Diagnostic value of dual-source, dual-energy computed tomography combined with the neutrophil-lymphocyte ratio for discriminating gastric signet ring cell from mixed signet ring cell and non-signet ring cell carcinomas.

PURPOSE: To explore the diagnostic value of dual-source computed tomography (DSCT) and neutrophil to lymphocyte ratio (NLR) for differentiating gastric signet ring cell carcinoma (SRC) from mixed SRC (mSRC) and non-SRC (nSRC). METHODS: This retrospective study included patients with gastric adenocarcinoma who underwent DSCT between August 2019 and June 2021 at our Hospital. The iodine concentration in the venous phase (ICvp), standardized iodine concentration (NICVP), and the slope of the energy spectrum curve (kVP) were extracted from DSCT data. NLR was determined from laboratory results. DSCT (including ICVP, NICVP, and kVP) and combination (including DSCT model and NLR) models were established based on the multinomial logistic regression analysis. The receiver operator characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the diagnostic value. RESULTS: A total of 155 patients (SRC [n = 45, aged 61.22 ± 11.4 years], mSRC [n = 60, aged 61.09 ± 12.7 years], and nSRC [n = 50, aged 67.66 ± 8.76 years]) were included. There were significant differences in NLR, ICVP, NICVP, and kVP among the SRC, mSRC, and nSRC groups (all P < 0.001). The AUC of the combination model for SRC vs. mSRC + nSRC was 0.964 (95% CI: 0.923-1.000), with a sensitivity of 98.3% and a specificity of 86.7%, higher than with DSCT (AUC: 0.959, 95% CI: 0.919-0.998, sensitivity: 90.0%, specificity: 89.9%) or NLR (AUC: 0.670, 95% CI: 0.577-0.768, sensitivity: 62.2%, specificity: 61.8%). CONCLUSION: DSCT combined with NLR showed high diagnostic efficacy in differentiating SRC from mSRC and nSRC.

A combined encoder-transformer-decoder network for volumetric segmentation of adrenal tumors.

BACKGROUND: The morphology of the adrenal tumor and the clinical statistics of the adrenal tumor area are two crucial diagnostic and differential diagnostic features, indicating precise tumor segmentation is essential. Therefore, we build a CT image segmentation method based on an encoder-decoder structure combined with a Transformer for volumetric segmentation of adrenal tumors. METHODS: This study included a total of 182 patients with adrenal metastases, and an adrenal tumor volumetric segmentation method combining encoder-decoder structure and Transformer was constructed. The Dice Score coefficient (DSC), Hausdorff distance, Intersection over union (IOU), Average surface distance (ASD) and Mean average error (MAE) were calculated to evaluate the performance of the segmentation method. RESULTS: Analyses were made among our proposed method and other CNN-based and transformer-based methods. The results showed excellent segmentation performance, with a mean DSC of 0.858, a mean Hausdorff distance of 10.996, a mean IOU of 0.814, a mean MAE of 0.0005, and a mean ASD of 0.509. The boxplot of all test samples' segmentation performance implies that the proposed method has the lowest skewness and the highest average prediction performance. CONCLUSIONS: Our proposed method can directly generate 3D lesion maps and showed excellent segmentation performance. The comparison of segmentation metrics and visualization results showed that our proposed method performed very well in the segmentation.

Differentiation of localized pneumonic-type lung adenocarcinoma from localized pulmonary inflammatory lesion based on clinical data and multi-slice spiral computed tomography imaging features.

