Clinical­imaging­radiomic nomogram based on unenhanced CT effectively predicts adrenal metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas.

The aim of the present study was to develop and evaluate a clinical-imaging-radiomic nomogram based on pre-enhanced computed tomography (CT) for pre-operative differentiation lipid-poor adenomas (LPAs) from metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas (AIs). A total of 196 consecutive patients with lung cancer, who underwent initial chest or abdominal pre-enhanced CT scan with small hyperattenuating AIs, were included. The patients were randomly divided into a training cohort with 71 cases of LPAs and 66 cases of metastases, and a testing cohort with 31 cases of LPAs and 28 cases of metastases. Plain CT radiological and clinical features were evaluated, including sex, age, size, pre-enhanced CT value (CTpre), shape, homogeneity and border. A total of 1,316 radiomic features were extracted from the plain CT images of the AIs, and the significant features selected by the least absolute shrinkage and selection operator were used to establish a Radscore. Subsequently, a clinical-imaging-radiomic model was developed by multivariable logistic regression incorporating the Radscore with significant clinical and imaging features. This model was then presented as a nomogram. The performance of the nomogram was assessed by calibration curves and decision curve analysis (DCA). A total of 4 significant radiomic features were incorporated in the Radscore, which yielded notable area under the receiver operating characteristic curves (AUCs) of 0.920 in the training dataset and 0.888 in the testing dataset. The clinical-imaging-radiomic nomogram incorporating the Radscore, CTpre, sex and age revealed favourable differential diagnostic performance (AUC: Training, 0.968; testing, 0.915) and favourable calibration curves. The nomogram was revealed to be more useful than the Radscore and the clinical-imaging model in clinical practice by DCA. The clinical-imaging-radiomics nomogram based on initial plain CT images by integrating the Radscore and clinical-imaging factors provided a potential tool to effectively differentiate LPAs from metastases in patients with lung cancer with small hyperattenuating AIs.

18F-FDG-PET/CT-based machine learning model evaluates indeterminate adrenal nodules in patients with extra-adrenal malignancies.

BACKGROUND: To assess the value of an 18F-FDG-positron emission tomography/computed tomography (PET/CT)-based machine learning model for distinguishing between adrenal benign nodules (ABNs) and adrenal metastases (AMs) in patients with indeterminate adrenal nodules and extra-adrenal malignancies. METHODS: A total of 303 patients who underwent 18F-FDG-PET/CT with indeterminate adrenal nodules and extra-adrenal malignancies from March 2015 to June 2021 were included in this retrospective study (training dataset (n = 182): AMs (n = 97), ABNs (n = 85); testing dataset (n = 121): AMs (n = 68), ABNs (n = 55)). The clinical and PET/CT imaging features of the two groups were analyzed. The predictive model and simplified scoring system for distinguishing between AMs and ABNs were built based on clinical and PET/CT risk factors using multivariable logistic regression in the training cohort. The performances of the predictive model and simplified scoring system in both the training and testing cohorts were evaluated by the areas under the receiver operating characteristic curves (AUCs) and calibration curves. The comparison of AUCs was evaluated by the DeLong test. RESULTS: The predictive model included four risk factors: sex, the ratio of the maximum standardized uptake value (SUVmax) of adrenal lesions to the mean liver standardized uptake value, the value on unenhanced CT (CTU), and the clinical stage of extra-adrenal malignancies. The model achieved an AUC of 0.936 with a specificity, sensitivity and accuracy of 0.918, 0.835, and 0.874 in the training dataset, respectively, while it yielded an AUC of 0.931 with a specificity, sensitivity, and accuracy of 1.00, 0.735, and 0.851 in the testing dataset, respectively. The simplified scoring system had comparable diagnostic value to the predictive model in both the training (AUC 0.938, sensitivity: 0.825, specificity 0.953, accuracy 0.885; P = 0.5733) and testing (AUC 0.931, sensitivity 0.735, specificity 1.000, accuracy 0.851; P = 1.00) datasets. CONCLUSIONS: Our study showed the potential ability of a machine learning model and a simplified scoring system based on clinical and 18F-FDG-PET/CT imaging features to predict AMs in patients with indeterminate adrenal nodules and extra-adrenal malignancies. The simplified scoring system is simple, convenient, and easy to popularize.

Small hyperattenuating adrenal nodules in patients with lung cancer: Differentiation of metastases from adenomas on biphasic contrast-enhanced computed tomography.

