Springer Science and Business Media LLC

To evaluate the role of pelvic MR imaging in differentiating between low rectal and anal cancers using the pathological results as the gold standard. In this study, retrospective analysis of 100 patients with a history of low rectal (n = 50) or anal (n = 50) cancers who underwent staging pelvic MR imaging before treatment was performed. The following parameters were analyzed: distance from the anal verge to the tumor, percentage of tumor above puborectalis muscle, tumor size, T2W signal intensity, sphincter/levator muscles invasion, organ invasion, and MRI diagnosis. Multivariable logistic regression was performed to determine factors associated with low rectal and anal cancers. Distances from the anal verge to the tumor were compared using receiver-operating characteristic (ROC) curves. From the ROC curves, the cut-off value for the distance from the anal verge to the tumor in differentiating between low rectal and anal cancers was 2.1 cm and the area under the ROC curve was 0.90 (95% CI 0.84–0.97). Multivariate logistic regression revealed three significant factors in differentiating between low rectal and anal cancers, including T2 mixed hyper- and hyposignal intensity (OR 66.00, 95% CI 4.66–934.81), distance cut-off value (OR 34.72, 95% CI 5.73–210.27), and absence of sphincter invasion (OR 18.75, 95% CI 1.91–183.96), with sensitivity, specificity, PPV, and NPV of 98%, 88%, 89%, and 97%, respectively, and diagnostic accuracy increased from 79% (reader 1) and 82% (reader 2) to 93%. MR imaging can be useful to differentiating between low rectal and anal cancers which benefits staging and treatment planning.

Adrenal incidentalomas are common lesions found on abdominal imaging, most of which are lipid-rich adrenal adenomas. Imaging diagnoses differentiating lipid-poor adrenal adenomas (LPA) from non-adenomas (NA) are presently challenging to perform. The aim of the study was to investigate the diagnostic performance of the relative enhancement ratio parameter in identifying LPA from NA. We retrospectively evaluated consecutively presenting patients with lipid-poor adrenal lesions (January 2015 to August 2021). Lesions were divided into LPA and NA (including hyperenhancing and hypoenhancing NA). Kruskal–Wallis and Bonferroni tests were used to determine the differences in feature parameters between these three groups. Receiver operating characteristic curve analysis was performed to determine the sensitivity for diagnosing LPA and NA at 95% specificity; the parameters were compared using the McNemar test. A total of 253 patients (mean age, 55 ± 12 years; 135 men), 121 with LPA and 132 with NA, were analyzed herein. The sensitivity (achieved at 95% specificity) of the relative enhancement ratio was higher than that of unenhanced attenuation in differentiating LPA from NA (60% vs. 52%, p = 0.064). The relative enhancement ratio yielded a higher sensitivity than unenhanced attenuation (79% vs. 59%, p < 0.001) in differentiating LPA from hypoenhancing NA, and a lower sensitivity (26% vs. 69%, p < 0.001) in differentiating LPA from hyperenhancing NA. The relative enhancement ratio showed better diagnostic performance than unenhanced attenuation in differentiating LPA from hypoenhancing NA, while simultaneously showing poor diagnostic performance in identifying LPA from all NA.

The current study developed an ultrasound-based deep learning model to make preoperative differentiation among hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular–cholangiocarcinoma (cHCC-ICC). The B-mode ultrasound images of 465 patients with primary liver cancer were enrolled in model construction, comprising 264 HCCs, 105 ICCs, and 96 cHCC-ICCs, of which 50 cases were randomly selected to form an independent test cohort, and the rest of study population was assigned to a training and validation cohorts at the ratio of 4:1. Four deep learning models (Resnet18, MobileNet, DenseNet121, and Inception V3) were constructed, and the fivefold cross-validation was adopted to train and validate the performance of these models. The following indexes were calculated to determine the differential diagnosis performance of the models, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F-1 score, and area under the receiver operating characteristic curve (AUC) based on images in the independent test cohort. Based on the fivefold cross-validation, the Resnet18 outperformed other models in terms of accuracy and robustness, with the overall training and validation accuracy as 99.73% (± 0.07%) and 99.35% (± 0.53%), respectively. Furthers validation based on the independent test cohort suggested that Resnet 18 yielded the best diagnostic performance in identifying HCC, ICC, and cHCC-ICC, with the sensitivity, specificity, accuracy, PPV, NPV, F1-score, and AUC of 84.59%, 92.65%, 86.00%, 85.82%, 92.99%, 92.37%, 85.07%, and 0.9237 (95% CI 0.8633, 0.9840). Ultrasound-based deep learning algorithm appeared a promising diagnostic method for identifying cHCC-ICC, HCC, and ICC, which might play a role in clinical decision making and evaluation of prognosis.

