Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.

Evaluation of the clinical application value of artificial intelligence in diagnosing head and neck aneurysms.

OBJECTIVE: To evaluate the performance of a semi-automated artificial intelligence (AI) software program (CerebralDoc® system) in aneurysm detection and morphological measurement. METHODS: In this study, 354 cases of computed tomographic angiography (CTA) were retrospectively collected in our hospital. Among them, 280 cases were diagnosed with aneurysms by either digital subtraction angiography (DSA) and CTA (DSA group, n = 102), or CTA-only (non-DSA group, n = 178). The presence or absence of aneurysms, as well as their location and related morphological features determined by AI were evaluated using DSA and radiologist findings. Besides, post-processing image quality from AI and radiologists were also rated and compared. RESULTS: In the DSA group, AI achieved a sensitivity of 88.24% and an accuracy of 81.97%, whereas radiologists achieved a sensitivity of 95.10% and an accuracy of 84.43%, using DSA results as the gold standard. The AI in the non-DSA group achieved 81.46% sensitivity and 76.29% accuracy, as per the radiologists' findings. The comparison of position consistency results showed better performance under loose criteria than strict criteria. In terms of morphological characteristics, both the DSA and the non-DSA groups agreed well with the diagnostic results for neck width and maximum diameter, demonstrating excellent ICC reliability exceeding 0.80. The AI-generated images exhibited superior quality compared to the standard software for post-processing, while also demonstrating a significantly reduced processing time. CONCLUSIONS: The AI-based aneurysm detection rate demonstrates a commendable performance, while the extracted morphological parameters exhibit a remarkable consistency with those assessed by radiologists, thereby showcasing significant potential for clinical application.

A multi-institutional study to predict the benefits of DEB-TACE and molecular targeted agent sequential therapy in unresectable hepatocellular carcinoma using a radiological-clinical nomogram.

OBJECTIVE: Exploring the efficacy of a Radiological-Clinical (Rad-Clinical) model in predicting prognosis of unresectable hepatocellular carcinoma (HCC) patients after drug eluting beads transcatheter arterial chemoembolization (DEB-TACE) to optimize the targeted sequential treatment. METHODS: In this retrospective analysis, we included 202 patients with unresectable HCC who received DEB-TACE treatment in 17 institutions from June 2018 to December 2022. Progression-free survival (PFS)-related radiomics features were computationally extracted from HCC patients to build a radiological signature (Rad-signature) model with least absolute shrinkage and selection operator regression. A Rad-Clinical model for postoperative PFS was further constructed according to the Rad-signature and clinical variables by Cox regression analysis. It was presented as a nomogram and evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. And further evaluate the application value of Rad-Clinical model in clinical stages and targeted sequential therapy of HCC. RESULTS: Tumor size, Barcelona Clinic Liver Cancer (BCLC) stage, and radiomics score (Rad-score) were found to be independent risk factors for PFS after DEB-TACE treatment for unresectable HCC, with the Rad-Clinical model being the greatest predictor of PFS in these patients (hazard ratio: 2.08; 95% confidence interval: 1.56-2.78; P < 0.001) along with high 6 months, 12 months, 18 months, and 24 months area under the curves of 0.857, 0.810, 0.843, and 0.838, respectively. In addition, compared to the radiomics and clinical nomograms, the Radiological-Clinical nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (45.2%, 95% CI 0.260-0.632, p < 0.05) and integrated discrimination improvement (14.9%, 95% CI 0.064-0.281, p < 0.05). Based on this model, low-risk patients had higher PFS than high-risk patients in BCLC-B and C stages (P = 0.021). Targeted sequential therapy for patients with high and low-risk HCC in BCLC-B stage exhibited significant benefits (P = 0.018, P = 0.012), but patients with high-risk HCC in BCLC-C stage did not benefit much (P = 0.052). CONCLUSION: The Rad-Clinical model may be favorable for predicting PFS in patients with unresectable HCC treated with DEB-TACE and for identifying patients who may benefit from targeted sequential therapy.

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma.

