Can the Abdominal Aortic Atherosclerotic Plaque Index Predict Functional Outcomes after Robot-Assisted Partial Nephrectomy?

This study aims to evaluate the abdominal aortic atherosclerotic plaque index (API)'s predictive role in patients with pre-operatively or post-operatively developed chronic kidney disease (CKD) treated with robot-assisted partial nephrectomy (RAPN) for renal cell carcinoma (RCC). One hundred and eighty-three patients (134 with no pre- and post-operative CKD (no CKD) and 49 with persistent or post-operative CKD development (post-op CKD)) who underwent RAPN between January 2019 and January 2022 were deemed eligible for the analysis. The API was calculated using dedicated software by assessing the ratio between the CT scan atherosclerotic plaque volume and the abdominal aortic volume. The ROC regression model demonstrated the influence of API on CKD development, with an increasing effect according to its value (coefficient 0.13; 95% CI 0.04-0.23; p = 0.006). The Model 1 multivariable analysis of the predictors of post-op CKD found that the following are independently associated with post-op CKD: Charlson Comorbidity Index (OR 1.31; p = 0.01), last follow-up (FU) Δ%eGFR (OR 0.95; p < 0.01), and API ≥ 10 (OR 25.4; p = 0.01). Model 2 showed API ≥ 10 as the only factor associated with CKD development (OR 25.2; p = 0.04). The median follow-up was 22 months. Our results demonstrate API to be a strong predictor of post-operative CKD, allowing the surgeon to tailor the best treatment for each patient, especially in those who might be at higher risk of CKD.

Acute pancreatitis after pancreatoduodenectomy: A prospective study of diffusion-weighted magnetic resonance imaging, serum biomarkers, and clinical features.

BACKGROUND: Postpancreatectomy acute pancreatitis is challenging to diagnose and poorly characterized in its early phases. However, it represents the ideal target for novel therapeutic opportunities possibly gleaned from medical acute pancreatitis. This study aims to systematically investigate early radiologic, biochemical, and clinical features of postpancreatectomy acute pancreatitis. METHODS: This was a prospective observational study of patients undergoing pancreatoduodenectomy from September 2019 to January 2021. Diffusion-weighted magnetic resonance imaging was performed on postoperative day 3. Serum pancreatic amylase and lipase were assessed daily until postoperative day 5. Postoperative serum hyperamylasemia and postpancreatectomy acute pancreatitis were defined based on the International Study Group for Pancreatic Surgery definition. RESULTS: A total of 65 patients were enrolled according to the sample size calculation. Patients with postoperative serum hyperamylasemia and postpancreatectomy acute pancreatitis had significantly lower apparent diffusion coefficient values at diffusion-weighted magnetic resonance imaging but no macroscopic features consistent with acute pancreatitis. Subsequently, 21 patients (32.3%) underwent computed tomography imaging for clinical worsening, and 6 had radiologic features of acute pancreatitis. All these latter patients had postoperative serum hyperamylasemia and worse outcomes, characterized by local (postoperative pancreatic fistula: 83%) and systemic morbidity (sepsis: 66.7%). The postoperative serum hyperamylasemia incidence was 21.5% (n = 14), and postpancreatectomy acute pancreatitis occurred in 6 patients (9.2%), with 4 grade B (6.1%) and 2 grade C (3%). CONCLUSION: Postpancreatectomy acute pancreatitis is characterized by early serum hyperamylasemia and hyperlipasemia. Although pancreatic changes may appear at postoperative day 3 diffusion-weighted magnetic resonance imaging, its standard use has no impact on postoperative management. Macroscopic radiologic features appear later and correlate with worse clinical scenarios. This paper paves the ground for including postpancreatectomy acute pancreatitis in the spectrum of acute pancreatitis, promoting the transfer of treatment strategies for acute pancreatitis into managing postpancreatectomy acute pancreatitis.

Toward autonomous robotic prostate biopsy: a pilot study.

PURPOSE: We present the validation of PROST, a robotic device for prostate biopsy. PROST is designed to minimize human error by introducing some autonomy in the execution of the key steps of the procedure, i.e., target selection, image fusion and needle positioning. The robot allows executing a targeted biopsy through ultrasound (US) guidance and fusion with magnetic resonance (MR) images, where the target was defined. METHODS: PROST is a parallel robot with 4 degrees of freedom (DOF) to orient the needle and 1 DOF to rotate the US probe. We reached a calibration error of less than 2 mm, computed as the difference between the needle positioning in robot coordinates and in the US image. The autonomy of the robot is given by the image analysis software, which employs deep learning techniques, the integrated image fusion algorithms and automatic computation of the needle trajectory. For safety reasons, the insertion of the needle is assigned to the doctor. RESULTS: System performance was evaluated in terms of positioning accuracy. Tests were performed on a 3D printed object with nine 2-mm spherical targets and on an anatomical commercial phantom that simulates human prostate with three lesions and the surrounding structures. The average accuracy reached in the laboratory experiments was [Formula: see text] in the first test and [Formula: see text] in the second test. CONCLUSIONS: We introduced a first prototype of a prostate biopsy robot that has the potential to increase the detection of clinically significant prostate cancer and, by including some level of autonomy, to simplify the procedure, to reduce human errors and shorten training time. The use of a robot for the biopsy of the prostate will create the possibility to include also a treatment, such as focal ablation, to be delivered through the same system.

