Application of predictive model based on CT radiomics and machine learning in diagnosis for occult locally advanced esophageal squamous cell carcinoma before treatment: A two-center study.

PURPOSE: Development and validation of a radiomics model for predicting occult locally advanced esophageal squamous cell carcinoma (LA-ESCC) on computed tomography (CT) radiomic features before implementation of treatment. METHODS: The study retrospectively collected 574 patients with esophageal squamous cell carcinoma (ESCC) from two medical centers, which were divided into three cohorts for training, internal and external validation. After delineating volume of interest (VOI), radiomics features were extracted and subjected to feature selection using three robust methods. Subsequently, 10 machine learning models were constructed, among which the optimal model was utilized to establish a radiomics signature. Furthermore, a predictive nomogram incorporating both clinical and radiomics signatures was developed. The performance of these models was evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis as well as measures including accuracy, sensitivity, and specificity. RESULTS: A total of 19 radiomics features were selected. The multilayer perceptron (MLP), which was found to be optimal, achieved an AUC of 0.919, 0.864 and 0.882 in the training, internal and external validation cohorts, respectively. Similarly, MLP showed good accuracy in distinguish occult LA-ESCC in subgroup of cT1-2N0M0 diagnosed by clinicians with 0.803 and 0.789 in two validation cohorts respectively. By incorporating the radiomics signature with clinical signature, a predictive nomogram demonstrated superior prediction performance with an AUC of 0.877 and accuracy of 0.85 in external validation cohort. CONCLUSION: The radiomics and machine learning model can offers improved accuracy in prediction of occult LA-ESCC, providing valuable assistance to clinicians when choosing treatment plans.

Development of a Preoperative Prediction Model Based on Spectral CT to Evaluate Axillary Lymph Node With Macrometastases in Clinical T1/2N0 Invasive Breast Cancer.

OBJECTIVES: To develop a prediction model based on spectral computed tomography (CT) to evaluate axillary lymph node (ALN) with macrometastases in clinical T1/2N0 invasive breast cancer. METHODS: A total of 217 clinical T1/2N0 invasive breast cancer patients who underwent spectral CT scans were retrospectively enrolled and categorized into a training cohort (n = 151) and validation cohort (n = 66). These patients were classified into ALN nonmacrometastases (stage pN0 or pN0 [i+] or pN1mi) and ALN macrometastases (stage pN1-3) subgroups. The morphologic criteria and quantitative spectral CT parameters of the most suspicious ALN were measured and compared. Least absolute shrinkage and selection operator (Lasso) was used to screen predictive indicators to build a logistic model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the models. RESULTS: The combined arterial-venous phase spectral CT model yielded the best diagnostic performance in discrimination of ALN nonmacrometastases and ALN macrometastases with the highest AUC (0.963 in the training cohort and 0.945 in validation cohorts). Among single phase spectral CT models, the venous phase spectral CT model showed the best performance (AUC = 0.960 in the training cohort and 0.940 in validation cohorts). There was no significant difference in AUCs among the 3 models (DeLong test, P > .05 for each comparison). CONCLUSION: A Lasso-logistic model that combined morphologic features and quantitative spectral CT parameters based on contrast-enhanced spectral imaging potentially be used as a noninvasive tool for individual preoperative prediction of ALN status in clinical T1/2N0 invasive breast cancers.

A nomogram model of spectral CT quantitative parameters and clinical characteristics predicting lymphovascular invasion of gastric cancer.

Objective: The study established a nomogram based on quantitative parameters of spectral computed tomography (CT) and clinical characteristics, aiming to evaluate its predictive value for preoperative lymphovascular invasion (LVI) in gastric cancer (GC). Methods: From December 2019 to December 2021, 171 patients with pathologically confirmed GC were retrospectively collected with corresponding clinical data and spectral CT quantitative data. Patients were divided into LVI-positive and LVI-negative groups based on their pathological results. The univariate and multivariate logistic regression analyses were used to identify the risk factors and construct a nomogram. The calibration curve and receiver operating characteristic (ROC) curve were adopted to evaluate the predictive accuracy of nomogram. Results: Four clinical characteristics or spectral CT quantitative parameters, including Borrmann classification (P = 0.039), CA724 (P = 0.007), tumor thickness (P = 0.031), and iodine concentration in the venous phase (VIC) (P = 0.004) were identified as independent factors for LVI in GC patients. The nomogram was established based on the four factors, which had a potent predictive accuracy in the training, internal validation and external validation cohorts, with the area under the ROC curve (AUC) of 0.864 (95% CI, 0.798-0.930), 0.964 (95% CI, 0.903-1.000) and 0.877 (95% CI, 0.759-0.996), respectively. Conclusion: This study constructed a comprehensive nomogram consisting spectral CT quantitative parameters and clinical characteristics of GC, which exhibited a robust efficiency in predicting LVI in GC patients.

