Influence of abdominal fat distribution and inflammatory status on post-operative prognosis in non-small cell lung cancer patients: a retrospective cohort study.

PURPOSE: To evaluate the influence of visceral fat area (VFA), subcutaneous fat area (SFA), the systemic immune-inflammation index (SII) and total inflammation-based systemic index (AISI) on the postoperative prognosis of non-small cell lung cancers (NSCLC) patients. METHODS: 266 NSCLC patients received surgery from two academic medical centers were included. To assess the effect of abdominal fat measured by computed tomography (CT) imaging and inflammatory indicators on patients' overall survival (OS) and progression-free survival (PFS), Kaplan-Meier survival analysis and Cox proportional hazards models were used. RESULTS: Kaplan-Meier analysis showed the OS and PFS of patients in high-VFA group was better than low-VFA group (p < 0.05). AISI and SII were shown to be risk factors for OS and PFS (p < 0.05) after additional adjustment for BMI (Cox regression model II). After further adjustment for VFA (Cox regression model III), low-SFA group had longer OS (p < 0.05). Among the four subgroups based on VFA (high/low) and SFA (high/low) (p < 0.05), the high-VFA & low-SFA group had the longest median OS (108 months; 95% CI 74-117 months) and PFS (85 months; 95% CI 65-117 months), as well as the lowest SII and AISI (p < 0.05). Low-SFA was a protective factor for OS with different VFA stratification (p < 0.05). CONCLUSION: VFA, SFA, SII and AISI may be employed as significant prognostic markers of postoperative survival in NSCLC patients. Moreover, excessive SFA levels may encourage systemic inflammation decreasing the protective impact of VFA, which may help to provide targeted nutritional support and interventions for postoperative NSCLC patients with poor prognosis.

Prediction of osteoporosis using radiomics analysis derived from single source dual energy CT.

BACKGROUND: With the aging population of society, the incidence rate of osteoporosis is increasing year by year. Early diagnosis of osteoporosis plays a significant role in the progress of disease prevention. As newly developed technology, computed tomography (CT) radiomics could discover radiomic features difficult to recognize visually, providing convenient, comprehensive and accurate osteoporosis diagnosis. This study aimed to develop and validate a clinical-radiomics model based on the monochromatic imaging of single source dual-energy CT for osteoporosis prediction. METHODS: One hundred sixty-four participants who underwent both single source dual-energy CT and quantitative computed tomography (QCT) lumbar-spine examination were enrolled in a study cohort including training datasets (n = 114 [30 osteoporosis and 84 non-osteoporosis]) and validation datasets (n = 50 [12 osteoporosis and 38 non-osteoporosis]). One hundred seven radiomics features were extracted from 70-keV monochromatic CT images. With QCT as the reference standard, a radiomics signature was built by using least absolute shrinkage and selection operator (LASSO) regression on the basis of reproducible features. A clinical-radiomics model was constructed by incorporating the radiomics signature and a significant clinical predictor (age) using multivariate logistic regression analysis. Model performance was assessed by its calibration, discrimination and clinical usefulness. RESULTS: The radiomics signature comprised 14 selected features and showed good calibration and discrimination in both training and validation cohorts. The clinical-radiomics model, which incorporated the radiomics signature and a significant clinical predictor (age), also showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval, 0.903-0.952) in the training cohort and an AUC of 0.988 (95% confidence interval, 0.967-0.998) in the validation cohort, and good calibration. The clinical-radiomics model stratified participants into groups with osteoporosis and non-osteoporosis with an accuracy of 94.0% in the validation cohort. Decision curve analysis (DCA) demonstrated that the radiomics signature and the clinical-radiomics model were clinically useful. CONCLUSIONS: The clinical-radiomics model incorporating the radiomics signature and a clinical parameter had a good ability to predict osteoporosis based on dual-energy CT monoenergetic imaging.

Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics.

The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.

Combination of 3.0T magnetic resonance imaging T2 mapping with texture analysis for evaluating the degeneration of lumbar facet joints. / 3.0Tç£å ±æŒ¯æˆåƒT2 mappingè”åˆçº¹ç†åˆ†æžè¯„ä¼°è °æ¤Žå°å ³èŠ‚é€€å˜ç¨‹åº¦.

