Development and Validation of a Diagnostic Model for Identifying Clear Cell Renal Cell Carcinoma in Small Renal Masses Based on CT Radiological Features: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to develop a diagnostic model based on clinical and CT features for identifying clear cell renal cell carcinoma (ccRCC) in small renal masses (SRMs). MATERIAL AND METHODS: This retrospective multi-centre study enroled patients with pathologically confirmed SRMs. Data from three centres were used as training set (n = 229), with data from one centre serving as an independent test set (n = 81). Univariate and multivariate logistic regression analyses were utilised to screen independent risk factors for ccRCC and build the classification and regression tree (CART) diagnostic model. The area under the curve (AUC) was used to evaluate the performance of the model. To demonstrate the clinical utility of the model, three radiologists were asked to diagnose the SRMs in the test set based on professional experience and re-evaluated with the aid of the CART model. RESULTS: There were 310 SRMs in 309 patients and 71% (220/310) were ccRCC. In the testing cohort, the AUC of the CART model was 0.90 (95% CI: 0.81, 0.97). For the radiologists' assessment, the AUC of the three radiologists based on the clinical experience were 0.78 (95% CI:0.66,0.89), 0.65 (95% CI:0.53,0.76), and 0.68 (95% CI:0.57,0.79). With the CART model support, the AUC of the three radiologists were 0.93 (95% CI:0.86,0.97), 0.87 (95% CI:0.78,0.95) and 0.87 (95% CI:0.78,0.95). Interobserver agreement was improved with the CART model aids (0.323 vs 0.654, P < 0.001). CONCLUSION: The CART model can identify ccRCC with better diagnostic efficacy than that of experienced radiologists and improve diagnostic performance, potentially reducing the number of unnecessary biopsies.

Abdominal image quality and dose reduction with energy-integrating or photon-counting detectors dual-source CT: A phantom study.

PURPOSE: The purpose of this study was to assess image-quality and dose reduction potential using a photon-counting computed tomography (PCCT) system by comparison with two different dual-source CT (DSCT) systems using two phantoms. MATERIALS AND METHODS: Acquisitions on phantoms were performed using two DSCT systems (DSCT1 [Somatom Force] and DSCT2 [Somatom Pro.Pulse]) and one PCCT system (Naeotom Alpha) at four dose levels (13/6/3.4/1.8 mGy). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed to assess noise magnitude and noise texture and spatial resolution (f50), respectively. Detectability indexes (d') were computed to model the detection of abdominal lesions: one unenhanced high-contrast task, one contrast-enhanced high-contrast task and one unenhanced low-contrast task. Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists. RESULTS: For all dose levels, noise magnitude values were lower with PCCT than with DSCTs. For all CT systems, similar noise texture values were found at 13 and 6 mGy, but the greatest noise texture values were found for DSCT2 and the lowest for PCCT at 3.4 and 1.8 mGy. For high-contrast inserts, similar or lower f50 values were found with PCCT than with DSCT1 and the opposite pattern was found for the low-contrast insert. For the three simulated lesions, d' values were greater with PCCT than with DSCTs. Abdominal images were rated satisfactory for clinical use by the radiologists for all dose levels with PCCT and for 13 and 6 mGy with DSCTs. CONCLUSION: By comparison with DSCTs, PCCT reduces image-noise and improves detectability of simulated abdominal lesions without altering the spatial resolution and image texture. Image-quality obtained with PCCT seem to indicate greater potential for dose optimization than those obtained with DSCTs.

Extracellular volume fraction derived from dual-energy CT: a potential predictor for acute pancreatitis after pancreatoduodenectomy.