Detecting bone marrow infiltration in nonosteolytic multiple myeloma through separation of hydroxyapatite via the two-material decomposition technique in spectral computed tomography.

Background: Conventional computed tomography (CT) has low sensitivity for the diagnosis of bone marrow infiltration in nonosteolytic multiple myeloma (NOL-MM). This study aimed to compare the performance of the two-material decomposition technique of spectral CT with the removal of X-ray absorption components of calcium (Ca) versus that of hydroxyapatite (HAP) for diagnosis of NOL-MM. Methods: From October 2022 to March 2023, a total of 41 consecutive patients with MM without focal bone lesions undergoing chest spectral CT and thoracic spine magnetic resonance imaging (MRI) in Fujian Medical University Union Hospital were prospectively enrolled; meanwhile, another set of 41 age- and sex-matched healthy consecutive participants were selected as a comparison group. Based on MRI findings, patients with MM were classified with a diffuse infiltration pattern MM (DP-MM) or a normal pattern MM (NP-MM). Regions of interest (ROIs) were manually drawn on vertebrae. CT values of 70-keV images and basic material density within the ROIs were stored. The basic two-material pairs included a Ca-related pair (Ca-X) and an HAP-related pair (HAP-X), with X referring to fat, water, or muscle. Material density values DCa(X), DX(Ca), DHAP(X), and DX(HAP) were each used to diagnose MM, and the area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance. Results: The 41 patients with NOL-MM included 30 with DP-MM and 11 with NP-MM. CT value, DCa(X), and DHAP(X) were comparable between the NOL-MM, DP-MM, NP-MM, and comparison groups. DX(HAP) was better than DX(Ca) for distinguishing the NOL-MM group from the comparison group {AUC [95% confidence interval (CI)], 0.874 (0.800, 0.949) vs. 0.737 (0.630, 0.844); P=0.02}, the DP-MM group from the comparison group [AUC (95% CI), 0.933 (0.878, 0.989) vs. 0.785 (0.677, 0.894); P=0.01], the NP-MM group from the comparison group [AUC (95% CI), 0.714 (0.540, 0.888) vs. 0.605 (0.429, 0.782); P=0.03], and the DP-MM group from the NP-MM group [AUC (95% CI), 0.809 (0.654, 0.964) vs. 0.736 (0.566, 0.907); P=0.049]. The diagnostic performance of DX(HAP) and DX(Ca) was influenced only by the removed material, while the X material had no influence. Conclusions: The spectral CT two-material decomposition technique with removal of X-ray absorption components of HAP is useful for diagnosis of NOL-MM, irrespective of the paired material.

Ultra-high b-value DWI accurately identifies isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas.

