CT semi-quantitative score used as risk factor for hyponatremia in patients with COVID-19: a cross-sectional study.

Purpose: Chest computed tomography (CT) is used to determine the severity of COVID-19 pneumonia, and pneumonia is associated with hyponatremia. This study aims to explore the predictive value of the semi-quantitative CT visual score for hyponatremia in patients with COVID-19 to provide a reference for clinical practice. Methods: In this cross-sectional study, 343 patients with RT-PCR confirmed COVID-19, all patients underwent CT, and the severity of lung lesions was scored by radiologists using the semi-quantitative CT visual score. The risk factors of hyponatremia in COVID-19 patients were analyzed and combined with laboratory tests. The thyroid function changes caused by SARS-CoV-2 infection and their interaction with hyponatremia were also analyzed. Results: In patients with SARS-CoV-2 infection, the total severity score (TSS) of hyponatremia was higher [M(range), 3.5(2.5-5.5) vs 3.0(2.0-4.5) scores, P=0.001], implying that patients with hyponatremia had more severe lung lesions. The risk factors of hyponatremia in the multivariate regression model included age, vomiting, neutrophils, platelet, and total severity score. SARS-CoV-2 infection impacted thyroid function, and patients with hyponatremia showed a lower free triiodothyronine (3.1 ± 0.9 vs 3.7 ± 0.9, P=0.001) and thyroid stimulating hormone level [1.4(0.8-2.4) vs 2.2(1.2-3.4), P=0.038]. Conclusion: Semi-quantitative CT score can be used as a risk factor for hyponatremia in patients with COVID-19. There is a weak positive correlation between serum sodium and free triiodothyronine in patients with SARS-CoV-2 infection.

Reinforcement learning for individualized lung cancer screening schedules: A nested case-control study.

BACKGROUND: The current guidelines for managing screen-detected pulmonary nodules offer rule-based recommendations for immediate diagnostic work-up or follow-up at intervals of 3, 6, or 12 months. Customized visit plans are lacking. PURPOSE: To develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL-based policy models. METHODS: Using a nested case-control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer-free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we trained RL-based policy models, incorporating nodule diameter alone, combined with nodule appearance (attenuation and margin) and/or patient information (age, sex, smoking status, pack-years, and family history). We calculated rates of misdiagnosis, missed diagnosis, and delayed diagnosis, and compared the performance of RL-based policy models with rule-based follow-up protocols (National Comprehensive Cancer Network guideline; China Guideline for the Screening and Early Detection of Lung Cancer). RESULTS: We identified significant interactions between certain variables (e.g., nodule shape and patient smoking pack-years, beyond those considered in guideline protocols) and the selection of follow-up testing intervals, thereby impacting the quality of the decision sequence. In validation, one RL-based policy model achieved rates of 12.3% for misdiagnosis, 9.7% for missed diagnosis, and 11.7% for delayed diagnosis. Compared with the two rule-based protocols, the three best-performing RL-based policy models consistently demonstrated optimal performance for specific patient subgroups based on disease characteristics (benign or malignant), nodule phenotypes (size, shape, and attenuation), and individual attributes. CONCLUSIONS: This study highlights the potential of using an RL-based approach that is both clinically interpretable and performance-robust to develop personalized lung cancer screening schedules. Our findings present opportunities for enhancing the current cancer screening system.

Acceptability of adding a non-contrast abdominal CT scan to screen for kidney cancer and other abdominal pathology within a community-based CT screening programme for lung cancer: A qualitative study.