BACKGROUND: Small (< 4 cm) clear cell renal cell carcinoma (ccRCC) is the most common type of small renal cancer and its prognosis is poor. However, conventional radiological characteristics obtained by computed tomography (CT) are not sufficient to predict the nuclear grade of small ccRCC before surgery. METHODS: A total of 113 patients with histologically confirmed ccRCC were randomly assigned to the training set (n = 67) and the testing set (n = 46). The baseline and CT imaging data of the patients were evaluated statistically to develop a clinical model. A radiomics model was created, and the radiomics score (Rad-score) was calculated by extracting radiomics features from the CT images. Then, a clinical radiomics nomogram was developed using multivariate logistic regression analysis by combining the Rad-score and critical clinical characteristics. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of small ccRCC in both the training and testing sets. RESULTS: The radiomics model was constructed using six features obtained from the CT images. The shape and relative enhancement value of the nephrographic phase (REV of the NP) were found to be independent risk factors in the clinical model. The area under the curve (AUC) values for the training and testing sets for the clinical radiomics nomogram were 0.940 and 0.902, respectively. Decision curve analysis (DCA) revealed that the radiomics nomogram model was a better predictor, with the highest degree of coincidence. CONCLUSION: The CT-based radiomics nomogram has the potential to be a noninvasive and preoperative method for predicting the WHO/ISUP grade of small ccRCC.

Evaluation of symptomatic small bowel stricture in Crohn's disease by double-balloon endoscopy.

PURPOSE: To assess the efficacy of double-balloon endoscopy (DBE) for the detection of small-bowel strictures in Crohn's disease (CD). METHODS: This tertiary-referral hospital cohort study was conducted between January 2018 and May 2022. CD patients with symptoms of small-bowel stricture were enrolled sequentially. All of the patients were subjected to both computed tomography enterography (CTE) and DBE, and their symptoms of stricture were assessed using the Crohn's Disease Obstructive Score (CDOS). The diagnostic yield of DBE was compared to that of CTE, and the relationship between the DBE findings and CDOS was investigated. The factors influencing the DBE diagnosis were examined using Cox regression analysis. RESULTS: This study included 165 CD patients. The CDOS scores were higher in 95 patients and lower in 70 patients. DBE detected 92.7% (153/165) and CTE detected 85.5% (141/165) of the strictures. The DBE diagnostic yields were 94.7% (90/95) in the high CDOS patients and 91.4% (64/70) in the low CDOS patients (P = 0.13). Patients with a history of abdominal surgery and abscess had a lower diagnosis rate in the multivariate analysis. CONCLUSION: DBE has been demonstrated to be an efficient diagnostic method for detecting small bowel strictures in CD patients. Additionally, there was no difference in the diagnostic yields between patients with low and high obstructive scores.

A CT-based radiomics nomogram for predicting the progression-free survival in small cell lung cancer: a multicenter cohort study.

PURPOSE: To develop a radiomics nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients with small cell lung cancer (SCLC) and assess its incremental value to the clinical risk factors for individual PFS estimation. METHODS: 558 patients with pathologically confirmed SCLC were retrospectively recruited from three medical centers. A radiomics signature was generated by using the Pearson correlation analysis, univariate Cox analysis, and multivariate Cox analysis. Association of the radiomics signature with PFS was evaluated. A radiomics nomogram was developed based on the radiomics signature, then its calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: In total, 6 CT radiomics features were finally selected. The radiomics signature was significantly associated with PFS (hazard ratio [HR] 4.531, 95% confidence interval [CI] 3.524-5.825, p < 0.001). Incorporating the radiomics signature into the radiomics nomogram resulted in better performance for the estimation of PFS (concordance index [C-index] 0.799) than with the clinical nomogram (C-index 0.629), as well as high 6 months and 12 months area under the curves of 0.885 and 0.846, respectively. Furthermore, the radiomics nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (33.7%, 95% CI 0.216-0.609, p < 0.05) and integrated discrimination improvement (22.7%, 95% CI 0.168-0.278, p < 0.05). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram. CONCLUSION: A CT-based radiomics nomogram exhibited a promising performance for predicting PFS in patients with SCLC, which could provide valuable information for individualized treatment.