Objective: The objective of this study was to evaluate the value of biphasic contrast-enhanced computed tomography (CECT) in the differential diagnosis of metastasis and lipid-poor adenomas (LPAs) in lung cancer patients with unilateral small hyperattenuating adrenal nodule. Materials and methods: This retrospective study included 241 lung cancer patients with unilateral small hyperattenuating adrenal nodule (metastases, 123; LPAs, 118). All patients underwent plain chest or abdominal computed tomography (CT) scan and biphasic CECT scan, including arterial and venous phases. Qualitative and quantitative clinical and radiological characteristics of the two groups were compared using univariate analysis. An original diagnostic model was developed using multivariable logistic regression, and then, according to odds ratio (OR) of the risk factors of metastases, a diagnostic scoring model was developed. The areas under the receiver operating characteristic curves (AUCs) of the two diagnostic models were compared by DeLong test. Results: Compared with LAPs, metastases were older and showed more frequently irregular in shape and cystic degeneration/necrosis (all p < 0.05). Enhancement ratios on venous (ERV) and arterial (ERA) phase of LAPs were noticeably higher than that of metastases, whereas CT values in unenhanced phase (UP) of LPAs were noticeably lower than that of metastases (all p < 0.05). Compared with LAPs, the proportions of male and III/IV clinical stage and small-cell lung cancer (SCLL) were significantly higher for metastases (all p < 0.05). As for peak enhancement phase, LPAs showed relatively faster wash-in and earlier wash-out enhancement pattern than metastases (p < 0.001). Multivariate analysis revealed age ≥ 59.5 years (OR: 2.269; p = 0.04), male (OR: 3.511; p = 0.002), CT values in UP ≥ 27.5 HU (OR: 6.968; p < 0.001), cystic degeneration/necrosis (OR: 3.076; p = 0.031), ERV ≤ 1.44 (OR: 4.835; p < 0.001), venous phase or equally enhanced (OR: 16.907; p < 0.001 or OR: 14.036; p < 0.001), and clinical stage II or III or IV (OR: 3.550; p = 0.208 or OR: 17.535; p = 0.002 or OR: 20.241; p = 0.001) were risk factors for diagnosis of metastases. AUCs of the original diagnostic model and the diagnostic scoring model for metastases were 0.919 (0.883-0.955) and 0.914 (0.880-0.948), respectively. There was no statistical significance of AUC between the two diagnostic model (p = 0.644). Conclusions: Biphasic CECT performed well diagnostic ability in differentiating metastases from LAPs. The diagnostic scoring model is easy to popularize due to simplicity and convenience.

Value of CT spectral imaging in the differential diagnosis of sarcoidosis and Hodgkin's lymphoma based on mediastinal enlarged lymph node: A STARD compliant article.

To investigate the imaging characteristics of sarcoidosis and Hodgkin's lymphoma based on mediastinal enlarged lymph node using spectral CT and evaluate whether the quantitative information can improve the differential diagnosis of these diseases. This retrospective study was approved by the institutional review board, and written informed consent was obtained from all patients. Overall, 21 patients with sarcoidosis and 39 patients with Hodgkin's lymphoma were examined with CT spectral imaging during the arterial phase (AP) and venous phase (VP). The CT values on 40 to 140 keV monochromatic images and iodine (water) concentrations of enlarged lymph nodes were obtained in AP and VP. Iodine concentrations (ICs) were normalized to the iodine concentration in the aorta. The differences in normalized iodine concentrations (NICs) and hounsfield units (HU) curve slop (λHU) were calculated. Anatomical distribution of mediastinal lymph nodes and morphologic features were also compared. Receiver operating characteristic curves were generated to help establish threshold values for the parameters required for the significant differentiation of sarcoidosis from lymphomas. The CT values on 40 to 100 keV monochromatic images in AP and 40 to 50 keV in VP were higher in sarcoidosis than those in Hodgkin's lymphoma, the differences were statistically significant (P < .05); NICs during the AP and λHU during the AP (VP) in patients with sarcoidosis differed significantly from those in patients with Hodgkin's lymphoma. Receiver operating characteristic curves analysis showed that the monochromatic CT value on 40 keV in AP had the highest sensitivity (71.4%) and specificity (100%) in differentiating sarcoidosis from Hodgkin's lymphoma. The anatomic distribution, coalescence, calcification, compression, enhancement pattern and enhancement degree of the mediastinal enlarged lymph node differed significantly between the groups (P < .05). The combination of monochromatic CT value, NICs and λHU had higher sensitivity and specificity than did those of conventional qualitative CT image analysis during the combined phases. CT spectral imaging has promising potential for the diagnostic differentiation of Hodgkin's lymphomas and sarcoidosis. The monochromatic CT value, iodine content and λHU could be valuable parameters for differentiating Hodgkin's lymphomas and sarcoidosis based on mediastinal enlarged lymph node.

Radiomics approach based on biphasic CT images well differentiate "early stage" of adrenal metastases from lipid-poor adenomas: A STARD compliant article.

