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Archive for the ‘Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound’ Category


Comparison of four methods in diagnosing acute myocarditis: The diagnostic performance of native T1, T2, ECV to LLC

 

Reporter: Aviva Lev-Ari, PhD, RN

 

Abstract

Background:

The Lake Louise Criteria (LLC) were established in 2009 and are the recommended cardiac magnetic resonance imaging criterion for diagnosing patients with suspected myocarditis. Subsequently, newer parametric imaging techniques which can quantify T1, T2, and the extracellular volume (ECV) have been developed and may provide additional utility in the diagnosis of myocarditis. However, whether their diagnostic accuracy is superior to LLC remains unclear. In this meta-analysis, we compared the diagnostic performance of native T1, T2, ECV to LLC in diagnosing acute myocarditis.

Methods and Results:

We searched PubMed for published studies of LLC, native T1, ECV, and T2 diagnostic criteria used to diagnose acute myocarditis. Seventeen studies were included, with a total of 867 myocarditis patients and 441 control subjects. Pooled sensitivity, specificity, and diagnostic odds ratio of all diagnostic tests were assessed by bivariate analysis. LLC had a pooled sensitivity of 74%, specificity of 86%, and diagnostic odds ratio of 17.7. Native T1 had a significantly higher sensitivity than LLC (85% versus 74%, P=0.025). Otherwise, there was no significant difference in sensitivity, specificity, and diagnostic odds ratio when comparing LLC to native T1, T2, or ECV.

Conclusions:

Native T1, T2, and ECV mapping provide comparable diagnostic performance to LLC. Although only native T1 had significantly better sensitivity than LLC, each technique offers distinct advantages for evaluating and characterizing myocarditis when compared with the LLC.

SOURCE

https://www.ahajournals.org/doi/10.1161/CIRCIMAGING.118.007598

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Stanford University researchers have developed a scanner that unites optical, radioluminescence, and photoacoustic imaging to evaluate for Thin-Cap Fibro Atheroma (TCFA)

Reporter: Aviva Lev-Ari, RN

 

Early diagnosis and treatment could save lives by preventing the progression, and subsequent rupture, of these plaques. That is precisely why researchers designed the Circumferential-Intravascular-Radioluminescence-Photoacoustic-Imaging (CIRPI) system, which allows not just high-acuity optical imaging via beta-sensitive probe, but also radioluminescent marking inside the artery to determine the extent of inflammation. Photoacoustic imaging also provides information about the often-complex biological makeup of the plaques (how much is calcified or comprised of cholesterol or triglycerides).

SOURCE

https://www.mdtmag.com/news/2017/06/pet-imaging-atherosclerosis-reveals-risk-plaque-rupture?cmpid=horizontalcontent

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Live Conference Coverage @Medcitynews Converge 2018 Philadelphia: The Davids vs. the Cancer Goliath Part 2

8:40 – 9:25 AM The Davids vs. the Cancer Goliath Part 2

Startups from diagnostics, biopharma, medtech, digital health and emerging tech will have 8 minutes to articulate their visions on how they aim to tame the beast.

Start Time End Time Company
8:40 8:48 3Derm
8:49 8:57 CNS Pharmaceuticals
8:58 9:06 Cubismi
9:07 9:15 CytoSavvy
9:16 9:24 PotentiaMetrics

Speakers:
Liz Asai, CEO & Co-Founder, 3Derm Systems, Inc. @liz_asai
John M. Climaco, CEO, CNS Pharmaceuticals @cns_pharma 

John Freyhof, CEO, CytoSavvy
Robert Palmer, President & CEO, PotentiaMetrics @robertdpalmer 
Moira Schieke M.D., Founder, Cubismi, Adjunct Assistant Prof UW Madison @cubismi_inc

 

3Derm Systems

3Derm Systems is an image analysis firm for dermatologic malignancies.  They use a tele-medicine platform to accurately triage out benign malignancies observed from the primary care physician, expediate those pathology cases if urgent to the dermatologist and rapidly consults with you over home or portable device (HIPAA compliant).  Their suite also includes a digital dermatology teaching resource including digital training for students and documentation services.

 

CNS Pharmaceuticals

developing drugs against CNS malignancies, spun out of research at MD Anderson.  They are focusing on glioblastoma and Berubicin, an anthracycline antiobiotic (TOPOII inhibitor) that can cross the blood brain barrier.  Berubicin has good activity in a number of animal models.  Phase I results were very positive and Phase II is scheduled for later in the year.  They hope that the cardiotoxicity profile is less severe than other anthracyclines.  The market opportunity will be in temazolamide resistant glioblastoma.

Cubismi

They are using machine learning and biomarker based imaging to visualize tumor heterogeneity. “Data is the new oil” (Intel CEO). We need prediction machines so they developed a “my body one file” system, a cloud based data rich file of a 3D map of human body.

