Non-contrast CT Research Articles

Revolutionizing Intracranial Hemorrhage Diagnosis: A Retrospective Analytical Study of Viz.ai ICH for Enhanced Diagnostic Accuracy.

Introduction Artificial intelligence (AI) alerts the radiologist to the presence of intracranial hemorrhage (ICH) as fast as 1-2 minutes from scan completion, leading to faster diagnosis and treatment. We wanted to validate a new AI application called Viz.ai ICH to improve the diagnosis of suspected ICH. Methods We performed a retrospective analysis of 4,203 consecutive non-contrast brain computed tomography (CT) reports in a single institution between September 1, 2021, and January 31, 2022. The reports were made by neuroradiologists who reviewed each case for the presence of ICH. Reports and identified cases with positive findings for ICH were reviewed. Positive cases were categorized based on subtype, timing, and size/volume. Viz.ai ICH output was reviewed for positive cases. This AI model was validated by assessing its performance with Viz.ai ICH as the index test compared to the neuroradiologists' interpretation as the gold standard. Results According to neuroradiologists, 9.2% of non-contrast brain CT reports were positive for ICH. The sensitivity of Viz.ai ICH was 85%, specificity was 98%, positive predictive value was 81%, and negative predictive value was 99%. Subgroup analysis was performed based on intraparenchymal, subarachnoid, subdural, and intraventricular subtypes. Sensitivities were 94%, 79%, 83%, and 44%, respectively. Further stratification revealed sensitivity improves with higher acuity and volume/size across subtypes. Conclusion Our analysis indicates that AI can accurately detect ICH's presence, particularly for large-volume/large-size ICH. The paper introduces a novel AI model for detecting ICH. This advancement contributes to the field by revolutionizing ICH detection and improving patient outcomes.

Segmentation Guided Crossing Dual Decoding Generative Adversarial Network for Synthesizing Contrast-Enhanced Computed Tomography Images.

Although contrast-enhanced computed tomography (CE-CT) images significantly improve the accuracy of diagnosing focal liver lesions (FLLs), the administration of contrast agents imposes a considerable physical burden on patients. The utilization of generative models to synthesize CE-CT images from non-contrasted CT images offers a promising solution. However, existing image synthesis models tend to overlook the importance of critical regions, inevitably reducing their effectiveness in downstream tasks. To overcome this challenge, we propose an innovative CE-CT image synthesis model called Segmentation Guided Crossing Dual Decoding Generative Adversarial Network (SGCDD-GAN). Specifically, the SGCDD-GAN involves a crossing dual decoding generator including an attention decoder and an improved transformation decoder. The attention decoder is designed to highlight some critical regions within the abdominal cavity, while the improved transformation decoder is responsible for synthesizing CE-CT images. These two decoders are interconnected using a crossing technique to enhance each other's capabilities. Furthermore, we employ a multi-task learning strategy to guide the generator to focus more on the lesion area. To evaluate the performance of proposed SGCDD-GAN, we test it on an in-house CE-CT dataset. In both CE-CT image synthesis tasks-namely, synthesizing ART images and synthesizing PV images-the proposed SGCDD-GAN demonstrates superior performance metrics across the entire image and liver region, including SSIM, PSNR, MSE, and PCC scores. Furthermore, CE-CT images synthetized from our SGCDD-GAN achieve remarkable accuracy rates of 82.68%, 94.11%, and 94.11% in a deep learning-based FLLs classification task, along with a pilot assessment conducted by two radiologists.

CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer

BackgroundProgrammed cell death ligand 1 (PD-L1), as a reliable predictive biomarker, plays an important role in guiding immunotherapy of lung cancer. To investigate the value of CT-based deep learning radiomics signature to predict PD-L1 expression in non-small cell lung cancers(NSCLCs).Methods259 consecutive patients with pathological confirmed NSCLCs were retrospectively collected and divided into the training cohort and validation cohort according to the chronological order. The univariate and multivariate analyses were used to build the clinical model. Radiomics and deep learning features were extracted from preoperative non-contrast CT images. After feature selection, Radiomics score (Rad-score) and deep learning radiomics score (DLR-score) were calculated through a linear combination of the selected features and their coefficients. Predictive performance for PD-L1 expression was evaluated via the area under the curve (AUC) of receiver operating characteristic, the calibration curves, and the decision curve analysis.ResultsThe clinical model based on Cytokeratin 19 fragment and lobulated shape obtained an AUC of 0.767(95% CI: 0.673–0.860) in the training cohort and 0.604 (95% CI:0.477–0.731) in the validation cohort. 11 radiomics features and 15 deep learning features were selected by LASSO regression. AUCs of the Rad-score were 0.849 (95%CI: 0.783–0.914) and 0.717 (95%CI: 0.607–0.826) in the training cohort and validation cohort, respectively. AUCs of DLR-score were 0.938 (95%CI: 0.899–0.977) and 0.818(95%CI:0.727–0.910) in the training cohort and validation cohort, respectively. AUCs of the DLR-score were significantly higher than those of the Rad-score and the clinical model.ConclusionThe CT-based deep learning radiomics signature could achieve clinically acceptable predictive performance for PD-L1 expression, which showed potential to be a surrogate imaging biomarker or a complement of immunohistochemistry assessment.

Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans

Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT. Clinical validation was performed using an external validation set including 398 patients designated for true nonenhanced CT (NECT), from multiple vendors at three institutes. Detection of lesions was performed by three radiologists with only NECT in the first session and an additionally provided DL-SynCCT in the second session. The mean peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) of the DL-SynCCT compared to CECT were 43.25 ± 0.41 and 0.92 ± 0.01, respectively. With DL-SynCCT, the pooled sensitivity for lesion detection (72.0% to 76.4%, P < 0.001) and level of diagnostic confidence (3.0 to 3.6, P < 0.001) significantly increased. In conclusion, DL-SynCCT generated by weakly supervised learning showed significant benefit in terms of sensitivity in detecting abnormal findings when added to NECT in patients designated for nonenhanced CT scans.

Radiodensities of Skeletal Muscle and Visceral Adipose Tissues Are Prognostic Factors in Clear-Cell Renal Cell Carcinoma.

Body composition may be related to survival in patients with clear-cell renal cell carcinoma (ccRCC), but studies have not simultaneously considered adipose and muscle tissue quantity and radiodensity. We analyzed data from 1,022 patients with ccRCC who underwent nephrectomy between 2000 and 2020 at Memorial Sloan Kettering Cancer Center. Skeletal muscle, visceral adipose tissue, and subcutaneous adipose tissue indexes (cm2/m2) and radiodensities [Hounsfield units (HU)] were assessed from noncontrast presurgical CT scans; clinical and demographic characteristics were available from the time of surgery. HRs and confidence intervals were estimated for overall (OS) and disease-free survival (DFS) through March 2023 in multivariable models that simultaneously accounted for all body composition measures. The median age of the patients was 58 years, 69% were male, and 90% were White. There were 169 OS events over 8,392 person-years and 253 DFS events over 7,753 person-years of follow-up. In adjusted analyses, poor OS was associated with lower skeletal muscle radiodensity [-10 HU, HR (95% confidence interval), 1.37 (1.05-1.77)] and greater visceral adipose tissue radiodensity [+10 HU, 1.66 (1.06-2.59)], with similar findings for DFS. Poor survival was also associated with greater visceral adipose tissue index [+40 cm2/m2, OS: 1.32 (0.97, 1.79); DFS: 1.33 (1.04, 1.71)]. Associations with skeletal muscle radiodensity were limited to patients with stage 1/2 disease. Radiodensities of skeletal muscle and visceral adipose tissues may be novel presurgical prognostic factors for patients with ccRCC. The findings underscore the importance of evaluating the full range of body composition features simultaneously in multivariable models.

Deep learning-based automatic ASPECTS calculation can improve diagnosis efficiency in patients with acute ischemic stroke: a multicenter study.

The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL). This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians' readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts. The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system's diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency. The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention. The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications. The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS. This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan. 94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.

Laparoscopic management of small bowel obstruction secondary to a mesodiverticular band of a Meckel's diverticulum in an adult: A case report and literature review.