OBJECTIVES: To distinguish isocitrate dehydrogenase (IDH) genotypes and tumor subtypes of adult-type diffuse gliomas based on the fifth edition of the World Health Organization classification of central nervous system tumors (WHO CNS5) in 2021 using standard, high, and ultra-high b-value diffusion-weighted imaging (DWI). MATERIALS AND METHODS: This prospective study enrolled 70 patients with adult-type diffuse gliomas who underwent multiple b-value DWI. Apparent diffusion coefficient (ADC) values including ADCb500/b1000, ADCb500/b2000, ADCb500/b3000, ADCb500/b4000, ADCb500/b6000, ADCb500/b8000, and ADCb500/b10000 in tumor parenchyma (TP) and contralateral normal-appearing white matter (NAWM) were calculated. The ADC ratios of TP/NAWM were assessed for correlations with IDH genotypes, tumor subtypes, and Ki-67 status; diagnostic performances were compared. RESULTS: All ADCs were significantly higher in IDH mutant gliomas than in IDH wild-type gliomas (p < 0.01 for all); ADCb500/b8000 had the highest area under the curve (AUC) of 0.866. All ADCs were significantly lower in glioblastoma than in astrocytoma (p < 0.01 for all). ADCs other than ADCb500/b1000 were significantly lower in glioblastoma than in oligodendroglioma (p < 0.05 for all). ADCb500/b8000 and ADCb500/b10000 were significantly higher in oligodendroglioma than in astrocytoma (p = 0.034 and 0.023). The highest AUCs were 0.818 for ADCb500/b6000 when distinguishing glioblastoma from astrocytoma, 0.979 for ADCb500/b8000 and ADCb500/b10000 when distinguishing glioblastoma from oligodendroglioma, and 0.773 for ADCb500/b10000 when distinguishing astrocytoma from oligodendroglioma. Additionally, all ADCs were negatively correlated with Ki-67 status (p < 0.05 for all). CONCLUSION: Ultra-high b-value DWI can reliably separate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using WHO CNS5 criteria. CLINICAL RELEVANCE STATEMENT: Ultra-high b-value diffusion-weighted imaging can accurately distinguish isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas, which may facilitate personalized treatment and prognostic assessment for patients with glioma. KEY POINTS: • Ultra-high b-value diffusion-weighted imaging can accurately distinguish subtle differences in water diffusion among biological tissues. • Ultra-high b-value diffusion-weighted imaging can reliably separate isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas. • Compared with standard b-value diffusion-weighted imaging, high and ultra-high b-value diffusion-weighted imaging demonstrate better diagnostic performances.

Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning.

BACKGROUND: Lymphovascular invasion (LVI) and perineural invasion (PNI) are important prognostic factors for gastric cancer (GC) that indicate an increased risk of metastasis and poor outcomes. Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment decisions. However, prior models using conventional computed tomography (CT) images to predict LVI or PNI separately have had limited accuracy. Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion. We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients. AIM: To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately. METHODS: This study used a retrospective dataset involving 257 GC patients (training cohort, n = 172; validation cohort, n = 85). First, several clinical indicators, including serum tumor markers, CT-TN stages and CT-detected extramural vein invasion (CT-EMVI), were extracted, as were quantitative spectral CT parameters from the delineated tumor regions. Next, a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters. A logistic regression (LR)-based nomogram model was subsequently constructed to predict LVI/PNI status, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: In both the training and validation cohorts, CT T3-4 stage, CT-N positive status, and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant (P < 0.05). LR analysis of the training group showed preoperative CT-T stage, CT-EMVI, single-energy CT values of 70 keV of venous phase (VP-70 keV), and the ratio of standardized iodine concentration of equilibrium phase (EP-NIC) were independent influencing factors. The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824, respectively, which were slightly greater than those of CT-T and CT-EMVI (AUC = 0.793, 0.762). The nomogram combining CT-T stage, CT-EMVI, VP-70 keV and EP-NIC yielded AUCs of 0.918 (0.866-0.954) and 0.874 (0.784-0.936) in the training and validation cohorts, which are significantly higher than using each of single independent factors (P < 0.05). CONCLUSION: The study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC, with accuracy boosted by integrating clinical markers.

Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer.

PURPOSE: To investigate the potential of using histogram analysis of synthetic MRI (SyMRI) images before and after contrast enhancement to predict axillary lymph node (ALN) status in patients with invasive ductal carcinoma (IDC). METHODS: From January 2022 to October 2022, a total of 212 patients with IDC underwent breast MRI examination including SyMRI. Standard T2 weight images, DCE-MRI and quantitative maps of SyMRI were obtained. 13 features of the entire tumor were extracted from these quantitative maps, standard T2 weight images and DCE-MRI. Statistical analyses, including Student's t-test, Mann-Whiney U test, logistic regression, and receiver operating characteristic (ROC) curves, were used to evaluate the data. The mean values of SyMRI quantitative parameters derived from the conventional 2D region of interest (ROI) were also evaluated. RESULTS: The combined model based on T1-Gd quantitative map (energy, minimum, and variance) and clinical features (age and multifocality) achieved the best diagnostic performance in the prediction of ALN between N0 (with non-metastatic ALN) and N+ group (metastatic ALN ≥ 1) with the AUC of 0.879. Among individual quantitative maps and standard sequence-derived models, the synthetic T1-Gd model showed the best performance for the prediction of ALN between N0 and N+ groups (AUC = 0.823). Synthetic T2_entropy and PD-Gd_energy were useful for distinguishing N1 group (metastatic ALN ≥ 1 and ≤ 3) from the N2-3 group (metastatic ALN > 3) with an AUC of 0.722. CONCLUSIONS: Whole-tumor histogram features derived from quantitative parameters of SyMRI can serve as a complementary noninvasive method for preoperatively predicting ALN metastases.