A novel clinical radiomics nomogram at baseline to predict mucosal healing in Crohn's disease patients treated with infliximab.

OBJECTIVES: Mucosal healing (MH) is currently the gold standard in Crohn's disease (CD) management. Noninvasive assessment of MH in CD patients is increasingly a concern of clinicians. METHODS: This retrospective study included 106 patients with confirmed CD who were divided into a training cohort (n = 73) and a testing cohort (n = 33). Patient demographics were evaluated to establish a clinical model. Radiomics features were extracted from computed tomography enterography (CTE) images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated by using the radiomics signature-based formula. A clinical radiomics nomogram was then built by incorporating the Rad-score and significant clinical features. The diagnostic performance of the three models was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Of the 106 patients with CD, 37 exhibited MH after 26 weeks of infliximab (IFX) treatment. The area under the ROC curve (AUC) of the clinical radiomics nomogram for distinguishing MH from non-MH, which was based on the disease duration and Rad-score, was 0.880 (95% confidence interval [CI]: 0.809-0.943) in the training cohort and 0.877 (95% CI: 0.745-0.983) in the testing cohort. Decision curve analysis (DCA) confirmed the clinical utility of the clinical radiomics nomogram. CONCLUSIONS: This is a preliminary study suggesting that this CTE-based radiomics model has potential value for predicting MH in CD patients. A nomogram constructed by combining radiomics signatures and clinical features can help clinicians select appropriate therapeutic strategies for CD patients. KEY POINTS: • The disease duration (odds ratio (OR) = 0.969, 95% confidence interval (CI) = 0.943-0.995, p = 0.021) was an independent predictor of MH in the clinical model. • The AUC of the radiomics model constructed by the five radiomics features was 0.846 (95% CI: 0.759-0.921) in the training cohort and 0.817 (95% CI: 0.665-0.945) in the testing cohort for differentiating MH from non-MH. • The AUC of the clinical radiomics nomogram was 0.880 (95% CI: 0.809-0.943) in the training cohort and 0.877 (95% CI: 0.745-0.983) in the testing cohort for distinguishing MH from non-MH.

Characterizing microRNAs and their targets in different organs of Camellia sinensis var. assamica.

To comprehensively annotate miRNAs and their targets in tea plant, Camellia sinensis, we sequenced small and messenger RNAs of 9 samples of Camellia sinensis var. assamica (YK-10), a diploid elite cultivar widely grown in southwest China. In order to identify targets of miRNAs, we sequenced two degradome sequencing profiles from leaves and roots of YK-10, respectively. By analyzing the small RNA-Seq profiles, we newly identified 137 conserved miRNAs and 23 species specific miRNAs in the genome of YK-10, which significantly improved the annotation of miRNAs in tea plant. Approximately 2000 differently expressed genes were identified when comparing RNA-Seq profiles of any two of the three organs selected in the study. Totally, more than 5000 targets of conserved miRNAs were identified in the two degradome profiles. Furthermore, our results suggest that a few miRNAs play roles in the biosynthesis pathways of theanine, caffeine and flavonoid. These results enhance our understanding of small RNA guided gene regulations in different organs of tea plant.

Exposure-time-dependent subcellular staging of gold nanoparticles deposition and vesicle destruction in mice livers.

Several advances in nanomedicine have been accompanied by rising concerns about the bioaccumulation and toxicity of gold nanoparticles (AuNPs). Here, we assessed the in vivo fate of diversely sized AuNPs that were injected into mice as a computed tomography contrast agent and examined with multi-scale analyses across the organ, tissue, cell, and subcellular levels. After focusing on the strong detected accumulation in livers, our data revealed a set of three clear, exposure-time-dependent patterns based on i) AuNPs deposit morphology and ii) readily identifiable phenotypes for AuNP-impacted subcellular vesicles. Importantly, we detected no obvious differences in liver function, liver cell apoptosis, or autophagy upon exposure to AuNPs. Thus, our study illustrates an accessible experimental and high-resolution data interpretation framework for quickly obtaining and contextualizing informative trends about any AuNP-triggered patterns of subcellular damage in nanomedicine studies; these can help guide cytotoxity and safety testing of diagnostic nanomedical technologies.

