Revisiting Temozolomide's role in solid tumors: Old is gold?

Temozolomide is an imidazotetrazine with a long history in oncology especially for the high grade malignant glioma and metastatic melanoma. However, last year's new indications for its use are added. Its optimum pharmacodynamic profile, its ability to penetrate the blood-brain barrier, the existence of methylation of MGMT in solid tumors which enhances its efficacy, the identification of new agents that can overcome temozolomide's resistance, the promising role of temozolomide in turning immune cold tumors to hot ones, are leading to expand its use in other solid tumors, giving oncologists an additional tool for the treatment of advanced and aggressive neoplasms.

Microsatellite Instability Is Insufficiently Used as a Biomarker for Lynch Syndrome Testing in Clinical Practice.

PURPOSE: The pan-cancer presence of microsatellite instability (MSI)-positive tumors demonstrates its clinical utility as an agnostic biomarker for identifying immunotherapy-eligible patients. Additionally, MSI is a hallmark of Lynch syndrome (LS), the most prevalent cancer susceptibility syndrome among patients with colorectal and endometrial cancer. Therefore, MSI-high results should inform germline genetic testing for cancer-predisposing genes. However, in clinical practice, such analysis is frequently disregarded. METHODS: A next-generation sequencing (NGS)-based technique was used for MSI analysis in 4,553 patients with various tumor types. Upon request, somatic BRAF gene analysis was conducted. In addition, hereditary testing of cancer-associated genes was performed in MSI-high cases using a capture-based NGS protocol. MLH1 promoter methylation analysis was conducted retrospectively in patients with colorectal and endometrial cancer to further investigate the origin of MSI at the tumor level. RESULTS: The MSI positivity rate for the entire cohort was 5.27%. Endometrial, gastric, colorectal, urinary tract, and prostate cancers showed the highest proportion of MSI-high cases (15.69%, 8.54%, 7.40%, 4.55%, and 3.19%, respectively). A minority of 45 patients (22.73%) among the MSI-high cases underwent germline testing to determine whether the mismatch repair pathway deficiency was inherited. 24.44% of those who performed the genetic test carried a pathogenic variant in an LS-associated gene. Three MSI-high individuals had non-LS gene alterations, including BRCA1, BRCA2, and CDKN2A pathogenic variants, indicating the presence of non-LS-associated gene alterations among MSI-high patients. CONCLUSION: Although MSI analysis is routinely performed in clinical practice, as many as 77% of MSI-high patients do not undergo LS genetic testing, despite international guidelines strongly recommending it. BRAF and MLH1 methylation analysis could shed light on the somatic origin of MSI in 42.50% of the MSI-high patients; however, MLH1 analysis is barely ever requested in clinical practice.

Co-Designing Digital Technologies for Improving Clinical Care in People with Parkinson's Disease: What Did We Learn?

The healthcare model is shifting towards integrated care approaches. This new model requires patients to be more closely involved. The iCARE-PD project aims to address this need by developing a technology-enabled, home-based, and community-centered integrated care paradigm. A central part of this project is the codesign process of the model of care, exemplified by the active participation of patients in the design and iterative evaluation of three sensor-based technological solutions. We proposed a codesign methodology used for testing the usability and acceptability of these digital technologies and present initial results for one of them, MooVeo. Our results show the usefulness of this approach in testing the usability and acceptability as well as the opportunity to incorporate patients' feedback into the development. This initiative will hopefully help other groups incorporate a similar codesign approach and develop tools that are well adapted to patients' and care teams' needs.

Evaluating Gait Impairment in Parkinson's Disease from Instrumented Insole and IMU Sensor Data.

Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients' mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.

An All-in-One Tool for 2D Atherosclerotic Disease Assessment and 3D Coronary Artery Reconstruction.