CUBISMI IS ON A MISSION TO HELP DELIVER THE FUTURE PROMISE OF PRECISION MEDICINE TO CURE DISEASE AND ASSURE YOUR OPTIMAL HEALTH.  WE ARE BUILDING A PATIENT-DOCTOR HEALTH DATA EXCHANGE PLATFORM THAT WILL LEVERAGE REVOLUTIONARY MEDICAL IMAGING TECHNOLOGY AND PUT THE POWER OF HEALTH DATA INTO THE HANDS OF YOU AND YOUR DOCTORS.

 

CytoSavvy

CytoSavvy is a digital pathology company.  They feel AI has a fatal flaw in that no way to tell how a decision was made. Use a Shape Based Model Segmentation algorithm which uses automated image analysis to provide objective personalized pathology data.  They are partnering with three academic centers (OSU, UM, UPMC) and pool data and automate the rule base for image analysis.

CytoSavvy’s patented diagnostic dashboards are intuitive, easy–to-use and HIPAA compliant. Our patented Shape-Based Modeling Segmentation (SBMS) algorithms combine shape and color analysis capabilities to increase reliability, save time, and improve decisions. Specifications and capabilities for our web-based delivery system follow.

link to their white paper: https://www.cytosavvy.com/resources/healthcare-ai-value-proposition.pdf

PotentialMetrics

They were developing a diagnostic software for cardiology epidemiology measuring outcomes however when a family member got a cancer diagnosis felt there was a need for outcomes based models for cancer treatment/care.  They deliver real world outcomes for persoanlized patient care to help patients make decisions on there care by using a socioeconomic modeling integrated with real time clinical data.

Featured in the Wall Street Journal, using the informed treatment decisions they have generated achieve a 20% cost savings on average.  There research was spun out of Washington University St. Louis.

They have concentrated on urban markets however the CEO had mentioned his desire to move into more rural areas of the country as there models work well for patients in the rural setting as well.

Please follow on Twitter using the following #hash tags and @pharma_BI 

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

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What is the Role of Noninvasive Diagnostic Fractional Flow Reserve (FFR) CT vs Invasive FFR for PCI?

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 7/17/2018

WATCH VIDEO – Interview with Patrick Serruys, MD, PhD, Prof. of Interventional Cardiology, Imperial College, London

VIDEO: Will CT-FFR Replace Diagnostic Angiograms?

VIDEOS | COMPUTED TOMOGRAPHY (CT) | JULY 17, 2018

An interview with Patrick Serruys, M.D., Ph.D., Imperial College London, principal investigator of the SYNTAX III Trial presented earlier this year as a late-breaker at EuroPCR. He presented the trial again at the Society of Cardiovascular Computed Tomography (SCCT) 2018 meeting.

Read the article “SYNTAX III Revolution Trial Shows CT-FFR Could Replace Cine-angiography in Coming Years.”

SOURCE

https://www.dicardiology.com/videos/video-will-ct-ffr-replace-diagnostic-angiograms-0

What is the Role of Noninvasive Diagnostic Fractional Flow Reserve (FFR) CT vs Invasive FFR for PCI?

02/27/2018

We know that FFRCT, the method of obtaining FFR from computed tomography angiographic (CTA) images, has been approved by Medicare and other third-party payers. It is used before patients come to the cath lab. The use of FFRCT in the PLATFORM study1reduced the number of unnecessary cardiac caths that had normal coronary angiography, while maintaining the same number of patients needing PCI.  Before discussing the role of angio-derived FFR, let’s review how FFRCT is obtained (Figure 1). Starting with any good quality CTA, the images are sent, offline, to HeartFlow Inc.2 To derive the FFR, the CTA images are reconstructed into a 3-dimensional coronary tree, segmenting it into individual points with each point undergoing processing by specialized equations (i.e., Navier-Stokes equations) to compute pressure loss along the course of the artery at rest and again during an assumed hyperemic state. These computational fluid dynamic equations require several assumptions from a population model regarding the myocardial blood flow rates as a function of the myocardial arterial branches and the resistance of the myocardium. These values are put into the computational flow dynamics (CFD) model, and using high-power computers, the FFR is generated along the entire course of each vessel. FFRCT has been validated against invasive FFR and found to be about 80% correlative in several studies.3,4 FFRCT has better correlation with FFR than most stress tests, and based on clinical outcome data, will likely replace traditional stress testing, with a reduction in procedures in patients without significant coronary disease. However, there are some operators who may be confused, thinking that FFRCT will replace invasive FFR. FFRCT screens for important coronary artery disease (CAD) before the patient comes to the cath lab, and then once in the lab, the operators can confirm lesion significance with FFR.

Noninvasive FFR Derived From Angiography: Wireless FFR in the Lab?