OBJECTIVES: The Yorkshire Kidney Screening Trial (YKST) is a feasibility study of adding non-contrast abdominal CT scanning to screen for kidney cancer and other abdominal malignancies to community-based CT screening for lung cancer within the Yorkshire Lung Screening Trial (YLST). This study explored the acceptability of the combined screening approach to participants and healthcare professionals (HCPs) involved in the trial. METHODS: We conducted semi-structured interviews with eight HCPs and 25 participants returning for the second round of scanning within YLST, 20 who had taken up the offer of the additional abdominal CT scan and five who had declined. Transcripts were analysed using thematic analysis, guided by the Theoretical Framework of Acceptability. RESULTS: Overall, combining the offer of a non-contrast abdominal CT scan alongside the low-dose thoracic CT was considered acceptable to participants, including those who had declined the abdominal scan. The offer of the additional scan made sense and fitted well within the process, and participants could see benefits in terms of efficiency, cost and convenience both for themselves as individuals and also more widely for the NHS. Almost all participants made an instant decision at the point of initial invitation based more on trust and emotions than the information provided. Despite this, there was a clear desire for more time to decide whether to accept the scan or not. HCPs also raised concerns about the burden on the study team and wider healthcare system arising from additional workload both within the screening process and downstream following findings on the abdominal CT scan. CONCLUSIONS: Adding a non-contrast abdominal CT scan to community-based CT screening for lung cancer is acceptable to both participants and healthcare professionals. Giving potential participants prior notice and having clear pathways for downstream management of findings will be important if it is to be offered more widely.

A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma.

Background: Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable treatment responses. The purpose of this study is to establish and validate a radiomics based on enhanced computed tomography (CT) and combined with clinical data to predict the major pathological response to NIT in ESCC patients. Methods: This retrospective study included 82 ESCC patients who were randomly divided into the training group (n = 57) and the validation group (n = 25). Radiomic features were derived from the tumor region in enhanced CT images obtained before treatment. After feature reduction and screening, radiomics was established. Logistic regression analysis was conducted to select clinical variables. The predictive model integrating radiomics and clinical data was constructed and presented as a nomogram. Area under curve (AUC) was applied to evaluate the predictive ability of the models, and decision curve analysis (DCA) and calibration curves were performed to test the application of the models. Results: One clinical data (radiotherapy) and 10 radiomic features were identified and applied for the predictive model. The radiomics integrated with clinical data could achieve excellent predictive performance, with AUC values of 0.93 (95% CI 0.87-0.99) and 0.85 (95% CI 0.69-1.00) in the training group and the validation group, respectively. DCA and calibration curves demonstrated a good clinical feasibility and utility of this model. Conclusion: Enhanced CT image-based radiomics could predict the response of ESCC patients to NIT with high accuracy and robustness. The developed predictive model offers a valuable tool for assessing treatment efficacy prior to initiating therapy, thus providing individualized treatment regimens for patients.

Automated eyeball volume measurement based on CT images using neural network-based segmentation and simple estimation.

With the increase in the dependency on digital devices, the incidence of myopia, a precursor of various ocular diseases, has risen significantly. Because myopia and eyeball volume are related, myopia progression can be monitored through eyeball volume estimation. However, existing methods are limited because the eyeball shape is disregarded during estimation. We propose an automated eyeball volume estimation method from computed tomography images that incorporates prior knowledge of the actual eyeball shape. This study involves data preprocessing, image segmentation, and volume estimation steps, which include the truncated cone formula and integral equation. We obtained eyeball image masks using U-Net, HFCN, DeepLab v3 +, SegNet, and HardNet-MSEG. Data from 200 subjects were used for volume estimation, and manually extracted eyeball volumes were used for validation. U-Net outperformed among the segmentation models, and the proposed volume estimation method outperformed comparative methods on all evaluation metrics, with a correlation coefficient of 0.819, mean absolute error of 0.640, and mean squared error of 0.554. The proposed method surpasses existing methods, provides an accurate eyeball volume estimation for monitoring the progression of myopia, and could potentially aid in the diagnosis of ocular diseases. It could be extended to volume estimation of other ocular structures.

Common double-lumen tube selection methods overestimate adequate tube sizes in individual patients - a 3D reconstruction study.

