are 21 synchronous second primary lung cancers (SPLCs) and 76 metachronous ones. 55SPLCs and 82 pulmonary metastases (PMs) were included in the training set, while 42 SPLCs and 73 PMs were included in the validation set
Summary
Over the last few decades, owing to advancements in cancer screening and treatment, the life expectancy of cancer survivors continues to improve. It was estimated that approximately 16.9 million Americans were living with cancer as of January 1, 2019, and this number is expected to increase to 20 million by January 1, 2030 [1]. Cancer survivors have a higher risk of developing new primary malignant tumors than the general population. The most common newly developed primary malignant tumor is lung cancer [2]. Lung cancer remains the leading cause of cancerrelated death worldwide [3]. 30% of cancer survivors develop lung metastases [4]. The objective of this study was to assess the value of quantitative radiomics features in discriminating second primary lung cancers (SPLCs) from pulmonary metastases (PMs)
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