A virtual reality study on postretrieval extinction of smoking memory reconsolidation in smokers.

Exposure to smoking-related stimuli may induce the reconsolidation of smoking-related memories in smokers. Research has proposed that extinction applied after the retrieval of a smoking memory may inhibit reconsolidation and prevent craving. The aim of this study was to test the effect of postretrieval extinction (PRE) on the reconsolidation of smoking memory by using a virtual reality (VR) simulation in smokers. On the day 1 session, the study exposed 46 smokers to a neutral and then to a smoking VR scenario under a fixed-block protocol. On day 2, the study randomized participants into three groups (G) and exposed them to a 15-s VR immersion in smoking (G1, G3) or neutral (G2) scenario for memory retrieval. After 15 min, the study exposed G1 and G2 to a VR PRE during the temporal window of memory vulnerability, whereas the study exposed G3 to extinction immediately after retrieval. On day 3, the study exposed all groups to neutral and smoking scenarios similar to day 1. All groups significantly increased craving for cigarettes after exposure to the smoking scenario on day 1 (p < 0.01). On day 3, VR PRE after a 15-second VR smoking memory retrieval was able to inhibit reconsolidation in G1, but not in G3 exposed to PRE before the window of vulnerability, or in G2 not exposed to the smoking memory retrieval. These findings show the superiority of VR PRE after smoking memory retrieval compared to a standard extinction procedure.

Quantitative Evaluation of an Automated Cone-Based Breast Ultrasound Scanner for MRI-3D US Image Fusion.

Breast cancer is one of the most diagnosed types of cancer worldwide. Volumetric ultrasound breast imaging, combined with MRI can improve lesion detection rate, reduce examination time, and improve lesion diagnosis. However, to our knowledge, there are no 3D US breast imaging systems available that facilitate 3D US - MRI image fusion. In this paper, a novel Automated Cone-based Breast Ultrasound System (ACBUS) is introduced. The system facilitates volumetric ultrasound acquisition of the breast in a prone position without deforming it by the US transducer. Quality of ACBUS images for reconstructions at different voxel sizes (0.25 and 0.50 mm isotropic) was compared to quality of the Automated Breast Volumetric Scanner (ABVS) (Siemens Ultrasound, Issaquah, WA, USA) in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and resolution using a custom made phantom. The ACBUS image data were registered to MRI image data utilizing surface matching and the registration accuracy was quantified using an internal marker. The technology was also evaluated in vivo. The phantom-based quantitative analysis demonstrated that ACBUS can deliver volumetric breast images with an image quality similar to the images delivered by a currently commercially available Siemens ABVS. We demonstrate on the phantom and in vivo that ACBUS enables adequate MRI-3D US fusion. To our conclusion, ACBUS might be a suitable candidate for a second-look breast US exam, patient follow-up, and US guided biopsy planning.

Correlation of MR features and histogram-derived parameters with aggressiveness and outcomes after resection in pancreatic ductal adenocarcinoma.

PURPOSE: To evaluate MR-derived histogram parameters in predicting aggressiveness and surgical outcomes in patients with PDAC, by correlating them to pathological features, recurrence-free survival (RFS), and overall survival (OS). METHODS: Pre-operative MR examinations of 103 patients with PDAC between July 2014 and September 2018 were retrospectively analyzed. Morphologic features and whole-tumor histogram-derived parameters were correlated to pathological features using Fisher's exact or Mann-Whitney U tests and receiver operating characteristic (ROC) curves were constructed for significant parameters. Cox regression analysis and Kaplan-Meier curves were used to determine the association of clinical-pathological variables, morphological features, and histogram-derived parameters with RFS and OS. RESULTS: T1entropy, ADCentropy, T2kurtosis, and ADCuniformity had the highest area under the curve (AUC) for prediction of vascular infiltration, nodal metastases, microscopic vascular invasion, and peripancreatic fat invasion (.657, .742, .760, and .818, respectively). Poor tumor differentiation (P = 0.002, hazard ratio-HR = 4.08), nodal ratio (P = 0.034, HR 6.95), and ADCmaximum (P = 0.021, HR 1.01) were significant predictors of RFS. Poor tumor differentiation (P = 0.05, HR 2.82), ADCuniformity (P = 0.02, HR 3.32), and arterialentropy (P = 0.02, HR 6.84) were the only significant predictors of death; patients with higher arterialentropy had significantly shorter OS than patients who did not meet this criterion (P = 0.02; median OS 24 vs 31 months). CONCLUSION: Histogram-derived parameters may predict adverse pathological features in PDACs. High arterialentropy seems to be associated with short OS after surgery in patients with PDAC.