Clinical application of super-low-positioned intestinal decompression tube in the treatment of intestinal obstruction: a pilot study.

OBJECTIVE: This study aimed to evaluate the clinical application and efficacy of a super-low-positioned intestinal decompression tube in the treatment of intestinal obstruction. METHODS: A total of 130 patients with postoperative small bowel obstruction were included in this study. The patients were divided into a super-low-positioned intestinal decompression group and a conventional intestinal decompression group. The clinical data, treatment outcomes, and complications were compared between the two groups. RESULTS: The technical success rate of placing the super-low-positioned intestinal decompression tube was 100%, with no intraoperative complications. The patients in the super-low-positioned intestinal decompression group had a significantly shorter hospital stay (8.3 ± 5.2 vs 17.7 ± 13.3, P < 0.001) and a higher non-operative treatment success rate (83.6% vs 57.9%, P = 0.001) compared to the conventional intestinal decompression group. Multivariate logistic regression analysis showed that the placement of a super-low-positioned intestinal decompression tube was an independent protective factor for treatment outcomes (P = 0.001). The hospital stay was significantly shorter in the super-low-positioned intestinal decompression group compared to the conventional group in both successful non-operative treatment patients (6.9 ± 3.0 vs 11.2 ± 7.5, P < 0.001) and failed non-operative treatment patients (16.2 ± 7.4 vs 26.6 ± 14.4, P < 0.001). The super-low-positioned intestinal decompression tube effectively relieved the "Self-strangulation" phenomenon in patients with intestinal obstruction. CONCLUSION: The super-low-positioned intestinal decompression tube is a safe and effective method for the treatment of intestinal obstruction, with better treatment outcomes and shorter hospital stays compared to conventional intestinal decompression. Further prospective studies are needed to validate these findings.

The value of diffusion-weighted imaging in the natural history of meningiomas: a predictor of tumor growth.

OBJECTIVE: The general trend in meningioma treatment is shifting from surgery to active surveillance. However, the natural history of meningioma still needs to be clarified, and a simple, practical method is needed to identify fast-growing tumors. The authors aimed to determine whether diffusion-weighted imaging (DWI) could be a valuable imaging modality for predicting meningioma growth. METHODS: Consecutive asymptomatic patients with a meningioma diagnosed on MRI and followed up at the authors' institution between July 2011 and July 2019 were eligible for inclusion in this retrospective study. Univariable and multivariable Cox regression analyses were used to explore whether the relative apparent diffusion coefficient (rADC) was an independent predictor of meningioma growth. Correlations between tumor growth rate (TGR), tumor volume doubling time (VDT), Ki-67, and rADC were assessed using the Pearson correlation coefficient. The predictive ability of rADC was evaluated using receiver operating characteristic (ROC) curves and validated with internal validation data. RESULTS: Sixty-four patients (47 females, 17 males) with a mean age of 62.2 ± 1.4 years were included in this study. Univariable and multivariable analyses revealed that rADC was an independent predictor of meningioma growth (p < 0.05). ROC curve analysis showed that baseline rADC had good predictive power for growing meningiomas (AUC = 0.88, 95% CI 0.78-0.96), as well as slow- or fast-growing meningiomas (AUC = 0.83, 95% CI 0.59-0.98). Moreover, rADC still had a good ability to discriminate between growing and nongrowing meningiomas in the validation set (AUC = 0.85, 95% CI 0.64-1.00). In the 20 patients with tumor growth, baseline rADC was moderately negatively correlated with TGR (r = -0.50, p = 0.02) and strongly positively correlated with VDT (r = 0.63, p = 0.003). Moreover, Ki-67 was significantly associated with rADC in 8 patients who had undergone surgery (r = -0.75, p = 0.03). CONCLUSIONS: In asymptomatic meningiomas, the lower the rADC at baseline, the faster the TGR and the shorter the VDT. DWI could be a valuable tool in predicting meningioma growth in asymptomatic patients.