OBJECTIVES: To investigate the value of extracellular volume (ECV) fraction and fat fraction (FF) derived from dual- energy CT (DECT) for predicting postpancreatectomy acute pancreatitis (PPAP) after pancreatoduodenectomy (PD). METHODS: This retrospective study included patients who underwent DECT and PD between April 2022 and September 2022. PPAP was determined according to the International Study Group for Pancreatic Surgery (ISGPS) definition. Iodine concentration (IC) and FF of the pancreatic parenchyma were measured on preoperative DECT. The ECV fraction was calculated from iodine map images of the equilibrium phase. The independent predictors for PPAP were assessed by univariate and multivariable logistic regression analysis and receiver operating characteristic (ROC) curve analysis. RESULTS: Sixty-nine patients were retrospectively enrolled (median age, 60 years; interquartile range, 55-70 years; 47 men). Of these, nine patients (13.0%) developed PPAP. These patients had lower portal venous phase IC, equilibrium phase IC, FF, and ECV fraction, and higher pancreatic parenchymal-to-portal venous phase IC ratio and pancreatic parenchymal-to-equilibrium phase IC ratio, compared with patients without PPAP. After multivariable analysis, ECV fraction was independently associated with PPAP (odd ratio [OR], 0.87; 95% confidence interval [CI]: 0.79, 0.96; p < 0.001), with an area under the curve (AUC) of 0.839 (sensitivity 100.0%, specificity 58.3%). CONCLUSIONS: A lower ECV fraction is independently associated with the occurrence of PPAP after PD. ECV fraction may serve as a potential predictor for PPAP after PD. CLINICAL RELEVANCE STATEMENT: DECT-derived ECV fraction of pancreatic parenchyma is a promising biomarker for surgeons to preoperatively identify patients with higher risk for postpancreatectomy acute pancreatitis after PD and offer selective perioperative management. KEY POINTS: PPAP is a complication of pancreatic surgery, early identification of higher-risk patients allows for risk mitigation. Lower DECT-derived ECV fraction was independently associated with the occurrence of PPAP after PD. DECT aids in preoperative PAPP risk stratification, allowing for appropriate treatment to minimize complications.

Imaging of Spondylodiscitis: A Comprehensive Updated Review-Multimodality Imaging Findings, Differential Diagnosis, and Specific Microorganisms Detection.

Spondylodiscitis is defined by infectious conditions involving the vertebral column. The incidence of the disease has constantly increased over the last decades. Imaging plays a key role in each phase of the disease. Indeed, radiological tools are fundamental in (i) the initial diagnostic recognition of spondylodiscitis, (ii) the differentiation against inflammatory, degenerative, or calcific etiologies, (iii) the disease staging, as well as (iv) to provide clues to orient towards the microorganisms involved. This latter aim can be achieved with a mini-invasive procedure (e.g., CT-guided biopsy) or can be non-invasively supposed by the analysis of the CT, positron emission tomography (PET) CT, or MRI features displayed. Hence, this comprehensive review aims to summarize all the multimodality imaging features of spondylodiscitis. This, with the goal of serving as a reference for Physicians (infectious disease specialists, spine surgeons, radiologists) involved in the care of these patients. Nonetheless, this review article may offer starting points for future research articles.

Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial.