The mesodiverticular band (MDB) of a Meckel's diverticulum (MD) is a rare, yet notable etiology of small bowel obstruction (SBO) in adults. Due to the nonspecific symptoms and challenging diagnosis thereof, preoperative clinical suspicion and strategic management are crucial for achieving optimal outcomes. Therefore, we presented a case in which laparoscopic surgery was strategically performed to alleviate ileus, due to a preoperative diagnosis of SBO, suspected to be secondary to an MD with a concomitant MDB. A 32-year-old male patient presented with a half-day's duration of epigastric pain, abdominal distension, and tenderness, resulting in the working diagnosis of SBO. Initial non-contrast computed tomography (CT) revealed SBO without signs of strangulation, postulated to be caused by an MD and concomitant MDB, resulting in conservative management. The symptoms persisted, necessitating contrast-enhanced CT. However, the dilated bowel loop suggestive of an MD that had been observed on non-contrast CT could not be confirmed on contrast-enhanced CT. Decompression therapy using a long tube provided minimal relief, prompting laparoscopic surgery on the 5th day post-admission for diagnostic and therapeutic purposes. An MD resection effectively relieved the SBO. The histopathological analysis revealed a true diverticulum with ectopic pancreatic tissue, confirming the diagnosis of an MD. At the band site, vascular and neural structures were encased in a sheath, consistent with the remnants of the vitelline duct mesentery; and histopathologically diagnosed as an MDB. The postoperative course was uneventful, and the patient was discharged on the 9th day, postoperatively. Decompression therapy and strategic laparoscopic surgery based on the preoperative working diagnosis of SBO yielded favorable outcomes, highlighting the importance of the early clinical suspicion of an MD and a concomitant MDB, as the etiology of SBO. The imaging variability and rarity of an MD in adults emphasizes the need for a heightened awareness and an accurate diagnosis for optimal management. Early intervention should be deliberated for patients with suspected intestinal ischemia. However, this case accentuates the clinical implications of strategic planning and employing minimally invasive techniques in the management of an MD-related SBO in adults.

Stroke Evaluation in the Interventional Suite Using Dual-Layer Detector Cone-Beam CT: aFirst-in-human Prospective Cohort Study (the Next GenerationX-ray Imaging System Trial).

Cone-beam CT in the interventional suite could be an alternative to CT to shorten door-to-thrombectomy time. However, image quality in cone-beam CT is limited by artifacts and poor differentiation between gray and white matter. This study compared non-contrast brain dual-layer cone-beam CT in the interventional suite to reference standard CT in stroke patients. Aprospective single-center study enrolled consecutive participants with ischemic or hemorrhagic stroke. The hemorrhage detection accuracy, per-region ASPECTS accuracy and subjective image quality (Likert scales for gray-white matter differentiation, structure perception and artifacts) were assessed by three neuroradiologists blinded to clinical data on dual-layer cone-beam CT 75 keV monoenergetic images compared to CT. Objective image quality was assessed by region-of-interest metrics. Non-inferiority for hemorrhage detection and ASPECTS accuracy was determined by the exact binomial test with aone-sided lower performance boundary prospectively set to 80% (98.75% CI). 27participants were included (74years ± 9; 19female) in the hyperacute or acute stroke phase. One reader missed asmall bleeding, but all hemorrhages were detected in the majority analysis (100% accuracy, CI lower boundary 86%, p = 0.002). ASPECTS majority analysis showed 90% accuracy (CI lower boundary 85%, p < 0.001). Sensitivity was 66% (individual readers 67%, 69%, and 76%), specificity was 97% (97%, 96%, 89%). Subjective and objective image quality were inferior to CT. In asmall single-center cohort, dual-layer cone-beam CT showed non-inferior hemorrhage detection and ASPECTS accuracy to CT. Despite inferior image quality, the technique may be useful for stroke evaluation in the interventional suite. NCT04571099 (clinicaltrials.gov). Prospectively registered 2020-09-04.

Neutrophil to lymphocyte ratio and serum procalcitonin level as a predictor of spontaneous ureteral stone passage: A prospective study.

