Computed tomography pulmonary angiogram (CTPA) is important to evaluate suspected pulmonary embolism in pregnancy but has maternal/fetal radiation risks. The objective of this study was to estimate maternal and fetal radiation-induced cancer risk from CTPA during pregnancy. Simulation modeling via the National Cancer Institute's Radiation Risk Assessment Tool was used to estimate excess cancer risks from 17 organ doses from CTPA during pregnancy, with doses determined by a radiation dose indexing monitoring system. Organ doses were obtained from a radiation dose indexing monitoring system. Maternal and fetal cancer risks per 100,000 were calculated for male and female fetuses and several maternal ages. The 534 CTPA examinations had top 3 maternal organ doses to the breast, lung, and stomach of 17.34, 15.53, and 9.43 mSv, respectively, with a mean uterine dose of 0.21 mSv. The total maternal excess risks of developing cancer per 100,000 were 181, 151, 121, 107, 94.5, 84, and 74.4, respectively, for a 20-, 25-, 30-, 35-, 40-, 45-, and 50-year-old woman undergoing CTPA, compared with baseline cancer risks of 41,408 for 20-year-old patients. The total fetal excess risks of developing cancer per 100,000 were 12.3 and 7.3 for female and male fetuses, respectively, when compared with baseline cancer risks of 41,227 and 48,291. Excess risk of developing cancer from CTPA was small relative to baseline cancer risk for pregnant patients and fetuses, decreased for pregnant patients with increasing maternal age, and was greater for female fetuses than male fetuses.

Artificial intelligence (AI)-assisted medical imaging technology is a new research area of great interest that has developed rapidly over the last decade. However, there has been no bibliometric analysis of published studies in this field. The present review focuses on AI-related studies on computed tomography imaging in the Web of Science database and uses CiteSpace and VOSviewer to generate a knowledge map and conduct the basic information analysis, co-word analysis, and co-citation analysis. A total of 7265 documents were included and the number of documents published had an overall upward trend. Scholars from the United States and China have made outstanding achievements, and there is a general lack of extensive cooperation in this field. In recent years, the research areas of great interest and difficulty have been the optimization and upgrading of algorithms, and the application of theoretical models to practical clinical applications. This review will help researchers understand the developments, research areas of great interest, and research frontiers in this field and provide reference and guidance for future studies.

This study aimed to investigate the effectiveness and practicality of using models like convolutional neural network and transformer in detecting and precise segmenting meningioma from magnetic resonance images. The retrospective study on T1-weighted and contrast-enhanced images of 523 meningioma patients from 3 centers between 2010 and 2020. A total of 373 cases split 8:2 for training and validation. Three independent test sets were built based on the remaining 150 cases. Six convolutional neural network detection models trained via transfer learning were evaluated using 4 metrics and receiver operating characteristic analysis. Detected images were used for segmentation. Three segmentation models were trained for meningioma segmentation and were evaluated via 4 metrics. In 3 test sets, intraclass consistency values were used to evaluate the consistency of detection and segmentation models with manually annotated results from 3 different levels of radiologists. The average accuracies of the detection model in the 3 test sets were 97.3%, 93.5%, and 96.0%, respectively. The model of segmentation showed mean Dice similarity coefficient values of 0.884, 0.834, and 0.892, respectively. Intraclass consistency values showed that the results of detection and segmentation models were highly consistent with those of intermediate and senior radiologists and lowly consistent with those of junior radiologists. The proposed deep learning system exhibits advanced performance comparable with intermediate and senior radiologists in meningioma detection and segmentation. This system could potentially significantly improve the efficiency of the detection and segmentation of meningiomas.

This study aimed to determine the methodological quality and evaluate the diagnostic performance of radiomics features in detecting lymph node metastasis on preoperative images in patients with cholangiocarcinoma and gallbladder cancer. Publications between January 2005 and October 2022 were considered for inclusion. Databases such as Pubmed/Medline, Scopus, Embase, and Google Scholar were searched for relevant studies. The quality of the methodology of the manuscripts was determined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2. Pooled results with corresponding 95% confidence intervals (CIs) were calculated using the DerSimonian-Liard method (random-effect model). Forest plots were used to visually represent the diagnostic profile of radiomics signature in each of the data sets pertaining to each study. Fagan plot was used to determine clinical applicability. Overall sensitivity was 0.748 (95% CI, 0.703-0.789). Overall specificity was 0.795 (95% CI, 0.742-0.839). The combined negative likelihood ratio was 0.299 (95% CI, 0.266-0.350), and the positive likelihood ratio was 3.545 (95% CI, 2.850-4.409). The combined odds ratio of the studies was 12.184 (95% CI, 8.477-17.514). The overall summary receiver operating characteristics area under the curve was 0.83 (95% CI, 0.80-0.86). Three studies applied nomograms to 8 data sets and achieved a higher pooled sensitivity and specificity (0.85 [0.80-0.89] and 0.85 [0.71-0.93], respectively). The pooled analysis showed that predictive models fed with radiomics features achieve good sensitivity and specificity in detecting lymph node metastasis in computed tomography and magnetic resonance imaging images. Supplementation of the models with biological correlates increased sensitivity and specificity in all data sets.