Wouldn’t it be great to get the FFR from the angiogram without having to put in a guidewire? This is in our near future. The generation of a “virtual” FFR derived from angiography or other modalities (Table 1A-B, Figures 2-4) has been proposed using computational flow dynamics (CFD) or rapid flow analysis to obtain wireless image-based FFR, incorporated into the diagnostic angiography workflow. As one might expect, online implementation of angio-derived FFR requires novel concepts and systems to reduce computation time and make the analysis process acceptable to in-lab functions. Early data shows that angio-derived FFR can be obtained within several minutes during a regular coronary angiogram.5

Angio-FFR Validation StudiesTwo contenders for introduction to the cath labs in the near future are QFR and FFRangio. QFR (Quantitative Flow Ratio, Medis Medical Imaging Systems) validation was reported in the FAVOR II China study, which reported the vessel-level diagnostic accuracy of QFR in identifying hemodynamically-significant coronary stenosis was 97.7% and patient-level diagnostic accuracy was 92.4% (P<0.001 for both).6 In addition, the FAVOR II Europe-Japan trial demonstrated that QFR had superior sensitivity and specificity in comparison to 2-D QCA with FFR as the gold standard: 88% vs 46% and 88% vs 77% (P<0.001 for both). The overall diagnostic accuracy of QFR was 88%.7 For FFRangio (CathWorks), the sensitivity, specificity, and diagnostic accuracy of FFRangio were 88%, 95%, and 93%, respectively.5 The strong concordance with invasive, wire-based FFR will likely make these methods widely available, but of course, early favorable results require confirmation. Once confirmed in larger studies and for a wider spectrum of coronary lesions, angio-derived FFR should become a routine part of diagnostic angiography.

Accuracy in computing noninvasive FFR is based on the implementation of complex computational methods that can differ among the various competing methods. In contrast to FFRCT, which creates a complete and detailed 3D model of the coronary tree from CTA scans, Tu et al8 constructed vessel geometry from routine angiography, applying a simpler model for flow, derived from the division of coronary branches (as opposed to using an estimate of flow from myocardial mass)2, and an approximate algebraic computational method from experimental studies of flow through single arterial stenosis models8 (as opposed to CFD equations) to solve for pressure drop and FFR (Figure 5). Because Tu et al8 do not employ the complicated Navier-Stokes equations, the computational time is almost instantaneous once the geometry is segmented into “sub segments” from the 3D rendering. Pellicano et al5 constructed 3D artery geometry from routing angiography alone, applying rapid flow analysis where all stenoses are converted into resistances in a lumped model, while scaling laws (of branches) are used to estimate the microcirculatory bed resistance.

Competition for a winning method of angiographically-derived FFR is underway, with different companies using different models and different assumptions regarding flow and resistance inputs (Table 1A-B). An example is QFR that uses several assumptions related to flow variables. fQFR is specified hyperemic inflow, assuming a fixed inflow velocity of 0.35 m/s. cQFR is “virtual” hyperemic flow, determined from a model based on TIMI [Thrombolysis In Myocardial Infarction] frame count, relating measured flow under baseline conditions to hyperemic flow. Lastly, aQFR is the variable of directly measured hyperemic flow. From these assumptions, QFR gives highly comparable results to invasive FFR.

Advantages of Angio-Derived FFR

The in-lab computations of angio-derived FFR are fast and have the potential to provide wireless FFR lesion assessment to every angiographic procedure. Other advantages of angio-derived FFR are obvious. There is no need to insert a pressure guidewire. Pharmacologic hyperemia is not necessary. It is nearly operator independent. The angio-derived FFR is also co-registered on the angiogram with accurate and reproducible results. In addition, 3D reconstruction of the coronary tree can enhance the identification of reference vessel diameters for selection of stent sizing, and ultimately predict anatomic and physiological outcomes.5

Limitations of Angio-Derived FFR 

The image acquisition requirements and the user interface of an image-based FFR system should be seamlessly incorporated into the standard work of the catheterization laboratory. Data acquisition should minimally disrupt routine angiography. Angio-derived FFR should only require the acquisition of 2 to 3 conventional radiographic projections in which the lesions can be clearly seen. It is important to visualize the entire coronary tree on the screen and to optimize vessel opacification. Poor images or overlapped segments will limit the accuracy of angio-derived FFR. The image acquisition angles needed for angio-derived FFR are the same as those used for routine procedures. High resolution imaging at >10 frames/sec are needed.5

On the technical side, coronary microvascular resistance (CMV) is a fundamental assumption to compute pressure from flow. CMV in one study was derived from invasive measurements, something which will limit future acceptance.9 As the data sets are accumulated, it is hoped that invasive CMV will not be needed. One angio-derived FFR method, vFFR9,10, requires rotational angiography, which is not yet widely available, and may produce asymmetric coronary segmentations — a concern for accurate analysis.