BACKGROUND: Appropriate selection of double-lumen tube sizes for one-lung ventilation is crucial to prevent airway damage. Current selection methods rely on demographic factors or 2D radiography. Prediction of left bronchial diameter is indispensable for choosing the adequate tube size. This prospective observational study investigates if current selection methods sufficiently predict individuals' left bronchial diameters for DLT selection compared to the 3D reconstruction. METHODS: 100 patients necessitating thoracic surgery with one-lung ventilation and left-sided double-lumen tubes, ≥ 18 years of age, and a set of chest X-rays and 2D thorax CT scans for 3D reconstruction of the left main bronchus were included between 07/2021 and 06/2023. The cross-validated prediction error and the width of the 95%-prediction intervals of the 3D left main bronchial diameter utilizing linear prediction models were based on current selection methods. RESULTS: The mean bronchial diameter in 3D reconstruction was 13.6 ± 2.1 mm. The ranges of the 95%-prediction intervals for the bronchial diameter were 6.4 mm for demographic variables, 8.3 mm for the tracheal diameter from the X-ray, and 5.9 mm for bronchial diameter from the 2D-CT scans. Current methods violated the suggested '≥1 mm' safety criterion in up to 7% (men) and 42% (women). Particularly, 2D radiography overestimated women's left bronchial diameter. Current methods even allowed the selection of double-lumen tubes with bronchial tube sections greater than the bronchial diameter in women. CONCLUSIONS: Neither demographic nor 2D-radiographic methods sufficiently account for the variability of the bronchial diameter. Wide 95%-prediction intervals for the bronchial diameter hamper accurate individual double-lumen tube selection. This increases women's risk of bronchial damage, particularly if they have other predisposing factors. These patients may benefit from 3D reconstruction of the left main bronchus. TRIAL REGISTRATION: Not applicable.

A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities.

PURPOSE: To examine whether there is a significant difference in image quality between the deep learning reconstruction (DLR [AiCE, Advanced Intelligent Clear-IQ Engine]) and hybrid iterative reconstruction (HIR [AIDR 3D, adaptive iterative dose reduction three dimensional]) algorithms on the conventional enhanced and CE-boost (contrast-enhancement-boost) images of indirect computed tomography venography (CTV) of lower extremities. MATERIALS AND METHODS: In this retrospective study, seventy patients who underwent CTV from June 2021 to October 2022 to assess deep vein thrombosis and varicose veins were included. Unenhanced and enhanced images were reconstructed for AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images were obtained using subtraction software. Objective and subjective image qualities were assessed, and radiation doses were recorded. RESULTS: The CT values of the inferior vena cava (IVC), femoral vein ( FV), and popliteal vein (PV) in the CE-boost images were approximately 1.3 (1.31-1.36) times higher than in those of the enhanced images. There were no significant differences in mean CT values of IVC, FV, and PV between AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images. Noise in AiCE, AiCE-boost images was significantly lower than in AIDR 3D and AIDR 3D-boost images ( P < 0.05). The SNR (signal-to-noise ratio), CNR (contrast-to-noise ratio), and subjective scores of AiCE-boost images were the highest among 4 groups, surpassing AiCE, AIDR 3D, and AIDR 3D-boost images (all P < 0.05). CONCLUSION: In indirect CTV of the lower extremities images, DLR with the CE-boost technique could decrease the image noise and improve the CT values, SNR, CNR, and subjective image scores. AiCE-boost images received the highest subjective image quality score and were more readily accepted by radiologists.

Automation of Wilms' tumor segmentation by artificial intelligence.

