Project Virtual Tumor Cancer in silico and Alternative Cancer Therapies
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Performance evaluation of the PET component of a hybrid PET/CT-ultrafast ultrasound imaging instrument

Mailyn Perez-Liva, Thomas Viel, Thulaciga Yoganathan, Anikitos Garofalakis, Joevin Sourdon, Caterina Facchin, Mickael Tanter, Jean Provost, Bertrand Tavitian


Physics in Medicine & Biology, Volume 63, Number 19, 21 September 2018

DOI: 10.1088/1361-6560/aad946

We recently introduced a hybrid imaging instrument, PETRUS, based on a combination of positron emission tomography (PET) for molecular imaging, x-ray computed tomography (CT) for anatomical imaging, co-registration and attenuation correction, and ultrafast ultrasound imaging (UUI) for motion-correction, hemodynamic and biomechanical imaging. In order to ensure a precise co-registration of simultaneous PET-UUI acquisitions, ultrasound probes attached to an ultrafast ultrasound scanner are operated in the field of view (FOV) of a small animal PET/CT scanner using a remote-controlled micro-positioner. Here we explore the effect of the presence of ultrasound probes on PET image quality. We compare the performance of PET and image quality with and without the presence of probes in the PET field of view, both in vitro following the NEMA-NU-4-2008 standard protocol, and in vivo in small animals. Overall, deviations in the quality of images acquired with and without the ultrasound probes were under 10% and under 7% for the NEMA protocol and in vivo tests, respectively. Our results demonstrate the capability of the PETRUS device to acquire multimodal images in vivo without significant degradation of image quality.

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Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation

Constance D. Lehman , Adam Yala, Tal Schuster, Brian Dontchos, Manisha Bahl, Kyle Swanson, Regina Barzilay


Radiology Oct 16 2018


DOI: 10.1148/radiol.2018180694

Purpose

To develop a deep learning (DL) algorithm to assess mammographic breast density.


Materials and Methods

In this retrospective study, a deep convolutional neural network was trained to assess Breast Imaging Reporting and Data System (BI-RADS) breast density based on the original interpretation by an experienced radiologist of 41 479 digital screening mammograms obtained in 27 684 women from January 2009 to May 2011. The resulting algorithm was tested on a held-out test set of 8677 mammograms in 5741 women. In addition, five radiologists performed a reader study on 500 mammograms randomly selected from the test set. Finally, the algorithm was implemented in routine clinical practice, where eight radiologists reviewed 10 763 consecutive mammograms assessed with the model. Agreement on BI-RADS category for the DL model and for three sets of readings—(a) radiologists in the test set, (b) radiologists working in consensus in the reader study set, and (c) radiologists in the clinical implementation set—were estimated with linear-weighted κ statistics and were compared across 5000 bootstrap samples to assess significance.


Results

The DL model showed good agreement with radiologists in the test set (κ = 0.67; 95% confidence interval [CI]: 0.66, 0.68) and with radiologists in consensus in the reader study set (κ = 0.78; 95% CI: 0.73, 0.82). There was very good agreement (κ = 0.85; 95% CI: 0.84, 0.86) with radiologists in the clinical implementation set; for binary categorization of dense or nondense breasts, 10 149 of 10 763 (94%; 95% CI: 94%, 95%) DL assessments were accepted by the interpreting radiologist.


Conclusion

This DL model can be used to assess mammographic breast density at the level of an experienced mammographer.

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Tumor Metastasis in the Microcirculation

Bingmei M. Fu


Molecular, Cellular, and Tissue Engineering of the Vascular System pp 201-218, 13 October 2018


DOI: 10.1007/978-3-319-96445-4_11


Tumor cell metastasis through blood circulation is a complex process and is one of the great challenges in cancer research as metastatic spread is responsible for ∼90% of cancer-related mortality. Tumor cell intravasation into, arrest and adhesion at, and extravasation from the microvessel walls are critical steps in metastatic spread. Understanding these steps may lead to new therapeutic concepts for tumor metastasis. Vascular endothelium forming the microvessel wall and the glycocalyx layer at its surface are the principal barriers to and regulators of the material exchange between circulating blood and body tissues. The cleft between adjacent endothelial cells is the principal pathway for water and solute transport through the microvessel wall in health. Recently, this cleft has been found to be the location for tumor cell adhesion and extravasation. The blood-flow-induced hydrodynamic factors such as shear rates and stresses, shear rate and stress gradients, as well as vorticities, especially at the branches and turns of microvasculatures, also play important roles in tumor cell arrest and adhesion. This chapter therefore reports the current advances from in vivo animal studies and in vitro culture cell studies to demonstrate how the endothelial integrity or microvascular permeability, hydrodynamic factors, microvascular geometry, cell adhesion molecules, and surrounding extracellular matrix affect critical steps of tumor metastasis in the microcirculation.

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Optimal control problems for the Gompertz model under the Norton-Simon hypothesis in chemotherapy

Luis A. Fernández, Cecilia Pola


Discrete & Continuous Dynamical Systems - B, 2018


DOI: 10.3934/dcdsb.2018266


We study a collection of problems associated with the optimization of cancer chemotherapy treatments, under the assumptions of Gomperztian-type tumor growth and that the drug killing effect is proportional to the rate of growth for the untreated tumor (Norton-Simon hypothesis). Classical pharmacokinetics and different pharmacodynamics (Skipper and Emax) are considered, together with a toxicity limit or the penalization of the accumulated drug effect. Existence and uniqueness of the optimal control is proved in some cases, while in others the total amount of drug is the unique relevant aspect to take into account and the existence of an infinite number of optimal controls is shown. In all cases, explicit expressions for the solutions are derived in terms of the problem data. Finally, numerical results of illustrative examples and some conclusions are presented.