OBJECTIVES: To investigate the feasibility of low-radiation dose and low iodinated contrast medium (ICM) dose protocol combining low-tube voltage and deep-learning reconstruction (DLR) algorithm in thin-slice abdominal CT. METHODS: This prospective study included 148 patients who underwent contrast-enhanced abdominal CT with either 120-kVp (600 mgL/kg, n = 74) or 80-kVp protocol (360 mgL/kg, n = 74). The 120-kVp images were reconstructed using hybrid iterative reconstruction (HIR) (120-kVp-HIR), while 80-kVp images were reconstructed using HIR (80-kVp-HIR) and DLR (80-kVp-DLR) with 0.5 mm thickness. Size-specific dose estimate (SSDE) and iodine dose were compared between protocols. Image noise, CT attenuation, and contrast-to-noise ratio (CNR) were quantified. Noise power spectrum (NPS) and edge rise slope (ERS) were used to evaluate noise texture and edge sharpness, respectively. The subjective image quality was rated on a 4-point scale. RESULTS: SSDE and iodine doses of 80-kVp were 40.4% (8.1 ± 0.9 vs. 13.6 ± 2.7 mGy) and 36.3% (21.2 ± 3.9 vs. 33.3 ± 4.3 gL) lower, respectively, than those of 120-kVp (both, p < 0.001). CT attenuation of vessels and solid organs was higher in 80-kVp than in 120-kVp images (all, p < 0.001). Image noise of 80-kVp-HIR and 80-kVp-DLR was higher and lower, respectively than that of 120-kVp-HIR (both p < 0.001). The highest CNR and subjective scores were attained in 80-kVp-DLR (all, p < 0.001). There were no significant differences in average NPS frequency and ERS between 120-kVp-HIR and 80-kVp-DLR (p ≥ 0.38). CONCLUSION: Compared with the 120-kVp-HIR protocol, the combined use of 80-kVp and DLR techniques yielded superior subjective and objective image quality with reduced radiation and ICM doses at thin-section abdominal CT. CLINICAL RELEVANCE STATEMENT: Scanning at low-tube voltage (80-kVp) combined with the deep-learning reconstruction algorithm may enhance diagnostic efficiency and patient safety by improving image quality and reducing radiation and contrast doses of thin-slice abdominal CT. KEY POINTS: Reducing radiation and iodine doses is desirable; however, contrast and noise degradation can be detrimental. The 80-kVp scan with the deep-learning reconstruction technique provided better images with lower radiation and contrast doses. This technique may be efficient for improving diagnostic confidence and patient safety in thin-slice abdominal CT.

Image Quality and Lesion Detection of Multiplanar Reconstruction Images Using Deep Learning: Comparison with Hybrid Iterative Reconstruction.

Background: We assessed and compared the image quality of normal and pathologic structures as well as the image noise in chest computed tomography images using "adaptive statistical iterative reconstruction-V" (ASiR-V) or deep learning reconstruction "TrueFidelity". Methods: Forty consecutive patients with suspected lung disease were evaluated. The 1.25-mm axial images and 2.0-mm coronal multiplanar images were reconstructed under the following three conditions: (i) ASiR-V, lung kernel with 60% of ASiR-V; (ii) TF-M, standard kernel, image filter (Lung) with TrueFidelity at medium strength; and (iii) TF-H, standard kernel, image filter (Lung) with TrueFidelity at high strength. Two radiologists (readers) independently evaluated the image quality of anatomic structures using a scale ranging from 1 (best) to 5 (worst). In addition, readers ranked their image preference. Objective image noise was measured using a circular region of interest in the lung parenchyma. Subjective image quality scores, total scores for normal and abnormal structures, and lesion detection were compared using Wilcoxon's signed-rank test. Objective image quality was compared using Student's paired t-test and Wilcoxon's signed-rank test. The Bonferroni correction was applied to the P value, and significance was assumed only for values of P < 0.016. Results: Both readers rated TF-M and TF-H images significantly better than ASiR-V images in terms of visualization of the centrilobular region in axial images. The preference score of TF-M and TF-H images for reader 1 were better than that of ASiR-V images, and the preference score of TF-H images for reader 2 were significantly better than that of ASiR-V and TF-M images. TF-M images showed significantly lower objective image noise than ASiR-V or TF-H images. Conclusion: TrueFidelity showed better image quality, especially in the centrilobular region, than ASiR-V in subjective and objective evaluations. In addition, the image texture preference for TrueFidelity was better than that for ASiR-V.

Modern imaging of acute pulmonary embolism.

The first-choice imaging test for visualization of thromboemboli in the pulmonary vasculature in patients with suspected acute pulmonary embolism (PE) is multidetector computed tomography pulmonary angiography (CTPA) - a readily available and widely used imaging technique. Through technological advancements over the past years, alternative imaging techniques for the diagnosis of PE have become available, whilst others are still under investigation. In particular, the evolution of artificial intelligence (AI) is expected to enable further innovation in diagnostic management of PE. In this narrative review, current CTPA techniques and the emerging technology photon-counting CT (PCCT), as well as other modern imaging techniques of acute PE are discussed, including CTPA with iodine maps based on subtraction or dual-energy acquisition, single-photon emission CT (SPECT), magnetic resonance angiography (MRA), and magnetic resonance direct thrombus imaging (MRDTI). Furthermore, potential applications of AI are discussed.