Ureteric stone is responsible for around 20% of urinary tract stones and among them 70% of these are located in distal portion of the ureter. Stone causing ureter obstruction produce inflammatory changes in ureteric wall and prevent spontaneous passage of stone. The objective of the study is to compare the predictive role of procalcitonin and Neutrophil-to-lymphocyte ratio (NLR) for spontaneous passage of stone. Total 150 participants having ureteric stone of 4-8 mm, were included in prospective observational study. The patients were followed up for 4 weeks. Spontaneous Stone Passage (SSP) was confirmed with either the patient collecting the stone during urination or by Non-Contrast CT performed at 4 weeks. Blood samples of the patients were analysed and White blood cells, sedimentation, Neutrophile to Lymphocyte (NLR), procalcitonin level compared to analyse predictors of future SSP. The procalcitonin levels of the Spontaneous stone passing SSP (-ve) group (209.05 ± 78.45 pg/ml) were significantly higher than the not passing the SSP (+ve) group (130.76 ± 24.18) (p < 0.001). NLR is significantly higher in the SSP -ve (3.84 ± 0.41) than the SSP +ve (2.18 ± 0.38) group (p < 0.001). In single and multivariate analysis, significant activity was found for procalcitonin in SP +ve group. The findings of the study suggests that high level of procalcitonin, and high NLR have a negative effect on passage of stone. So early intervention can be planned to these patients to prevent complications.

Artificial Intelligence-based automated CT brain interpretation to accelerate treatment for acute stroke in rural India: An interrupted time series study.

In resource-limited settings, timely treatment of acute stroke is dependent upon accurate diagnosis that draws on non-contrast computed tomography (NCCT) scans of the head. Artificial Intelligence (AI) based devices may be able to assist non-specialist physicians in NCCT interpretation, thereby enabling faster interventions for acute stroke patients in these settings. We evaluated the impact of an AI device by comparing the time to intervention (TTI) from NCCT imaging to significant intervention before (baseline) and after the deployment of AI, in patients diagnosed with stroke (ischemic or hemorrhagic) through a retrospective interrupted time series analysis at a rural hospital managed by non-specialists in India. Significant intervention was defined as thrombolysis or antiplatelet therapy in ischemic strokes, and mannitol for hemorrhagic strokes or mass effect. We also evaluated the diagnostic accuracy of the software using the teleradiologists' reports as ground truth. Impact analysis in a total of 174 stroke patients (72 in baseline and 102 after deployment) demonstrated a significant reduction of median TTI from 80 minutes (IQR: 56·8-139·5) during baseline to 58·50 (IQR: 30·3-118.2) minutes after AI deployment (Wilcoxon rank sum test-location shift: -21·0, 95% CI: -38·0, -7·0). Diagnostic accuracy evaluation in a total of 864 NCCT scans demonstrated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) in detecting intracranial hemorrhage to be 0·89 (95% CI: 0·83-0·93), 0·99 (0·98-1·00), 0·96 (0·91-0·98) and 0·97 (0·96-0·98) respectively, and for infarct these were 0·84 (0·79-0·89), 0·81 (0·77-0·84), 0·58 (0·52-0·63), and 0·94 (0·92-0·96), respectively. AI-based NCCT interpretation supported faster abnormality detection with high accuracy, resulting in persons with acute stroke receiving significantly earlier treatment. Our results suggest that AI-based NCCT interpretation can potentially improve uptake of lifesaving interventions for acute stroke in resource-limited settings.

Sociodemographic biases in a commercial AI model for intracranial hemorrhage detection.

To evaluate whether a commercial AI tool for intracranial hemorrhage (ICH) detection on head CT exhibited sociodemographic biases. Our retrospective study reviewed 9736 consecutive, adult non-contrast head CT scans performed between November 2021 and February 2022 in a single healthcare system. Each CT scan was evaluated by a commercial ICH AI tool and a board-certified neuroradiologist; ground truth was defined as final radiologist determination of ICH presence/absence. After evaluating the AI tool's aggregate diagnostic performance, sub-analyses based on sociodemographic groups (age, sex, race, ethnicity, insurance status, and Area of Deprivation Index [ADI] scores) assessed for biases. χ2 test or Fisher's exact tests evaluated for statistical significance with p ≤ 0.05. Our patient population was 50% female (mean age 60 ± 19 years). The AI tool had an aggregate accuracy of 93% [9060/9736], sensitivity of 85% [1140/1338], specificity of 94% [7920/ 8398], positive predictive value (PPV) of 71% [1140/1618] and negative predictive value (NPV) of 98% [7920/8118]. Sociodemographic biases were identified, including lower PPV for patients who were females (67.3% [62,441/656] vs. 72.7% [699/962], p = 0.02), Black (66.7% [454/681] vs. 73.2% [686/937], p = 0.005), non-Hispanic/non-Latino (69.7% [1038/1490] vs. 95.4% [417/437]), p = 0.009), and who had Medicaid/Medicare (69.9% [754/1078]) or Private (66.5% [228/343]) primary insurance (p = 0.003). Lower sensitivity was seen for patients in the third quartile of national (78.8% [241/306], p = 0.001) and state ADI scores (79.0% [22/287], p = 0.001). In our healthcare system, a commercial AI tool had lower performance for ICH detection than previously reported and demonstrated several sociodemographic biases.