Diagnosis of coronary artery disease is mainly based on invasive imaging modalities such as X-ray angiography, intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Computed tomography coronary angiography (CTCA) is also used as a non-invasive imaging alternative. In this work, we present a novel and unique tool for 3D coronary artery reconstruction and plaque characterization using the abovementioned imaging modalities or their combination. In particular, image processing and deep learning algorithms were employed and validated for the lumen and adventitia borders and plaque characterization at the IVUS and OCT frames. Strut detection is also achieved from the OCT images. Quantitative analysis of the X-ray angiography enables the 3D reconstruction of the lumen geometry and arterial centerline extraction. The fusion of the generated centerline with the results of the OCT or IVUS analysis enables hybrid coronary artery 3D reconstruction, including the plaques and the stent geometry. CTCA image processing using a 3D level set approach allows the reconstruction of the coronary arterial tree, the calcified and non-calcified plaques as well as the detection of the stent location. The modules of the tool were evaluated for efficiency with over 90% agreement of the 3D models with the manual annotations, while a usability assessment using external evaluators demonstrated high usability resulting in a mean System Usability Scale (SUS) score equal to 0.89, classifying the tool as "excellent".

Front-Line Bevacizumab plus Chemotherapy with or without Maintenance Therapy for Metastatic Breast Cancer: An Observational Study by the Hellenic Oncology Research Group.

Front-line bevacizumab (BEV) in combination with taxanes offers benefit in progression-free survival (PFS) in metastatic breast cancer (mBC). The medical records of mBC patients, treated with front-line BEV-based chemotherapy, were retrospectively reviewed in order to generate real life safety and efficacy data. Patients with human epidermal growth factor receptor 2 (HER2)-negative mBC treated with front-line BEV in combination with chemotherapy were eligible. Maintenance therapy with BEV and/or hormonal agents was at the physicians' discretion. Among the 387 included patients, the most common adverse events were anemia (61.9%, mainly grade 1), grade 3/4 neutropenia (16.5%), grade 1/2 fatigue (22.3%), and grade 1/2 neuropathy (19.6%). Dose reductions were required in 164 cycles (7.1%) and toxicity led to treatment discontinuation in 21 patients (5.4%). The median PFS and the median overall survival (OS) were 13.3 (95% CI: 11.7-14.8) and 32.3 months (95% CI: 27.7-36.9), respectively. Maintenance therapy, with hormonal agents (ET) and/or BEV, was associated with longer OS versus no maintenance therapy (47.2 versus 23.6 months; p < 0.001) in patients with hormone receptor (HR)-positive disease and BEV maintenance offered longer OS versus no maintenance in patients with HR-negative disease (52.8 versus 23.3; p = 0.023). These real-life data show that front-line BEV-based chemotherapy in HER2-negative mBC patients is an effective treatment with an acceptable toxicity profile. The potential benefit of maintenance treatment, especially ET, is important and warrants further research.

Configurable Offline Sensor Placement Identification for a Medical Device Monitoring Parkinson's Disease.

Sensor placement identification in body sensor networks is an important feature, which could render such a system more robust, transparent to the user, and easy to wear for long term data collection. It can be considered an active measure to avoid the misuse of a sensing system, specifically as these platforms become more ubiquitous and, apart from their research orientation, start to enter industries, such as fitness and health. In this work we discuss the offline, fixed class, sensor placement identification method implemented in PDMonitor®, a medical device for long-term Parkinson's disease monitoring at home. We analyze the stepwise procedure used to accurately identify the wearables depending on how many are used, from two to five, given five predefined body positions. Finally, we present the results of evaluating the method in 88 subjects, 61 Parkinson's disease patients and 27 healthy subjects, when the overall average accuracy reached 99.1%.

SmartFFR, a New Functional Index of Coronary Stenosis: Comparison With Invasive FFR Data.

Aims: In this study, we evaluate the efficacy of SmartFFR, a new functional index of coronary stenosis severity compared with gold standard invasive measurement of fractional flow reserve (FFR). We also assess the influence of the type of simulation employed on smartFFR (i.e. Fluid Structure Interaction vs. rigid wall assumption). Methods and Results: In a dataset of 167 patients undergoing either computed tomography coronary angiography (CTCA) and invasive coronary angiography or only invasive coronary angiography (ICA), as well as invasive FFR measurement, SmartFFR was computed after the 3D reconstruction of the vessels of interest and the subsequent blood flow simulations. 202 vessels were analyzed with a mean total computational time of seven minutes. SmartFFR was used to process all models reconstructed by either method. The mean FFR value of the examined dataset was 0.846 ± 0.089 with 95% CI for the mean of 0.833-0.858, whereas the mean SmartFFR value was 0.853 ± 0.095 with 95% CI for the mean of 0.84-0.866. SmartFFR was significantly correlated with invasive FFR values (RCCTA = 0.86, p CCTA < 0.0001, RICA = 0.84, p ICA < 0.0001, R overall = 0.833, p overall < 0.0001), showing good agreement as depicted by the Bland-Altman method of analysis. The optimal SmartFFR threshold to diagnose ischemia was ≤0.83 for the overall dataset, ≤0.83 for the CTCA-derived dataset and ≤0.81 for the ICA-derived dataset, as defined by a ROC analysis (AUCoverall = 0.956, p < 0.001, AUCICA = 0.975, p < 0.001, AUCCCTA = 0.952, p < 0.001). Conclusion: SmartFFR is a fast and accurate on-site index of hemodynamic significance of coronary stenosis both at single coronary segment and at two or more branches level simultaneously, which can be applied to all CTCA or ICA sequences of acceptable quality.

