Mixed primary prostatic carcinoma with acinar, neuroendocrine and ductal components.

Mixed tumors in the prostate gland have been described; they are primarily originate in the gland or are the product of metastatic compromise. Mixed primary epithelial carcinomas of the prostate are very rare; here we report the case of a 72-years-old man with histopathologic findings of a primary prostate mixed carcinoma, showing characteristics of acinar, ductal and neuroendocrine adenocarcinoma. We also discuss the clinical, diagnostic, and therapeutic aspects of these uncommon mixed neoplasms.

Biventricular Strain Imaging with Cardiac MRI in Genotyped and Histology Validated Amyloid Cardiomyopathy.

Cardiac amyloidosis (CA) is a common and potentially fatal infiltrative cardiomyopathy. Contrast-enhanced cardiac MRI (CMR) is used as a diagnostic tool. However, utility of CMR for the comprehensive analysis of biventricular strains and strain rates is not reported as extensively as echocardiography. In addition, RV strain analysis using CMR has not been described previously. OBJECTIVES: We sought to study the global and regional indices of biventricular strain and strain rates in endomyocardial biopsy (EMB)-proven, genotyped cases of CA. METHODS: A database of 80 EMBs was curated from 2012 to 2019 based on histology. A total of 19 EMBs positive for CA were subjected to further tissue-characterization with histology, and compared with four normal biopsy specimens. Samples were genotyped for ATTR- or AL-subtypes. Five patients, with both echocardiography and contrast-enhanced CMR performed 72-h apart, were subjected to comprehensive analysis of biventricular strain and strain-rates. RESULTS: Histology confirmed that the selected samples were indeed positive for cardiac amyloid. Echocardiography showed reduced global and regional left-ventricular (LV) longitudinal strain indices. CMR with tissue-characterization of LV showed global reductions in circumferential, radial and longitudinal strains and strain-rates, following a general trend with the echocardiographic findings. The basal right-ventricular (RV) segments had reduced circumferential strains with no changes in longitudinal strain. CONCLUSIONS: In addition to providing a clinical diagnosis of CA based on contrast clearance-dynamics, CMR can be a potent tool for accurate functional assessment of global and regional changes in strain and strain-rates involving both LV and RV. Further studies are warranted to validate and curate the strain imaging capacity of CMR in CA.

Patient-specific 3D-printed coronary models based on coronary computed tomography angiography volumes to investigate flow conditions in coronary artery disease.

BACKGROUND: 3D printed patient-specific coronary models have the ability to enable repeatable benchtop experiments under controlled blood flow conditions. This approach can be applied to CT-derived patient geometries to emulate coronary flow and related parameters such as Fractional Flow Reserve (FFR). METHODS: This study uses 3D printing to compare such benchtop FFR results with a non-invasive CT-FFR research software algorithm and catheter based invasive FFR (I-FFR) measurements. Fifty-two patients with a clinical indication for I-FFR underwent a research Coronary CT Angiography (CCTA) prior to catheterization. CT images were used to measure CT-FFR and to generate patient-specific 3D printed models of the aortic root and three main coronary arteries. Each patient-specific model was connected to a programmable pulsatile pump and benchtop FFR (B-FFR) was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. B-FFR was measured for two coronary outflow rates ('normal', 250 ml min-1; and 'hyperemic', 500 ml min-1) by adjusting the model's distal coronary resistance. RESULTS: Pearson correlations and ROC AUC were calculated using invasive I-FFR as reference. The Pearson correlation factor of CT-FFR and B-FFR-500 was 0.75 and 0.71, respectively. Areas under the ROCs for CT-FFR and B-FFR-500 were 0.80 (95%CI: 0.70-0.87) and 0.81 (95%CI: 0.64-0.91) respectively. CONCLUSION: Benchtop flow simulations with 3D printed models provide the capability to measure pressure changes at any location in the model, for ultimately emulating the FFR at several simulated physiological blood flow conditions. CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/show/NCT03149042.

Initial evaluation of a convolutional neural network used for noninvasive assessment of coronary artery disease severity from coronary computed tomography angiography data.

PURPOSE: Coronary computed tomography angiography (CTA) has one of the highest diagnostic sensitivities for detection of the significance of coronary artery disease (CAD); however, sensitivity is moderate and may result in increased catheterization rates. We performed an efficacy study to determine whether a trained machine learning algorithm that uses coronary CTA data may improve CAD diagnosis accuracy. METHODS: Sixty-four-patient image datasets based on coronary CTA were retrospectively collected to generate eight views considering 45° increments around the coronary artery centerline. The dataset was randomly split into training and testing cohorts. Invasive FFR measurements were used as ground truth labels. A convolutional neural network (CNN) was trained and the model's capacity to predict severity of CAD was assessed on the testing cohort. Classification accuracy and area under the receiver operating characteristic curve (AUROC) analysis were performed. Similar CAD severity classification accuracy and AUROC analyses were performed using only percent diameter stenosis (%DS) and CT-derived FFR performed by 13 operators with various levels of expertise. RESULTS: Classification accuracy over the test cohort was 80.9% using the trained network and 72.4% using the user-operated CT-derived FFR software. AUROC over the test cohort was 0.862 using the trained network, 0.807 using %DS, and 0.758 using the human-operated CT-derived FFR software. CONCLUSIONS: A trained neural network compared noninferiorly in-terms of classification accuracy and AUROC with human operators of a CT-derived FFR software, and in-terms of AUROC with clinical decision-making using %DS.

Reliability of nine programs of topological predictions and their application to integral membrane channel and carrier proteins.

We evaluated topological predictions for nine different programs, HMMTOP, TMHMM, SVMTOP, DAS, SOSUI, TOPCONS, PHOBIUS, MEMSAT-SVM (hereinafter referred to as MEMSAT), and SPOCTOPUS. These programs were first evaluated using four large topologically well-defined families of secondary transporters, and the three best programs were further evaluated using topologically more diverse families of channels and carriers. In the initial studies, the order of accuracy was: SPOCTOPUS > MEMSAT > HMMTOP > TOPCONS > PHOBIUS > TMHMM > SVMTOP > DAS > SOSUI. Some families, such as the Sugar Porter Family (2.A.1.1) of the Major Facilitator Superfamily (MFS; TC #2.A.1) and the Amino Acid/Polyamine/Organocation (APC) Family (TC #2.A.3), were correctly predicted with high accuracy while others, such as the Mitochondrial Carrier (MC) (TC #2.A.29) and the K(+) transporter (Trk) families (TC #2.A.38), were predicted with much lower accuracy. For small, topologically homogeneous families, SPOCTOPUS and MEMSAT were generally most reliable, while with large, more diverse superfamilies, HMMTOP often proved to have the greatest prediction accuracy. We next developed a novel program, TM-STATS, that tabulates HMMTOP, SPOCTOPUS or MEMSAT-based topological predictions for any subdivision (class, subclass, superfamily, family, subfamily, or any combination of these) of the Transporter Classification Database (TCDB; www.tcdb.org) and examined the following subclasses: α-type channel proteins (TC subclasses 1.A and 1.E), secreted pore-forming toxins (TC subclass 1.C) and secondary carriers (subclass 2.A). Histograms were generated for each of these subclasses, and the results were analyzed according to subclass, family and protein. The results provide an update of topological predictions for integral membrane transport proteins as well as guides for the development of more reliable topological prediction programs, taking family-specific characteristics into account.