Abbreviated MRI in the evaluation of dizziness: report turnaround times and impact on length of stay compared to CT, CTA, and conventional MRI.

Neuroimaging is often used in the emergency department (ED) to evaluate for posterior circulation strokes in patients with dizziness, commonly with CT/CTA due to speed and availability. Although MRI offers more sensitive evaluation, it is less commonly used, in part due to slower turnaround times. We assess the potential for abbreviated MRI to improve reporting times and impact on length of stay (LOS) compared to conventional MRI (as well as CT/CTA) in the evaluation of acute dizziness. We performed a retrospective analysis of length of stay via LASSO regression for patients presenting to the ED with dizziness and discharged directly from the ED over 4 years (1/1/2018-12/31/2021), controlling for numerous patient-level and logistical factors. We additionally assessed turnaround time between order and final report for various imaging modalities. 14,204 patients were included in our analysis. Turnaround time for abbreviated MRI was significantly lower than for conventional MRI (4.40h vs. 6.14h, p < 0.001) with decreased impact on LOS (0.58h vs. 2.02h). Abbreviated MRI studies had longer turnaround time (4.40h vs. 1.41h, p < 0.001) and was associated with greater impact on ED LOS than non-contrast CT head (0.58h vs. 0.00h), however there was no significant difference in turnaround time compared to CTA head and neck (4.40h vs. 3.86h, p = 0.06) with similar effect on LOS (0.58h vs. 0.53h). Ordering both CTA and conventional MRI was associated with a greater-than-linear increase in LOS (additional 0.37h); the same trend was not seen combining CTA and abbreviated MRI (additional 0.00h). In the acute settings where MRI is available, abbreviated MRI protocols may improve turnaround times and LOS compared to conventional MRI protocols. Since recent guidelines recommend MRI over CT in the evaluation of dizziness, implementation of abbreviated MRI protocols has the potential to facilitate rapid access to preferred imaging, while minimizing impact on ED workflows.

Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke

ObjectiveTo establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular treatment.MethodsA retrospective analysis was conducted on 174 patients who underwent endovascular treatment for acute anterior circulation ischemic stroke between January 2020 and December 2023. FR was defined as successful recanalization but poor prognosis at 90 days (modified Rankin Scale, mRS 4–6). Radiomic features were extracted from non-contrast CT and selected using the least absolute shrinkage and selection operator (LASSO) regression method. Logistic regression (LR) model was used to build models based on radiomic and clinical features. A radiomics-clinical nomogram model was developed, and the predictive performance of the models was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity.ResultsA total of 174 patients were included. 2016 radiomic features were extracted from non-contrast CT, and 9 features were selected to build the radiomics model. Univariate and stepwise multivariate analyses identified admission NIHSS score, hemorrhagic transformation, NLR, and admission blood glucose as independent factors for building the clinical model. The AUC of the radiomics-clinical nomogram model in the training and testing cohorts were 0.860 (95%CI 0.801–0.919) and 0.775 (95%CI 0.605–0.945), respectively.ConclusionThe radiomics-clinical nomogram model based on non-contrast CT demonstrated satisfactory performance in predicting futile recanalization in patients with anterior circulation acute ischemic stroke.

Risk Factors of Asymptomatic Kidney Stone Passage in Adults with Recurrent Kidney Stones.