IVUS Longitudinal and Axial Registration for Atherosclerosis Progression Evaluation.

Intravascular ultrasound (IVUS) imaging offers accurate cross-sectional vessel information. To this end, registering temporal IVUS pullbacks acquired at two time points can assist the clinicians to accurately assess pathophysiological changes in the vessels, disease progression and the effect of the treatment intervention. In this paper, we present a novel two-stage registration framework for aligning pairs of longitudinal and axial IVUS pullbacks. Initially, we use a Dynamic Time Warping (DTW)-based algorithm to align the pullbacks in a temporal fashion. Subsequently, an intensity-based registration method, that utilizes a variant of the Harmony Search optimizer to register each matched pair of the pullbacks by maximizing their Mutual Information, is applied. The presented method is fully automated and only required two single global image-based measurements, unlike other methods that require extraction of morphology-based features. The data used includes 42 synthetically generated pullback pairs, achieving an alignment error of 0.1853 frames per pullback, a rotation error 0.93° and a translation error of 0.0161 mm. In addition, it was also tested on 11 baseline and follow-up, and 10 baseline and post-stent deployment real IVUS pullback pairs from two clinical centres, achieving an alignment error of 4.3±3.9 for the longitudinal registration, and a distance and a rotational error of 0.56±0.323 mm and 12.4°±10.5°, respectively, for the axial registration. Although the performance of the proposed method does not match that of the state-of-the-art, our method relies on computationally lighter steps for its computations, which is crucial in real-time applications. On the other hand, the proposed method performs even or better that the state-of-the-art when considering the axial registration. The results indicate that the proposed method can support clinical decision making and diagnosis based on sequential imaging examinations.

Accurate Monitoring of Parkinson's Disease Symptoms With a Wearable Device During COVID-19 Pandemic.

BACKGROUND: Accurate assessment of symptoms in Parkinson's disease (PD) is essential for optimal treatment decisions. During the past few years, different monitoring modalities have started to be used in the everyday clinical practice, mainly for the evaluation of motor symptoms. However, monitoring technologies for PD have not yet gained wide acceptance among physicians, patients, and caregivers. The COVID-19 pandemic disrupted the patients' access to healthcare, bringing to the forefront the need for wearable sensors, which provide effective remote symptoms' evaluation and follow-up. CASE REPORT: We report two cases with PD, whose symptoms were monitored with a new wearable CE-marked system (PDMonitor®), enabling appropriate treatment modifications. CONCLUSION: Objective assessment of the patient's motor symptoms in his daily home environment is essential for an accurate monitoring in PD and enhances treatment decisions.

A Novel Approach to Generate a Virtual Population of Human Coronary Arteries for In Silico Clinical Trials of Stent Design.

Goal: To develop a cardiovascular virtual population using statistical modeling and computational biomechanics. Methods: A clinical data augmentation algorithm is implemented to efficiently generate virtual clinical data using a real clinical dataset. An atherosclerotic plaque growth model is employed to 3D reconstructed coronary arterial segments to generate virtual coronary arterial geometries (geometrical data). Last, the combination of the virtual clinical and geometrical data is achieved using a methodology that allows for the generation of a realistic virtual population which can be used in in silico clinical trials. Results: The results show good agreement between real and virtual clinical data presenting a mean gof 0.1 ± 0.08. 400 virtual coronary arteries were generated, while the final virtual population includes 10,000 patients. Conclusions: The virtual arterial geometries are efficiently matched to the generated clinical data, both increasing and complementing the variability of the virtual population.