Impact of pulmonary artery intramural hematoma on patients with acute type A aortic dissection.

OBJECTIVES: To investigate the short-term/long-term impact of pulmonary artery intramural hematoma (PA-IMH) in patients with acute Stanford type A aortic dissection (ATAAD) following surgical repair. MATERIALS AND METHODS: Consecutive patients with ATAAD who received surgical repair at Beijing and Yunnan Fuwai Hospital in 2010-2021 were retrospectively reviewed. Patients with hemorrhage extending along the PA were identified as the PA-IMH group. Multivariable logistics regression was used to obtain the odds ratio (OR), and the Kaplan-Meier method was used to estimate the survival rate. RESULTS: Of the 2046 ATAAD patients, 324 (15.8%) patients were identified with PA-IMH, and 1722 (84.2%) were without PA-IMH. PA-IMH had a higher prevalence in patients with older age, female gender, aortic IMH, and type II aortic dissection. PA-IMH patients incurred excess early mortality compared with non-PA-IMH patients (9.3% vs. 5.6%, OR = 1.86, 95%CI 1.19-2.91, p = 0.006). The results were stable in the subgroup analysis, with an increased risk in older (> 70 years) or DeBakey type II ATAAD patients. Notably, an increase in the degree and extent of PA-IMH exacerbated the risk of early mortality. However, after landmark analysis at 30-day postsurgery, no significant difference was noted in the long-term outcomes between PA-IMH and non-PA-IMH groups (p = 0.440). The 5-year survival rates were 87.1% (95%CI: 83.3%, 91.1%) and 90.1% (95%CI: 88.5%, 91.7%), respectively. CONCLUSIONS: The presence of PA-IMH in ATAAD patients is common and is independently associated with increased early mortality after surgical repair, especially in those with older age (> 70) or type II dissection. However, such detrimental effects do not persist in the long-term follow-up among patients who survived hospital discharge. CLINICAL RELEVANCE STATEMENT: We confirmed that PA-IMH significantly increases early postoperative mortality in patients with acute type A aortic dissection, especially in older patients or DeBakey type II dissection. This should prompt further investigation of the incremental role of PA-IMH in this pathology. KEY POINTS: Acute type A aortic dissection mortality gets worse when pulmonary artery intramural hematoma is present. Pulmonary artery-intramural hematoma increased the risk of early mortality but not affect long-term prognosis. Further research should investigate the effects of pulmonary artery intramural thrombus on aortic dissection.

Aortic Valve Calcification Density Measured by MDCT in the Assessment of Aortic Stenosis Severity.

BACKGROUND: Aortic valve calcification (AVC) indexation to the aortic annulus (AA) area measured by Doppler echocardiography (AVCdEcho) provides powerful prognostic information in patients with aortic stenosis (AS). However, the indexation by AA measured by multidetector computed tomography (AVCdCT) has never been evaluated. The aim of this study was to compare AVC, AVCdCT, and AVCdEcho with regard to hemodynamic correlations and clinical outcomes in patients with AS. METHODS: Data from 889 patients, mainly White, with calcific AS who underwent Doppler echocardiography and multidetector computed tomography within the same episode of care were retrospectively analyzed. AA was measured both by Doppler echocardiography and multidetector computed tomography. AVCdCT severity thresholds were established using receiver operating characteristic curve analyses in men and women separately. The primary end point was the occurrence of all-cause mortality. RESULTS: Correlations between gradient/velocity and AVCd were stronger (both P≤0.005) using AVCdCT (r=0.68, P<0.001 and r=0.66, P<0.001) than AVC (r=0.61, P<0.001 and r=0.60, P<0.001) or AVCdEcho (r=0.61, P<0.001 and r=0.59, P<0.001). AVCdCT thresholds for the identification of severe AS were 334 Agatston units (AU)/cm2 for women and 467 AU/cm2 for men. On a median follow-up of 6.62 (6.19-9.69) years, AVCdCT ratio was superior to AVC ratio and AVCdEcho ratio to predict all-cause mortality in multivariate analyses (hazard ratio [HR], 1.59 [95% CI, 1.26-2.00]; P<0.001 versus HR, 1.53 [95% CI, 1.11-1.65]; P=0.003 versus HR, 1.27 [95% CI, 1.11-1.46]; P<0.001; all likelihood test P≤0.004). AVCdCT ratio was superior to AVC ratio and AVCdEcho ratio to predict survival under medical treatment in multivariate analyses (HR, 1.80 [95% CI, 1.27-1.58]; P<0.001 compared with HR, 1.55 [95% CI, 1.13-2.10]; P=0.007; HR, 1.28 [95% CI, 1.03-1.57]; P=0.01; all likelihood test P<0.03). AVCdCT ratio predicts mortality in all subgroups of patients with AS. CONCLUSIONS: AVCdCT appears to be equivalent or superior to AVC and AVCdEcho to assess AS severity and predict all-cause mortality. Thus, it should be used to evaluate AS severity in patients with nonconclusive echocardiographic evaluations with or without low-flow status. AVCdCT thresholds of 300 AU/cm2 for women and 500 AU/cm2 for men seem to be appropriate to identify severe AS. Further studies are needed to validate these thresholds, especially in diverse populations.