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas.

BACKGROUND: The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery. METHODS: Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student's t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs. RESULTS: DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03). CONCLUSION: Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.

Molybdenum target mammography-based prediction model for metastasis of axillary sentinel lymph node in early-stage breast cancer.

Sentinel lymph node (SLN) status is closely related to axillary lymph node metastasis in breast cancer. However, SLN biopsy has certain limitations due to invasiveness and diagnostic efficiency. This study aimed to develop a model to predict the risk of axillary SLN metastasis in early-stage breast cancer based on mammography, a noninvasive, cost-effective, and potential complementary way. Herein, 649 patients with early-stage breast cancer (cT1-T2) who received SLN biopsy were assigned to the training cohort (n = 487) and the validation cohort (n = 162). A prediction model based on specific characteristics of tumor mass in mammography was developed and validated with R software. The performance of model was evaluated by receiver operating characteristic curve, calibration plot, and decision curve analysis. Tumor margins, spicular structures, calcification, and tumor size were independent predictors of SLN metastasis (all P < .05). A nomogram showed a satisfactory performance with an AUC of 0.829 (95% CI = 0.792-0.865) in the training cohort and an AUC of 0.825 (95% CI = 0.763-0.888) in validation cohort. The consistency between model-predicted results and actual observations showed great Hosmer-Lemeshow goodness-of-fit (P = .104). Patients could benefit from clinical decisions guided by the present model within the threshold probabilities of 6% to 84%. The prediction model for axillary SLN metastasis showed satisfactory discrimination, calibration abilities, and wide clinical practicability. These findings suggest that our prediction model based on mammography characteristics is a reliable tool for predicting SLN metastasis in patients with early-stage breast cancer.

Artificial intelligence as diagnostic aiding tool in cases of Prostate Imaging Reporting and Data System category 3: the results of retrospective multi-center cohort study.

PURPOSE: To study the effect of artificial intelligence (AI) on the diagnostic performance of radiologists in interpreting prostate mpMRI images of the PI-RADS 3 category. METHODS: In this multicenter study, 16 radiologists were invited to interpret prostate mpMRI cases with and without AI. The study included a total of 87 cases initially diagnosed as PI-RADS 3 by radiologists without AI, with 28 cases being clinically significant cancers (csPCa) and 59 cases being non-csPCa. The study compared the diagnostic efficacy between readings without and with AI, the reading time, and confidence levels. RESULTS: AI changed the diagnosis in 65 out of 87 cases. Among the 59 non-csPCa cases, 41 were correctly downgraded to PI-RADS 1-2, and 9 were incorrectly upgraded to PI-RADS 4-5. For the 28 csPCa cases, 20 were correctly upgraded to PI-RADS 4-5, and 5 were incorrectly downgraded to PI-RADS 1-2. Radiologists assisted by AI achieved higher diagnostic specificity and accuracy than those without AI [0.695 vs 0.000 and 0.736 vs 0.322, both P < 0.001]. Sensitivity with AI was not significantly different from that without AI [0.821 vs 1.000, P = 1.000]. AI reduced reading time significantly compared to without AI (mean: 351 seconds, P < 0.001). The diagnostic confidence score with AI was significantly higher than that without AI (Cohen Kappa: -0.016). CONCLUSION: With the help of AI, there was an improvement in the diagnostic accuracy of PI-RADS category 3 cases by radiologists. There is also an increase in diagnostic efficiency and diagnostic confidence.

Basilar artery plaque distribution is associated with pontine infarction and vertebrobasilar artery geometry.

