LAUSR

Objective This article aimed to differentiate noncalcified hamartoma from pulmonary carcinoid preoperatively using computed tomography (CT) radiomics approaches. Materials and Methods The unenhanced CT (UECT) and contrast-enhanced CT (CECT) data of noncalcified hamartoma (n = 73) and pulmonary carcinoid (n = 54; typical/atypical carcinoid = 13/41) were retrospectively analyzed. The patients were randomly divided into the training and validation sets. A total of 396 radiomics features were extracted from UECT and CECT, respectively. The features were selected by using the minimum redundancy maximum relevance and the least absolute shrinkage and selection operator to construct a radiomics model. Clinical factors and radiomics features were integrated to build a nomogram model. The performance of clinical factors, radiomics, and nomogram models on the differential diagnosis between noncalcified hamartoma and carcinoid were investigated. Diagnostic performance of radiologists was also explored. Result In regard to distinguishing noncalcified hamartoma from carcinoid, the areas under the receiver operating characteristic curves of the clinical, radiomics, and nomogram models were 0.88, 0.94, and 0.96 in the training set UECT, and were 0.85, 0.92, and 0.96 in the training set CECT, respectively. The areas under the curve of the 3 models were 0.89, 0.96, and 0.96 in the validation set UECT, and were 0.79, 0.90, and 0.94 in the validation set CECT, respectively. The nomogram model exhibited good calibration and was clinically useful by decision curve analysis. Nomogram did not show significant improvement compared with radiomics, neither for UECT nor for CECT. Diagnostic performance of radiologists was lower than both radiomics and nomogram model. Conclusions Radiomics approaches may be useful in distinguishing peripheral pulmonary noncalcified hamartoma from carcinoid. Radiomics features extracted from CECT provided no significant benefit when compared with UECT.