BACKGROUND: 3D reconstruction of Wilms' tumor provides several advantages but are not systematically performed because manual segmentation is extremely time-consuming. The objective of our study was to develop an artificial intelligence tool to automate the segmentation of tumors and kidneys in children. METHODS: A manual segmentation was carried out by two experts on 14 CT scans. Then, the segmentation of Wilms' tumor and neoplastic kidney was automatically performed using the CNN U-Net and the same CNN U-Net trained according to the OV2ASSION method. The time saving for the expert was estimated depending on the number of sections automatically segmented. RESULTS: When segmentations were performed manually by two experts, the inter-individual variability resulted in a Dice index of 0.95 for tumor and 0.87 for kidney. Fully automatic segmentation with the CNN U-Net yielded a poor Dice index of 0.69 for Wilms' tumor and 0.27 for kidney. With the OV2ASSION method, the Dice index varied depending on the number of manually segmented sections. For the segmentation of the Wilms' tumor and neoplastic kidney, it varied respectively from 0.97 to 0.94 for a gap of 1 (2 out of 3 sections performed manually) to 0.94 and 0.86 for a gap of 10 (1 section out of 6 performed manually). CONCLUSION: Fully automated segmentation remains a challenge in the field of medical image processing. Although it is possible to use already developed neural networks, such as U-Net, we found that the results obtained were not satisfactory for segmentation of neoplastic kidneys or Wilms' tumors in children. We developed an innovative CNN U-Net training method that makes it possible to segment the kidney and its tumor with the same precision as an expert while reducing their intervention time by 80%.

Comparison of image quality between Deep learning image reconstruction and Iterative reconstruction technique for CT Brain- a pilot study.

Background: Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blotchy appearance and decreased resolution for subtle contrasts. The deep-learning image reconstruction (DLIR) algorithm, which integrates a convolutional neural network (CNN) into the reconstruction process, generates high-quality images with minimal noise. Hence, the objective of this study was to assess the IQ of the Precise Image (DLIR) and the IR technique (iDose 4) for the NCCT brain. Methods: This is a prospective study. Thirty patients who underwent NCCT brain were included. The images were reconstructed using DLIR-standard and iDose 4. Qualitative IQ analysis parameters, such as overall image quality (OQ), subjective image noise (SIN), and artifacts, were measured. Quantitative IQ analysis parameters such as Computed Tomography (CT) attenuation (HU), image noise (IN), posterior fossa index (PFI), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in the basal ganglia (BG) and centrum-semiovale (CSO) were measured. Paired t-tests were performed for qualitative and quantitative IQ analyses between the iDose 4 and DLIR-standard. Kappa statistics were used to assess inter-observer agreement for qualitative analysis. Results: Quantitative IQ analysis showed significant differences (p<0.05) in IN, SNR, and CNR between the iDose 4 and DLIR-standard at the BG and CSO levels. IN was reduced (41.8-47.6%), SNR (65-82%), and CNR (68-78.8%) were increased with DLIR-standard. PFI was reduced (27.08%) the DLIR-standard. Qualitative IQ analysis showed significant differences (p<0.05) in OQ, SIN, and artifacts between the DLIR standard and iDose 4. The DLIR standard showed higher qualitative IQ scores than the iDose 4. Conclusion: DLIR standard yielded superior quantitative and qualitative IQ compared to the IR technique (iDose4). The DLIR-standard significantly reduced the IN and artifacts compared to iDose 4 in the NCCT brain.

A study on the correlation of Doppler ultrasound in the diagnosis of cervical lymph nodes in patients with laryngeal and hypopharyngeal cancers: An observational study.

The objective was to assess the diagnostic efficacy of Doppler ultrasound in detecting cervical lymph nodes in patients diagnosed with laryngeal and hypopharyngeal cancers. Patients undergoing surgery for laryngeal and hypopharyngeal cancers in the Otolaryngology Department from January 2021 to January 2023 were included. Two groups, with equal numbers, underwent ultrasound examination and intensive CT examination in the experimental and control groups, respectively, along with routine cervical lymph node dissection. A resident with over 6 years of clinical experience in the otolaryngology department performed routine bilateral cervical lymph node palpation. Sensitivity, specificity, and validity were compared among different examination methods. The McNemar test assessed specificity and sensitivity between palpation, color Doppler ultrasonography, and enhanced CT, while the Kappa concordance test evaluated the concordance between the 2 examination methods. Data were statistically analyzed using SPSS 23.0. Palpation showed a diagnostic sensitivity (DS) of 52.83% and specificity of 91.11% for all patients with cervical lymph node metastasis. Ultrasonography demonstrated a DS of 77.78% and specificity of 81.82% in patients with cervical lymph node metastasis, while intensive CT had a DS of 75.86% and specificity of 60.00%. Statistical significance (P < .05) was observed in the sensitivity between palpation and ultrasonography, and between palpation and enhanced CT. The specificity between enhanced CT and ultrasonography (P = .021) and between palpation and enhanced CT scan (P = .003) both showed statistical significance (P < .05). Doppler ultrasound yields diagnostic results highly consistent with pathological diagnoses in patients with laryngeal and hypopharyngeal cancers. Utilizing Doppler ultrasound can enhance the accuracy of diagnosing these cancers, aiding physicians in devising more suitable treatment plans for patients.

