NIMG-41. RAPID AND ACCURATE CREATION OF PATIENT-SPECIFIC COMPUTATIONAL MODELS FOR GBM PATIENTS RECEIVING OPTUNE THERAPY WITH CONVENTIONAL IMAGING (T1w/PD)

Cornelia Wenger; Hadas Sara Hershkovich; Catherine Tempel-Brami; Moshe Giladi; Ze’ev Bomzon

doi:10.1093/neuonc/noz175.711

NIMG-41. RAPID AND ACCURATE CREATION OF PATIENT-SPECIFIC COMPUTATIONAL MODELS FOR GBM PATIENTS RECEIVING OPTUNE THERAPY WITH CONVENTIONAL IMAGING (T1w/PD)

Neuro-Oncology ◽

10.1093/neuonc/noz175.711 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi170-vi170 ◽

Cited By ~ 1

Author(s):

Cornelia Wenger ◽

Hadas Sara Hershkovich ◽

Catherine Tempel-Brami ◽

Moshe Giladi ◽

Ze’ev Bomzon

Keyword(s):

Water Content ◽

Computational Models ◽

Specific Treatment ◽

Mixture Theory ◽

Specific Model ◽

Patient Specific ◽

Great Promise ◽

Head Model ◽

Tissue Segmentation ◽

Binary Segmentation

Abstract BACKGROUND For understanding the electric field distributions in glioblastoma (GBM) patients receiving OptuneTM therapy computational head models are employed. Accurate and fast model creation is of high importance to patient-specific treatment planning for improving efficacy, i.e., for maximizing intensity delivered to the tumor which depends on the tissues’ electric properties (EPs). Traditional model creation relies on time-consuming tissue segmentation and troublesome binary categorization of distinct tumor areas for assigning homogenous EPs. Here, we present a feasibility study of a new approach for fast model creation that uses individually created, heterogeneous EP maps from conventional MRIs. METHODS In a previous animal study we adapted water-content based electrical properties tomography (wEPT) for creating electrical conductivity (σ) maps at 200 kHz, the operating frequency of OptuneTM therapy. This adapted wEPT approach uses a T1w and a PD image to map the tissues’ water-content (WC) with a simple function. Subsequently the σ map is calculated as a function of WC based on Maxwell’s mixture theory. Three patients of the EF-14 trial were selected for calculating WC and σ maps. One patient was chosen to create a computational head model for simulating OptuneTM treatment. RESULTS The wEPT-estimated values of WC and σ in the healthy brain are accurate, homogenous and consistent among patients. Contrary, wEPT-estimates of WC and σ in tumor tissues are very heterogeneous and variable between patients. The patient-specific model with wEPT reveals more detailed current pathways during OptuneTM therapy. CONCLUSIONS The results emphasize the need for individual head model creation, since binary segmentation masks with pre-defined σ values are not recommended for the heterogeneous and variable tumor. The presented approach holds great promise for rapid creation of patient-specific computational models because only conventional MRIs are needed. However, this method needs to be validated and further established with analyzing more patients.

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P11.37 Evaluating water content and electrical properties at 200 kHz in brain and GBM tumor tissue of three TTFields patients with conventional imaging

Neuro-Oncology ◽

10.1093/neuonc/noz126.183 ◽

2019 ◽

Vol 21 (Supplement_3) ◽

pp. iii51-iii51

Author(s):

Z Bomzon ◽

C Wenger ◽

H K Hershkovich ◽

C Tempel Brami ◽

M Giladi

Keyword(s):