Decision Support for Medication Change of Parkinson's Disease Patients.

BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is a degenerative disorder of the central nervous system for which currently there is no cure. Its treatment requires long-term, interdisciplinary disease management, and usage of typical medications, including levodopa, dopamine agonists, and enzymes, such as MAO-B inhibitors. The key goal of disease management is to prolong patients' independence and keep their quality of life. Due to the different combinations of motor and non-motor symptoms from which PD patients suffer, in addition to existing comorbidities, the change of medications and their combinations is difficult and patient-specific. To help physicians, we developed two decision support models for PD management, which suggest how to change the medication treatment. METHODS: The models were developed using DEX methodology, which integrates the qualitative multi-criteria decision modelling with rule-based expert systems. The two DEX models differ in the way the decision rules were defined. In the first model, the decision rules are based on the interviews with neurologists (DEX expert model), and in the second model, they are formed from a database of past medication change decisions (DEX data model). We assessed both models on the Parkinson's Progression Markers Initiative (PPMI) and on a questionnaire answered by 17 neurologists from 4 European countries using accuracy measure and the Jaccard index. RESULTS: Both models include 15 sub-models that address possible medication treatment changes based on the given patients' current state. In particular, the models incorporate current state changes in patients' motor symptoms (dyskinesia intensity, dyskinesia duration, OFF duration), mental problems (impulsivity, cognition, hallucinations and paranoia), epidemiologic data (patient's age, activity level) and comorbidities (cardiovascular problems, hypertension and low blood pressure). The highest accuracy of the developed sub-models for 15 medication treatment changes ranges from 69.31 to 99.06 %. CONCLUSIONS: Results show that the DEX expert model is superior to the DEX data model. The results indicate that the constructed models are sufficiently adequate and thus fit for the purpose of making "second-opinion" suggestions to decision support users.

Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial.

BACKGROUND: Mobile health, predominantly wearable technology and mobile apps, have been considered in Parkinson disease to provide valuable ecological data between face-to-face visits and improve monitoring of motor symptoms remotely. OBJECTIVE: We explored the feasibility of using a technology-based mHealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in this study as the PD_manager system, to collect clinically meaningful data. We also explored outcomes and disease-related factors that are important determinants to establish feasibility. Finally, we further validated a tremor evaluation method with data collected while patients performed their daily activities. METHODS: PD_manager trial was an open-label parallel group randomized study.The mHealth platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps) and a knowledge platform serving as the cloud backend. Compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective Unified Parkinson's Disease Rating Scale estimations by clinicians were explored. RESULTS: Of the 75 study participants, 65 (87%) completed the protocol. They used the PD_manager system for a median 11.57 (SD 3.15) days. Regression analysis suggests that the main factor associated with high use was caregivers' burden. Motor Aspects of Experiences of Daily Living and patients' self-rated health status also influence the system's use. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor. CONCLUSIONS: We found that PD patients, regardless of their demographics and disease characteristics, used the system for 11 to 14 days. The study further supports that mHealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mHealth platform can improve disease management and care. TRIAL REGISTRATION: ISRCTN Registry ISRCTN17396879; http://www.isrctn.com/ISRCTN17396879. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-018-2767-4.

Cancer classification from time series microarray data through regulatory Dynamic Bayesian Networks.

Genomic profiling of cancer studies has generated comprehensive gene expression patterns for diverse phenotypes. Computational methods which employ transcriptomics datasets have been proposed to model gene expression data. Dynamic Bayesian Networks (DBNs) have been used for modeling time series datasets and for the inference of regulatory networks. Furthermore, cancer classification through DBN-based approaches could reveal the importance of exploiting knowledge from statistically significant genes and key regulatory molecules. Although microarray datasets have been employed extensively by several classification methods for decision making, the use of new knowledge from the pathway level has not been addressed adequately in the literature in terms of DBNs for cancer classification. In the present study, we identify the genes that act as regulators and mediate the activity of transcription factors that have been found in all promoters of our differentially expressed gene sets. These features serve as potential priors for distinguishing tumor from normal samples using a DBN-based classification approach. We employed three microarray datasets from the Gene Expression Omnibus (GEO) public functional repository and performed differential expression analysis. Promoter and pathway analysis of the identified genes revealed the key regulators which influence the transcription mechanisms of these genes. We applied the DBN algorithm on selected genes and identified the features that can accurately classify the samples into tumors and controls. Both accuracy and Area Under the Curve (AUC) were high for the gene sets comprising of the differentially expressed genes along with their master regulators (accuracy: 70.8%-98.5%; AUC: 0.562-0.985).