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Development of novel PLGA nanoparticles with co‐encapsulation of docetaxel and abiraterone acetate for a highly efficient delivery into tumor cells

Mariya B. Sokol, Elena D. Nikolskaya, Nikita G. Yabbarov, Vladimir A. Zenin, Mariya R. Faustova, Alexey V. Belov, Olga A. Zhunina


J Biomed Mater Res Part B,  03 October 2018


DOI: 10.1002/jbm.b.34208

Co‐encapsulation of abiraterone acetate (AbrA) and docetaxel (Dtx) in polymeric nanoparticles as novel prototypes for prostate cancer treatment combining hormonal and chemotherapy was designed. Nanoparticles (NPs) composed of poly(dl‐lactide‐co‐glycolide) (PLGA) were prepared by single‐emulsion solvent evaporation technique and characterized in terms of morphology with atomic force microscopy and transmission electron microscopy. HPLC method for simultaneous determination of AbrA and Dtx encapsulation efficacy was developed. Also differential scanning calorimetry and Fourier‐transform infrared spectroscopy were provided. To study the effectiveness of cellular internalization and distribution of NPs with AbrA and Dtx co‐encapsulation (NP‐AbrA/Dtx), a fluorescence microscopy was utilized. NPs prepared had size 256.3 ±9.4 nm and zeta potential −18.4 ±1.4 mV. Encapsulation efficacy for AbrA was 68.7% and for Dtx was 74.3%. NPs were able to control the AbrA and Dtx release within 24 h. The mathematical model of drug release was performed. The results obtained from confocal microscopy showed the effective accumulation of the NP‐AbrA/Dtx in the cytoplasm of cells. Synthesized NPs possessed satisfactory parameters and a biphasic release profile, proceeding by the Fick diffusion mechanism, which provide prolonged release of the drugs and maintenance of their concentration. It was shown that NPs loaded with AbrA and Dtx exhibited a high cytotoxic activity on the LNCaP cell line, similar to the combination of free drugs of AbrA and Dtx, but in contrast to the combination of substances, had a synergistic mechanism of action. Our findings support the potential use of developed NPs in further in vivo studies.

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Biosynthetic energy cost for amino acids decreases in cancer evolution

Biosynthetic energy cost for amino acids decreases in cancer evolution | Project Virtual Tumor Cancer in silico and Alternative Cancer Therapies | Scoop.it
Hong Zhang, Yirong Wang, Jun Li, Han Chen, Xionglei He, Huiwen Zhang, Han Liang, Jian Lu


Nature Communications volume 9, Article number: 4124 (2018)

Rapidly proliferating cancer cells have much higher demand for proteinogenic amino acids than normal cells. The use of amino acids in human proteomes is largely affected by their bioavailability, which is constrained by the biosynthetic energy cost in living organisms. Conceptually distinct from gene-based analyses, we introduce the energy cost per amino acid (ECPA) to quantitatively characterize the use of 20 amino acids during protein synthesis in human cells. By analyzing gene expression data from The Cancer Genome Atlas, we find that cancer cells evolve to utilize amino acids more economically by optimizing gene expression profile and ECPA shows robust prognostic power across many cancer types. We further validate this pattern in an experimental evolution of xenograft tumors. Our ECPA analysis reveals a common principle during cancer evolution.

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Evaluation of Drug-Loaded Gold Nanoparticle Cytotoxicity as a Function of Tumor Vasculature-Induced Tissue Heterogeneity

Hunter A. Miller, Hermann B. Frieboes


Annals of Biomedical Engineering, pp 1–15, 08 October 2018


DOI:10.1007/s10439-018-02146-4


The inherent heterogeneity of tumor tissue presents a major challenge to nanoparticle-mediated drug delivery. This heterogeneity spans from the molecular (genomic, proteomic, metabolomic) to the cellular (cell types, adhesion, migration) and to the tissue (vasculature, extra-cellular matrix) scales. In particular, tumor vasculature forms abnormally, inducing proliferative, hypoxic, and necrotic tumor tissue regions. As the vasculature is the main conduit for nanotherapy transport into tumors, vasculature-induced tissue heterogeneity can cause local inadequate delivery and concentration, leading to subpar response. Further, hypoxic tissue, although viable, would be immune to the effects of cell-cycle specific drugs. In order to enable a more systematic evaluation of such effects, here we employ computational modeling to study the therapeutic response as a function of vasculature-induced tumor tissue heterogeneity. Using data with three-layered gold nanoparticles loaded with cisplatin, nanotherapy is simulated interacting with different levels of tissue heterogeneity, and the treatment response is measured in terms of tumor regression. The results quantify the influence that varying levels of tumor vascular density coupled with the drug strength have on nanoparticle uptake and washout, and the associated tissue response. The drug strength affects the proportion of proliferating, hypoxic, and necrotic tissue fractions, which in turn dynamically affect and are affected by the vascular density. Higher drug strengths may be able to achieve stronger tumor regression but only if the intra-tumoral vascular density is above a certain threshold that affords sufficient transport. This study establishes an initial step towards a more systematic methodology to assess the effect of vasculature-induced tumor tissue heterogeneity on the response to nanotherapy.