Kidney stones are a common health problem and are characterized by a high risk of recurrence. A certain number of kidney stones passed asymptomatically. Data regarding the frequency of asymptomatic spontaneous stone passages are limited. To assess the frequency of asymptomatic spontaneous stone passage and its covariates, we conducted a post-hoc analysis of the prospective randomized NOSTONE trial. All asymptomatic spontaneous stone passages were identified by comparing the total number of kidney stones on low-dose non-contrast CT imaging at the beginning and end of the study, considering symptomatic stone passages and surgical stone removal. The statistical analysis focused on the association of independent variables and the number of asymptomatic spontaneous stone passages using linear regression analyses. Of the 416 randomized patients, 383 with both baseline and end-of-study CT were included in this analysis. The median follow-up period was 35 months, the median patient age was 49 years (IQR:40 - 55), and 20% of the patients were female. A total of 442 stone events occurred in 209 out of 383 (55%) patients: 217 out of 442 (49%) were symptomatic spontaneous stone passages, 67 out of 442 (15%) were surgically removed stones, and 158 out of 442 (36%) were asymptomatic spontaneous stone passages. The median size of asymptomatic stones (2.4 mm; IQR:1.95-3.4) and the size of symptomatic stones (2.15 mm; IQR:1.68-2.79) that passed spontaneously were not significantly different (p=0.37). The number of asymptomatic spontaneous stone passages was significantly associated with a higher number of stones on CT at randomization (P=0.001). Limitations include the lack of data on stone size at the time of passage and overrepresentation of White men. Asymptomatic stone passage was common in patients with recurrent calcium-containing kidney stones. The higher the number of stones at presentation, the more likely it was that a kidney stone would spontaneously pass over time without causing any symptoms.

Integrating Clinical Data and Radiomics and Deep Learning Features for End-to-End Delayed Cerebral Ischemia Prediction on Noncontrast CT.

Delayed cerebral ischemia is hard to diagnose early due to gradual, symptomless development. This study aimed to develop an automated model for predicting delayed cerebral ischemia following aneurysmal SAH on NCCT. This retrospective study included 400 patients with aneurysmal SAH (156 with delayed cerebral ischemia) who underwent NCCT. The study used ATT-Deeplabv3+ for automatically segmenting hemorrhagic regions using semisupervised learning. Principal component analysis was used for reducing the dimensionality of deep learning features extracted from the average pooling layer of ATT-DeepLabv3+. The classification model integrated clinical data, radiomics, and deep learning features to predict delayed cerebral ischemia. Feature selection involved Pearson correlation coefficients, least absolute shrinkage, and selection operator regression. We developed models based on clinical features, clinical-radiomics, and a combination of clinical, radiomics, and deep learning. The study selected logistic regression, Naive Bayes, Adaptive Boosting (AdaBoost), and multilayer perceptron as classifiers. The performance of segmentation and classification models was evaluated on their testing sets using the Dice similarity coefficient for segmentation, and the area under the receiver operating characteristic curve (AUC) and calibration curves for classification. The segmentation process achieved a Dice similarity coefficient of 0.91 and the average time of 0.037 s/image. Seventeen features were selected to calculate the radiomics score. The clinical-radiomics-deep learning model with multilayer perceptron achieved the highest AUC of 0.84 (95% CI, 0.72-0.97), which outperformed the clinical-radiomics model (P = .002) and the clinical features model (P = .001) with multilayer perceptron. The performance of clinical-radiomics-deep learning model using AdaBoost was significantly superior to its clinical-radiomics model (P = .027). The performance of the clinical-radiomics-deep learning model and the clinical-radiomics model with logistic regression notably exceeded that of the model based solely on clinical features (P = .028; P = .046). The AUC of the clinical-radiomics-deep learning model with multilayer perceptron (P < .001) and the clinical-radiomics model with logistic regression (P = .046) were significantly higher than the clinical model with logistic regression. Of all models, the clinical-radiomics-deep learning model with multilayer perceptron showed best calibration. The proposed 2-stage end-to-end model not only achieves rapid and accurate segmentation but also demonstrates superior diagnostic performance with high AUC values and good calibration in the clinical-radiomics-deep learning model, suggesting its potential to enhance delayed cerebral ischemia detection and treatment strategies.

Differential Impact of Subcutaneous and Visceral Fat on Bone Changes after Gastrectomy.