Quantitative measurements of dual-energy CT parameters in the diagnosis of osteoporosis - a highly sensitive and specific technique: An observational study.

With the aging of the population in our society, osteoporosis (OP) has become one of the chronic diseases that seriously threaten the physical health of the elderly, leading to a heavy burden on healthcare. In recent years, with the continuous development of dual-energy CT (DECT) technology, quantitative measurements of DECT parameters, which is highly sensitive to OP, provides accurate results, is convenient and cost-effective, and is expected to be widely used in bone density testing. This study was aimed to explore the value of quantitative measurements of DECT parameters in diagnosing OP, in order to better guide clinical judgments and treatment. A total of 187 patients who underwent dual-energy X-ray and DECT examinations at Tianjin hospital between January 2022 and June 2023 were included as participants in this study. The bone mineral density (BMD) values of the lumbar spine (L1-L4) were determined using dual-energy X-ray absorptiometry. Simultaneously, CT scans of the lumbar spine (L1-L4) were conducted to measure the CT values of contrast media (CM), mixed-energy image CT values (regular CT value [rCT]), calcium concentration (CaD), as well as fat fraction (FF). Pearson correlation analysis was used to examine the relationship between the quantitative measurements of L1 to L4 vertebral bodies obtained from DECT and BMD. The values of CM, rCT, and CaD in the OP group were all lower than those in the non-OP group with statistical significance (P < .001). Conversely, the fat fraction parameter value in the OP group was significantly higher in contrast with the non-OP group (P = .004); there was a positive correlation between CM, rCT, CaD, and BMD values (R = 0.579, P < .001; R = 0.604, P < .001; R = 0.563, P < .001); CM, rCT, and CaD had high diagnostic value for OP, as evidenced by AUCs of 0.935 (95% CI: 0.900-0.971), 0.956 (95% CI: 0.925-0.987), and 0.926 (95% CI: 0.858-0.954), respectively, all with P values < .001. Quantitative measurement of DECT parameters showed a high sensitivity as well as a high specificity in the diagnosis of OP. It is also highly feasible and holds significant clinical diagnostic value, making it a suitable candidate for widespread application.

Robotic-assisted total hip arthroplasty utilizing a fluoroscopy-guided system produced similar cup accuracy and precision relative to a computerized tomography-based robotic platform.

Robotic assistance for total hip arthroplasty (THA) has been demonstrated to improve accuracy of acetabular cup placement relative to manual, unassisted technique. The purpose of this investigation was to compare the accuracy and precision between a fluoroscopy-based robotic total hip arthroplasty platform (FL-RTHA) and a computerized tomography-based (CT-RTHA) platform. The study included 98 consecutive FL-RTHA and 159 CT-RTHA procedures performed via direct anterior approach (DAA). All cases were performed for a pre-operative diagnosis of osteoarthritis, avascular necrosis, or rheumatoid arthritis. Primary outcome variables included cup implantation accuracy and precision (variance). Implantation accuracy was calculated as the absolute value of the difference between pre-operative target cup angles (inclination and anteversion) and the same post-operative angles. Percentage placement in the Lewinnek safe zone was also measured for both cohorts. The FL-RTHA and CT-RTHA cohorts demonstrated a 1.2° difference in absolute values for cup inclination accuracy (4.6° ± 3.6 vs. 3.4 ± 2.7; p = 0.005), and no difference in absolute values for cup anteversion accuracy (4.7° ± 4.1 vs. 4.6 ± 3.4; p = 0.991). Cohorts demonstrated similar precision for cup inclination and anteversion placement parameters, as well as equivalent Lewinnek safe zone placement. The use of a fluoroscopy-based robotic assistance platform for primary DAA THA resulted in similar accuracy and precision of acetabular cup placement when compared to a CT-based robotic assistance system.