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The influence of time delay in a chaotic cancer model

Subhas Khajanchi, Matjaž Perc, Dibakar Ghosh


Chaos 28, 103101 (2018);


DOI: 10.1063/1.5052496


The tumor-immune interactive dynamics is an evergreen subject that continues to draw attention from applied mathematicians and oncologists, especially so due to the unpredictable growth of tumor cells. In this respect, mathematical modeling promises insights that might help us to better understand this harmful aspect of our biology. With this goal, we here present and study a mathematical model that describes how tumor cells evolve and survive the brief encounter with the immune system, mediated by effector cells and host cells. We focus on the distribution of eigenvalues of the resulting ordinary differential equations, the local stability of the biologically feasible singular points, and the existence of Hopf bifurcations, whereby the time lag is used as the bifurcation parameter. We estimate analytically the length of the time delay to preserve the stability of the period-1 limit cycle, which arises at the Hopf bifurcation point. We also perform numerical simulations, which reveal the rich dynamics of the studied system. We show that the delayed model exhibits periodic oscillations as well as chaotic behavior, which are often indicators of long-term tumor relapse.

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A Deep Learning Line to Assess Patient’s Lung Cancer Stages

André Dias, João Fernandes, Rui Monteiro, Joana Machado, Filipa Ferraz, João Neves, Luzia Sampaio, Jorge Ribeiro, Henrique Vicente, Victor Alves, José Neves


Third International Congress on Information and Communication Technology pp 599-607, 29 September 2018


DOI: 10.1007/978-981-13-1165-9_55


Our goal is to pursue a vision of developing and maintaining a comprehensive and integrated computer model to help physicians plan the most appropriate treatment and anticipate a patient’s prospects for the extent of cancer. For example, cancer can be treated at an early stage by surgery or radiation, while chemotherapy may be the care for more advanced stages. In fact, early detection of this type of cancer facilitates its treatment and may rise the patients’ prospect of a continued existence. Thus, a formal view of an intelligent system for performing cancer feature extraction and analysis in order to establish the bases that will help physicians plan treatment and predict patient’s prognosis is presented. It is based on the Logic Programming Language and draws a line between Deep Learning and Knowledge Representation and Reasoning, and is supported by a Case Based attitude to computing. In fact, despite the fact that each patient’s condition is different, treating cancer at the same stage is often similar.

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Ultrafast Dynamic Contrast-Enhanced Breast MRI: Kinetic Curve Assessment Using Empirical Mathematical Model Validated with Histological Microvessel Density

Ultrafast Dynamic Contrast-Enhanced Breast MRI: Kinetic Curve Assessment Using Empirical Mathematical Model Validated with Histological Microvessel Density | Project Virtual Tumor Cancer in silico and Alternative Cancer Therapies | Scoop.it

Naoko Mori, Hiroyuki Abe, Shunji Mugikura, Chiaki Takasawa, Satoko Sato, Minoru Miyashita, Yu Mori, Federico D. Pineda, Gregory S. Karczmar, Hajime Tamura, Shoki Takahashi, Kei Takase


Academic Radiology, 28 September 2018


DOI: 10.1016/j.acra.2018.08.016

Rationale and Objectives

To evaluate whether parameters from empirical mathematical model (EMM) for ultrafast dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) correlate with histological microvessel density (MVD) in invasive breast cancer.


Materials and Methods

Ninety-eight consecutive patients with invasive breast cancer underwent an institutional review board-approved ultrafast DCE-MRI including a pre- and 18 postcontrast whole breast ultrafast scans (3 seconds) followed by four standard scans (60 seconds) using a 3T system. Region of interest was placed within each lesion where the highest signal increase was observed on ultrafast DCE-MRI, and the increase rate of enhancement was calculated as follows: ΔS = (SIpost − SIpre)/SIpre. The kinetic curve obtained from ultrafast DCE-MRI was analyzed using a truncated EMM: ΔS(t) = A(1 − e−αt), where A is the upper limit of the signal intensity, α (min−1) is the rate of signal increase. The initial slope of the kinetic curve is given by Aα. Initial area under curve (AUC30) and time of initial enhancement was calculated. From the standard DCE-MRI, the initial enhancement rate (IER) and the signal enhancement ratio (SER) were calculated as follows: IER = (SIearly − SIpre)/SIpre, SER = (SIearly − SIpre)/(SIdelayed − SIpre). The parameters were compared to MVD obtained from surgical specimens.


Results

A, α, Aα, AUC30, and time of initial enhancement significantly correlated with MVD (r = 0.29, 0.40, 0.51, 0.43, and −0.32 with p = 0.0027, p < 0.0001, p < 0.0001, p < 0.0001, and p = 0.0012, respectively), whereas IER and SER from standard DCE-MRI did not.


Conclusion

The parameters of the EMM, especially the initial slope or Aα, for ultrafast DCE-MRI correlated with MVD in invasive breast cancer.

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Structure and evolution of double minutes in diagnosis and relapse brain tumors

Structure and evolution of double minutes in diagnosis and relapse brain tumors | Project Virtual Tumor Cancer in silico and Alternative Cancer Therapies | Scoop.it

Ke Xu, Liang Ding, Ti-Cheng Chang, Ying Shao, Jason Chiang, Heather Mulder, Shuoguo Wang, Tim I. Shaw, Ji Wen, Laura Hover, Clay McLeod, Yong-Dong Wang, John Easton, Michael Rusch, James Dalton, James R. Downing, David W. Ellison, Jinghui Zhang, Suzanne J. Baker, Gang Wu