Osteoporosis and fragility fractures are crucial musculoskeletal complications in long-term survivors of gastric cancer. However, the relationship between changes in body composition after gastrectomy and bone loss has not been investigated. Therefore, this study aimed to explore whether computed tomography (CT)-derived body composition parameters are associated with bone loss after gastrectomy in patients with gastric cancer. We retrospectively reviewed medical records and abdomen CT scans of patients who underwent gastrectomy at Yonsei University Severance Hospital between 2009 and 2018. Patients with non-metastatic gastric adenocarcinoma and preoperative and postoperative non-contrast CT scans were analyzed. Section area of skeletal muscle (SMA), visceral fat (VFA), and subcutaneous fat (SFA) were assessed using semi-automatic segmentation software. Changes in trabecular bone attenuation of L1 mid-vertebra level (L1 Hounsfield units [HU]) were measured. Fifty-seven patients (mean age, 65.5±10.6; 70.2% males) were analyzed, and the median duration was 31 months. Fortyseven patients (82.5%) lost weight after gastrectomy. Baseline SMA and VFA did not differ between the bone loss and preserved groups; however, baseline SFA was significantly higher in the bone preserved group than in the bone loss group (P=0.020). In a multivariable linear regression model adjusted for confounding factors, one standard deviation higher VFA at baseline was associated with greater annualized L1 HU loss (%) (P=0.034). However, higher preoperative SFA was associated with protection against bone loss after gastrectomy (P=0.025). Higher preoperative SFA exhibited a protective effect against bone loss after gastrectomy in patients with non-metastatic gastric cancer, whereas VFA exhibited a negative effect.

Exploring the relationship between epicardial fat and coronary plaque burden and characteristics: insights from cardiac ct imaging.

Epicardial adipose tissue (EAT) may enhance the risk of coronary artery disease (CAD). We investigated the relationship between EAT density (a maker of local inflammation) and coronary plaque characteristics in stable CAD patients. This study included 123 individuals who underwent coronary artery calcium scan and coronary CT angiography to evaluate CAD. Plaque characteristics were analyzed by semi-automated software (QAngio, Leiden, Netherlands). Non-contrast CT scans were used to measure EAT density (HU) and volume (cc) (Philips, Cleveland, OH). Multivariate regression models were used to evaluate the association of EAT density and volume with different plaque types. The mean (SD) age was 59.4±10.1 years, 53% were male, the mean (SD) EAT density was -77.2±4.6 HU and the volume was 118.5±41.2 cc. After adjustment for cardiovascular risk factors, EAT density was associated with fibrous fatty (FF) plaque (p<0.03). A 1 unit increase in HU was associated with a 7% higher FF plaque, and lower EAT density is independently associated to FF plaque. The association between EAT density and fibrous (p=0.08), and total noncalcified (p=0.09) plaque trended toward but did not reach significance. There was no association between EAT volume and any plaque type. These results suggest that inflammatory EAT may promote coronary atherosclerosis. Therefore, non-contrast cardiac CT evaluation of EAT quality can help better assess cardiovascular risk.

AI-based abdominal CT measurements of orthotopic and ectopic fat predict mortality and cardiometabolic disease risk in adults.

To evaluate the utility of CT-based abdominal fat measures for predicting the risk of death and cardiometabolic disease in an asymptomatic adult screening population. Fully automated AI tools quantifying abdominal adipose tissue (L3 level visceral [VAT] and subcutaneous [SAT] fat area, visceral-to-subcutaneous fat ratio [VSR], VAT attenuation), muscle attenuation (L3 level), andliver attenuation were applied to non-contrast CT scans in asymptomatic adults undergoing CT colonography (CTC). Longitudinal follow-up documented subsequent deaths, cardiovascular events, and diabetes. ROC andtime-to-event analyses were performed to generate AUCs andhazard ratios (HR) binned by octile. A total of 9223 adults (mean age, 57 years; 4071:5152 M:F) underwent screening CTC from April 2004 to December 2016. 549 patients died on follow-up (median, nine years). Fat measures outperformed BMI for predicting mortality risk-5-year AUCs for muscle attenuation, VSR, and BMI were 0.721, 0.661, and 0.499, respectively. Higher visceral, muscle, and liver fat were associated with increased mortality risk-VSR > 1.53, HR = 3.1; muscle attenuation < 15 HU, HR = 5.4; liver attenuation < 45 HU, HR = 2.3. Higher VAT area and VSR were associated with increased cardiovascular event and diabetes risk-VSR > 1.59, HR = 2.6 for cardiovascular event; VAT area > 291 cm2, HR = 6.3 for diabetes (p < 0.001). A U-shaped association was observed for SAT with a higher risk of death for very low and very high SAT. Fully automated CT-based measures of abdominal fat are predictive of mortality and cardiometabolic disease risk in asymptomatic adults and uncover trends that are not reflected in anthropomorphic measures. Fully automated CT-based measures of abdominal fat soundly outperform anthropometric measures for mortality and cardiometabolic risk prediction in asymptomatic patients. Abdominal fat depots associated with metabolic dysregulation and cardiovascular disease can be derived from abdominal CT. Fully automated AI body composition tools can measure factors associated with increased mortality and cardiometabolic risk. CT-based abdominal fat measuresuncover trends in mortality and cardiometabolic risk not captured by BMI in asymptomatic outpatients.