Prediction-Augmented Shared Decision-Making and Lung Cancer Screening Uptake.

Importance: Addressing poor uptake of low-dose computed tomography lung cancer screening (LCS) is critical, especially for those having the most to gain-high-benefit persons with high lung cancer risk and life expectancy more than 10 years. Objective: To assess the association between LCS uptake and implementing a prediction-augmented shared decision-making (SDM) tool, which enables clinicians to identify persons predicted to be at high benefit and encourage LCS more strongly for these persons. Design, Setting, and Participants: Quality improvement interrupted time series study at 6 Veterans Affairs sites that used a standard set of clinical reminders to prompt primary care clinicians and screening coordinators to engage in SDM for LCS-eligible persons. Participants were persons without a history of LCS who met LCS eligibility criteria at the time (aged 55-80 years, smoked ≥30 pack-years, and current smoking or quit <15 years ago) and were not documented to be an inappropriate candidate for LCS by a clinician during October 2017 through September 2019. Data were analyzed from September to November 2023. Exposure: Decision support tool augmented by a prediction model that helps clinicians personalize SDM for LCS, tailoring the strength of screening encouragement according to predicted benefit. Main outcome and measure: LCS uptake. Results: In a cohort of 9904 individuals, the median (IQR) age was 64 (57-69) years; 9277 (94%) were male, 1537 (16%) were Black, 8159 (82%) were White, 5153 (52%) were predicted to be at intermediate (preference-sensitive) benefit and 4751 (48%) at high benefit, and 1084 (11%) received screening during the study period. Following implementation of the tool, higher rates of LCS uptake were observed overall along with an increase in benefit-based LCS uptake (higher screening uptake among persons anticipated to be at high benefit compared with those at intermediate benefit; primary analysis). Mean (SD) predicted probability of getting screened for a high-benefit person was 24.8% (15.5%) vs 15.8% (11.8%) for a person at intermediate benefit (mean absolute difference 9.0 percentage points; 95% CI, 1.6%-16.5%). Conclusions and Relevance: Implementing a robust approach to personalized LCS, which integrates SDM, and a decision support tool augmented by a prediction model, are associated with improved uptake of LCS and may be particularly important for those most likely to benefit. These findings are timely given the ongoing poor rates of LCS uptake.

The contribution of E3D imaging integrated with robotic navigation: analysis of the first 80 consecutive posterior spinal fusion cases.

Eighty consecutive complex spinal robotic cases utilizing intraoperative 3D CT imaging (E3D, Group 2) were compared to 80 age-matched controls using the Excelsius robot alone with C-arm Fluoroscopic registration (Robot Only, Group 1). The demographics between the two groups were similar-severity of deformity, ASA Score for general anesthesia, patient age, gender, number of spinal levels instrumented, number of patients with prior spinal surgery, and amount of neurologic compression. The intraoperative CT scanning added several objective factors improving patient safety. There were significantly fewer complications in the E3D group with only 3 of 80 (4%) patients requiring a return to the operating room compared to 11 of 80 (14%) patients in the Robot Only Group requiring repeat surgery for implant related problems (Chi squared analysis = 5.00, p = 0.025). There was a significant reduction the amount of fluoroscopy time in the E3D Group (36 s, range 4-102 s) compared to Robot only group (51 s, range 15-160 s) (p = 0.0001). There was also shorter mean operative time in the E3D group (257 ± 59.5 min) compared to the robot only group (306 ± 73.8 min) due to much faster registration time (45 s). A longer registration time was required in the Robot only group to register each vertebral level with AP and Lateral fluoroscopy shots. The estimated blood loss was also significantly lower in Group 2 (mean 345 ± 225 ml) vs Group 1 (474 ± 397 ml) (p = 0.012). The mean hospital length of stay was also significantly shorter for Group 2 (3.77 ± 1.86 days) compared to Group 1 (5.16 ± 3.40) (p = 0.022). There was no significant difference in the number of interbody implants nor corrective osteotomies in both groups-Robot only 52 cases vs. 42 cases in E3D group.Level of evidence: IV, Retrospective review.