A three-dimensional quantification of calcified and non-calcified plaques in coronary arteries based on computed tomography coronary angiography images: Comparison with expert's annotations and virtual histology intravascular ultrasound.

The detection, quantification and characterization of coronary atherosclerotic plaques has a major effect on the diagnosis and treatment of coronary artery disease (CAD). Different studies have reported and evaluated the noninvasive ability of Computed Tomography Coronary Angiography (CTCA) to identify coronary plaque features. The identification of calcified plaques (CP) and non-calcified plaques (NCP) using CTCA has been extensively studied in cardiovascular research. However, NCP detection remains a challenging problem in CTCA imaging, due to the similar intensity values of NCP compared to the perivascular tissue, which surrounds the vasculature. In this work, we present a novel methodology for the identification of the plaque burden of the coronary artery and the volumetric quantification of CP and NCP utilizing CTCA images and we compare the findings with virtual histology intravascular ultrasound (VH-IVUS) and manual expert's annotations. Bland-Altman analyses were employed to assess the agreement between the presented methodology and VH-IVUS. The assessment of the plaque volume, the lesion length and the plaque area in 18 coronary lesions indicated excellent correlation with VH-IVUS. More specifically, for the CP lesions the correlation of plaque volume, lesion length and plaque area was 0.93, 0.84 and 0.85, respectively, whereas the correlation of plaque volume, lesion length and plaque area for the NCP lesions was 0.92, 0.95 and 0.81, respectively. In addition to this, the segmentation of the lumen, CP and NCP in 1350 CTCA slices indicated that the mean value of DICE coefficient is 0.72, 0.7 and 0.62, whereas the mean HD value is 1.95, 1.74 and 1.95, for the lumen, CP and NCP, respectively.

Noninvasive CT-based hemodynamic assessment of coronary lesions derived from fast computational analysis: a comparison against fractional flow reserve.

OBJECTIVES: Application of computational fluid dynamics (CFD) to three-dimensional CTCA datasets has been shown to provide accurate assessment of the hemodynamic significance of a coronary lesion. We aim to test the feasibility of calculating a novel CTCA-based virtual functional assessment index (vFAI) of coronary stenoses > 30% and ≤ 90% by using an automated in-house-developed software and to evaluate its efficacy as compared to the invasively measured fractional flow reserve (FFR). METHODS AND RESULTS: In 63 patients with chest pain symptoms and intermediate (20-90%) pre-test likelihood of coronary artery disease undergoing CTCA and invasive coronary angiography with FFR measurement, vFAI calculations were performed after 3D reconstruction of the coronary vessels and flow simulations using the finite element method. A total of 74 vessels were analyzed. Mean CTCA processing time was 25(± 10) min. There was a strong correlation between vFAI and FFR, (R = 0.93, p < 0.001) and a very good agreement between the two parameters by the Bland-Altman method of analysis. The mean difference of measurements from the two methods was 0.03 (SD = 0.033), indicating a small systematic overestimation of the FFR by vFAI. Using a receiver-operating characteristic curve analysis, the optimal vFAI cutoff value for identifying an FFR threshold of ≤ 0.8 was ≤ 0.82 (95% CI 0.81 to 0.88). CONCLUSIONS: vFAI can be effectively derived from the application of computational fluid dynamics to three-dimensional CTCA datasets. In patients with coronary stenosis severity > 30% and ≤ 90%, vFAI performs well against FFR and may efficiently distinguish between hemodynamically significant from non-significant lesions. KEY POINTS: Virtual functional assessment index (vFAI) can be effectively derived from 3D CTCA datasets. In patients with coronary stenoses severity > 30% and ≤ 90%, vFAI performs well against FFR. vFAI may efficiently distinguish between functionally significant from non-significant lesions.

InSilc: 3D Reconstruction and plaque characterization tool.