Acta Neuropathologica, pp 1–15, 28 September 2018


DOI: 10.1007/s00401-018-1912-1


Double minute chromosomes are extrachromosomal circular DNA fragments frequently found in brain tumors. To understand their evolution, we characterized the double minutes in paired diagnosis and relapse tumors from a pediatric high-grade glioma and four adult glioblastoma patients. We determined the full structures of the major double minutes using a novel approach combining multiple types of supporting genomic evidence. Among the double minutes identified in the pediatric patient, only one carrying EGFR was maintained at high abundance in both samples, whereas two others were present in only trace amounts at diagnosis but abundant at relapse, and the rest were found either in the relapse sample only or in the diagnosis sample only. For the EGFR-carrying double minutes, we found a secondary somatic deletion in all copies at relapse, after erlotinib treatment. However, the somatic mutation was present at very low frequency at diagnosis, suggesting potential resistance to the EGFR inhibitor. This mutation caused an in-frame RNA transcript to skip exon 16, a novel transcript isoform absent in EST database, as well as about 700 RNA-seq of normal brains that we reviewed. We observed similar patterns involving longitudinal copy number shift of double minutes in another four pairs (diagnosis/relapse) of adult glioblastoma. Overall, in three of five paired tumor samples, we found that although the same oncogenes were amplified at diagnosis and relapse, they were amplified on different double minutes. Our results suggest that double minutes readily evolve, increasing tumor heterogeneity rapidly. Understanding patterns of double minute evolution can shed light on future therapeutic solutions to brain tumors carrying such variants.

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Simultaneous Model Selection and Model Calibration for the Proliferation of Tumor and Normal Cells During In Vitro Chemotherapy Experiments

JosÉ M.J. Costa, Helcio R.B. Orlande, Viviane O.F. Lione, Antonio G.F. Lima, TaynÁ C.S. Cardoso, Leonardo A.B. Varón

Journal of Computational Biology, 22 Sep 2018


DOI: 10.1089/cmb.2017.0130

In vitro experiments were conducted in this work to analyze the proliferation of tumor (DU-145) and normal (macrophage RAW 264.7) cells under the influence of a chemotherapeutic drug (doxorubicin). Approximate Bayesian Computation (ABC) was used to select among four competing models to represent the number of cells and to estimate the model parameters, based on the experimental data. For one case, the selected model was validated in a replicated experiment, through the solution of a state estimation problem with a particle filter algorithm, thus demonstrating the robustness of the ABC procedure used in this work.

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Skin lesion segmentation and recognition using multichannel saliency estimation and M-SVM on selected serially fused features

Tallha Akram, Muhammad Attique Khan, Muhammad Sharif, Mussarat Yasmin


Journal of Ambient Intelligence and Humanized Computing, pp 1–20, 24 September 2018


DOI: 10.1007/s12652-018-1051-5


The number of deaths caused by melanoma has increased remarkably in the last few years which are the carcinogenic type of skin cancer. Lately, computer based methods are introduced which are intelligent enough to support dermatologist in initial judgment of skin lesion. However, there still exists a gap for an optimal solution; therefore, machine learning community is still considering it a great challenge. The primary objective of this article is to efficiently detect and classify skin lesion with the utilization of an improved segmentation and feature selection criteria. Presented contribution is threefold; First, ternary color spaces are exploited to separate foreground from the background—utilizing multilevel approach of contrast stretching. Second, a weighting criterion is designed which is able to select the best solution based on extended texture feature analysis, related labels, boundary connections and central distance. Third, an improved feature extraction and dimensionality reduction criteria is proposed which combines conventional as well as recent feature extraction techniques. The proposed method is tested on PH2, ISBI 2016 and ISIC benchmark data sets and evaluated on the basis of multiple parameters including FPR, sensitivity, specificity, FNR and accuracy. From the statistics, it is quite clear that the proposed method outperforms numerous existing techniques with considerable margin.

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PETRUS hybrid device acquires multimodal images in vivo

PETRUS hybrid device acquires multimodal images in vivo | Project Virtual Tumor Cancer in silico and Alternative Cancer Therapies | Scoop.it
By Andrew Williams

A research team in France has evaluated the PETRUS hybrid imaging instrument both in vitro and in vivo in small animals. The researchers demonstrated the capability of the device, which simultaneously performs PET/CT and ultrafast ultrasound, to acquire multimodal images in vivo without significant degradation of image quality (Phys. Med. Biol. 63 19NT01).

The researchers — based at Inserm, the Université Paris Descartes and ESPCI Paris — explored the effect of using a PETRUS system on image quality, finding deviations of below 10% between images acquired with and without ultrasound probes.
Image quality

As Mailyn Pérez-Liva, a post-doctoral researcher at Inserm, explains, the PETRUS (PET registered ultrafast sonography) device combines PET, X-ray CT and ultrafast ultrasound imaging (UUI) into a single device by operating ultrasound probes in the field-of-view of a nanoScan small-animal PET/CT scanner.

Although previous work has demonstrated that the device yields “unprecedented multiparametric information for preclinical oncology and cardiology studies”, Pérez-Liva highlights the well-known fact that the presence of objects attenuating the 511 keV annihilation gamma rays inside a PET gantry may degrade image quality and create artefacts in the reconstructed images.

In view of the fact that the exact compositions of ultrasound probes are not provided by manufacturers, Pérez-Liva notes that the effects of their presence in the PET field-of-view cannot be reliably estimated using models. This motivated the researchers to investigate the effect on image quality experimentally and, since PET is a quantitative molecular imaging modality, to “appreciate their influence on measurements of tissue radioactivity concentrations”.

To achieve this, the team examined the effects of ultrasound probes inside the PET gantry on the quality of PET images and performed tests under the conditions described by the NEMA NU 4-2008 standard protocol for small-animal PET systems. They also investigated the effects of ultrasound probes on the quantification of in vivo dynamic studies of 18F-FDG uptake in beating mouse and rat hearts.