Interventional Radiology for Bleeding Ectopic Varices: Individualized Approach Based on Vascular Anatomy.

Ectopic varices are rare but potentially life-threatening conditions usually resulting from a combination of global portal hypertension and local occlusive components. As imaging, innovative devices, and interventional radiologic techniques evolve and are more widely adopted, interventional radiology is becoming essential in the management of ectopic varices. The interventional radiologist starts by diagnosing the underlying causes of portal hypertension and evaluating the afferent and efferent veins of ectopic varices with CT. If decompensated portal hypertension is causing ectopic varices, placement of a transjugular intrahepatic portosystemic shunt is considered the first-line treatment, although this treatment alone may not be effective in managing ectopic variceal bleeding because it may not sufficiently resolve focal mesenteric venous obstruction causing ectopic varices. Therefore, additional variceal embolization should be considered after placement of a transjugular intrahepatic portosystemic shunt. Retrograde transvenous obliteration can serve as a definitive treatment when the efferent vein connected to the systemic vein is accessible. Antegrade transvenous obliteration is a vital component of interventional radiologic management of ectopic varices because ectopic varices often exhibit complex anatomy and commonly lack catheterizable portosystemic shunts. Superficial veins of the portal venous system such as recanalized umbilical veins may provide safe access for antegrade transvenous obliteration. Given the absence of consensus and guidelines, a multidisciplinary team approach is essential for the individualized management of ectopic varices. Interventional radiologists must be knowledgeable about the anatomy and hemodynamic characteristics of ectopic varices based on CT images and be prepared to consider appropriate options for each specific situation. ©RSNA, 2024 Supplemental material is available for this article.

Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty.

BACKGROUND: This study aimed to develop a novel deep convolutional neural network called Dual-path Double Attention Transformer (DDA-Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic-assisted total knee arthroplasty (TKA). METHODS: The femoral, tibial, patellar, and fibular segmentation performance and speed were evaluated and the accuracy of component sizing, bone resection and alignment of the robotic-assisted TKA system constructed using this deep learning network was clinically validated. RESULTS: Overall, DDA-Transformer outperformed six other networks in terms of the Dice coefficient, intersection over union, average surface distance, and Hausdorff distance. DDA-Transformer exhibited significantly faster segmentation speeds than nnUnet, TransUnet and 3D-Unet (p < 0.01). Furthermore, the robotic-assisted TKA system outperforms the manual group in surgical accuracy. CONCLUSIONS: DDA-Transformer exhibited significantly improved accuracy and robustness in knee joint segmentation, and this convenient and stable knee joint CT image segmentation network significantly improved the accuracy of the TKA procedure.

Diagnostic efficiency of intravoxel incoherent motion-based virtual magnetic resonance elastography in pulmonary neoplasms.