Finally, the amount of time required to acquire and process the data to produce angio-derived FFR is likely to be longer than the 3-minute computation time. Acquisition time should realistically include the time to overcome the difficulties of imaging complex anatomy, eliminate artifacts, upload the study for CFD analysis, and create the volumetric mesh. Furthermore, there will probably be patient-specific errors related to abnormal coronary physiology which may account for outliers in the correlations between angiography-derived and invasive FFR measurements.11

Angio-derived FFR is currently reported for off-line results, but, recently, online applications have also been presented. Minimal operator interaction is necessary in the flow calculation process, which results in low inter-operator variability.

The Bottom Line

When FFRCT and angio-derived FFR technology ultimately become more widely available, they will radically change the way diagnostic angiography is performed in the same way that invasive FFR changed the way we approach patients needing PCI

References

  1. Douglas PS, De Bruyne B, Pontone G, et al. 1-Year Outcomes of FFRCT-Guided Care in Patients With Suspected Coronary Disease: The PLATFORM Study.  J Am Coll Cardiol. 2016 Aug 2; 68(5): 435-445. doi: 10.1016/j.jacc.2016.05.057.
  2. Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J Am Coll Cardiol. 2013; 61(22): 2233-2241.
  3. Norgaard BL, Leipsic J, Gaur S, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease. J Am Coll Cardiol. 2014; 63: 1145-1155.
  4. Min JK, Leipsic J, Pencina MJ, et al. Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA. 2012; 308: 1237-1234.
  5. Pellicano M, Lavi I, Bruyne B, et al. Validation study of image-based fractional flow reserve during coronary angiography. Circ Cardiovasc Interv. 2017; 10: e005259. doi: 10.1161/CIRCINTERVENTIONS.116.005259.
  6. Xu B, Tu S, Qiao S, et al. Diagnostic accuracy of angiography-based quantitative flow ratio measurements for online assessment of coronary stenosis. J Am Coll Cardiol. 2017 Dec 26; 70(25): 3077-3087. doi: 10.1016/j.jacc.2017.10.035.
  7. Westra J. Late-Breaking Clinical Trials 2. Presented at: TCT Scientific Symposium; Oct. 29-Nov. 2, 2017; Denver, Colorado.
  8. Tu S, Westra J, Yang J, et al. Diagnostic accuracy of fast computational approaches to derive fractional flow reserve from diagnostic coronary angiography: the international multicenter FAVOR pilot study. J Am Coll Cardiol Intv. 2016; 9: 2024-2035.
  9. Morris PD, van de Vosse FN, Lawford PV, et al. “Virtual” (computed) fractional flow reserve: current challenges and limitations. JACC Cardiovasc Interv. 2015; 8: 1009-1017. doi: 10.1016/j.jcin.2015.04.006.
  10. Morris PD, Ryan D, Morton AC, et al. Virtual fractional flow reserve from coronary angiography: modeling the significance of coronary lesions: results from the VIRTU-1 (VIRTUal Fractional Flow Reserve From Coronary Angiography) study. JACC Cardiovasc Interv. 2013; 6: 149-157. doi: 10.1016/j.jcin.2012.08.024.
  11. Papafaklis MI, Muramatsu T, Ishibashi Y, et al. Fast virtual functional assessment of intermediate coronary lesions using routine angiographic data and blood flow simulation in humans: comparison with pressure wire – fractional flow reserve. EuroIntervention. 2014; 10: 574-583. doi: 10.4244/EIJY14M07_01
  12. Tu S, Barbato E, Köszegi Z, et al. Fractional flow reserve calculation from 3-dimensional quantitative coronary angiography and TIMI frame count: a fast computer model to quantify the functional significance of moderately obstructed coronary arteries. JACC Cardiovasc Interv. 2014 Jul; 7(7): 768-777. doi: 10.1016/j.jcin.2014.03.004.

Disclosure: Dr. Kern is a consultant for Abiomed, Merit Medical, Abbott Vascular, Philips Volcano, ACIST Medical, Opsens Inc., and Heartflow Inc. 

SOURCE

https://www.cathlabdigest.com/article/Noninvasive-Angiographic-Derived-FFR-Wireless-Physiology-Coming-Your-Cath-Lab-Soon

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Diagnostic and Prognostic value of structural MRI: preliminary evidence of common and specific gray matter changes in Depression and Anxiety patients

Reporter: Aviva Lev-Ari, PhD, RN

 

Cortical thickening in the insular cortex, a brain region vital to perception and self-awareness in Patients with

  • Social anxiety disorder (SAD)
  • Major depressive disorder (MDD)

greater cortical thickness may reflect a

  • compensatory mechanism that is related to inflammation or other aspects of the pathophysiology,” she said.
  • greater anterior cingulate cortical thickness could be the result of both the continuous coping efforts and emotion regulation attempts of MDD and SAD patients.”

