Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma

Predictive Value of Quantitative Parameters of 18F-FDG PET/CT in Patients with Liposarcoma

The purpose of this study was to evaluate the predictive features of baseline F-18-fluorodeoxy-D-glucose positron emission tomography (18F-FDG PET)/computed tomography (CT) parameters in patients with dedifferentiated liposarcomas (DDLPSs) and well-differentiated liposarcomas (WDLPSs) receiving systemic treatment. A total of 24 patients with liposarcoma who underwent longitudinal 18F-FDG PET/CT in systemic therapy were included. All volumetric segmentation of each tumor section and semiquantitative imaging parameters were extracted from the axial field of view from both PET and CT images. Maximum, mean, and minimum standardized uptake values (SUVmax, SUVmean, and SUVmin), Hounsfield units (HUs), and their respective changes from baseline and posttreatment were calculated. The voxel values from unenhanced CT images were correlated with PET-derived parameters. The 18F-FDG uptake decreased by more than 56% on average in responders for both SUVmax and SUVmean in DDLPS. There was a decrease in HUmax in DDLPS among responders. Using AUC > 0.8 as a reasonable predictor, we found that the ratios of SUVmaxD/HUmean, SUVmaxD/HUmedian, and SUVmeanD/HUmedian at baseline were significant indicators of the response to treatment in patients with liposarcoma. The changes in SUVmean and not just SUVmax parameters could be considered as accurate tumor response indicators. For the first time, we introduced baseline SUV/HU ratios as a valuable diagnostic tool in predicting liposarcoma treatment outcomes. This ability was not revealed by classic semiquantitative PET or CT parameters at baseline.

CT-based radiomics for differentiating peripherally located pulmonary sclerosing pneumocytoma from carcinoid

BACKGROUND

Pulmonary sclerosing pneumocytoma (PSP) and pulmonary carcinoid (PC) are difficult to distinguish based on conventional imaging examinations. In recent years, radiomics has been used to discriminate benign from malignant pulmonary lesions. However, the value of radiomics based on computed tomography (CT) images to differentiate PSP from PC has not been well explored.

PURPOSE

We aimed to investigate the feasibility of radiomics in the differentiation between PSP and PC.

METHODS

Fifty-three PSP and fifty-five PC were retrospectively enrolled and then were randomly divided into the training and test sets. Univariate and multivariable logistic analyses were carried to select clinical predictor related to differential diagnosis of PSP and PC. A total of 1316 radiomics features were extracted from the unenhanced CT (UECT) and contrast-enhanced CT (CECT) images, respectively. The minimum redundancy maximum relevance and the least absolute shrinkage and selection operator were used to select the most significant radiomics features to construct radiomics models. The clinical predictor and radiomics features were integrated to develop combined models. Two senior radiologists independently categorized each patient into PSP or PC group based on traditional CT method. The performances of clinical, radiomics, and combined models in differentiating PSP from PC were investigated by the receiver operating characteristic (ROC) curve. The diagnostic performance was also compared between the combined models and radiologists.

RESULTS

In regard to differentiating PSP from PC, the area under the curves (AUCs) of the clinical, radiomics, and combined models were 0.87, 0.96, and 0.99 in the training set UECT, and were 0.87, 0.97, and 0.98 in the training set CECT, respectively. The AUCs of the clinical, radiomics, and combined models were 0.84, 0.92, and 0.97 in the test set UECT, and were 0.84, 0.93, and 0.98 in the test set CECT, respectively. In regard to the differentiation between PSP and PC, the combined model was comparable to the radiomics model, but outperformed the clinical model and the two radiologists, whether in the test set UECT or CECT.

CONCLUSIONS

Radiomics approaches show promise in distinguishing between PSP and PC. Moreover, the integration of clinical predictor (gender) has the potential to enhance the diagnostic performance even further.