The coronavirus disease 2019 (COVID-19) pandemic disrupted health care systems, including implementation of lung cancer screening programs. The impact and recovery from this disruption on screening processes is not well appreciated. Herein, the radiology database of a Northeast tertiary health care network was reviewed before and during the pandemic (2013-2022). In the 3 months before the pandemic, an average of 77.3 lung cancer screening with computed tomography scans (LCS-CT) were performed per month. The average dropped to 23.3 between April and June of 2020, whereas COVID-19 hospitalizations peaked at 1604. By July, average hospitalizations dropped to 50, and LCS-CTs rose to >110 per month for the remaining year. LCS-CTs did not decline during COVID-19 surges in December of 2021 and 2022. The LCS-CT performance grew by 4.5% in 2020, 69.6% in 2021, and 27.0% in 2022, exceeding projected growth by 722 examinations. This resiliency indicates a potentially smaller impact of COVID-19 on lung cancer diagnoses than initially feared.

Neuroendocrine neoplasms have shown a linear increase in incidence and prevalence in recent decades, primarily due to improved cross-sectional imaging, expanded use of endoscopic procedures, and advanced genetic analysis. However, diagnosis of hereditary neuroendocrine tumors is still challenging because of heterogeneity in their presentation, the variety of tumor locations, and multiple associated syndromes. Radiologists should be familiar with the spectrum of these tumors and associated hereditary syndromes. Furthermore, as the assessment of multiple tumor elements such as morphology, biochemical markers, and presence of metastatic disease are essential for the treatment plan, conventional anatomic and functional imaging methods are fundamental in managing and surveilling these cases. Our article illustrates the role of different cross-sectional imaging modalities in diagnosing and managing various hereditary abdominopelvic neuroendocrine tumors.

The increasing number of coronary computed tomography angiography (CCTA) requests raised concerns about dose exposure. New dose reduction strategies based on artificial intelligence have been proposed to overcome limitations of iterative reconstruction (IR) algorithms. Our prospective study sought to explore the added value of deep-learning image reconstruction (DLIR) in comparison with a hybrid IR algorithm (adaptive statistical iterative reconstruction-veo [ASiR-V]) in CCTA, even in clinical challenging scenarios, as obesity, heavily calcified vessels and coronary stents. We prospectively included 103 consecutive patients who underwent CCTA. Data sets were reconstructed with ASiR-V and DLIR. For each reconstruction signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) was calculated, and qualitative assessment was made with a four-point Likert scale by two independent and blinded radiologists with different expertise. Both SNR and CNR were significantly higher in DLIR (SNR-DLIR median value [interquartile range] of 13.89 [11.06-16.35] and SNR-ASiR-V 25.42 [22.46-32.22], P < 0.001; CNR-DLIR 16.84 [9.83-27.08] vs CNR-ASiR-V 10.09 [5.69-13.5], P < 0.001).Median qualitative score was 4 for DLIR images versus 3 for ASiR-V ( P < 0.001), with a good interreader reliability [intraclass correlation coefficient(2,1)e intraclass correlation coefficient(3,1) 0.60 for DLIR and 0.62 and 0.73 for ASiR-V].In the obese and in the "calcifications and stents" groups, DLIR showed significantly higher values of SNR (24.23 vs 11.11, P < 0.001 and 24.55 vs 14.09, P < 0.001, respectively) and CNR (16.08 vs 8.04, P = 0.008 and 17.31 vs 10.14, P = 0.003) and image quality. Deep-learning image reconstruction in CCTA allows better SNR, CNR, and qualitative assessment than ASiR-V, with an added value in the most challenging clinical scenarios.