Coronary artery disease (CAD) is the leading cause of mortality in Europe and worldwide. Atherosclerosis is the most common pathologic process that is highly related with CAD, while the implantation of drug-eluting Bioresorbable Vascular Scaffolds (BVS) is the most promising procedure for treating patients with CAD. InSilc is an textbfin silico clinical trial (ISCT) platform for the development and assessment of drugeluting BVS. The InSilc platform provides insight in the performance of drug-eluting BVS in their short term and medium/long term through the Mechanical Modelling Module, the Deployment Module, the Fluid Dynamics Module, the Myocardial Perfusion Module, the Drug-delivery Module and the Degradation Module. In order for the aforementioned modules to be developed, the utilization of the reconstructed patient specific arterial segment and the BVS design are required, which is achieved through the 3D reconstruction and plaque characterization tool.In this study, the overall architecture of the InSilc platform is presented with special emphasis on the 3D reconstruction and plaque characterization tool. The tool will be able to implement different medical image processing workflows. The workflows will require minimum user intervention in order to be used in large scale clinical trials.

SMARTool: A tool for clinical decision support for the management of patients with coronary artery disease based on modeling of atherosclerotic plaque process.

SMARTool aims to the development of a clinical decision support system (CDSS) for the management and stratification of patients with coronary artery disease (CAD). This will be achieved by performing computational modeling of the main processes of atherosclerotic plaque growth. More specifically, computed tomography coronary angiography (CTCA) is acquired and 3-dimensional (3D) reconstruction is performed for the arterial trees. Then, blood flow and plaque growth modeling is employed simulating the major processes of atherosclerosis, such as the estimation of endothelial shear stress (ESS), the lipids transportation, low density lipoprotein (LDL) oxidation, macrophages migration and plaque development. The plaque growth model integrates information from genetic and biological data of the patients. The SMARTool system enables also the calculation of the virtual functional assessment index (vFAI), an index equivalent to the invasively measured fractional flow reserve (FFR), to provide decision support for patients with stenosed arteries. Finally, it integrates modeling of stent deployment. In this work preliminary results are presented. More specifically, the reconstruction methodology has mean value of Dice Coefficient and Hausdorff Distance is 0.749 and 1.746, respectively, while low ESS and high LDL concentration can predict plaque progression.

A novel hybrid approach for reconstruction of coronary bifurcations using angiography and OCT.

The aim of this study is to present a new method for three-dimensional (3D) reconstruction of coronary bifurcations using biplane Coronary Angiographies and Optical Coherence Tomography (OCT) imaging. The method is based on a five step approach by improving a previous validated work in order to reconstruct coronary arterial bifurcations. In the first step the lumen borders are detected on the Frequency Domain (FD) OCT images. In the second step a semi-automated method is implemented on two angiographies for the extraction of the 2D bifurcation coronary artery centerline. In the third step the 3D path of the bifurcation artery is extracted based on a back projection algorithm. In the fourth step the lumen borders are placed onto the 3D catheter path. Finally, in the fifth step the intersection of the main and side branches produces the reconstructed model of the coronary bifurcation artery. Data from three patients are acquired for the validation of the proposed methodology and the results are compared against a reconstruction method using quantitative coronary angiography (QCA). The comparison between the two methods is achieved using morphological measures of the vessels as well as comparison of the wall shear stress (WSS) mean values.

Computational estimation of the hemodynamic significance of coronary stenoses in arterial branches deriving from CCTA: A proof-of-concept study.

The development of non-invasive methods for the accurate hemodynamic assessment of the coronary vasculature has become a non-trivial matter for the everyday clinical practice. Virtual Functional Assessment Index has already been suggested as a valid alternative to the invasively measured FFR but only on coronary arterial segments. In this work, we propose a novel method for the estimation of the severity of coronary lesions in arterial branches from CCTA derived images. Four left arterial branches were reconstructed in 3D using our in-house developed 3D reconstruction algorithm, and were subjected to computational blood flow simulations for the final calculation of the vFAI through the whole arterial branch. Strong correlation was found (r=0.82) between the two methods. A small relative error of 3.2% and a small trend of overestimation (0.023, SD=0.088) were also observed. All pathological cases presenting ischemia, were correctly discriminated by our method as hemodynamically significant lesions.