Image Source: There are significant cortical thickness differences among the three groups. All regions survived clusterwise-correction (p<0.001).(Credit: Radiological Society of North America)

SOURCE

MRI Uncovers Brain Abnormalities in People with Depression and Anxiety

https://www.mdtmag.com/news/2017/11/mri-uncovers-brain-abnormalities-people-depression-and-anxiety?et_cid=6181014&et_rid=461755519&location=top&et_cid=6181014&et_rid=461755519&linkid=https%3a%2f%2fwww.mdtmag.com%2fnews%2f2017%2f11%2fmri-uncovers-brain-abnormalities-people-depression-and-anxiety%3fet_cid%3d6181014%26et_rid%3d%%subscriberid%%%26location%3dtop

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cancerandoncologyseriesccover

Series C: e-Books on Cancer & Oncology

Series C Content Consultant: Larry H. Bernstein, MD, FCAP

 

VOLUME ONE 

Cancer Biology and Genomics

for

Disease Diagnosis

2015

http://www.amazon.com/dp/B013RVYR2K

Stephen J. Williams, PhD, Senior Editor

sjwilliamspa@comcast.net

Tilda Barliya, PhD, Editor

tildabarliya@gmail.com

Ritu Saxena, PhD, Editor

ritu.uab@gmail.com

Leaders in Pharmaceutical Business Intelligence 

Part I

Historical Perspective of Cancer Demographics, Etiology, and Progress in Research

Chapter 1:  The Occurrence of Cancer in World Populations

1.1   Understanding Cancer

Prabodh Kandala, PhD

1.2  Cancer Metastasis

Tilda Barliya, PhD

1.3      2013 Perspective on “War on Cancer” on December 23, 1971

Aviva Lev-Ari, PhD, RN

1.4   Global Burden of Cancer Treatment & Women Health: Market Access & Cost Concerns

Aviva Lev-Ari, PhD, RN

1.5    The Importance of Cancer Prevention Programs: New Perspectives for Fighting Cancer

Ziv Raviv, PhD

1.6      The “Cancer establishments” examined by James Watson, co-discoverer of DNA w/Crick, 4/1953,  

Larry H Bernstein, MD, FCAP

1.7      New Ecosystem of Cancer Research: Cross Institutional Team Science

Aviva Lev-Ari, PhD, RN

1.8       Cancer Innovations from across the Web

Larry H Bernstein, MD, FCAP

1.9         Exploring the role of vitamin C in Cancer therapy

Ritu Saxena PhD

1.10        Relation of Diet and Cancer

Sudipta Saha, PhD

1.11      Association between Non-melanoma Skin Cancer and subsequent Primary Cancers in White Population 

Aviva Lev-Ari, PhD, RN

1.12       Men With Prostate Cancer More Likely to Die from Other Causes

Prabodh Kandala, PhD

1.13      Battle of Steve Jobs and Ralph Steinman with Pancreatic Cancer: How we Lost

Ritu Saxena, PhD

Chapter 2.  Rapid Scientific Advances Changes Our View on How Cancer Forms

2.1     All Cancer Cells Are Not Created Equal: Some Cell Types Control Continued Tumor Growth, Others Prepare the Way for Metastasis 

Prabodh Kandala, PhD

2.2      Hold on. Mutations in Cancer do Good

Prabodh Kandala, PhD

2.3       Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

Larry H Bernstein, MD, FCAP

2.4          Naked Mole Rats Cancer-Free

Larry H Bernstein, MD, FCAP

2.5           Zebrafish—Susceptible to Cancer

Larry H Bernstein, MD, FCAP

2.6         Demythologizing Sharks, Cancer, and Shark Fins,

Larry H Bernstein, MD, FCAP

2.7       Tumor Cells’ Inner Workings Predict Cancer Progression

Prabodh Kandala, PhD

2.8      In Focus: Identity of Cancer Stem Cells

Ritu Saxena, PhD

2.9      In Focus: Circulating Tumor Cells

Ritu Saxena, PhD

2.10     Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers 

Stephen J. Williams, PhD

2.11     Role of Primary Cilia in Ovarian Cancer

Aashir Awan, PhD

Chapter 3:  A Genetic Basis and Genetic Complexity of Cancer Emerges

3.1       The Binding of Oligonucleotides in DNA and 3-D Lattice Structures

Larry H Bernstein, MD, FCAP

3.2      How Mobile Elements in “Junk” DNA Promote Cancer. Part 1: Transposon-mediated Tumorigenesis. 

Stephen J. Williams, PhD

3.3      DNA: One Man’s Trash is another Man’s Treasure, but there is no JUNK after all

Demet Sag, PhD

3.4 Issues of Tumor Heterogeneity

3.4.1    Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

Stephen J. Williams, PhD

3.4.2       Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

Stephen J. Williams, PhD

3.5        arrayMap: Genomic Feature Mining of Cancer Entities of Copy Number Abnormalities (CNAs) Data

Aviva Lev-Ari, PhD, RN

3.6        HBV and HCV-associated Liver Cancer: Important Insights from the Genome

Ritu Saxena, PhD

3.7      Salivary Gland Cancer – Adenoid Cystic Carcinoma: Mutation Patterns: Exome- and Genome-Sequencing @ Memorial Sloan-Kettering Cancer Center

