A Rare Case of Squamous Cell Carcinoma of the Bladder Presenting as a Metastatic Right Ventricular Mass

A 74-year-old woman presented with bilateral lower extremity swelling, worsening dyspnea on exertion, and mild hemoptysis. An echocardiogram at time of admission showed a mass in the right ventricle. The pathology of a sample obtained via transvenous biopsy was consistent with squamous cell carcinoma; no primary source could initially be identified. Severe thrombocytopenia, likely consumptive, precluded surgical intervention, so the patient underwent palliative radiation. Unfortunately, she developed fatal respiratory failure. Upon autopsy, the bladder was found to contain polyps of invasive squamous cell carcinoma, similar in morphology to the tumor mass in the heart. Her lungs contained multiple tumor emboli at different stages, which was likely the final cause of her death. Squamous cell carcinoma metastases to the endocardium are extremely rare and without defined treatment. Surgery can improve prognosis in those with primary tumors that are benign or without metastases. In those with symptomatic metastatic tumors, palliative debulking can done although generally will not improve prognosis. It is currently unknown whether radiation improves survival. In this case, irradiation did destroy a portion of the tumor as the final pathology showed extensive necrosis of the tumor; unfortunately, it did not change her symptoms and did not change the final outcome

Role of biomechanical forces in the natural history of coronary atherosclerosis.

Atherosclerosis remains a major cause of morbidity and mortality worldwide, and a thorough understanding of the underlying pathophysiological mechanisms is crucial for the development of new therapeutic strategies. Although atherosclerosis is a systemic inflammatory disease, coronary atherosclerotic plaques are not uniformly distributed in the vascular tree. Experimental and clinical data highlight that biomechanical forces, including wall shear stress (WSS) and plaque structural stress (PSS), have an important role in the natural history of coronary atherosclerosis. Endothelial cell function is heavily influenced by changes in WSS, and longitudinal animal and human studies have shown that coronary regions with low WSS undergo increased plaque growth compared with high WSS regions. Local alterations in WSS might also promote transformation of stable to unstable plaque subtypes. Plaque rupture is determined by the balance between PSS and material strength, with plaque composition having a profound effect on PSS. Prospective clinical studies are required to ascertain whether integrating mechanical parameters with medical imaging can improve our ability to identify patients at highest risk of rapid disease progression or sudden cardiac events.This work was supported by the British Heart Foundation (FS/13/33/30168), Heart Research UK (RG2638/14/16), the Cambridge NIHR Biomedical Research Centre, and the BHF Cambridge Centre for Research Excellence.This is the author accepted manuscript. The final version is available from Nature Publishing Group at http://dx.doi.org/10.1038/nrcardio.2015.203

Semi-Automatic Reconstruction of Patient-Specific Stented Coronaries based on Data Assimilation and Computer Aided Design

Purpose The interplay between geometry and hemodynamics is a significant factor in the development of cardiovascular diseases. This is particularly true for stented coronary arteries. To elucidate this factor, an accurate patient-specific analysis requires the reconstruction of the geometry following the stent deployment for a computational fluid dynamics (CFD) investigation. The image-based reconstruction is troublesome for the different possible positions of the stent struts in the lumen and the coronary wall. However, the accurate inclusion of the stent footprint in the hemodynamic analysis is critical for detecting abnormal stress conditions and flow disturbances, particularly for thick struts like in bioresorbable scaffolds. Here, we present a novel reconstruction methodology that relies on Data Assimilation and Computer Aided Design. Methods The combination of the geometrical model of the undeployed stent and image-based data assimilated by a variational approach allows the highly automated reconstruction of the skeleton of the stent. A novel approach based on computational mechanics defines the map between the intravascular frame of reference (called L-view) and the 3D geometry retrieved from angiographies. Finally, the volumetric expansion of the stent skeleton needs to be self-intersection free for the successive CFD studies; this is obtained by using implicit representations based on the definition of Nef-polyhedra. Results We assessed our approach on a vessel phantom, with less than 10% difference (properly measured) vs. a customized manual (and longer) procedure previously published, yet with a significant higher level of automation and a shorter turnaround time. Computational hemodynamics results were even closer. We tested the approach on two patient-specific cases as well. Conclusions The method presented here has a high level of automation and excellent accuracy performances, so it can be used for larger studies involving patient-specific geometries

Predicting coronary stenosis progression using plaque fatigue from IVUS-based thin-slice models: A machine learning random forest approach