BACKGROUND: The aim of the study were as below. (1) To investigate the feasibility of intravoxel incoherent motion (IVIM)-based virtual magnetic resonance elastography (vMRE) to provide quantitative estimates of tissue stiffness in pulmonary neoplasms. (2) To verify the diagnostic performance of shifted apparent diffusion coefficient (sADC) and reconstructed virtual stiffness values in distinguishing neoplasm nature. METHODS: This study enrolled 59 patients (37 males, 22 females) with one pulmonary neoplasm who underwent computed tomography-guided percutaneous transthoracic needle biopsy (PTNB) with pathological diagnosis (26 adenocarcinoma, 10 squamous cell carcinoma, 3 small cell carcinoma, 4 tuberculosis and 16 non-specific benign; mean age, 60.81 ± 9.80 years). IVIM was performed on a 3 T magnetic resonance imaging scanner before biopsy. sADC and virtual shear stiffness maps reflecting lesion stiffness were reconstructed. sADC and virtual stiffness values of neoplasm were extracted, and the diagnostic performance of vMRE in distinguishing benign and malignant and detailed pathological type were explored. RESULTS: Compared to benign neoplasms, malignant ones had a significantly lower sADC and a higher virtual stiffness value (P < 0.001). Subsequent subtype analyses showed that the sADC values of adenocarcinoma and squamous cell carcinoma groups were significantly lower than non-specific benign group (P = 0.013 and 0.001, respectively). Additionally, virtual stiffness values of the adenocarcinoma and squamous cell carcinoma subtypes were significantly higher than non-specific benign group (P = 0.008 and 0.001, respectively). However, no significant correlation was found among other subtype groups. CONCLUSIONS: Non-invasive vMRE demonstrated diagnostic efficiency in differentiating the nature of pulmonary neoplasm. vMRE is promising as a new method for clinical diagnosis.

Improved Precision and Safety of Supra-Tentorial Intracranial Hematoma Puncture Using C-Arm CT Four-Dimensional Navigation.

BACKGROUND Spontaneous intracerebral hemorrhage has a high fatality rate within the initial month after onset. This study determined the safety and therapeutic efficacy of minimally invasive puncture for supra-tentorial intracranial hematoma under C-arm computed tomography (CT) 4-dimensional navigation. MATERIAL AND METHODS We retrospectively analyzed 64 patients with supra-tentorial cerebral hemorrhage from June 2020 to May 2023; 31 patients were assigned to the study group (C-arm CT navigation puncture) and 33 patients were in the control group (conventional CT-guided puncture). The analysis focused on assessment of puncture error, postoperative complication rate, and the Glasgow Outcome Scale (GOS) and National Institute of Health Stroke Scale (NIHSS) scores 30 and 90 days after surgery. RESULTS C-arm CT navigation puncture had improved precision, with significantly reduced transverse (3.17±1.75 mm) and longitudinal (1.83±1.21 mm) deviations, compared with the control group (7.88±1.74 mm and 5.50±1.84 mm, respectively; P<0.05). The overall postoperative complication rate was significantly lower in the study group than in the control group (12.90% vs 36.36%, P<0.05). The mean GOS score was higher in the study group than in the control group 30 and 90 days postoperatively (3.42±0.96 and 3.97±0.95 vs 2.94±0.79 and 3.46±0.90, respectively; P<0.05), while the mean NIHSS score was lower in the study group than in the control group 30 and 90 days postoperatively (10.58±6.52 and 5.97±4.55 vs 14.42±8.13 and 9.55±8.31, respectively; P<0.05). CONCLUSIONS Supra-tentorial intracranial hematoma puncture under C-arm CT 4-dimensional navigation is accurate, safe, and beneficial.

Placental Transmogrification of the Lung Presenting as Cystic-Solid Mass: A Case Report.

The gradually progressive solitary cystic-solid mass of chest CT scans is highly suggestive of lung cancer. We report a case of a 29-year-old woman with a persistent cystic-solid lesion in the right upper lobe. A chest CT scan showed a 35 mm × 44 mm × 51 mm focal cystic-solid mass in the anterior segment of the right upper lobe. The size of lesion had increased over 3 years, especially for the solid component. The right upper lobe pneumonectomy was performed. Postoperative pathological examination showed placental transmogrification of the lung, which is a rare cause of pulmonary cystic lesion.