White Matter ◽

Electrical Properties ◽

Water Content ◽

Brain Tissue ◽

Gray Matter ◽

Tumor Tissue ◽

Specific Treatment ◽

Patient Specific ◽

Tissue Samples ◽

Human Gbm

Abstract BACKGROUND Electrical properties (EPs) of brain tissue, specifically brain tumors, crucially determine the field distribution of Tumor Treating Fields (TTFields), an anti-mitotic treatment approved for glioblastoma multiforme (GBM). Due to the correlation of TTFields efficacy and field intensity at the tumor region, the knowledge of EPs in each patient is of great importance for patient-specific planning of treatment. Water content electrical properties tomography (wEPT) is a non-invasive imaging technique using water content (WC) maps obtained from rapidly acquired and processed conventional sequences to estimate the EPs of brain tissue at 128 MHz. The WC maps of this approach are constructed from two spin echo sequences similar to a T1 and a PD image. Following previous studies in rat tumor models demonstrating promising wEPT mapping of EPs in the brain at 200, this study examines the feasibility of this approach in human GBM patients. MATERIAL AND METHODS For three patients of the EF-14 trial population, we divided T1 and PD images pixel-by-pixel to obtain the image ratio. Using a transfer function, WC maps were generated and maps of the electrical conductivity σ and the relative permittivity ε r at 200 kHz were calculated with two different equations. RESULTS The median value of estimated WC remains similar in healthy brain tissues among all patients, ~73.5% in the white matter, ~82% in the gray matter. The median values of wEPT-estimated σ at 200 kHz in the white matter is ~0.09 S/m and in the gray matter ~0.18 S/m, corresponding median values of ε r at 200 kHz are ~2100 and ~3000 in white and gray matter respectively. Contrary, in the tumor the spread between the median values of WC and EPs is much higher. Stating the most important findings, in the necrosis median WC are 90.3%, 92.3%, 85.2% in patients 1–3 respectively with corresponding median σ values of 0.494, 0.657, 0.25 S/m. In the enhancing tumor the spread of median WC is even higher (67.2%, 83.6%, 85.5%), yet lower spread but also very heterogeneous median σ values of 0.075 S/m, 0.208, 0.259 S/m are estimated with wEPT. CONCLUSION Our results demonstrate the adaption of wEPT for mapping of WC and EPs at 200 kHz in three human GBM patients. In contrast to the vastly irregular tumor tissue, our estimations in healthy brain tissue are similar between patients and in accordance with EPs experimentally measured during our animal experiments and consistent with reported values in the literature. Hence, wEPT is a promising, fast technique based on regular MRI that might help patient-specific treatment planning of TTFields therapy, although the mapping of tumor tissue needs further confirmation in a greater population and investigations of EPs of excised tumor tissue samples should be conducted.

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Using BH3 Profiling As a Predictive Indicator for Myeloma Patient Response to Bortezomib,

Blood ◽

10.1182/blood.v118.21.3952.3952 ◽

2011 ◽

Vol 118 (21) ◽

pp. 3952-3952

Author(s):

Michael Thomenius ◽

Jan Cerny ◽

Ryan Lena ◽

Triona Chonghaile ◽

Stephanie Walters ◽

...

Keyword(s):