Introduction: Coronary stenosis due to atherosclerosis restricts blood flow. Stenosis progression would lead to increased clinical risk such as heart attack. Although many risk factors were found to contribute to atherosclerosis progression, factors associated with fatigue is underemphasized. Our goal is to investigate the relationship between fatigue and stenosis progression based on in vivo intravascular ultrasound (IVUS) images and finite element models. Methods: Baseline and follow-up in vivo IVUS and angiography data were acquired from seven patients using Institutional Review Board approved protocols with informed consent obtained. Three hundred and five paired slices at baseline and follow-up were matched and used for plaque modeling and analysis. IVUS-based thin-slice models were constructed to obtain the coronary biomechanics and stress/strain amplitudes (stress/strain variations in one cardiac cycle) were used as the measurement of fatigue. The change of lumen area (DLA) from baseline to follow-up were calculated to measure stenosis progression. Nineteen morphological and biomechanical factors were extracted from 305 slices at baseline. Correlation analyses of these factors with DLA were performed. Random forest (RF) method was used to fit morphological and biomechanical factors at baseline to predict stenosis progression during follow-up. Results: Significant correlations were found between stenosis progression and maximum stress amplitude, average stress amplitude and average strain amplitude (p < 0.05). After factors selection implemented by random forest (RF) method, eight morphological and biomechanical factors were selected for classification prediction of stenosis progression. Using eight factors including fatigue, the overall classification accuracy, sensitivity and specificity of stenosis progression prediction with RF method were 83.61%, 86.25% and 80.69%, respectively. Conclusion: Fatigue correlated positively with stenosis progression. Factors associated with fatigue could contribute to better prediction for atherosclerosis progression

Local fluid dynamics in patients with bifurcated coronary lesions undergoing percutaneous coronary interventions

Although the coronary arteries are uniformly exposed to systemic cardiovascular risk factors, atherosclerosis development has a non-random distribution, which follows the local mechanical stresses including flow-related hemodynamic forces. Among these, wall shear stress plays an essential role and it represents the major flow-related factor affecting the distribution of atherosclerosis in coronary bifurcations. Furthermore, an emerging body of evidence suggests that hemodynamic factors such as low and oscillating wall shear stress may facilitate the development of in-stent restenosis and stent thrombosis after successful drug-eluting stent implantation. Drug-eluting stent implantation represents the gold standard for bifurcation interventions. In this specific setting of interventions on bifurcated lesions, the impact of fluid dynamics is expected to play a major role and constitutes substantial opportunity for future technicalimprovement. In the present review, available data is summarized regarding the role of local fluid dynamics in the clinical outcome of patients with bifurcated lesions

Numerical Simulation of Magnetic Nanoparticles Targeted at an Atherosclerotic Lesion in the Left Coronary Artery of Patient

ABSTRACT A numerical investigation simulating feasibility of magnetic drug targeting (MDT) at an atherosclerotic lesion of the left coronary artery of a patient using iron nano-particles coated with a therapeutic agent is reported. Progression of a plaque in the left coronary artery over a six month period was previously determined by intravascular ultrasound (IVUS). The site where the progression is active is located on the leeward side of the plaque. The proximal segment of the left coronary artery including the lesion was reconstructed by our 3D IVUS technique, and a Doppler measurement provided velocity waveforms in the lumen. These data are used to simulate blood flow employing computational fluid dynamics (CFD). Wall shear stress (WSS) and flow pathlines show that few nanoparticles would reach the active lesion region of the plaque. Therefore, MDT is considered as a possible effective therapy. Numerical investigations are performed to examine the feasibility for treatment by modeling hypothetical magnet fields, iron nano-particles, and coronary artery flow conditions. The magnetic field in the lesion segment produced by a permanent magnet located outside the lumen is calculated. The motion of the nanoparticles in the segment is a combined result of the velocities produced by hemodynamic and magnetic forces. Various particles and magnets are investigated in the simulations. Two kinds of results are presented: the distribution of the magnetic force produced by the magnets, and the quantity of captured particles at the lesion during various time intervals (number of heart beats)

Combining IVUS + OCT data, biomechanical models and machine learning method for accurate coronary plaque morphology quantification and cap thickness and stress/strain index predictions

Assessment and prediction of vulnerable plaque progression and rupture risk are of utmost importance for diagnosis, management and treatment of cardiovascular diseases and possible prevention of acute cardiovascular events such as heart attack and stroke. However, accurate assessment of plaque vulnerability assessment and prediction of its future changes require accurate plaque cap thickness, tissue component and structure quantifications and mechanical stress/strain calculations. Multi-modality intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography image data with follow-up were acquired from ten patients to obtain accurate and reliable plaque morphology for model construction. Three-dimensional thin-slice finite element models were constructed for 228 matched IVUS + OCT slices to obtain plaque stress/strain data for analysis. Quantitative plaque cap thickness and stress/strain indices were introduced as substitute quantitative plaque vulnerability indices (PVIs) and a machine learning method (random forest) was employed to predict PVI changes with actual patient IVUS + OCT follow-up data as the gold standard. Our prediction results showed that optimal prediction accuracies for changes in cap-PVI (C-PVI), mean cap stress PVI (meanS-PVI) and mean cap strain PVI (meanSn-PVI) were 90.3% (AUC = 0.877), 85.6% (AUC = 0.867) and 83.3% (AUC = 0.809), respectively. The improvements in prediction accuracy by the best combination predictor over the best single predictor were 6.6% for C-PVI, 10.0% for mean S-PVI and 8.0% for mean Sn-PVI. Our results demonstrated the potential using multi-modality IVUS + OCT image to accurately and efficiently predict plaque cap thickness and stress/strain index changes. Combining mechanical and morphological predictors may lead to better prediction accuracies