Impact of iron deposit on the accuracy of quantifying liver fat fraction using multi-material decomposition algorithm in dual-energy spectral computed tomography.

OBJECTIVES: To investigate the accuracy of using multi-material decomposition (MMD) algorithm in dual-energy spectral computed tomography (CT) for quantifying fat fraction (FF) in the presence of iron. MATERIALS: Nine tubes with various proportions of fat and iron were prepared. FF were divided into three levels (10%, 20%, and 30%), recorded as references (FFref ). Iron concentrations (in mg/100 g) were divided into three ranges (25.25-25.97, 50.38-51.55 and 75.57-77.72). The nine-tube phantom underwent dual-energy CT and MR. CT attenuation was measured and FF were determined using MMD in CT (FFCT ) and Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation (IDEAL-IQ) in MR (FFMR ) for each tube. Statistical analyses used were: Spearman rank correlation for correlations between FFref and CT attenuation, FFCT , and FFMR ; one-way ANOVA, and one-sample t-test for the differences between FFCT and FFref and between FFMR and FFref . A multivariate linear regression model was established to analyze the differences between the corresponding values with different iron concentrations under the same FFref . RESULTS: Fat fraction on CT (FFCT) and FFMR were positively correlated with FFref (all p < 0.001), while the CT attenuation was negatively correlated with FFref in the three iron concentration ranges. For a given FFref , FFCT decreased and FFMR increased as the iron concentration increased. The mean difference between FFCT and FFref over the nine tube measurements was 0.25 ± 2.45%, 5.7% lower the 5.98 ± 3.33% value between FFMR and FFref (F = 310.017, p < 0.01). CONCLUSION: The phantom results indicate that MMD in dual-energy CT can directly quantify volumetric FF and is less affected by iron concentration than MR IDEAL-IQ method.

Recent Progress on Relevant microRNAs in Autism Spectrum Disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder whose pathogenesis is unclear and is affected by both genetic and environmental factors. The microRNAs (miRNAs) are a kind of single-stranded non-coding RNA with 20-22 nucleotides, which normally inhibit their target mRNAs at a post-transcriptional level. miRNAs are involved in almost all biological processes and are closely related to ASD and many other diseases. In this review, we summarize relevant miRNAs in ASD, and analyze dysregulated miRNAs in brain tissues and body fluids of ASD patients, which may contribute to the pathogenesis and diagnosis of ASD.

Clinical values of of gemstone spectral CT in diagnosing thyroid disease.

OBJECTIVE: To quantitatively evaluate the diagnostic value of gemstone spectral CT in thyroid disease. PATIENTS AND METHODS: A total of 123 patients with thyroid diseases were enrolled in the retrospective study. All the patients underwent spectral CT scan, and 39 of them underwent dual-phase enhanced scan. Iodine concentration (IC) and normalized IC (NIC) were compared between benign and malignant nodules. The optimal threshold to predict malignancy was obtained by receiver operating characteristic curve (ROC). Multivariate ROC analysis was performed to evaluate the efficacy of combining the IC (NIC) and conventional morphological characteristics. RESULTS: Ten diffuse diseases and 113 nodular diseases were confirmed by clinical laboratory examination and histopathology. In total, 122 nodules (87 benign and 35 malignant) were detected, 41 nodules in enhanced cases. The IC and NIC_{VP} of malignant nodules were significantly lower than benign nodules in noncontrast and venous phase (VP) (P=0.000). The optimal IC and NIC_{VP} threshold was 0.68 mg/mL and 0.38, achieving 74.3% and 76.9% sensitivity, 66.7% and 80.0% specificity, respectively. For multivariate analysis, the area under ROC (AUC) was 0.88 and 0.94 in noncontrast and VP, respectively. Sensitivities of 77.1% and 84.6%, and specificities of 84.5% and 88.0% were achieved. CONCLUSION: Gemstone spectral CT can generally evaluate the abnormal iodine content in thyroid disease. Lower iodine can help differentiating malignant from benign nodules. A comprehensive analysis combining spectrum parameters and morphological characteristics is suggested in differential diagnosis.