Iterative simulations to estimate the elastic properties from a series of MRI images followed by MRI-US validation.

The modeling of breast deformations is of interest in medical applications such as image-guided biopsy, or image registration for diagnostic purposes. In order to have such information, it is needed to extract the mechanical properties of the tissues. In this work, we propose an iterative technique based on finite element analysis that estimates the elastic modulus of realistic breast phantoms, starting from MRI images acquired in different positions (prone and supine), when deformed only by the gravity force. We validated the method using both a single-modality evaluation in which we simulated the effect of the gravity force to generate four different configurations (prone, supine, lateral, and vertical) and a multi-modality evaluation in which we simulated a series of changes in orientation (prone to supine). Validation is performed, respectively, on surface points and lesions using as ground-truth data from MRI images, and on target lesions inside the breast phantom compared with the actual target segmented from the US image. The use of pre-operative images is limited at the moment to diagnostic purposes. By using our method we can compute patient-specific mechanical properties that allow compensating deformations. Graphical Abstract Workflow of the proposed method and comparative results of the prone-to-supine simulation (red volumes) validated using MRI data (blue volumes).

Analytical derivation of elasticity in breast phantoms for deformation tracking.

PURPOSE: Patient-specific biomedical modeling of the breast is of interest for medical applications such as image registration, image guided procedures and the alignment for biopsy or surgery purposes. The computation of elastic properties is essential to simulate deformations in a realistic way. This study presents an innovative analytical method to compute the elastic modulus and evaluate the elasticity of a breast using magnetic resonance (MRI) images of breast phantoms. METHODS: An analytical method for elasticity computation was developed and subsequently validated on a series of geometric shapes, and on four physical breast phantoms that are supported by a planar frame. This method can compute the elasticity of a shape directly from a set of MRI scans. For comparison, elasticity values were also computed numerically using two different simulation software packages. RESULTS: Application of the different methods on the geometric shapes shows that the analytically derived elongation differs from simulated elongation by less than 9% for cylindrical shapes, and up to 18% for other shapes that are also substantially vertically supported by a planar base. For the four physical breast phantoms, the analytically derived elasticity differs from numeric elasticity by 18% on average, which is in accordance with the difference in elongation estimation for the geometric shapes. The analytic method has shown to be multiple orders of magnitude faster than the numerical methods. CONCLUSION: It can be concluded that the analytical elasticity computation method has good potential to supplement or replace numerical elasticity simulations in gravity-induced deformations, for shapes that are substantially supported by a planar base perpendicular to the gravitational field. The error is manageable, while the calculation procedure takes less than one second as opposed to multiple minutes with numerical methods. The results will be used in the MRI and Ultrasound Robotic Assisted Biopsy (MURAB) project.

Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors?

OBJECTIVES: To evaluate MRI derived whole-tumour histogram analysis parameters in predicting pancreatic neuroendocrine neoplasm (panNEN) grade and aggressiveness. METHODS: Pre-operative MR of 42 consecutive patients with panNEN >1 cm were retrospectively analysed. T1-/T2-weighted images and ADC maps were analysed. Histogram-derived parameters were compared to histopathological features using the Mann-Whitney U test. Diagnostic accuracy was assessed by ROC-AUC analysis; sensitivity and specificity were assessed for each histogram parameter. RESULTS: ADCentropy was significantly higher in G2-3 tumours with ROC-AUC 0.757; sensitivity and specificity were 83.3 % (95 % CI: 61.2-94.5) and 61.1 % (95 % CI: 36.1-81.7). ADCkurtosis was higher in panNENs with vascular involvement, nodal and hepatic metastases (p= .008, .021 and .008; ROC-AUC= 0.820, 0.709 and 0.820); sensitivity and specificity were: 85.7/74.3 % (95 % CI: 42-99.2 /56.4-86.9), 36.8/96.5 % (95 % CI: 17.2-61.4 /76-99.8) and 100/62.8 % (95 % CI: 56.1-100/44.9-78.1). No significant differences between groups were found for other histogram-derived parameters (p >.05). CONCLUSIONS: Whole-tumour histogram analysis of ADC maps may be helpful in predicting tumour grade, vascular involvement, nodal and liver metastases in panNENs. ADCentropy and ADCkurtosis are the most accurate parameters for identification of panNENs with malignant behaviour. KEY POINTS: • Whole-tumour ADC histogram analysis can predict aggressiveness in pancreatic neuroendocrine neoplasms. • ADC entropy and kurtosis are higher in aggressive tumours. • ADC histogram analysis can quantify tumour diffusion heterogeneity. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information for prognostication.