Clinical calculator based on CT and clinicopathologic characteristics predicts short-term prognosis following resection of microsatellite-stabilized diffuse gastric cancer.

PURPOSE: Although microsatellite stability/Epithelial-mesenchymal transition (MSS/EMT) subtypes have been reported in multiple cancer prognosis studies, strong confounding factors between MSS/EMT (usually with Lauren's diffuse phenotype) and diffuse gastric cancer (GC) may obscure the independent prognostic value of diffuse GC. Additionally, recent studies suggest a strong correlation between mural stratification based on CT and diffuse GC. This study aims to investigate potential prognostic factors of MSS diffuse GC using mural stratification and to develop a risk assessment model. METHODS: This retrospective study included 131 patients with MSS diffuse GC who underwent radical surgery. Univariate and multivariate Cox proportional hazards regression analysis was used to identify model predictors and construct a nomogram for overall survival (OS) and recurrence-free survival (RFS) risks. The model's performance was evaluated using ROC, accuracy, and C-index. Internal validation of the model was conducted using the bootstrap resampling method. RESULTS: Among 131 cases, 60 cases (45.8%) exhibited grade 2 mural stratification, which correlated with a poorer tumor prognosis and a more invasive phenotype. Furthermore, a nomogram for predicting OS and RFS prognosis was established based on multivariate results (age, extranodal invasion, mural stratification, and/or P53). The nomogram demonstrated excellent performance, with an AUC of 0.859 (95% CI 0.794-0.924) for OS and 0.859 (95% CI 0.789-0.929) for RFS. Internal validation using 1000 bootstrap samples yielded AUC values of 0.845 and 0.846 for OS and RFS, respectively. CONCLUSION: Grade 2 mural stratification based on CT imaging revealed a more aggressive invasive phenotype, characterized by increased LN metastasis, higher rates of peritoneal metastasis, and a poorer short-term prognosis. Furthermore, the CT phenotype-based nomogram demonstrates favorable discrimination and calibration, enabling convenient individual short-term prognostic evaluation following resection of MSS diffuse GC.

An Evaluation of Renal Sinus Fat Accumulation Using the Anteroposterior Diameter of the Renal Sinus on a Computed Tomography Axial Image.

