Enhanced in-stent luminal visualization and restenosis diagnosis in coronary computed tomography angiography via coronary stent decomposition algorithm from dual-energy image.

Stent implantation is a principal therapeutic approach for coronary artery diseases. Nonetheless, the presence of stents significantly interferes with in-stent luminal (ISL) visualization and complicates the diagnosis of in-stent restenosis (ISR), thereby increasing the risk of misdiagnoses and underdiagnoses in coronary computed tomography angiography (CCTA). Dual-energy (DE) CT could calculate the volume fraction for voxels from low- and high-energy images (LHEI) and provide information on specific three basic materials. In this study, the innovative coronary stent decomposition algorithm (CSDA) was developed from the DECT three materials decomposition (TMD), through spectral simulation to determine the scan and attenuation coefficient for the stent, and preliminary execution for an in vitro sophisticated polyether ether ketone (PEEK) 3D-printed right coronary artery (RCA) replica. Furthermore, the whole-coronary-artery replica with multi-stent implantation, the RCA replica with mimetic plaque embedded, and two patients with stent further validated the effectiveness of CSDA. Post-CSDA images manifested no weakened attenuation values, no elevated noise values, and maintained anatomical integrity in the coronary lumen. The stents were effectively removed, allowing for the ISL and ISR to be clearly visualized with a discrepancy in diameters within 10%. We believe that CSDA presents a promising solution for enhancing CCTA diagnostic accuracy post-stent implantation.

Diagnostic Accuracy of F18-fluorodeoxyglucose Positron Emission Tomography- computed Tomography for the Detection of Non-small Cell Lung Cancer Recurrence: A Systematic Review and Meta-analysis.

BACKGROUND: The Non-Small Cell Variant of Lung Cancer (NSCLC) has a poorer prognosis. It is typically diagnosed through non-invasive imaging. Of particular note has been FDGPET/ CT, which has been investigated across various settings with differing results. OBJECTIVE: This study is to pool the available information on the diagnostic performance of 18-F FDG PET/CT for detecting NSCLC recurrence. METHODS: A systematic literature search was conducted across electronic databases for studies published before May 2021. The QUADAS tool was applied to assess study quality, and a metaanalysis was performed to retrieve pooled estimates. Chi-squared tests and I2 statistics were used to assess heterogeneity. Egger's test and funnel plots were used to assess publication bias. RESULTS: The literature search yielded 20 studies featuring data on 1,973 patients. The majority of the studies had a low bias risk. The pooled sensitivity and specificity were 96% (95% CI: 91%- 98%) and 93% (95% CI: 89%-95%), respectively. The LRP and LRN estimates were in the left upper quadrant of the LR scattergram, indicating that F18-FDG PET/CT can be utilized for both confirmation and exclusion. The AUC was 0.98 (95% CI: 0.92-0.99). Fagan's nomogram showed that F18-FDG PET/CT had good clinical utility for recurrent NSCLC diagnosis. There was considerable between-study variability (p=0.02). The funnel plot was asymmetrical, indicating the possibility of publication bias. CONCLUSION: This meta-analysis found FDG-PET/CT to be highly accurate for identifying NSCLC recurrence. However, more studies assessing this modality across different patient situations are required to strengthen the argument for changing international guidelines and practices.

The Value of Artificial Intelligence Film Reading System Based on Deep Learning in the Diagnosis of Non-Small-Cell Lung Cancer and the Significance of Efficacy Monitoring: A Retrospective, Clinical, Nonrandomized, Controlled Study.

Objective: To explore the value of artificial intelligence (AI) film reading system based on deep learning in the diagnosis of non-small-cell lung cancer (NSCLC) and the significance of curative effect monitoring. Methods: We retrospectively selected 104 suspected NSCLC cases from the self-built chest CT pulmonary nodule database in our hospital, and all of them were confirmed by pathological examination. The lung CT images of the selected patients were introduced into the AI reading system of pulmonary nodules, and the recording software automatically identified the nodules, and the results were compared with the results of the original image report. The nodules detected by the AI software and film readers were evaluated by two chest experts and recorded their size and characteristics. Comparison of calculation sensitivity, false positive rate evaluation of the NSCLC software, and physician's efficiency of nodule detection whether there was a significant difference between the two groups. Results: The sensitivity, specificity, accuracy, positive predictive rate, and false positive rate of NSCLC diagnosed by radiologists were 72.94% (62/85), 92.06% (58/63), 81.08% (62+58/148), 92.53% (62/67), and 7.93% (5/63), respectively. The sensitivity, specificity, accuracy, positive prediction rate, and false positive rate of AI film reading system in the diagnosis of NSCLC were 94.12% (80/85), 77.77% (49/63), 87.161% (80 + 49/148), 85.11% (80/94), and 22.22% (14/63), respectively. Compared with radiologists, the sensitivity and false positive rate of artificial intelligence film reading system in the diagnosis of NSCLC were higher (P < 0.05). The sensitivity, specificity, accuracy, positive prediction rate, and negative prediction rate of artificial intelligence film reading system in evaluating the efficacy of patients with NSCLC were 87.50% (63/72), 69.23% (9/13), 84.70% (63 + 9)/85, 94.02% (63/67), and 50% (9/18), respectively. Conclusion: The AI film reading system based on deep learning has higher sensitivity for the diagnosis of NSCLC than radiologists and can be used as an auxiliary detection tool for doctors to screen for NSCLC, but its false positive rate is relatively high. Attention should be paid to identification. Meanwhile, the AI film reading system based on deep learning also has a certain guiding significance for the diagnosis and treatment monitoring of NSCLC.