“We observed that the presence of a UUI probe inside the field-of-view of the nanoScan PET/CT has a minor effect on the radioactivity concentration measurements in PET images, and does not degrade significantly the quantitative and qualitative PET data derived from the images — with discrepancies below 10%,” says Pérez-Liva.
Miniaturized probes

According to Pérez-Liva, it is particularly noteworthy that custom-made ultralight probes (used to image the microvascular network in mice) had a smaller effect on image quality than commercial ones. She suggested that “careful design of next-generation miniaturized probes will render them even more stealthy”.

MR Safe 4D Motion QA for Planning, Adaptive MRgRT and SimulationAdvertisement“With the significant advantages of simultaneous UUI and PET acquisitions, which offer the unique possibility of co-registering metabolism, vascularization, tissue elasticity and anatomy with a low-cost add-on, the PET/CT–UUI device is a remarkable means to increase the range of services offered by molecular imaging with PET,” she adds.

The PET/CT–UUI instrument was assembled from existing, commercially available devices using what Pérez-Liva describes as lightweight and portable UUI instrumentation for which dedicated, customized sequences were developed.

“Remarkably, PET/CT–UUI can produce multi-parametric information that is currently unobtainable with any other non-invasive imaging method,” she says. “PET/CT devices have been integrated in the clinic for many years, and UUI is also highly translational, since imaging modes developed for small animals can be readily applied clinically using adapted ultrasound probes. Nevertheless, UUI is a modality for which clinical applications are still in early phases of development.”

In this light, Pérez-Liva stresses that the specific clinical applications for PET/CT–UUI remain naturally speculative at this stage. But she points out that they are likely to involve a large array of organs, because UUI can be applied to any organs that are accessible with conventional ultrasound imaging.

“Typically, the deeper the organ is situated, the lower the ultrasound frequency used, and the lower the frequency, the worse the spatial resolution,” she explains. “In the worst case, the upper limit of resolution is typically in the order of 500 μm, corresponding to a 3 MHz centre frequency. This is better than the inherent resolution of PET and comparable to CT and MRI spatial resolutions.”

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Deep-learning algorithm identifies dense tissue in mammograms

Deep-learning algorithm identifies dense tissue in mammograms | Project Virtual Tumor Cancer in silico and Alternative Cancer Therapies | Scoop.it
By Tami Freeman


Dense breast tissue can mask cancers on mammograms, making screening more difficult, and is also an independent risk factor for breast cancer. Now, researchers from Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital (MGH) have developed a deep-learning algorithm that assesses breast density in mammograms as reliably as an experienced mammographer (Radiology 10.1148/radiol.2018180694).

In the USA, many states have laws mandating that women are notified if their mammograms indicate dense breast tissue. But breast density assessments rely on human assessment and results can vary among radiologists. “We’re dependent on human qualitative assessment of breast density, and that approach has significant flaws,” explains lead author Constance Lehman from MGH. “We need a more accurate tool.”

www.bruker.com/applications/preclinical-imaging.htmlAdvertisement“Our motivation was to create an accurate and consistent tool that can be shared and used across health care systems,” adds Adam Yala, a PhD student in MIT’s Computer Science and Artificial Intelligence Laboratory.

Every mammogram has a BI-RADS breast density rating in one of four categories: fatty; scattered fibroglandular; heterogeneously dense; or dense. The researchers developed a model, is built on a deep convolutional neural network, that can distinguish the different categories of breast tissue.

The researchers trained and tested the algorithm on a dataset of more than 58,000 digital screening mammograms. They used around 41,000 mammograms for training and about 8600 for testing. During training, the algorithm is given random mammograms to analyse and learns to map the mammogram with the original radiologist’s interpretation. Given a new mammogram, it can then predict the most likely density category.


Clinical application

In January of this year, the deep-learning algorithm was implemented in routine clinical practice at MGH. In a traditional workflow, mammograms are sent to a workstation for a radiologist to assess. For this study, the algorithm was applied first to assign each mammogram a density rating. Then when radiologists view a scan at their workstations, they see the model’s assigned rating, which they can then accept or reject.

The researchers note that this marks the first time that this type of deep-learning model has successfully been used in routine clinical practice. “It takes less than a second per image … [and it can be] easily and cheaply scaled throughout hospitals,” says Yala.

The study reports on 10,763 consecutive mammograms assessed by the algorithm and reviewed by eight radiologists. In a binary test determining whether breasts were heterogeneous and dense, or fatty and scattered, the algorithm achieved 94% agreement with the radiologists. Across all four BI-RADS categories, it matched radiologists’ assessments at 90%.

“We were thrilled with the results,” says Lehman. “Now at Mass General, the deep-learning algorithm processes all our screening mammograms and provides density, which is either accepted or rejected by the radiologists.”

The algorithm has the potential to standardize and automate routine breast density assessment. On a broader scale, the researchers see artificial intelligence (AI) as central to the development of personalized breast cancer risk assessment. AI is uniquely suited to breast imaging because it can draw upon a large, mature database with advanced, structured reporting that links images with outcomes.

“With AI, we now have the ability to leverage vast amounts of information into more personalized, more targeted care for our patients,” says Lehman. “In the case of breast cancer, we can better predict how likely a woman will have cancer in her future and improve the chances that it will be treated successfully.”