Backgrounds and objectives Renal sinus fat (RSF) is an indicator of obesity-related complications. However, the measurement and imaging process are complicated. For a simple measurement of RSF, we focused on the kidney's shape change caused by RSF accumulation. Thus, this study aimed to investigate whether the anteroposterior diameter of the renal sinus (APDRS) on a computed tomography (CT) axial image is useful for evaluating RSF accumulation. Materials and methods The correlation between APDRS and RSF was investigated in 98 outpatients who underwent abdominal CT. In addition, the correlation between APDRS or RSF and obesity indicators (estimated glomerular filtration rate from serum creatinine levels (eGFRcreat), body mass index (BMI), and visceral adipose tissue (VAT)) was also investigated. We classified patients based on the presence or absence of at least one underlying disease (chronic kidney disease (CKD), cardiovascular diseases (CVD), hypertension, and type 2 diabetes (T2D)) and investigated significant differences between the two groups at APDRS and RSF. The intraclass correlation coefficient (ICC) was also calculated for APDRS. Results There was a strong positive correlation between RSF and APDRS (r = 0.802, P < 0.01). The obesity indicators (eGFRcreat, BMI, and VAT) were correlated with RSF and APDRS (P < 0.01). Out of 98 outpatients, 48 had at least one underlying disease. There were statistically significant differences in APDRS and RSF between the patients with and without at least one of the underlying diseases caused by obesity (P < 0.01). The inter-reader ICC for the measurement of the APDRS was 0.98. Conclusions APDRS on a CT axial image may be useful for the evaluation of RSF accumulation.

Artificial Intelligence-Based Non-invasive Differentiation of Distinct Histologic Subtypes of Renal Tumors With Multiphasic Multidetector Computed Tomography.

INTRODUCTION: With rising cases of renal cell carcinoma (RCC), precise identification of tumor subtypes is essential, particularly for detecting small, heterogenous lesions often overlooked in traditional histopathological examinations. This study demonstrates the non-invasive use of deep learning for Histopathological differentiation of renal tumors through quadriphasic multidetector computed tomography (MDCT). PATIENTS AND METHODS: This prospective longitudinal study includes 50 subjects (32 males, 18 females) with suspected renal tumors. A deep neural network (DNN) is developed to predict RCC subtypes using peak attenuation values measured in Hounsfield Units (HUs) obtained from quadriphasic MDCT scans. The network then generates confidence scores for each of the four primary subtypes of renal tumors, effectively distinguishing between benign oncocytoma and various malignant subtypes. RESULTS: Our neural network accurately distinguishes Renal tumor subtypes, including clear cell, papillary, chromophobe, and benign oncocytoma, with a confidence score of 68% with the network's diagnosis aligning with Histopathological examinations. Our network was also able to accurately classify RCC subtypes on a synthetically generated dataset with 20,000 samples. CONCLUSION: We developed an artificial intelligence-based RCC subtype classification technique. Our approach is non-invasive and has the potential to transform the methodology in Renal oncology by providing accurate and timely diagnostic information and enhancing clinical decisions.

Pancreatic Parenchymal Atrophy and Pancreatic Fat Accumulation Measured by Multidetector Computed Tomography as a Stable Marker of Chronic Progressive Type 2 Diabetes Mellitus-A Cross Sectional Observational Study.

Background The most crucial step in the management of type 2 diabetes is identifying its pathogenesis and progression. Fat accumulation in the pancreas and decreased parenchymal volume can influence pancreatic function due to insulin resistance or ß-cell dysfunction. This study aims to find out the difference in pancreatic volume and fat content by using contrast-enhanced computed tomography (CECT) between normal subjects and patients with different durations of type 2 diabetes mellitus (T2DM). Methods This was a cross-sectional study. Patients who underwent CECT abdomen for the evaluation of conditions other than pancreatic origin were included. The study group was divided into three subgroups according to the duration of diabetes as <5 years, 5 to 10 years, and >10 years. In total, 40 nondiabetic controls were included. Pancreatic fat volume and parenchymal volume were measured in cm 3 using CECT. Correlation between pancreatic parenchymal and fat volume with the duration of T2DM as well as with levels of hemoglobin A1c, random blood sugar, serum triglyceride, low-density lipoproteins, and high-density lipoproteins was done. Results T2DM patients had significantly ( p < 0.001) lower pancreatic parenchymal volume (mean value of 57.08 ± 8.26 cm 3 in diabetics and 72.23 ± 3.41 cm 3 in controls) and higher pancreatic fat volume (mean value of 3.08 ± 1.90 cm 3 in diabetics and 0.67 ± 0.27cm 3 in controls) as compared to nondiabetic controls. In patients with T2DM, as the duration of T2DM increased, pancreatic parenchymal volume decreased and pancreatic fat volume increased. Conclusion Reduction in pancreatic volume and fat deposition may have a role in the onset and progression of diabetes. Determining the pancreatic volume and fat content would be useful for identifying high-risk patients and determining the pathogenesis of the development of diabetes.