To evaluate quantitative parameters derived from intravoxel incoherent motion diffusion-weighted MRI (IVIM) of renal parenchyma in patients with untreated acute leukemia (AL) and analyze its prognostic significance and probable pathological mechanism. From March 2019 to November 2021, 67 newly diagnosed AL patients and 67 healthy controls matched in age and sex were recruited. All participants underwent IVIM in the kidneys, and D, D*, f, standard ADC values were measured. The differences of all parameters between AL and controls were analyzed. The relationship between imaging parameters and estimated glomerular filtration rate (eGFR) was studied. Univariable and multivariable analyses were performed to investigate prognostic significance of possible indicators. The f and D value of renal medulla and D value of renal cortex in AL patients were lower than those in the healthy control group (t = − 2.173, t = − 3.463, t = − 2.030, respectively, all P < 0.05). The cortical f, cortical standard ADC, medullary f, and medullary standard ADC were correlated with the eGFR (r = 0.524, r = 0.401, r = 0.415, r = 0.325, respectively, all P < 0.05) in patients with AL. A medullary f value ≤ 9.51% (hazard ratio: 0.282; 95% confidence interval: 0.110, 0.719; P = 0.008) was associated with overall survival in a multivariable analysis. The f and standard ADC values in renal parenchyma were the probable imaging markers of renal function in patients with newly diagnosed de novo AL. Lower renal medullary f value was a potential independent predictor for overall survival.

We aim to establish diagnostic thresholds of sarcopenia and myosteatosis based on CT measurements, and to validate their prognostic value in a large cohort of kidney transplant recipients. Local healthy population with abdominal CT between 2010 and 2022, and patients underwent kidney transplantation between 2015 and 2019 at our center were retrospectively included. The skeletal muscle index and muscle attenuation of abdominal muscles were calculated based on CT image at the middle of the third lumbar vertebra. Primary endpoints included all-cause mortality and death censored allograft survival. Age- and sex-specific thresholds for sarcopenia and myosteatosis were established based on 1598 healthy local population. The final patient cohort consisted of 992 kidney transplant recipients (median age 34 years, interquartile range 28–44 years; 694 males), including 33 (3.3%) with sarcopenia and 95 (9.5%) with myosteatosis. Multivariate analysis revealed myosteatosis (adjusted hazard ratio = 3.08, p = 0.022) was an independent baseline risk factor of mortality after adjusting for age, the history of cancer, and the history of cardiovascular event. Multivariate analysis found preemptive transplantation (adjusted hazard ratio = 0.36, p = 0.037) was an independent protective factor of allograft loss. No difference was found in the prognosis between kidney transplant recipients with and without sarcopenia. Myosteatosis was an independent risk factor of mortality after kidney transplantation, but sarcopenia was not. Neither sarcopenia nor myosteatosis was associated with graft loss.

In the context of medical literature, a significant lacuna exists in understanding median arcuate ligament syndrome (MALS). While clinical aspects are well documented, literature lacks a robust exploration of the anatomical relationship between the celiac trunk and the median arcuate ligament (MAL). Morphometric parameters, including the vertebral level of MAL origin, MAL thickness, celiac trunk (CeT) origin level, diameter, and distances between CeT/Superior Mesenteric Artery (SMA) and the MAL center were observed on 250 CT angiograms. Cadavers (n = 11) were dissected to examine the same parameters and histo-morphological examination of MAL tissue was done. Radiological findings established average MAL thickness of 7.79 ± 2.58 mm. The celiac trunk typically originated at T12. The average distance between the celiac trunk and the MAL center was 1.32 ± 2.04 mm. The angle of the celiac trunk to the abdominal aorta was primarily obtuse. The average celiac trunk diameter was 5.53 ± 1.33 mm. Histological examinations revealed a diverse MAL composition, indicating variable mechanical properties. This study provides comprehensive morphometric data on the anatomical relationship between the MAL and the celiac trunk. In contrast to available literature which says the average MAL thickness of > 4 mm is an indicator of increased thickness, we observed much higher average thickness in the studied population. The findings contribute to a better understanding of normal anatomical variations which can serve as reference values for accurate radiological diagnosis of MALS. The histological examination revealed the heterogeneous nature of the MAL tissue composition, suggesting variable mechanical properties and functions in different regions.

To assess differences in FDG-PET/CT uptake among four subtypes of renal tumors: clear cell RCC (ccRCC), papillary type I and II RCC (pRCC), and oncocytoma. This retrospective study investigated 33 patients with 98 hereditary renal tumors. Lesions greater than 1 cm and patients with a timeframe of less than 18 months between preoperative imaging and surgery were considered. FDG-PET/CT images were independently reviewed by two nuclear medicine physicians, blinded to clinical information. Volumetric lesion SUVmean was measured and used to calculate a target-to-background ratio respective to liver (TBR). The Shrout-Fleiss intra-class correlation coefficient was used to assess reliability between readers. A linear mixed effects model, accounting for within-patient correlations, was used to compare TBR values of primary renal lesions with and without distant metastasis. The time interval between imaging and surgery for all tumors had a median of 77 (Mean: 139; Range: 1–512) days. Intra-class reliability of mean TBR resulted in a mean κ score of 0.93, indicating strong agreement between the readers. The mixed model showed a significant difference in mean TBR among the subtypes (p < 0.0001). Pairwise comparison showed significant differences between pRCC type II and ccRCC (p < 0.0001), pRCC type II and pRCC type I (p = 0.0001), and pRCC type II and oncocytoma (p = 0.0016). Furthermore, a significant difference in FDG uptake was present between primary pRCC type II renal lesions with and without distant metastasis (p = 0.023). pRCC type II lesions demonstrated significantly higher FDG activity than ccRCC, pRCC type I, or oncocytoma. These findings indicate that FDG may prove useful in studying the metabolic activity of renal neoplasms, identifying lesions of highest clinical concern, and ultimately optimizing active surveillance, and personalizing management plans.