Value of diffusion-weighted magnetic resonance imaging on the follow-up of nasopharyngeal carcinoma after radiotherapy.

PURPOSE: To explore the value of diffusion-weighted imaging (DWI) and magnetic resonance imaging (MRI) on the follow-up of nasopharyngeal carcinoma (NPC) after radiotherapy. MATERIAL AND METHODS: Eighty-three NPC patients after radiotherapy were divided into two groups: 4 cases of residual tumor and 33 cases of non-residual within 6 months, the cases of recurrent and non-recurrent were 5 and 41 over 6 months, respectively. MRI and DWI imaging of these cases were closely analyzed, and the apparent diffusion coefficient (ADC) of the nasopharyngeal residual mass and nasopharyngeal wall thickening, skull base destruction and lateral pterygoid muscle were measured. RESULTS: The ADC of the lateral pterygoid muscle was (1.501 ± 0.069) × 10^{ - 3} mm^{2}/s in 83 cases. The ADCs of the non-residual group, residual group, non-recurrent group and recurrent group were (1.843 ± 0.133.4) × 10^{ - 3} mm^{2}/s, (1.097 ± 0.183) × 10^{ - 3} mm^{2}/s, (1.884 ± 0.134) × 10^{ - 3} mm^{2}/s and (0.787 ± 0.067) × 10^{ - 3} mm^{2}/s respectively.The ADCs of the residual group and recurrent group were lower than that of the lateral pterygoid muscle, while the ADCs of the non-residual group and non-recurrent group were higher than that of the lateral pterygoid muscle.The differences were statistically significant (P < 0.05). CONCLUSION: DWI can provide qualitative and quantitative information about the tumor cellular level; it can also identify residual, local fibrosis and recurrence after NPC radiotherapy sensitively and accurately by measuring the ADC of the region of interest.

Heat exposure impacts on urban health: A meta-analysis.

The escalating health risks posed by warm weather in urban areas have become a pressing global public health issue. This study undertakes a meta-analysis to evaluate the impact of warm weather on health in urban settings. We comprehensively searched PubMed, Embase, Scopus, and Web of Science for literature published before September 6, 2023, evaluating evidence quality using the Navigation Guide Criteria. We included original studies utilizing high temperatures or heatwaves as exposure metrics and employing observational designs. A meta-analysis was carried out to assess the relative risk (RR) of the association between high temperatures (or heatwaves) and disease outcomes. Out of 12,893 studies identified, 188 met the inclusion criteria for meta-analysis. Results demonstrate a statistically significant association between a 1 °C temperature increase and a 2.1 % elevation in disease-related mortality (RR 1.021 [95 % CI 1.018-1.023]), alongside a 1.1 % increase in morbidity (RR 1.011 [95 % CI 1.007-1.016]). Heatwaves also showed associations with increased total mortality (RR 1.224 [95 % CI 1.186-1.264]) and morbidity (RR 1.038 [95 % CI 1.010-1.066]). Subgroup analyses for diseases, sex, age, climatic zones, countries, and time periods consistently indicated heightened disease-related mortality and morbidity linked to high temperatures. Notably, China's urban population faced an elevated mortality risk (RR 1.027 [95 % CI 1.018-1.036]) compared to other countries (RR 1.021 [95 % CI 1.019-1.024]). Mortality associated with high temperatures after 2007 (RR 1.022 [95 % CI 1.015-1.029]) was higher than before 2007 (RR 1.017 [95 % CI 1.013-1.021]), reflecting increased health risks as the global warming accelerates. Our findings underscore the positive association between rising temperatures and/or heatwaves and adverse health outcomes in urban populations. The widespread exposure to high temperatures amplifies health risks across various diseases, demographics, climates, and countries, with potential exacerbation under ongoing global warming. Further research is imperative to delineate factors influencing altered heat exposure impacts.

A comparative study of the predictive value of four models for death in patients with severe burns.