Computed tomography myocardial perfusion imaging to detect myocardial ischemia in patients with anxiety and obstructive coronary heart disease post-exposure to mental stressors.

This study aims to measure myocardial blood flow (MBF) using dynamic CT- myocardial perfusion imaging (CT-MPI) combined with mental stressors in patients with obstructive coronary artery disease (OCAD) and in patients with anxiety and no obstructive coronary artery disease (ANOCAD). A total of 30 patients with OCAD with 30 patients with ANOCAD were included in this analysis. Using the 17-segment model, the rest and stress phase MBF of major coronary arteries in participants were recorded respectively. Compared with ANOCAD patients, OCAD patients were more likely to have localized reduction of MBF (p < 0.05). For patients with ANOCAD, both global MBF and MBF of the main coronary arteries in the stress phase were lower than those in the rest phase (all p < 0.05), but there was no significant difference in MBF among the main coronary arteries in the rest or stress phase (p = 0.25, p = 0.15). For patients with OCAD, the MBF of the target area was lower than that of the non-target area in both the rest and stress phase, and the MBF of the target area in the stress phase was lower than that in the rest phase (all p < 0.05). However, there was no significant difference in MBF between the rest or stress phase in the non-target area (p = 0.73). Under mental stress, the decrease in MBF in ANOCAD patients was diffuse, while the decrease in MBF in OCAD patients was localized. Dynamic CT-MPI combined with mental stressors can be used to detect MBF changes in anxiety patients.

The mean Hounsfield unit range acquired from different slices produces superior predictive accuracy for pyonephrosis in obstructive uropathy.

PURPOSE: To determine the non-contrast computer tomography imaging features of pyonephrosis and evaluate the predictive value of Hounsfield units (HUs) in different hydronephrotic region slices. MATERIALS AND METHODS: We retrospectively reviewed data from patients with hydronephrosis who had renal-ureteral calculi. All patients were categorized into pyonephrosis and simple hydronephrosis groups. Baseline characteristics, the mean HU values in the maximal hydronephrotic region (uHU) slice, and the range of uHU in different slices (ΔuHU) were compared between the two groups. Univariate and multivariate analyses were performed to identify risk factors for pyonephrosis. RESULTS: Among the 181 patients enrolled in the current study, 71 patients (39.2%) were diagnosed with pyonephrosis. The mean dilated pelvis surface areas were comparable between patients with pyonephrosis and simple hydronephrosis (822.61 mm² vs. 877.23 mm², p=0.722). Collecting system debris (p=0.022), a higher uHU (p=0.038), and a higher ΔuHU (p<0.001) were identified as independent risk factors for pyonephrosis based on multivariate analysis. The ΔuHU sensitivity and specificity were 88.7% and 86.4%, respectively, at a cutoff value of 6.56 (p<0.001), whereas the sensitivity and specificity for detecting pyonephrosis at a uHU cutoff value of 7.96 was 50.7% and 70.9%, respectively (p=0.003). CONCLUSIONS: Non-contrast computer tomography was shown to accurately distinguish simple hydronephrosis from pyonephrosis in patients with obstructive uropathy. Evaluation of the ΔuHU in different slices may be more reliable than the uHU acquired from a single slice in predicting pyonephrosis.

Preoperative prediction for early recurrence of hepatocellular carcinoma using machine learning-based radiomics.