Background: Basilar artery (BA) atherosclerosis is a common cause of posterior-circulation ischemic stroke. In this study, we investigate the relationship between BA plaque distribution and pontine infarction (PI), further, explore the influence of vertebrobasilar artery (VBA) geometries on BA plaque distribution. Materials and methods: 303 patients were performed with MRI in this study, patients were divided into three groups: no cerebral infarction (NCI), anterior circulation cerebral infarction (ACCI), and posterior circulation cerebral infarction (PCCI), the VBA geometry was classified into four configurations: Walking, Tuning Fork, Lambda, and No Confluence. The AP-Mid-BA, Lateral-Mid-BA, and VA-BA angles were measured on three-dimensional time-of-flight magnetic resonance angiography. Patients underwent high-resolution magnetic resonance imaging to evaluate the BA plaque distribution (either anterior, posterior, or lateral wall). Acute and subacute cerebral infarction [including pontine infarction (PI)] were identified by T2 weighted imaging-fluid-attenuated inversion recovery and diffusion-weighted imaging. Results: The presence of BA plaque (P < 0.001) were associated with PCCI. Eighty-six patients all with BA plaque were further analyzed, compared with patients without pontine infarction, patients with pontine infarction were more likely to have plaque distributed at the posterior wall (P = 0.009) and have larger VA-BA anger (38.72° ± 26.01° vs. 26.59° ± 17.33°, P = 0.035). BA plaques in patients with pontine infarction were more frequently located at the posterior wall (50.00%) than at the anterior (10.00%) and lateral (37.50%) walls (P = 0.028). In Walking, Lambda and No Confluence geometry, BA plaques were prone to located at the lateral wall than at the anterior and posterior walls (all P ≤ 0.05). In the Tuning Fork group, BA plaques were evenly distributed. Conclusion: BA plaque was related to PCCI, BA plaque distribution was associated with PI, and VBA configuration strongly influences BA plaque distribution.

Short-term outcomes of a new gastrointestinal decompression tube combined with conservative treatment in patients with esophagojejunal anastomotic leakage after total gastrectomy.

To compare the short-term outcomes of a new gastrointestinal decompression tube combined with conservative treatment in patients with esophagojejunal anastomotic leakage (EJAL) after total gastrectomy. We retrospectively analyzed the data of 81 patients with EJAL who had undergone total gastrectomy and Roux-en-Y reconstruction at Fujian Medical University Union Hospital between January 2014 and December 2021. The patients were divided into experimental (12 patients with new gastrointestinal decompression tube plus conservative treatment) and control (69 patients with conservative treatment) groups, according to the different treatment methods they received. Anatomic defect size linearly correlated with time to clinical success, hospital stay, and hospital cost in the control group. The two groups showed no significant differences in anastomotic defect size, time of defect after surgery, hospitalization cost, and time of antibiotic use. However, the time to clinical success was significantly shorter in the experimental group than in the control group (16.0 ± 8.3 vs. 23.6 ± 17.8, P = 0.04), as was the length of hospital stay (30.1 ± 6.3 vs. 36.8 ± 16.7, P = 0.017). Furthermore, when the defect size was ≥ 4 mm, the time to clinical success, hospital stay, and hospital cost in the experimental group were lower than those in the control group (P < 0.05). Placement of a new gastrointestinal decompression tube is a safe treatment. When the defect size is ≥ 4 mm, the time to clinical success, length of hospital stay, and hospital cost can be reduced.

T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma.

OBJECTIVE: To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma. METHODS: This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman's rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression. RESULTS: High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (p = 0.001-0.009), lower minimum, and C10 of ADC (p = 0.013-0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance). CONCLUSION: T1 and ADC histogram parameters may represent an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma. KEY POINTS: • The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma. • The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status.

Background: Variations in axillary lymph node (ALN) metastatic potential between different breast cancers lead to microscopical alterations in tumor perfusion heterogeneity. This study investigated the usefulness of histogram metrics from iodine maps in the preoperative diagnosis of metastatic ALNs in patients with early-stage breast cancer. Methods: Between October 2020 and November 2021 enhanced spectral computed tomography (CT) was performed in female patients with breast cancer. Quantitative spectral CT parameters and histogram parameters (mean, median, maximum, minimum, 10th percentiles, 90th percentiles, kurtosis, skewness, energy, range, and variance) from iodine maps were compared between patients with metastatic and nonmetastatic ALNs. Continuous variables were compared using Student's t-test or Mann-Whitney U test. Categorical variables were compared using Pearson's chi-square tests or Fisher's exact tests. Associations between ALN status and imaging features were evaluated using Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis. Results: This study included 113 female patients (62 and 51 in the ALN-negative and ALN-positive groups, respectively). Tumor size, molecular subtypes, and location differed significantly between the ALN-negative and ALN-positive groups (P<0.05). None of the quantitative spectral CT parameters of mass between metastatic and nonmetastatic ALN groups were significantly different (P>0.05). Histogram parameters of iodine maps for breast cancers, including maximum, 10th percentile, range, and energy, were significantly higher in the metastatic ALNs group compared with the nonmetastatic ALNs group (P<0.05). Multivariable logistic regression analyses showed that tumor location and energy were independent predictors of metastatic ALNs in breast cancers. The combination of independent predictors yielded an area under the curve (AUC) of 0.824 (sensitivity 72.5%; specificity 74.2%). Conclusions: Whole-lesion histogram parameters derived from spectral CT iodine maps may be used as a complementary noninvasive means for the preoperative identification of ALN metastases in patients with early-stage breast cancer.