OBJECTIVE: To assess the prognostic value of the Ryan score, Belgian Outcome of Burn Injury (BOBI) score,revised Baux (rBaux) score, and a new model (a Logit(P)-based scoring method created in 2020) for predicting mortality risk in patients with extremely severe burns and to conduct a comparative analysis. METHODS: A retrospective analysis was conducted on 599 burn patients who met the inclusion criteria and were admitted to the burn unit of the First Affiliated Hospital of Nanchang University from 2017 to 2022. Relevant information was collected, and receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were plotted for each of the four models in assessing mortality in these burn patients using both age-stratified and unstratified forms. The ROC curve section was further compared with the area under the curve (AUC), optimal cutoff value, as well as its sensitivity and specificity. Additionally, the quality of the AUC was assessed using the Delong test. RESULT: Among the patients who met the inclusion criteria, 532 were in the survival group and 67 in the death group. Irrespective of age stratification, the novel model exhibited superior performance with an AUC of 0.868 (95% CI: 0.838-0.894) among all four models predicting mortality risk in included patients, and also demonstrated better AUC quality than other models; the calibration curves showed that the accuracy of all four models was good; the DCA curves showed that the clinical utility of the novel model and rBuax score were better. In the comparison of four scoring models across different age groups, the new model demonstrated the largest AUC in both 0-19 years (0.954, 95% CI 0.914-0.979) and 20-59 years groups (0.838, 95% CI 0.793-0.877), while rBuax score exhibited the highest AUC in ≥ 60 years group (0.708, 95% CI of 0.602-0.800). The calibration curves showed that the four models exhibited greater accuracy within the age range of 20-59 years, while the DCA curves indicated that both the novel model and rBuax score scale displayed better prediction in both the 20-59 and ≥ 60 years groups. CONCLUSIONS: All four models demonstrate accurate and effective prognostication for patients with severe burns. Both the novel model and rBaux score exhibit enhanced prediction utility. In terms of the model itself alone, the new model is not simpler than, for example, the rBaux score, and whether it can be applied clinicallyinvolves further study.

Evaluation of Mucosal Healing in Crohn's Disease: Radiomics Models of Intestinal Wall and Mesenteric Fat Based on Dual-Energy CT.

This study aims to assess the effectiveness of radiomics signatures obtained from dual-energy computed tomography enterography (DECTE) in the evaluation of mucosal healing (MH) in patients diagnosed with Crohn's disease (CD). In this study, 106 CD patients with a total of 221 diseased intestinal segments (79 with MH and 142 non-MH) from two medical centers were included and randomly divided into training and testing cohorts at a ratio of 7:3. Radiomics features were extracted from the enteric phase iodine maps and 40-kev and 70-kev virtual monoenergetic images (VMIs) of the diseased intestinal segments, as well as from mesenteric fat. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) logistic regression. Radiomics models were subsequently established, and the accuracy of these models in identifying MH in CD was assessed by calculating the area under the receiver operating characteristic curve (AUC). The combined-iodine model formulated by integrating the intestinal and mesenteric fat radiomics features of iodine maps exhibited the most favorable performance in evaluating MH, with AUCs of 0.989 (95% confidence interval (CI) 0.977-1.000) in the training cohort and 0.947 (95% CI 0.884-1.000) in the testing cohort. Patients categorized as high risk by the combined-iodine model displayed a greater probability of experiencing disease progression when contrasted with low-risk patients. The combined-iodine radiomics model, which is built upon iodine maps of diseased intestinal segments and mesenteric fat, has demonstrated promising performance in evaluating MH in CD patients.

Improving diagnostic confidence in low-dose dual-energy CTE with low energy level and deep learning reconstruction.