Assessment of Psoas Muscle Index in Middle-Aged Type 2 Diabetes Patients: Impact of Insulin Therapy on Sarcopenia.

Sarcopenia, characterized by progressive skeletal muscle loss, poses significant health risks, including physical impairment and mortality. The relationship between sarcopenia and insulin resistance suggests insulin therapy's potential in preserving muscle mass, particularly in Type 2 diabetes mellitus (T2DM) patients. This study aims to evaluate the Psoas Muscle Index (PMI) via computed tomography (CT) in middle-aged T2DM patients on insulin therapy versus oral antidiabetic drugs (OAD) and controls. This retrospective study included 107 middle-aged T2DM patients undergoing non-contrast CT scans and 58 age-matched controls. CT images were analyzed to calculate PMI. Statistical analysis included Chi-square tests, independent samples t-tests, Mann-Whitney U tests, and correlation analyses. Insulin-treated patients exhibited higher PMI than OAD users (p < 0.001), while OAD users had lower PMI than controls (p < 0.001). No significant difference was found between insulin-treated patients and controls (p = 0.616). Negative correlations were observed between T2DM duration/age and PMI across all groups, with a positive correlation between T2DM duration and BMI observed in the OAD group. Insulin therapy in T2DM patients, regardless of age or disease duration, positively impacts muscle mass, highlighting its potential in preserving muscular health and advocating for tailored treatment strategies in T2DM management.

CT-Based Radiomics Models for Differentiation of Benign and Malignant Thyroid Nodules: A Multicenter Development and Validation Study.

BACKGROUND. CT is increasingly detecting thyroid nodules. Prior studies indicated a potential role of CT-based radiomics models in characterizing thyroid nodules, although these studies lacked external validation. OBJECTIVE. The purpose of this study was to develop and validate a CT-based radiomics model for the differentiation of benign and malignant thyroid nodules. METHODS. This retrospective study included 378 patients (mean age, 46.3 ± 13.9 [SD] years; 86 men, 292 women) with 408 resected thyroid nodules (145 benign, 263 malignant) from two centers (center 1: 293 nodules, January 2018 to December 2022; center 2: 115 nodules, January 2020 to December 2022) who underwent preoperative multiphase neck CT (noncontrast, arterial, and venous phases). Nodules from center 1 were divided into training (n = 206) and internal validation (n = 87) sets; all nodules from center 2 formed an external validation set. Radiologists assessed nodules for morphologic CT features. Nodules were manually segmented on all phases, and radiomic features were extracted. Conventional (clinical and morphologic CT), noncontrast CT radiomics, arterial phase CT radiomics, venous phase CT radiomics, multiphase CT radiomics, and combined (clinical, morphologic CT, and multiphase CT radiomics) models were established using feature selection methods and evaluated by ROC curve analysis, calibration-curve analysis, and decision-curve analysis. RESULTS. The combined model included patient age, three morphologic features (cystic change, "edge interruption" sign, abnormal cervical lymph nodes), and 28 radiomic features (from all three phases). In the external validation set, the combined model had an AUC of 0.923, and, at an optimal threshold derived in the training set, sensitivity of 84.0%, specificity of 94.1%, and accuracy of 87.0%. In the external validation set, the AUC was significantly higher for the combined model than for the conventional model (0.827), noncontrast CT radiomics model (0.847), arterial phase CT radiomics model (0.826), venous phase CT radiomics model (0.773), and multiphase CT radiomics model (0.824) (all p < .05). In the external validation set, the calibration curves indicated the lowest (i.e., best) Brier score for the combined model; in the decision-curve analysis, the combined model had the highest net benefit for most of the range of threshold probabilities. CONCLUSION. A combined model incorporating clinical, morphologic CT, and multiphase CT radiomics features exhibited robust performance in differentiating benign and malignant thyroid nodules. CLINICAL IMPACT. The combined radiomics model may help guide further management for thyroid nodules detected on CT.