Role of pyroglutamic acid in cumulus cells of women with polycystic ovary syndrome.

PURPOSE: Polycystic ovary syndrome is a complex heterogeneous endocrine disorder associated with established metabolic abnormalities and is a common cause of infertility in females. Glutathione metabolism in the cumulus cells (CCs) of women with PCOS may be correlated to the quality of oocytes for infertility treatment; therefore, we used a metabolomics approach to examine changes in CCs from women with PCOS and oocyte quality. METHODS: Among 135 women undergoing fertility treatment in the present study, there were 43 women with PCOS and 92 without. CCs were collected from the two groups and levels of pyroglutamic acid were measured using LC-MS/MS followed by qPCR and Western blot analysis to examine genes and proteins involved in pyroglutamic acid metabolism related to glutathione synthesis. RESULTS: Women with PCOS showed increased levels of L-pyroglutamic acid, L-glutamate, and L-phenylalanine and decreased levels of Cys-Gly and N-acetyl-L-methionine. Gene expression of OPLAH, involved in pyroglutamic synthesis, was significantly increased in women with PCOS compared with those without. Gene expression of GSS was significantly decreased in women with PCOS and synthesis of glutathione synthetase protein was decreased. Expression of nuclear factor erythroid 2-related factor 2, involved in resistance to oxidative stress, was significantly increased in women with PCOS. CONCLUSIONS: CCs of women with PCOS showed high concentrations of pyroglutamic acid and reduced glutathione synthesis, which causes oxidative stress in CCs, suggesting that decreased glutathione synthesis due to high levels of pyroglutamic acid in CCs may be related to the quality of oocytes in women with PCOS.

Mehran 2 Contrast-Associated Acute Kidney Injury Risk Score: Is it Applicable to the Asian Percutaneous Coronary Intervention Population?

Contrast-associated acute kidney injury (CA-AKI) can occur after percutaneous coronary intervention (PCI). The Mehran score is the gold standard for predicting CA-AKI risk, and it has recently been updated. The Mehran 2 CA-AKI risk score, based on existing variables in patients undergoing PCI, can accurately differentiate the risk of CA-AKI. This study aimed to verify whether the new Mehran score is applicable to the Asian PCI population. The study included the clinical data of 2487 patients undergoing PCI from August 2020 to February 2022. The goodness-of-fit test (Hosmer-Lemeshow) was used to evaluate the correction ability of the Mehran 2 score, and the area under the receiver operating characteristic curve (ROC) was used to evaluate the accuracy of the Mehran 2 score in predicting CA-AKI. CA-AKI occurred in 28 of 2487 patients, with an incidence rate of 1.12%. The proportion of high risk factors for AKI in the cohort was lower than that in the Mehran 2 cohort (a large contemporary PCI cohort to develop the Mehran 2 score). The Mehran 2 risk score had excellent goodness-of-fit (χ2 = 5.320, df = 6, P = 0.503) and high predictive accuracy (area under the ROC curve 0.836, P < 0.0001). The Mehran 2 score shows good predictive and correction performance in the Asian population and has good clinical application value. The inclusion of the Mehran 2 risk score in patients hospitalised for coronary angiography appears to be good practice.

Alterations of arterial morphology in patients with aberrant subclavian artery and type B dissection and its association with dissection.