The aim of the study was to develop an optimal radiomics model based on abdominal contrast-enhanced computed tomography (CECT) for pre-operative differentiation of "early stage" adrenal metastases from lipid-poor adenomas (LPAs). This retrospective study included 188 patients who underwent abdominal CECT (training cohort: LPAs, 68; metastases, 64; validation cohort: LPAs, 29; metastases, 27). Abdominal CECT included plain, arterial, portal, and venous imaging. Clinical and CECT radiological features were assessed and significant features were selected. Radiomic features of the adrenal lesions were extracted from four-phase CECT images. Significant radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression. The clinical-radiological, unenhanced radiomics, arterial radiomics, portal radiomics, venous radiomics, combined radiomics, and clinical-radiological-radiomics models were established using a support vector machine (SVM). The DeLong test was used to compare the areas under the receiver operating characteristic curves (AUCs) of all models. The AUCs of the unenhanced (0.913), arterial (0.845), portal (0.803), and venous (0.905) radiomics models were all higher than those of the clinical-radiological model (0.788) in the testing dataset. The AUC of the combined radiomics model (incorporating plain and venous radiomics features) was further improved to 0.953, which was significantly higher than portal radiomics model (P = .033) and clinical-radiological model (P = .009), with the highest accuracy (89.13%) and a relatively stable sensitivity (91.67%) and specificity (86.36%). As the optimal model, the combined radiomics model based on biphasic CT images is effective enough to differentiate "early stage" adrenal metastases from LPAs by reducing the radiation dose.

[Different Energy Spectrum CT Findings between Anterior Mediastinal Lymphoma and Thymic Carcinoma].

Objective To investigate the differences in energy spectrum CT findings between anterior mediastinal lymphoma and thymic carcinoma. Methods Twenty-two cases of anterior mediastinal lymphoma and 28 cases of thymic carcinoma confirmed by biopsy in Tangshan People's Hospital were selected.The CT values and changes of iodine content and water content in lesion sites were measured by energy spectrum analysis software.The differences between anterior mediastinal lymphoma and thymic carcinoma were compared. Results The single-energy CT value of 40-80 keV in thymus carcinoma was higher than that in anterior mediastinal lymphoma(P=0.001,P=0.037,P=0.042,P=0.034,P=0.002;P=0.016,P=0.013,P=0.018,P=0.024,P=0.012).The difference in the single-energy CT value of 90-110 keV between anterior mediastinal lymphoma and thymic carcinoma showed no statistical significance(all P>0.05).The concentrations of water in the arterial and venous stages of thymic carcinoma were significantly lower than those in the anterior mediastinal lymphoma(P=0.030,P=0.037),whereas the iodine concentrations were significantly higher(P=0.026,P=0.000). Conclusion Anterior mediastinal lymphoma and thymic carcinoma have remarkably different 40-80 keV single energy CT value and iodine concentration in arterial and venous phases,which may be helpful for the differential diagnosis of these two malignancies.

Hepatocellular carcinoma and focal nodular hyperplasia of the liver: differentiation with CT spectral imaging.

OBJECTIVES: To investigate the value of CT spectral imaging in differentiating hepatocellular carcinoma (HCC) from focal nodular hyperplasia (FNH) during the arterial phase (AP) and portal venous phase (PP). METHODS: Fifty-eight patients with 42 HCCs and 16 FNHs underwent spectral CT during AP and PP. The lesion-liver contrast-to-noise ratio (CNR) at different energy levels, normalised iodine concentrations (NIC) and the lesion-normal parenchyma iodine concentration ratio (LNR) were calculated. The two-sample t test compared quantitative parameters. Two readers qualitatively assessed lesion types according to imaging features. Sensitivity and specificity of the qualitative and quantitative studies were compared. RESULTS: In general, CNRs at low energy levels (40-70 keV) were higher than those at high energy levels (80-140 keV). NICs and LNRs for HCC differed significantly from those of FNH: mean NICs were 0.25 mg/mL ± 0.08 versus 0.42 mg/mL ± 0.12 in AP and 0.52 mg/mL ± 0.14 versus 0.86 mg/mL ± 0.18 in PP. Mean LNRs were 2.97 ± 0.50 versus 6.15 ± 0.62 in AP and 0.99 ± 0.12 versus 1.22 ± 0.26 in PP. NICs and LNRs for HCC were lower than those of FNH. LNR in AP had the highest sensitivity and specificity in differentiating HCC from FNH. CONCLUSIONS: CT spectral imaging may help to increase detectability of lesions and accuracy of differentiating HCC from FNH. KEY POINTS: • CT spectral imaging may help to detect hepatocellular carcinoma (HCC). • CT spectral imaging may help differentiate HCC from focal nodular hyperplasia. • Quantitative analysis of iodine concentration provides greater diagnostic confidence. • Treatment can be given with greater confidence.