Multiple Myeloma ◽

Cancer Cells ◽

Specific Treatment ◽

Patient Specific ◽

Great Promise ◽

Apoptosis Resistance ◽

Test Set ◽

Patient Response ◽

Female Ratio ◽

Cell Death Pathway

Abstract Abstract 3952 The clinical course of multiple myeloma (MM) is highly variable and difficult to predict. Despite ongoing improvements in the treatment, relapse remains inevitable even with novel therapies. Thereis aneed to better tailor patient-specific treatment strategies to increase efficacy without increasing side effects.There are currently no prognostic tools to predict MM patient response to a particular chemotherapy regimen, and consequently this remains a critical unmet need. Eutropics isdeveloping a novel diagnostic assay called BH3 profiling for commercial use. Initial studies at Eutropics and at the Dana Farber Cancer Institute indicate that the BH3 profiling assay is predictive for MM patient response to treatment. The assay probes mitochondria of cancer cells and indicates when or if they are able to respond to upstream apoptosis signaling induced by these treatments. The underlying principle of the assay is that as a result of aberrant phenotypes, cancer cells develop blocks in apoptosis signaling pathways. These blocks make cancer cells both resistant to some therapies, but surprisingly, make some cancer cells hyper-sensitive to other therapies. BH3 profiling determines if a dependence on certain apoptosis regulating proteins for survival occurs in given cancer cells, and identifies the dependent protein. This understanding provides a unique insight to the best course of treatment, and in particular to treatment with apoptosis inducing bortezomib combination treatments. We use the test on CD138+ plasma cells purified from MM patients prior to or during the course of treatment with bortezomib based regimens. We perform the test on either fresh or viably frozen samples obtained from patients in a prospective manner. Here we report the results of test set data from both fresh and fresh frozen archived samples. BH3 profiling measures the functional state of the pro-survival Bcl-2 and pro-apoptotic family proteins for regulating or inducing apoptosis by determining the immediate response to BH3 proteins (e.g. PUMA, NOXA, BAD). By doing this, the assay identifies the mechanical defect that leads to apoptosis resistance in a given cancer cell. It does this by selectively antagonizing each of the anti-apoptotic proteins, and directly measuring the mitochondrial response. The signature response indicates if cells will respond to apoptosis-inducing signals. Cancer cells that are predicted to respond to pro-apoptotic signals are considered “primed”. The BH3 profile also identifies which of the pro-survival Bcl-2 family members are involved in the cell death pathway. In our initial test set, samples from 12 patients with MM were evaluated by BH3 profiling. The median age at the diagnosis was 64 years (49–79). The male to female ratio was 2:1 (8 M, 4 F). Seven MM patients displayed IgG, one displayed IgA, and 4 displayed light chain restriction. Four patients had high risk cytogenetics, the remaining had standard risk. Nine patients initiated treatment with a bortezomib based regimen to date. We have observed that the extent of priming in biopsied multiple myeloma cells prior to therapy correlates very closelywith the decrease in M-spike in response to bortezomibbased therapies. This technique shows great promise as a clinical diagnostic capable of predicting response to bortezomib based therapy and could provide physicians with invaluable information for predicting the course of treatment for multiple myeloma. Disclosures: Thomenius: Eutropics Pharmaceuticals: Employment, Salary. Lena:AEutropics Pharmaceuticals: Employment, salary. Chonghaile:Eutropics Pharmaceuticals: Consultancy, fee. Lyle:Eutropics Pharmceuticals: Consultancy, Employment. Letai:Eutropics Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees. Cardone:Eutropics Pharmaceuticals: Employment, Equity Ownership, salary.

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Patient-specific treatment allocation for carotid artery disease

Interventional Cardiology ◽

10.2217/ica.09.8 ◽

2009 ◽

Vol 1 (1) ◽

pp. 41-49

Author(s):

Marc Bosiers ◽

Koen Deloose ◽

Jurgen Verbist ◽

Patrick Peeters

Keyword(s):

Carotid Artery ◽

Carotid Artery Disease ◽

Specific Treatment ◽

Patient Specific ◽

Treatment Allocation ◽

Artery Disease

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The effect of beta-blockers on hemodynamic parameters in patient-specific blood flow simulations of type-B aortic dissection: a virtual study

Scientific Reports ◽

10.1038/s41598-021-95315-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mohammad Amin Abazari ◽

Deniz Rafieianzab ◽

M. Soltani ◽

Mona Alimohammadi

Keyword(s):