Background: Localized pneumonic-type lung adenocarcinoma (L-PLADC) is a special type of lung adenocarcinoma, which mimicking localized pulmonary inflammatory lesion (L-PIL), and many delayed diagnoses of L-PLADC have been identified due to insufficient clinical understanding or the lack of knowledge regarding the radiological findings. Multi-slice spiral computed tomography (MSCT) not only observes the fine structure of the lesion clearly, but also can evaluate the lesion and its surrounding tissues more intuitively, stereoscopically, and accurately using a variety of reconstruction techniques. The present study aimed to investigate the diagnostic value of clinical data and MSCT imaging features in differentiating L-PLADC from L-PIL. Methods: The clinical data and chest MSCT imaging features of 71 patients with L-PLADC and 70 patients with L-PIL were retrospectively analyzed. Seventy-one patients with L-PLADC underwent surgical resection or puncture and were confirmed as having invasive adenocarcinoma by pathology. Seventy patients with L-PIL were confirmed by clinical anti-inflammatory treatment or by puncture and surgery. The Chi-square and Mann-Whitney U tests were used to analyze the clinical data and MSCT imaging features of the included patients. Variables with P<0.05 in the univariate analysis were included in the multivariate logistic regression analysis to determine the independent risk factors for the diagnosis of L-PLADC. Results: The clinical data analysis showed that multivariate logistic regression analysis showed that irregular air bronchogram [odds ratio (OR) =15.946; P<0.001], ground-glass opacity (GGO) component (OR =12.369; P<0.001), pleural traction (OR =10.982; P<0.001), necrosis (OR =0.078; P<0.001), adjacent bronchial wall thickening (OR =0.017; P<0.001), pleural thickening (OR =0.074; P<0.001), and respiratory symptoms were independent risk factors for the diagnosis of L-PLADC [OR =0.117; the area under the curve (AUC), sensitivity, specificity, and accuracy values were 0.989, 97.2%, 94.3%, and 95.7%, respectively]. Conclusions: L-PLADC and L-PIL exhibit different clinical and MSCT imaging features. Determining these characteristics is conducive to the early diagnosis and clinical treatment of L-PLADC.

CT-based radiomic nomogram for predicting the severity of patients with COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severity of COVID-19 determines the management, treatment, and even prognosis. We aim to develop and validate a radiomics nomogram for identifying patients with severe COVID-19. METHODS: There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts, respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness. RESULTS: The radiomics signature consisting of four selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P < 0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19. CONCLUSION: We present an easy-to-use radiomics nomogram to identify the patients with severe COVID-19 for better guiding a prompt management and treatment.

Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19. METHODS: 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community-acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19. RESULTS: Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity; 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P < 0.001), had longer incubation period (P < 0.001), were more likely to have abnormal laboratory findings (P < 0.05), and be in severity status (P < 0.001). More lesions (including larger volume of lesion, consolidation, and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P < 0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation, and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three. CONCLUSIONS: Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.

Pre-existing Health Conditions and Epicardial Adipose Tissue Volume: Potential Risk Factors for Myocardial Injury in COVID-19 Patients.

Background: Myocardial injury is a life-threatening complication of coronavirus disease 2019 (COVID-19). Pre-existing health conditions and early morphological alterations may precipitate cardiac injury and dysfunction after contracting the virus. The current study aimed at assessing potential risk factors for COVID-19 cardiac complications in patients with pre-existing conditions and imaging predictors. Methods and Results: The multi-center, retrospective cohort study consecutively enrolled 400 patients with lab-confirmed COVID-19 in six Chinese hospitals remote to the Wuhan epicenter. Patients were diagnosed with or without the complication of myocardial injury by history and cardiac biomarker Troponin I/T (TnI/T) elevation above the 99th percentile upper reference limit. The majority of COVID-19 patients with myocardial injury exhibited pre-existing health conditions, such as hypertension, diabetes, hypercholesterolemia, and coronary disease. They had increased levels of the inflammatory cytokine interleukin-6 and more in-hospital adverse events (admission to an intensive care unit, invasive mechanical ventilation, or death). Chest CT scan on admission demonstrated that COVID-19 patients with myocardial injury had higher epicardial adipose tissue volume ([EATV] 139.1 (83.8-195.9) vs. 92.6 (76.2-134.4) cm2; P = 0.036). The optimal EATV cut-off value (137.1 cm2) served as a useful factor for assessing myocardial injury, which yielded sensitivity and specificity of 55.0% (95%CI, 32.0-76.2%) and 77.4% (95%CI, 71.6-82.3%) in adverse cardiac events, respectively. Multivariate logistic regression analysis showed that EATV over 137.1 cm2 was a strong independent predictor for myocardial injury in patients with COVID-19 [OR 3.058, (95%CI, 1.032-9.063); P = 0.044]. Conclusions: Augmented EATV on admission chest CT scan, together with the pre-existing health conditions (hypertension, diabetes, and hyperlipidemia) and inflammatory cytokine production, is associated with increased myocardial injury and mortality in COVID-19 patients. Assessment of pre-existing conditions and chest CT scan EATV on admission may provide a threshold point potentially useful for predicting cardiovascular complications of COVID-19.