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Structural Sensitivity of Control Models Arising in Combined Chemo-Radiotherapy

Marzena Dolbniak ; Jaroslaw Smieja ; Andrzej Swierniak


2018 23rd International Conference on Methods & Models in Automation & Robotics (MMAR)  27-30 Aug. 2018


DOI: 10.1109/MMAR.2018.8486088


Simple mathematical models of tumor growth are commonly used to predict efficiency of different therapy protocols and to optimize therapy outcome. Nowadays, the focus is on personalized oncology, in which treatment strategy is tailored to an individual patient. Simple models with minimal number of parameters can be used to find appropriate treatment strategy. The main idea of this study is to describe effects of combined radio-chemotherapy by two control actions. In this work we compare results of different control strategies (treatment protocols) for a family of models of tumor growth. Using simulations in silico we analyse responses of a thousand patients to three possible therapy protocols. Comparing Kaplan-Meier survival curves obtained for two different tumor growth models indicates that while Gompertz model is slightly less sensitive to changes in control action than exponential and logistic models, the difference is negligible. The important finding is that the shapes of survival curves are similar in all cases. It suggests that the family of models is structurally insensitive. Obtained results imply that more attention should be paid on estimation of parameters for a tumor growth model, than on model selection.

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Parameter identification of a model for prostate cancer treated by intermittent therapy

Clément Draghi, Fabrice Denis, Alain Tolédano, Christophe Letellier


Journal of Theoretical Biology, Volume 461, 14 January 2019, Pages 117-132


DOI: 10.1016/j.jtbi.2018.10.004

Adenocarcinoma is the most frequent cancer affecting the prostate walnut-size gland in the male reproductive system. Such cancer may have a very slow progression or may be associated with a “dark prognosis” when tumor cells are spreading very quickly. Prostate cancers have the particular properties to be marked by the level of prostate specific antigen (PSA) in blood which allows to follow its evolution. At least in its first phase, prostate adenocarcinoma is most often hormone-dependent and, consequently, hormone therapy is a possible treatment. Since few years, hormone therapy started to be provided intermittently for improving patient’s quality of life. Today, durations of on- and off-treatment periods are still chosen empirically, most likely explaining why there is no clear benefit from the survival point of view. We therefore developed a model for describing the interaction between the tumor environment, the PSA produced by hormone-dependent and hormone-independent tumor cells, respectively, and the level of androgens. Model parameters were identified using a genetic algorithm applied to the PSA time series measured in a few patients who initially received prostatectomy and were then treated by intermittent hormone therapy (LHRH analogs and anti-androgen). The measured PSA time series is quite correctly reproduced by free runs over the whole follow-up. Model parameter values allow for distinguishing different types of patient (age and Gleason score) meaning that the model can be individualized. We thus showed that the long-term evolution of the cancer can be affected by durations of on- and off-treatment periods.

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An efficient detection of brain tumor using fused feature adaptive firefly backpropagation neural network

A. R. Deepa, W. R. Sam Emmanuel


Multimedia Tools and Applications, pp 1–16, 06 October 2018


DOI: 10.1007/s11042-018-6731-9


Early diagnosis of tumor will increase the survival probability from a deadly disease called a brain tumor. Classification of the tumor from the tumor affected MRI using the concept of medical image processing assist better treatment and surgical planning. This paper proposes a fused feature adaptive firefly backpropagation neural network for classification which comprises preprocessing, feature extraction, selection, and fusion to achieve high classification accuracy. The preprocessing step uses the average filter for reducing the intensity variation of the images. The Gabor wavelet feature extraction extracts the locality, orientation, and frequency of the tumor image which provides texture information for classification. The kernel principal component analysis (KPCA) feature selection selects the small subset of features to reduce the redundancy and increase the relevancy of the feature. The Gaussian radial basis function (GRBF) for feature fusion provides the distinguished information from the multiple sets of features. Finally, the proposed approach accurately classify the tumor with high accuracy after applying the fusion results. The results are simulated in MATLAB and it proves the improved accuracy, sensitivity, specificity of the classified tumor of the proposed approach.

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In-silico dynamic analysis of cytotoxic drug administration to solid tumours: Effect of binding affinity and vessel permeability

In-silico dynamic analysis of cytotoxic drug administration to solid tumours: Effect of binding affinity and vessel permeability | Project Virtual Tumor Cancer in silico and Alternative Cancer Therapies | Scoop.it
Vasileios Vavourakis,Triantafyllos Stylianopoulos, Peter A. Wijeratne


PLOS x, October 8, 2018


DOI: 10.1371/journal.pcbi.1006460

The delivery of blood-borne therapeutic agents to solid tumours depends on a broad range of biophysical factors. We present a novel multiscale, multiphysics, in-silico modelling framework that encompasses dynamic tumour growth, angiogenesis and drug delivery, and use this model to simulate the intravenous delivery of cytotoxic drugs. The model accounts for chemo-, hapto- and mechanotactic vessel sprouting, extracellular matrix remodelling, mechano-sensitive vascular remodelling and collapse, intra- and extravascular drug transport, and tumour regression as an effect of a cytotoxic cancer drug. The modelling framework is flexible, allowing the drug properties to be specified, which provides realistic predictions of in-vivo vascular development and structure at different tumour stages. The model also enables the effects of neoadjuvant vascular normalisation to be implicitly tested by decreasing vessel wall pore size. We use the model to test the interplay between time of treatment, drug affinity rate and the size of the vessels’ endothelium pores on the delivery and subsequent tumour regression and vessel remodelling. Model predictions confirm that small-molecule drug delivery is dominated by diffusive transport and further predict that the time of treatment is important for low affinity but not high affinity cytotoxic drugs, the size of the vessel wall pores plays an important role in the effect of low affinity but not high affinity drugs, that high affinity cytotoxic drugs remodel the tumour vasculature providing a large window for the normalisation of the vascular architecture, and that the combination of large pores and high affinity enhances cytotoxic drug delivery efficiency. These results have implications for treatment planning and methods to enhance drug delivery, and highlight the importance of in-silico modelling in investigating the optimisation of cancer therapy on a personalised setting.