Aortic Dissection ◽

Clinical Decision Making ◽

Beta Blockers ◽

False Lumen ◽

Specific Treatment ◽

Left Ventricular ◽

Patient Specific ◽

Type B ◽

Windkessel Model ◽

Computed Tomography Images

AbstractAortic dissection (AD) is one of the fatal and complex conditions. Since there is a lack of a specific treatment guideline for type-B AD, a better understanding of patient-specific hemodynamics and therapy outcomes can potentially control the progression of the disease and aid in the clinical decision-making process. In this work, a patient-specific geometry of type-B AD is reconstructed from computed tomography images, and a numerical simulation using personalised computational fluid dynamics (CFD) with three-element Windkessel model boundary condition at each outlet is implemented. According to the physiological response of beta-blockers to the reduction of left ventricular contractions, three case studies with different heart rates are created. Several hemodynamic features, including time-averaged wall shear stress (TAWSS), highly oscillatory, low magnitude shear (HOLMES), and flow pattern are investigated and compared between each case. Results show that decreasing TAWSS, which is caused by the reduction of the velocity gradient, prevents vessel wall at entry tear from rupture. Additionally, with the increase in HOLMES value at distal false lumen, calcification and plaque formation in the moderate and regular-heart rate cases are successfully controlled. This work demonstrates how CFD methods with non-invasive hemodynamic metrics can be developed to predict the hemodynamic changes before medication or other invasive operations. These consequences can be a powerful framework for clinicians and surgical communities to improve their diagnostic and pre-procedural planning.

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Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

Communications Biology ◽

10.1038/s42003-020-01638-1 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Gaoyang Li ◽

Haoran Wang ◽

Mingzi Zhang ◽

Simon Tupin ◽

Aike Qiao ◽

...

Keyword(s):

Deep Learning ◽

Artery Bypass ◽

Computational Cost ◽

Specific Model ◽

Arterial System ◽

Patient Specific ◽

Cardiovascular Hemodynamics ◽

Before And After ◽

Deep Learning Network ◽

Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

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Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning

Journal of Dental Research ◽

10.1177/00220345211005338 ◽

2021 ◽

pp. 002203452110053

Author(s):

H. Wang ◽

J. Minnema ◽

K.J. Batenburg ◽

T. Forouzanfar ◽

F.J. Hu ◽

...

Keyword(s):

Gold Standard ◽

Orthodontic Treatment ◽

3D Models ◽

Similarity Coefficient ◽

Patient Specific ◽

Dice Similarity Coefficient ◽

Surface Deviations ◽

Surface Deviation ◽

Binary Segmentation ◽

Time Required

Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to segment the jaw or the teeth, there is still a lack of fully automated segmentation methods that can simultaneously segment both anatomic structures in CBCT scans (i.e., multiclass segmentation). In this study, we aimed to train and validate a mixed-scale dense (MS-D) convolutional neural network for multiclass segmentation of the jaw, the teeth, and the background in CBCT scans. Thirty CBCT scans were obtained from patients who had undergone orthodontic treatment. Gold standard segmentation labels were manually created by 4 dentists. As a benchmark, we also evaluated MS-D networks that segmented the jaw or the teeth (i.e., binary segmentation). All segmented CBCT scans were converted to virtual 3-dimensional (3D) models. The segmentation performance of all trained MS-D networks was assessed by the Dice similarity coefficient and surface deviation. The CBCT scans segmented by the MS-D network demonstrated a large overlap with the gold standard segmentations (Dice similarity coefficient: 0.934 ± 0.019, jaw; 0.945 ± 0.021, teeth). The MS-D network–based 3D models of the jaw and the teeth showed minor surface deviations when compared with the corresponding gold standard 3D models (0.390 ± 0.093 mm, jaw; 0.204 ± 0.061 mm, teeth). The MS-D network took approximately 25 s to segment 1 CBCT scan, whereas manual segmentation took about 5 h. This study showed that multiclass segmentation of jaw and teeth was accurate and its performance was comparable to binary segmentation. The MS-D network trained for multiclass segmentation would therefore make patient-specific orthodontic treatment more feasible by strongly reducing the time required to segment multiple anatomic structures in CBCT scans.

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Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Entropy ◽

10.3390/e23070898 ◽

2021 ◽

Vol 23 (7) ◽

pp. 898

Author(s):

Marta Saiz-Vivó ◽

Adrián Colomer ◽

Carles Fonfría ◽

Luis Martí-Bonmatí ◽

Valery Naranjo

Keyword(s):

High Performance ◽

Computational Models ◽

Domain Adaptation ◽

Semantic Segmentation ◽

Patient Specific ◽

Mr Images ◽

Training Samples ◽

Volumetric Images ◽

Acquisition Costs ◽

Left And Right

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

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