Objective: To develop a contrast-enhanced computed tomography (CECT) based radiomics model using machine learning method and assess its ability of preoperative prediction for the early recurrence of hepatocellular carcinoma (HCC). Methods: A total of 297 patients confirmed with HCC were assigned to the training dataset and test dataset based on the 8:2 ratio, and the follow-up period of the patients was from May 2012 to July 2017. The lesion sites were manually segmented using ITK-SNAP, and the pyradiomics platform was applied to extract radiomic features. We established the machine learning model to predict the early recurrence of HCC. The accuracy, AUC, standard deviation, specificity, and sensitivity were applied to evaluate the model performance. Results: 1,688 features were extracted from the arterial phase and venous phase images, respectively. When arterial phase and venous phase images were employed correlated with clinical factors to train a prediction model, it achieved the best performance (AUC with 95% CI 0.8300(0.7560-0.9040), sensitivity 89.45%, specificity 79.07%, accuracy 82.67%, p value 0.0064). Conclusion: The CECT-based radiomics may be helpful to non-invasively reveal the potential connection between CECT images and early recurrence of HCC. The combination of radiomics and clinical factors could boost model performance.

Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting.

OBJECTIVES: To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT). MATERIAL AND METHODS: Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted. Following preprocessing and addressing the class imbalance, a ML algorithm based on extreme gradient boosting trees (XGB) was employed to predict renal tumor subtypes using 10-fold cross-validation. The evaluation was conducted using the multiclass area under the receiver operating characteristic curve (AUC). Algorithms were trained on data from one center and independently tested on data from the other center. RESULTS: The training cohort comprised n = 297 patients (64.3% clear cell renal cell cancer [RCC], 13.5% papillary renal cell carcinoma (pRCC), 7.4% chromophobe RCC, 9.4% oncocytomas, and 5.4% angiomyolipomas (AML)), and the testing cohort n = 121 patients (56.2%/16.5%/3.3%/21.5%/2.5%). The XGB algorithm demonstrated a diagnostic performance of AUC = 0.81/0.64/0.8 for venous/arterial/combined contrast phase CT in the training cohort, and AUC = 0.75/0.67/0.75 in the independent testing cohort. In pairwise comparisons, the lowest diagnostic accuracy was evident for the identification of oncocytomas (AUC = 0.57-0.69), and the highest for the identification of AMLs (AUC = 0.9-0.94) CONCLUSION: Radiomic feature analyses can distinguish renal tumor subtypes on routinely acquired CTs, with oncocytomas being the hardest subtype to identify. CLINICAL RELEVANCE STATEMENT: Radiomic feature analyses yield robust results for renal tumor assessment on routine CTs. Although radiologists routinely rely on arterial phase CT for renal tumor assessment and operative planning, radiomic features derived from arterial phase did not improve the accuracy of renal tumor subtype identification in our cohort.

Location of the upper oesophageal sphincter during swallowing: Analysis using swallowing CT.

BACKGROUND: Upper oesophageal sphincter (UES) serves as an important anatomical and functional landmark during swallowing. However, the precise UES location before and during swallowing has not been well established. OBJECTIVE: This study aimed to determine upper oesophageal sphincter (UES) location and displacement during swallowing accounting for sex, age, and height in healthy adults using 320-row area detector computed tomography (320-ADCT). METHODS: Ninety-four healthy adults (43 males; 22-90 years) underwent 320-ADCT scanning while swallowing one trial of 10 mL honey thick barium. UES location at bolus hold and at maximum displacement and vertical displacement during swallowing were identified using the coordinates and the section classification of vertebrae (VERT scale). The differences and correlations of UES location and distance in terms of sex, age, and height were analysed using Mann-Whitney U test and Spearman's correlation coefficient. RESULTS: UES locations at bolus hold and at maximum displacement were significantly lower and UES vertical displacement was significantly larger in males than in females (p < .001). UES location at bolus hold became lower with increasing age (r = -.312, p = .002), but the negative correlation was low at maximum displacement (r = -.230, p = .026), resulting in larger vertical distance with ageing. UES locations showed high negative correlation at bolus hold with height (r = -.715, p < .001), and showed moderate negative correlation at maximum displacement with height (r = -.555, p < .001), although this effect was unclear when analysed by sex. CONCLUSION: Males showed lower UES location and larger displacement than females. The impact of age was evident with lower location before swallowing and larger displacement during swallowing. Differences observed by sex were not completely explained by using the VERT scale to adjust for height.