Diffusion-weighted magnetic resonance imaging in predicting the radiosensitivity of cervical cancer.

This study investigates the application value of diffusion-weighted magnetic resonance imaging in predicting cervical cancer radiosensitivity. Twenty-five patients who were newly diagnosed as cervical cancer and accepted simple radiotherapy were included in this study. Before external irradiation, 20 GY and at the end of irradiation, routine 1.5 T MRI and diffusion-weighted magnetic resonance imaging scanning were carried. Apparent diffusion coefficient (ADC) value of primary tumor was measured. Its correlation with tumor regression rate was analyzed. ADC values of before irradiation, 20 GY and at the end of irradiation was (0.93 ± 0.14) × 10(-3) mm(2)/s, (1.25 ± 0.17) × 10(-3) mm(2)/s and (1.55 ± 0.13) × 10(-3) mm(2)/s, respectively. There were statistical significant differences (P< 0.01). D-value of ADC values between before and 20 GY external irradiation was (0.33 ± 0.16) mm(2)/s. The tumor volume before and at the end of external irradiation were (37.48 ± 26.83) cm(3) and (4.41 ± 3.72) cm(3) respectively, with tumor regression rate of before and after external irradiation of (0.86 ± 0.11). ADC values of before irradiation, 20 GY and at the end of irradiation did not correlate with tumor regression rate. D-value of ADC values between before and 20 GY external irradiation positively correlated with tumor regression rate (r = 0.423, P = 0.035). ADC value of cervical cancer increased after radiotherapy and early changes of ADC value was positively correlated with tumor regression rate, thus, ADC value could be used as a potential prediction factor for cervical cancer radiosensitivity.

Mortality and Morbidity of Extremely Low Birth Weight Infants in the Mainland of China: A Multi-center Study.

BACKGROUND: With the progress of perinatal medicine and neonatal technology, more and more extremely low birth weight (ELBW) survived all over the world. This study was designed to investigate the short-term outcomes of ELBW infants during their Neonatal Intensive Care Unit (NICU) stay in the mainland of China. METHODS: All infants admitted to 26 NICUs with a birth weight (BW) < l000 g were included between January l, 2011 and December 31, 2011. All the data were collected retrospectively from clinical records by a prospectively designed questionnaire. The data collected from each NICU transmitted to the main institution where the results were aggregated and analyzed. Categorical variables were performed with Pearson Chi-square test. Binary Logistic regression analysis was used to detect risk factors. RESULTS: A total of 258 ELBW infants were admitted to 26 NICUs, of whom the mean gestational age (GA) was 28.1 ± 2.2 weeks, and the mean BW was 868 ± 97 g. The overall survival rate at discharge was 50.0%. Despite aggressive treatment 60 infants (23.3%) died and another 69 infants (26.7%) died after medical care withdrawal. Furthermore, the survival rate was significantly higher in coastal areas than inland areas (53.6% vs. 35.3%, P = 0.019). BW < 750 g and GA < 28 weeks were the largest risk factors, and being small for gestational age was a protective factor related to mortality. Respiratory distress syndrome was the most common complication. The incidence of patent ductus arteriosus, intraventricular hemorrhage, periventricular leukomalacia, bronchopulmonary dysplasia, retinopathy of prematurity was 26.2%, 33.7%, 6.7%, 48.1%, and 41.4%, respectively. Ventilator associated pneumonia was the most common hospital acquired infection during hospitalization. CONCLUSIONS: Our study was the first survey that revealed the present status of ELBW infants in the mainland of China. The mortality and morbidity of ELBW infants remained high as compared to other developed countries.