Photon-counting detector (PCD) computed tomography (CT) offers improved spatial and contrast resolution, which can impact quantitative measurements. This work aims to determine in human subjects the effect of dual-source PCD-CT on the quantitation of coronary artery calcification (CAC) compared with dual-source energy-integrating detector (EID) CT in both 1- and 3-mm images. This prospective study enrolled patients receiving a clinical EID-CT CAC examination to undergo a research PCD-CT CAC examination. Axial images were reconstructed with a 512 × 512 matrix, 200-mm field of view, 3-mm section thickness/1.5-mm interval using a quantitative kernel (Qr36). Sharper kernels (Qr56/QIR strength 4 for PCD and Qr49/ADMIRE strength 5 for EID) were used to reconstruct images with 1-mm section thickness/0.5-mm interval. Pooled analysis was performed for all calcifications with nonzero values, and volume and Agatston scores were compared between EID-CT and PCD-CT. A Wilcoxon signed-rank test was performed with P < 0.05 considered statistically significant. In 21 subjects (median age, 58 years; range, 50-75 years; 13 male [62%]) with a total of 42 calcified arteries detected at 3 mm and 46 calcified arteries at 1-mm images, EID-CT CAC volume and Agatston scores were significantly lower than those of PCD-CT ( P ≤ 0.001). At 3-mm thickness, the mean (standard deviation) volume and Agatston score for EID-CT were 55.5 (63.4) mm 3 and 63.8 (76.9), respectively, and 61.5 (69.4) mm 3 and 70.4 (85.3) for PCD-CT ( P = 0.0001 and P = 0.0013). At 1-mm thickness, the mean (standard deviation) volume and score for EID-CT were 50.0 (56.3) mm 3 and 61.1 (69.3), respectively, and 59.5 (63.9) mm 3 and 72.5 (79.9) for PCD-CT ( P < 0.0001 for both). The applied radiation dose (volume CT dose index) for the PCD-CT scan was 2.1 ± 0.6 mGy, which was 13% lower than for the EID-CT scan (2.4 ± 0.7 mGy, P < 0.001). Relative to EID-CT, PCD-CT demonstrated a small but significant increase in coronary artery calcium volume and Agatston score.

The aim of this study was to compare diatrizoate and iohexol regarding patient acceptance and fecal-tagging performance in noncathartic computed tomography colonography. This study enrolled 284 volunteers with fecal tagging by either diatrizoate or iohexol at an iodine concentration of 13.33 mg/mL and an iodine load of 24 g. Patient acceptance was rated on a 4-point scale of gastrointestinal discomfort. Two gastrointestinal radiologists jointly analyzed image quality, fecal-tagging density and homogeneity, and residual contrast agent in the small intestine. The results were compared by the generalized estimating equation method. Patient acceptance was comparable between the 2 groups (3.95 ± 0.22 vs 3.96 ± 0.20, P = 0.777). The diatrizoate group had less residual fluid and stool than the iohexol group ( P = 0.019, P = 0.004, respectively). There was no significant difference in colorectal distention, residual fluid, and stool tagging quality between the 2 groups (all P 's > 0.05). The mean 2-dimensional image quality score was 4.59 ± 0.68 with diatrizoate and 3.60 ± 1.14 with iohexol ( P < 0.001). The attenuation of tagged feces was 581 ± 66 HU with diatrizoate and 1038 ± 117 HU with iohexol ( P < 0.001). Residual contrast agent in the small intestine was assessed at 55.3% and 62.3% for the diatrizoate group and iohexol group, respectively ( P = 0.003). Compared with iohexol, diatrizoate had better image quality, proper fecal-tagging density, and more homogeneous tagging along with comparable excellent patient acceptance, and might be more suitable for fecal tagging in noncathartic computed tomography colonography.

A prediction model of benign and malignant differentiation was established by magnetic resonance signs of parotid gland tumors to provide an important basis for the preoperative diagnosis and treatment of parotid gland tumor patients. The data from 138 patients (modeling group) who were diagnosed based on a pathologic evaluation in the Department of Stomatology of Jilin University from June 2019 to August 2021 were retrospectively analyzed. The independent factors influencing benign and malignant differentiation of parotid tumors were selected by logistic regression analysis, and a mathematical prediction model for benign and malignant tumors was established. The data from 35 patients (validation group) who were diagnosed based on pathologic evaluation from September 2021 to February 2022 were collected for verification. Univariate and multivariate logistic regression analysis showed that tumor morphology, tumor boundary, tumor signal, and tumor apparent diffusion coefficient (ADC) were independent risk factors for predicting benign and malignant parotid gland tumors ( P < 0.05). Based on multivariate logistic regression analysis of the modeling group, a mathematical prediction model was established as follows: Y = the ex/(1 + ex) and X = 0.385 + (1.416 × tumor morphology) + (1.473 × tumor border) + (1.306 × tumor signal) + (2.312 × tumor ADC value). The results showed that the area under the receiver operating characteristic curve of the model was 0.832 (95% confidence interval, 0.75-0.91), the sensitivity was 82.6%, and the specificity was 70.65%. The validity of the model was verified using validation group data, for which the sensitivity was 85.71%, the specificity was 96.4%, and the correct rate was 94.3%. The results showed that the area under receiver operating characteristic curve was 0.936 (95% confidence interval, 0.83-0.98). Combined with tumor morphology, tumor ADC, tumor boundary, and tumor signal, the established prediction model provides an important reference for preoperative diagnosis of benign and malignant parotid gland tumors.