Aviva Lev-Ari, PhD, RN

3.8         Gastric Cancer: Whole-genome Reconstruction and Mutational Signatures

Aviva Lev-Ari, PhD, RN

3.9        Missing Gene may Drive more than a quarter of Breast Cancers

Aviva Lev-Ari, PhD, RN

3.10     Critical Gene in Calcium Reabsorption: Variants in the KCNJ and SLC12A1 genes – Calcium Intake and Cancer Protection

Aviva Lev-Ari,PhD, RN

Chapter 4: How Epigenetic and Metabolic Factors Affect Tumor Growth

4.1    Epigenetics

4.1.1     The Magic of the Pandora’s Box : Epigenetics and Stemness with Long non-coding RNAs (lincRNA)

Demet Sag, PhD, CRA, GCP

4.1.2     Stomach Cancer Subtypes Methylation-based identified by Singapore-Led Team

Aviva Lev-Ari, PhD, RN

4.1.3     The Underappreciated EpiGenome

Demet Sag, Ph.D., CRA, GCP

4.1.4     Differentiation Therapy – Epigenetics Tackles Solid Tumors

Stephen J. Williams, PhD

4.1.5      “The SILENCE of the Lambs” Introducing The Power of Uncoded RNA

Demet Sag, Ph.D., CRA, GCP

4.1.6      DNA Methyltransferases – Implications to Epigenetic Regulation and Cancer Therapy Targeting: James Shen, PhD

Aviva Lev-Ari, PhD, RN

4.2   Metabolism

4.2.1      Mitochondria and Cancer: An overview of mechanisms

Ritu Saxena, PhD

4.2.2     Bioenergetic Mechanism: The Inverse Association of Cancer and Alzheimer’s

Aviva Lev-Ari, PhD, RN

4.2.3      Crucial role of Nitric Oxide in Cancer

Ritu Saxena, PhD

4.2.4      Nitric Oxide Mitigates Sensitivity of Melanoma Cells to Cisplatin

Stephen J. Williams, PhD

4.2.5      Increased risks of obesity and cancer, Decreased risk of type 2 diabetes: The role of Tumor-suppressor phosphatase and tensin homologue (PTEN)

Aviva Lev-Ari, PhD, RN

4.2.6      Lipid Profile, Saturated Fats, Raman Spectrosopy, Cancer Cytology

Larry H Bernstein, MD, FCAP

4.3     Other Factors Affecting Tumor Growth

4.3.1      Squeezing Ovarian Cancer Cells to Predict Metastatic Potential: Cell Stiffness as Possible Biomarker

Prabodh Kandala, PhD

4.3.2      Prostate Cancer: Androgen-driven “Pathomechanism” in Early-onset Forms of the Disease

Aviva Lev-Ari, PhD, RN

Chapter 5: Advances in Breast and Gastrointestinal Cancer Research Supports Hope for Cure

5.1 Breast Cancer

5.1.1      Cell Movement Provides Clues to Aggressive Breast Cancer

Prabodh Kandala, PhD

5.1.2    Identifying Aggressive Breast Cancers by Interpreting the Mathematical Patterns in the Cancer Genome

Prabodh Kandala, PhD

5.1.3  Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment

Aviva Lev-Ari, PhD, RN

5.1.4       BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair

Sudipta Saha, PhD

5.1.5      Breast Cancer and Mitochondrial Mutations

Larry H Bernstein, MD, FCAP

5.1.6      MIT Scientists Identified Gene that Controls Aggressiveness in Breast Cancer Cells

Aviva Lev-Ari PhD RN

5.1.7       “The Molecular pathology of Breast Cancer Progression”

Tilda Barliya, PhD

5.1.8       In focus: Triple Negative Breast Cancer

Ritu Saxena, PhD

5.1.9       Automated Breast Ultrasound System (‘ABUS’) for full breast scanning: The beginning of structuring a solution for an acute need!

Dror Nir, PhD

5.1.10       State of the art in oncologic imaging of breast.

Dror Nir, PhD

 

5.2 Gastrointestinal Cancer

5.2.1         Colon Cancer

Tilda Barliya, PhD

5.2.2      PIK3CA mutation in Colorectal Cancer may serve as a Predictive Molecular Biomarker for adjuvant Aspirin therapy

Aviva Lev-Ari, PhD, RN

5.2.3     State of the art in oncologic imaging of colorectal cancers.

Dror Nir, PhD

5.2.4     Pancreatic Cancer: Genetics, Genomics and Immunotherapy

Tilda Barliya, PhD

5.2.5     Pancreatic cancer genomes: Axon guidance pathway genes – aberrations revealed

Aviva Lev-Ari, PhD, RN

Part II

Advent of Translational Medicine, “omics”, and Personalized Medicine Ushers in New Paradigms in Cancer Treatment and Advances in Drug Development

Chapter 6:  Treatment Strategies

6.1 Marketed and Novel Drugs

Breast Cancer                                   

6.1.1     Treatment for Metastatic HER2 Breast Cancer

Larry H Bernstein MD, FCAP

6.1.2          Aspirin a Day Tied to Lower Cancer Mortality

Aviva Lev-Ari, PhD, RN

6.1.3       New Anti-Cancer Drug Developed

Prabodh Kandala, Ph.D.