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Evaluation of Focal Laser Ablation of Prostate Cancer Using High Spectral and Spatial Resolution Imaging: A Pilot Study

Shiyang Wang, Xiaobing Fan, Ambereen Yousuf, Scott E. Eggener, Gregory Karczmar, Aytekin Oto


Journal of Magnetic Resonance Imaging, 06 October 2018


DOI: 10.1002/jmri.26538

Background

Focal laser ablation (FLA) is a minimally invasive thermal ablation, guided by MRI through an optical fiber, to induce coagulative necrosis in cancer.


Purpose

To evaluate the feasibility of high spectral and spatial resolution imaging using multiecho gradient echo (MEGE) MRI for identification of ablation zones, after FLA of prostate cancers.


Study Type

Prospective.


Population

Fourteen patients with biopsy‐confirmed localized prostate cancers.
Field Strength/Sequence

FLA was performed under monitored conscious sedation with a 1.5T MRI scanner. Axial MEGE images were acquired before and after the last FLA. Pre‐ and postcontrast enhanced T1‐weighted (pT1W) images were acquired to assess the FLA zone as a reference standard.


Assessment

The urn:x-wiley:10531807:media:jmri26538:jmri26538-math-0002 maps and water resonance peak height (WPH) images were calculated from the MEGE data. Ablation area was outlined using an active contour method. The maximum ablation area and total ablation volume were calculated from urn:x-wiley:10531807:media:jmri26538:jmri26538-math-0003 and WPH images, and compared with the sizes measured from pT1W images.


Statistical Tests

Nonparametric Kruskal–Wallis tests were performed to determine whether there was significant difference in calculated ablation areas and volumes between urn:x-wiley:10531807:media:jmri26538:jmri26538-math-0004, WPH, and pT1W images.


Results

Average urn:x-wiley:10531807:media:jmri26538:jmri26538-math-0005 (38.9 ± 14.1 msec) in the ablation area was significantly shorter (P = 0.03) than the preablation area urn:x-wiley:10531807:media:jmri26538:jmri26538-math-0006 (57.8 ± 25.3 msec). The normalized WPH value over the ablation area (1.3 ± 0.6) was significantly decreased (P = 0.02) more than the preablation area (2.0 ± 0.9). The maximum ablation areas measured by urn:x-wiley:10531807:media:jmri26538:jmri26538-math-0007 (295.7 ± 96.4 mm2), WPH (312.2 ± 63.0 mm2), and pT1W (320.3 ± 82.9 mm2) images were all similar. Furthermore, there was no significant difference (P = 0.31) for measured ablation volumes 3310.5 ± 649.5, 3406.4 ± 684.9, and 3672.5 ± 832.4 mm3 between urn:x-wiley:10531807:media:jmri26538:jmri26538-math-0008, WPH, and pT1W images, respectively.


Data Conclusion

urn:x-wiley:10531807:media:jmri26538:jmri26538-math-0009 and WPH images provide acceptable measurements of ablation zones during FLA treatment of prostate cancers without the need for contrast agent injection. This might allow repeated assessment following each heating period so that subsequent ablations can be optimized.

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Blood-Based Prediction of Tumor Relapse: The cfDNA Forecast

Giulia Siravegna,  Ryan B. Corcoran

Cancer Discovery, October 2018

DOI: 10.1158/2159-8290.CD-18-0952

Summary: Khan and colleagues demonstrate how serial blood-based liquid biopsies integrated with imaging and mathematical modeling can accurately “forecast” the time to treatment failure in patients with metastatic colorectal cancer treated with EGFR blockade, by early detection of molecular alterations associated with drug resistance in cell-free DNA. Cancer Discov; 8(10); 1213–5. ©2018 AACR.

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Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data

Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data | Project Virtual Tumor Cancer in silico and Alternative Cancer Therapies | Scoop.it
E. A. B. F. Lima, N. Ghousifam, A. Ozkan, J. T. Oden, A. Shahmoradi, M. N. Rylander, B. Wohlmuth, T. E. Yankeelov


Scientific Reports volume 8, Article number: 14558 (2018

Two of the central challenges of using mathematical models for predicting the spatiotemporal development of tumors is the lack of appropriate data to calibrate the parameters of the model, and quantitative characterization of the uncertainties in both the experimental data and the modeling process itself. We present a sequence of experiments, with increasing complexity, designed to systematically calibrate the rates of apoptosis, proliferation, and necrosis, as well as mobility, within a phase-field tumor growth model. The in vitro experiments characterize the proliferation and death of human liver carcinoma cells under different initial cell concentrations, nutrient availabilities, and treatment conditions. A Bayesian framework is employed to quantify the uncertainties in model parameters. The average difference between the calibration and the data, across all time points is between 11.54% and 14.04% for the apoptosis experiments, 7.33% and 23.30% for the proliferation experiments, and 8.12% and 31.55% for the necrosis experiments. The results indicate the proposed experiment-computational approach is generalizable and appropriate for step-by-step calibration of multi-parameter models, yielding accurate estimations of model parameters related to rates of proliferation, apoptosis, and necrosis.

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In Silico Design and Experimental Validation of Combination Therapy for Pancreatic Cancer

Haswanth Vundavilli ; Aniruddha Datta ; Chao Sima ; Jianping Hua ; Rosana Lopes ; Michael L. Bittner


IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018


DOI:10.1109/TCBB.2018.2872573 


The number of deaths associated with Pancreatic Cancer has been on the rise in the United States making it an especially dreaded disease. The overall prognosis for pancreatic cancer patients continues to be grim because of the complexity of the disease at the molecular level involving the potential activation/inactivation of several diverse signaling pathways. In this paper, we first model the aberrant signaling in pancreatic cancer using a multi-fault Boolean Network. Thereafter, we theoretically evaluate the efficacy of different drug combinations by simulating this boolean network with drugs at the relevant intervention points and arrive at the most effective drug(s) to achieve cell death. The simulation results indicate that drug combinations containing Cryptotanshinone, a traditional Chinese herb derivative, result in considerably enhanced cell death. These in silico results are validated using wet lab experiments we carried out on Human Pancreatic Cancer (HPAC) cell lines.