OBJECTIVES: Quantitative magnetic resonance imaging has been successfully applied to assess the status of cartilage biochemical components. This study aimed to investigate the performance of 3.0T magnetic resonance imaging T2 mapping combined with texture analysis for evaluating the early degeneration of lumbar facet joints. METHODS: A total of 38 patients (20 in the asymptomatic group and 18 in the symptomatic group) were enrolled. All patients underwent 3.0T magnetic resonance imaging conventional sequences, water excitation three-dimensional spoiled gradient echo sequence (3D-WATSc), and T2 mapping scans. The bilateral L4/5 and L5/S1 lumbar facet joints were morphological graded using the Weishaupt criteria, T2 values, and texture parameters derived from T2 mapping of cartilage. The Kruskal-Wallis H test was used to compare the differences of parameters among different groups. Multivariate logistic regression analysis was used to obtain the independent predictive factors for evaluating the early degeneration of lumbar facet joints. Receiver operating characteristic (ROC) curve was performed and the area under curve (AUC) was calculated. Spearman correlation analysis was used to evaluate the correlation of the independent predictors of cartilage T2 value and texture parameters with the subjects' Japanese Orthopedic Association (JOA) score or Visual Analogue Scale (VAS) score. RESULTS: A total of 148 facet joints were selected, including 70 in Weishaupt 0 (normal) group, 58 in Weishaupt 1 group, and 20 in Weishaupt 2-3 group. T2 value, entropy, and contrast increased significantly as the exacerbation of facet joint degeneration (all P<0.05), while the inverse difference moment, energy, and correlation decreased (all P<0.05). Entropy among different groups was significantly different (all P<0.05), and the differences of T2 value, contrast, inverse difference moment, and energy between Weishaupt 0 and Weishaupt 1 groups, or Weishaupt 0 and Weishaupt 2-3 groups were statistically significant (all P<0.05). Multivariate logistic regression analysis suggested that T2 value and inverse difference moment were the independent predictors for evaluating early degeneration of facet joints. The combination of T2 value with inverse difference moment achieved the best performance in distinguishing Weishaupt 0 from Weishaupt 1 (AUC=0.85), with sensitivity and specificity at 92.7% and 76.5%, respectively. In the symptom group, the cartilage T2 value combined inverse difference moment was positively correlated with JOA score (r=0.475, P<0.05) and VAS score (r=0.452, P<0.05). CONCLUSIONS: 3.0T magnetic resonance imaging T2 mapping combined with texture analysis is helpful to quantitatively evaluate the early degeneration of lumbar facet joints, in which the T2 value and inverse difference moment show an indicative significance．.

[Combination of prostate imaging reporting and data system with the apparent diffusion coefficient map for the diagnosis of peripheral zone prostate cancer].

OBJECTIVE: To explore the value of prostate imaging reporting and data system version 2 (PI-RADS V2) combined with quantitative parameters derived from apparent diffusion coefficient (ADC) map in the diagnosis of peripheral zone prostate cancer.
 Methods: A total of 50 patients who underwent prostate multiparametric MRI (mpMRI) with suspicious peripheral nodules were retrospectively enrolled, and all patients were biopsy-proven histologically. Two radiologists analyzed the position and category of peripheral zone lesions based on PI-RADS V2. Then 12 ADC quantitative parameters were calculated regarding each lesion on the ADC map by post-processing software. The lesions were divided into malignant group and benign group according to histopathological findings. The ADC quantitative parameters between groups were compared, and stepwise logistic regression analysis was used to build a discriminative model. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were performed to evaluate the diagnostic power and clinical benefit.
 Results: Twenty-eight peripheral zone prostate malignant lesions and 25 benign lesions were obtained finally. The area under the ROC curve, sensitivity and specificity to differentiate peripheral zone prostate malignant from benign lesions were as follows: 0.803, 60.71%, 92.00% (PI-RADS V2 score), 0.857, 89.29%, 76.00% (ADC model), and 0.891, 71.43%, 92.00% (combined model), respectively. The discriminative power of the combined model was significantly improved compared with PI-RADS V2 score (P=0.012). The combined model had relatively optimal overall net benefit, which outperformed the PI-RADS V2 score when threshold probability varied in the range of 0.05-0.27 and 0.46-0.81.
 Conclusion: PI-RADS V2 combined with quantitative analysis of ADC map improve the power in discriminating peripheral zone prostate cancer from benign lesions, and the clinical benefit as well.

Influence of Methods Used to Establish Sarcopenia Cutoff Values for Skeletal Muscle Measures Using Unenhanced and Contrast-Enhanced Computed Tomography Images.

BACKGROUND: Multiple cutoff values of computed tomography (CT)-based skeletal muscle measures have been proposed, but there is currently no consensus used to identify sarcopenia. We aimed to evaluate the influence of statistical methods used to establish sarcopenia cutoff values and to examine the impact of contrast enhancement on the skeletal muscle measures. METHODS: The skeletal muscle area (SMA) and muscle radiation attenuation (MRA) of 316 healthy individuals were measured on unenhanced CT images at the third lumbar vertebra level, and the skeletal muscle index (SMI) was SMA divided by height squared. Possible cutoff values were established using 2 methods: 5th percentile of individuals aged 20-60 years or mean - 2 × SD of individuals aged 20-50 years. The concordance was assessed using Cohen's κ coefficients and McNemar test. The skeletal muscle parameters on 3 phases from 30 CT examinations were compared. RESULTS: The concordance between the 2 methods was almost perfect (κ coefficients: 0.830-0.849) for low MRA but slight to moderate (κ coefficients: 0.189-0.591) for low SMI, especially in the men (P < 0.01). Compared with the unenhanced images, the mean SMA, SMI, and MRA on the contrast-enhanced images increased by 0.8%-1.7%, 0.8%-1.8%, and 14.8%-21.6% (all P < 0.001), respectively, and only the changes in MRA were clinically significant. CONCLUSIONS: The methods for establishing cutoff values and contrast enhancement influence the identification of low SMI and low MRA, respectively. Thus, the definition of sarcopenia should include the standardized method for establishing cutoff values and the phase of CT for analysis.