6.1.4         Pfizer’s Kidney Cancer Drug Sutent Effectively caused REMISSION to Adult Acute Lymphoblastic Leukemia (ALL)

Aviva Lev-Ari ,PhD, RN

6.1.5     “To Die or Not To Die” – Time and Order of Combination drugs for Triple Negative Breast Cancer cells: A Systems Level Analysis

Anamika Sarkar, PhD. and Ritu Saxena, PhD

Melanoma

6.1.6    “Thymosin alpha1 and melanoma”

Tilda Barliya, PhD

Leukemia

6.1.7    Acute Lymphoblastic Leukemia and Bone Marrow Transplantation

Tilda Barliya PhD

6.2 Natural agents

Prostate Cancer                 

6.2.1      Scientists use natural agents for prostate cancer bone metastasis treatment

Ritu Saxena, PhD

Breast Cancer

6.2.2        Marijuana Compound Shows Promise In Fighting Breast Cancer

Prabodh Kandala, PhD

Ovarian Cancer                  

6.2.3        Dimming ovarian cancer growth

Prabodh Kandala, PhD

6.3 Potential Therapeutic Agents

Gastric Cancer                 

6.3.1       β Integrin emerges as an important player in mitochondrial dysfunction associated Gastric Cancer

Ritu Saxena, PhD

6.3.2      Arthritis, Cancer: New Screening Technique Yields Elusive Compounds to Block Immune-Regulating Enzyme

Prabodh Kandala, PhD

Pancreatic Cancer                                   

6.3.3    Usp9x: Promising therapeutic target for pancreatic cancer

Ritu Saxena, PhD

Breast Cancer                 

6.3.4       Breast Cancer, drug resistance, and biopharmaceutical targets

Larry H Bernstein, MD, FCAP

Prostate Cancer

6.3.5        Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition

Stephen J. Williams, PhD

Glioblastoma

6.3.6      Gamma Linolenic Acid (GLA) as a Therapeutic tool in the Management of Glioblastoma

Raphael Nir, PhD, MSM, MSc

6.3.7   Akt inhibition for cancer treatment, where do we stand today?

Ziv Raviv, PhD

Chapter 7:  Personalized Medicine and Targeted Therapy

7.1.1        Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders

Aviva Lev-Ari, PhD, RN

7.1.2      Personalized medicine-based cure for cancer might not be far away

Ritu Saxena, PhD

7.1.3      Personalized medicine gearing up to tackle cancer

Ritu Saxena, PhD

7.1.4       Cancer Screening at Sourasky Medical Center Cancer Prevention Center in Tel-Aviv

Ziv Raviv, PhD

7.1.5       Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics

Aviva Lev-Ari, PhD, RN

7.1.6       Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)

Aviva Lev-Ari, PhD, RN

7.2 Personalized Medicine and Genomics

7.2.1       Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

Aviva Lev-Ari, PhD, RN

7.2.2       Whole exome somatic mutations analysis of malignant melanoma contributes to the development of personalized cancer therapy for this disease

Ziv Raviv, PhD

7.2.3       Genotype-based Analysis for Cancer Therapy using Large-scale Data Modeling: Nayoung Kim, PhD(c)

Aviva Lev-Ari, PhD, RN

7.2.4         Cancer Genomic Precision Therapy: Digitized Tumor’s Genome (WGSA) Compared with Genome-native Germ Line: Flash-frozen specimen and Formalin-fixed paraffin-embedded Specimen Needed