OBJECTIVE: The arterial morphology in patients with aberrant subclavian artery (ASA) and its association with type B aortic dissection are important for treatment and prevention. In the present study, we examined the arterial morphology of ASA patients with type B dissection and evaluated its association with type B dissection in vivo. METHODS: Patients with aortic dissection who had undergone computed tomography angiography were screened for the presence of ASA and type B dissection from January 2011 to May 2021. The angles of ascending aorta, aortic arch, and aortic deviation and the diameters of the ascending aorta, aortic arch, ASA ostium, and middle ASA segment were measured on the computed tomography angiography scans of the ASA patients with type B dissection (group 1; n = 16), clinically matched counterparts without type B dissection (group 2; n = 32), and patients with clinically matched type B dissection without ASA (group 3, n = 32). The correlation between ASA morphology and type B dissection was analyzed using variance analysis or the Wallis H test. RESULTS: Compared with group 2, group 1 had a sharper ascending aortic angle (131.5° ± 13.7° vs 148.1° ± 7.8°; P = .001), a larger aortic deviation angle in plane 2 (28.2° ± 6.0° vs 22.1° ±7.2°; P = .005) and plane 3 (26.4° ±7.3° vs 21.8° ± 6.3°; P = .028). Similarly, group 1 had a greater diameter in the ascending aorta and aortic arch and the ostium and middle of the ASA (38.3 ± 4.1 mm vs 33.6 ± 4.5 mm [P = .001]; 34.0 ± 9.3 mm vs 26.2 ± 2.9 mm [P = .004]; 20.3 ± 9.3 mm vs 14.0 ± 3.2 mm [P = .018]; 10.8 ± 2.3 mm vs 9.0 ± 1.5 mm [P = .002], respectively), without a significant difference in the aortic arch angle. Compared with group 3, group 1 had a sharper ascending aortic angle (131.5° ± 13.7° vs 142.5° ± 11.7°; P = .026) and smaller aortic deviation angle in plane 1 (21.7° ± 6.2° vs 28.9° ± 6.2°; P = .04) and plane 3 (26.4° ± 7.3° vs 21.8° ± 6.3°; P = .007), although with no significant differences in the aortic arch angle, aortic deviation angle in plane 2, and ascending aortic diameter. CONCLUSIONS: The diameters of the ostium and middle segment of the ASA and ascending aorta and the angles of the ascending aorta and aortic deviation are potential risk factors for type B dissection in patients with ASA, which could provide new insights into the mechanism of type B dissection in patients with ASA.

Feasibility of 3D Breath-Hold MR Cholangiopancreatography with a Spatially Selective Radiofrequency Excitation Pulse: Prospective Comparison with Parallel Imaging Technique and Compressed Sensing Method.

RATIONALE AND OBJECTIVES: The purpose of the present study was to evaluate the clinical feasibility of the modified 3D breath-hold magnetic resonance cholangiopancreatography with parallel imaging (3D-BH-PI-MRCP) using a spatially selective radiofrequency excitation pulse in patients with suspected pancreaticobiliary diseases. Moreover, we also compared its image quality with those of the original 3D-BH-PI-MRCP with a nonselective exciting pulse and the 3D breath hold compressed sensing magnetic resonance cholangiopancreatography (3D-BH-CS-MRCP). MATERIALS AND METHODS: Between January 2021 and July 2021, 106 patients prospectively underwent modified 3D-BH-PI-MRCP, original 3D-BH-PI-MRCP and 3D-BH-CS-MRCP at 3T in this study. The Friedman test was performed to compare the contrast, signal-to-noise-ratio (SNR), and contrast-noise-ratio, overall image quality, and duct visualization among the three protocols. RESULTS: The contrast ratio, SNR and contrast-to-noise ratio of the common bile duct differed significantly among the three sequences (p < 0.001). Compared to the 3D-BH-CS-MRCP protocol, the overall imaging quality of the two 3D-BH-PI-MRCP was higher but not significantly different. The scores for the anterior and posterior branches visualization were significantly higher in the original 3D-BH-PI-MRCP compared to the 3D-BH-CS-MRCP, but were no significant differences between the modified 3D-BH-PI-MRCP and the 3D-BH-CS-MRCP. CONCLUSION: The modified 3D-BH-PI-MRCP with a spatially selective radiofrequency excitation pulse could provide comparable image quality to the original 3D-BH-PI-MRCP and the 3D-BH-CS-MRCP during a single breath hold (22 seconds), and showed improved SNR and superior visualization of the pancreaticobiliary tree.