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A promising new vaccine against melanoma

A promising new vaccine against melanoma | Project Virtual Tumor Cancer in silico and Alternative Cancer Therapies | Scoop.it
By Samuel Vennin


Researchers from the University of Texas and the Scripps Research Institute have demonstrated that adding a new adjuvant, Diprovocim, to a cancer vaccine can draw cancer-fighting cells to the tumour site and boost the immune response (PNAS 10.1073/pnas.1809232115).

As cancer spreads, it inhibits the activity of T cells, the immune cells responsible for fighting off cancerous cells. Immunotherapy aims to harness the immune system to combat tumours and lately, cancer vaccines have been investigated as a potential trigger for this reaction. The vaccines are usually associated with adjuvants, molecules that are added to enhance the immune response to some specific antigens. This addition usually augments the response to cancer antigens both inside and outside of the tumour, making the vaccine more efficient.

Several adjuvants have been reported to improve the immune response in preclinical models for cancer treatment, but they are difficult to synthesize and can be toxic as they disseminate within the host organism. To find a safer alternative that would be easier to produce, a research team led by Bruce Beutler and Dale Boger screened a library of synthetic compounds. The researchers identified an adjuvant, Diprovocim, that is able to bind to the same immune receptors (TLR1/TLR2) as other adjuvants commonly used, while bearing no structural similarities and hence reducing the aforementioned shortcomings.


100% success in mice

The researchers tested this adjuvant on mice with a common form of aggressive melanoma. All mice in the experiment were given the anti-cancer therapy anti-PD-L1. They were then split into three groups: eight received the cancer vaccine, eight received the cancer vaccine plus Diprovocim, and eight received the cancer vaccine plus an alternative adjuvant derived from aluminium, alum.

sunpbiotech.com/AdvertisementThe results spoke for themselves. All mice who received the cancer vaccine/Diprovocim combination were alive after 54 days, while none of the mice who were only given the vaccine (without Diprovocim) survived longer than 38 days. Comparatively, only 25% of the mice treated with the cancer vaccine with alum survived past 54 days.

Further investigations showed that Diprovocim boosted the ability of the vaccine to fight tumours by stimulating the immune system to produce more T cells, a feat that the other two vaccines could not achieve. Those T cells contributed to eliminating about 70% of target cells in mice immunized with the vaccine containing Diprovocim, compared with about 10% in mice who received alum as an adjuvant.


Preventing tumour recurrence

The vaccine is not just effective at suppressing tumours, it can also prevent them from reappearing. When the researchers tried to re-establish tumours in the surviving mice (without giving them any further treatment), the tumours failed to expand in mice treated with Diprovocim, while they grew rapidly in those who received alum. This finding shows that Diprovocim produces antigen-specific responses that protect the mice from tumour regrowth.

One important feature of this technique compared with others lies in the site of injection. Unlike some vaccines being developed, this Diprovocim-based alternative does not need to be injected directly into the tumour. Here, the researchers gave it as an intramuscular injection away from the tumour site.

This new vaccine obviously requires further testing and trials on other types of tumours and in combination with different cancer therapies, but these promising results provide grounds for optimism in the quest for an efficient cancer treatment.

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Moderate Heat Application Enhances the Efficacy of Nanosecond Pulse Stimulation for the Treatment of Squamous Cell Carcinoma

Chelsea M. Edelblute, Siqi Guo, James Hornef

Technology in Cancer Research & Treatment, September 25, 2018


DOI: 10.1177/1533033818802305

Nanosecond pulse stimulation as a tumor ablation therapy has been studied for the treatment of various carcinomas in animal models and has shown a significant survival benefit. In the current study, we found that moderate heating at 43°C for 2 minutes significantly enhanced in vitro nanosecond pulse stimulation-induced cell death of KLN205 murine squamous cell carcinoma cells by 2.43-fold at 600 V and by 2.32-fold at 900 V, as evidenced by propidium iodide uptake. Furthermore, the ablation zone in KLN205 cells placed in a 3-dimensional cell-culture model and pulsed at a voltage of 900 V at 43°C was 3 times larger than in cells exposed to nanosecond pulse stimulation at room temperature. Application of moderate heating alone did not cause cell death. A nanosecond pulse stimulation electrode with integrated controllable laser heating was developed to treat murine ectopic squamous cell carcinoma. With this innovative system, we were able to quickly heat and maintain the temperature of the target tumor at 43°C during nanosecond pulse stimulation. Nanosecond pulse stimulation with moderate heating was shown to significantly extend overall survival, delay tumor growth, and achieve a high rate of complete tumor regression. Moderate heating extended survival nearly 3-fold where median overall survival was 22 days for 9.8 kV without moderate heating and over 63 days for tumors pulsed with 600, 100 ns pulses at 5 Hz, at voltage of 9.8 kV with moderate heating. Median overall survival in the control groups was 24 and 31 days for mice with untreated tumors and tumors receiving moderate heat alone, respectively. Nearly 69% (11 of 16) of tumor-bearing mice treated with nanosecond pulse stimulation with moderate heating were tumor free at the completion of the study, whereas complete tumor regression was not observed in the control groups and in 9.8 kV without moderate heating. These results suggest moderate heating can reduce the necessary applied voltage for tumor ablation with nanosecond pulse stimulation.

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