OBJECTIVE: To demonstrate the value of using 50 keV virtual monochromatic images with deep learning image reconstruction (DLIR) in low-dose dual-energy CT enterography (CTE). METHODS: In this prospective study, 114 participants (62 % M; 41.9 ± 16 years) underwent dual-energy CTE. The early-enteric phase was performed using standard-dose (noise index (NI): 8) and images were reconstructed at 70 keV and 50 keV with 40 % strength ASIR-V (ASIR-V40%). The late-enteric phase used low-dose (NI: 12) and images were reconstructed at 50 keV with ASIR-V40%, and DLIR at medium (DLIR-M) and high strength (DLIR-H). Image standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge-rise-slope (ERS) were computed. The quantitative comb sign score was calculated for the 27 patients with Crohn's disease. The subjective noise, image contrast, display of rectus artery were scored using a 5-point scale by two radiologists blindly. RESULTS: Effective dose was reduced by 50 % (P < 0.001) in the late-enteric phase to 3.26 mSv. The lower-dose 50 keV-DLIR-H images (SD:17.7 ± 0.5HU) had similar image noise (P = 0.97) as the standard-dose 70 keV-ASIR-V40% images (SD:17.7 ± 0.73HU), but with higher (P < 0.001) SNR, CNR, ERS and quantitative comb sign score (5.7 ± 0.17, 1.8 ± 0.12, 156.04 ± 5.21 and 5.05 ± 0.73, respectively). Furthermore, the lower-dose 50 keV-DLIR-H images obtained the highest score in the rectus artery visibility (4.27 ± 0.6). CONCLUSIONS: The 50 keV images in dual-energy CTE with DLIR provides high-quality images, with a 50 % reduction in radiation dose. Images with high contrast and density resolutions significantly enhance the diagnostic confidence of Crohn's disease and are essential for the clinical development of individualized treatment plans.

Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction.

OBJECTIVE: To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images. METHODS: In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5 mm and 1.25 mm slice thickness with ASIR-V at a level of 40% (ASIR-V40%). The late-enteric phase scan used low-dose (noise index: 12) and images were reconstructed at 1.25 mm slice thickness with ASIR-V40%, and DLIR at medium (DLIR-M) and high (DLIR-H). The 70 keV monochromatic images were used for image comparison and analysis. For objective assessment, image noise, artifact index, SNR and CNR were measured. For subjective assessment, subjective noise, image contrast, bowel wall sharpness, mesenteric vessel clarity, and small structure visibility were scored by two radiologists blindly. Radiation dose was compared between the early- and late-enteric phases. RESULTS: Radiation dose was reduced by 50% in the late-enteric phase [(6.31 ± 1.67) mSv] compared with the early-enteric phase [(3.01 ± 1.09) mSv]. For the 1.25 mm images, DLIR-M and DLIR-H significantly improved both objective and subjective image quality compared to those with ASIR-V40%. The low-dose 1.25 mm DLIR-H images had similar image noise, SNR, CNR values as the standard-dose 5 mm ASIR-V40% images, but significantly higher scores in image contrast [5(5-5), P < 0.05], bowel wall sharpness [5(5-5), P < 0.05], mesenteric vessel clarity [5(5-5), P < 0.05] and small structure visibility [5(5-5), P < 0.05]. CONCLUSIONS: DLIR significantly reduces image noise at the same slice thickness, but significantly improves spatial resolution and lesion conspicuity with thinner slice thickness in DECTE, compared to conventional ASIR-V40% 5 mm images, all while providing 50% radiation dose reduction.

A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study.

Purpose: To develop a predictive nomogram based on computed tomography (CT) radiomics to distinguish pulmonary tuberculosis (PTB) from community-acquired pneumonia (CAP). Methods: A total of 195 PTB patients and 163 CAP patients were enrolled from three hospitals. It is divided into a training cohort, a testing cohort and validation cohort. Clinical models were established by using significantly correlated clinical features. Radiomics features were screened by the least absolute shrinkage and selection operator (LASSO) algorithm. Radiomics scores (Radscore) were calculated from the formula of radiomics features. Clinical radiomics conjoint nomogram was established according to Radscore and clinical features, and the diagnostic performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. Results: Two clinical features and 12 radiomic features were selected as optimal predictors for the establishment of clinical radiomics conjoint nomogram. The results showed that the predictive nomogram had an outstanding ability to discriminate between the two diseases, and the AUC of the training cohort was 0.947 (95% CI, 0.916-0.979), testing cohort was 0.888 (95% CI, 0.814-0.961) and that of the validation cohort was 0.850 (95% CI, 0.778-0.922). Decision curve analysis (DCA) indicated that the nomogram has outstanding clinical value. Conclusions: This study developed a clinical radiomics model that uses radiomics features to identify PTB from CAP. This model provides valuable guidance to clinicians in identifying PTB.