Aviva Lev-Ari, PhD, RN

7.2.5         LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2

Aviva Lev-Ari, PhD, RN

7.2.6       Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk

Stephen J. Williams, PhD

7.3  Personalized Medicine and Targeted Therapy

7.3.1     The Development of siRNA-Based Therapies for Cancer

Ziv Raviv, PhD

7.3.2       mRNA interference with cancer expression

Larry H Bernstein, MD, FCAP

7.3.3       CD47: Target Therapy for Cancer

Tilda Barliya, PhD

7.3.4      Targeting Mitochondrial-bound Hexokinase for Cancer Therapy

Ziv Raviv, PhD

7.3.5       GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial”

Aviva Lev-Ari, PhD, RN

7.3.6         Personalized Pancreatic Cancer Treatment Option

Aviva Lev-Ari, PhD, RN

7.3.7        New scheme to routinely test patients for inherited cancer genes

Stephen J. Williams, PhD

7.3.8        Targeting Untargetable Proto-Oncogenes

Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

7.3.9        The Future of Translational Medicine with Smart Diagnostics and Therapies: PharmacoGenomics 

Demet Sag, PhD

7.4 Personalized Medicine in Specific Cancers

7.4.1      Personalized medicine and Colon cancer

Tilda Barliya, PhD

7.4.2      Comprehensive Genomic Characterization of Squamous Cell Lung Cancers

Aviva Lev-Ari, PhD, RN

7.4.3        Targeted Tumor-Penetrating siRNA Nanocomplexes for Credentialing the Ovarian Cancer Oncogene ID4

Sudipta Saha, PhD

7.4.4        Cancer and Bone: low magnitude vibrations help mitigate bone loss

Ritu Saxena, PhD

7.4.5         New Prostate Cancer Screening Guidelines Face a Tough Sell, Study Suggests

Prabodh Kandala, PhD

Part III

Translational Medicine, Genomics, and New Technologies Converge to Improve Early Detection

Diagnosis, Detection And Biomarkers

Chapter 8:  Diagnosis Diagnosis: Prostate Cancer

8.1        Prostate Cancer Molecular Diagnostic Market – the Players are: SRI Int’l, Genomic Health w/Cleveland Clinic, Myriad Genetics w/UCSF, GenomeDx and BioTheranostics

Aviva Lev-Ari PhD RN

8.2         Today’s fundamental challenge in Prostate cancer screening

Dror Nir, PhD

Diagnosis & Guidance: Prostate Cancer

8.3      Prostate Cancers Plunged After USPSTF Guidance, Will It Happen Again?

Aviva Lev-Ari, PhD, RN

Diagnosis, Guidance and Market Aspects: Prostate Cancer

8.4       New Prostate Cancer Screening Guidelines Face a Tough Sell, Study Suggests

Prabodh Kandala, PhD

Diagnossis: Lung Cancer

8.5      Diagnosing lung cancer in exhaled breath using gold nanoparticles

Tilda Barliya PhD

Chapter 9:  Detection

Detection: Prostate Cancer

9.1     Early Detection of Prostate Cancer: American Urological Association (AUA) Guideline

Dror Nir, PhD

Detection: Breast & Ovarian Cancer

9.2       Testing for Multiple Genetic Mutations via NGS for Patients: Very Strong Family History of Breast & Ovarian Cancer, Diagnosed at Young Ages, & Negative on BRCA Test

Aviva Lev-Ari, PhD, RN

Detection: Aggressive Prostate Cancer

9.3     A Blood Test to Identify Aggressive Prostate Cancer: a Discovery @ SRI International, Menlo Park, CA

Aviva Lev-Ari, PhD, RN

Diagnostic Markers & Screening as Diagnosis Method

9.4      Combining Nanotube Technology and Genetically Engineered Antibodies to Detect Prostate Cancer Biomarkers

Stephen J. Williams, PhD

Detection: Ovarian Cancer

9.5      Warning signs may lead to better early detection of ovarian cancer

Prabodh Kandala, PhD

9.6       Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?

Dror Nir, PhD

Chapter 10:  Biomarkers

                                                Biomarkers: Pancreatic Cancer

10.1        Mesothelin: An early detection biomarker for cancer (By Jack Andraka)

Tilda Barliya, PhD

Biomarkers: All Types of Cancer, Genomics and Histology

10.2                  Stanniocalcin: A Cancer Biomarker

Aashir Awan, PhD

10.3         Breast Cancer: Genomic Profiling to Predict Survival: Combination of Histopathology and Gene Expression Analysis

Aviva Lev-Ari, PhD, RN

Biomarkers: Pancreatic Cancer

10.4         Biomarker tool development for Early Diagnosis of Pancreatic Cancer: Van Andel Institute and Emory University

Aviva Lev-Ari, PhD, RN

10.5     Early Biomarker for Pancreatic Cancer Identified

Prabodh Kandala, PhD

Biomarkers: Head and Neck Cancer

10.6        Head and Neck Cancer Studies Suggest Alternative Markers More Prognostically Useful than HPV DNA Testing

Aviva Lev-Ari, PhD, RN

10.7      Opens Exome Service for Rare Diseases & Advanced Cancer @Mayo Clinic’s OncoSpire

Aviva Lev-Ari, PhD, RN

Diagnostic Markers and Screening as Diagnosis Methods

10.8         In Search of Clarity on Prostate Cancer Screening, Post-Surgical Followup, and Prediction of Long Term Remission

Larry H Bernstein, MD, FCAP

Chapter 11  Imaging In Cancer

11.1  Introduction by Dror Nir, PhD

11.2  Ultrasound

11.2.1        2013 – YEAR OF THE ULTRASOUND

Dror Nir, PhD

11.2.2      Imaging: seeing or imagining? (Part 1)

Dror Nir, PhD

11.2.3        Early Detection of Prostate Cancer: American Urological Association (AUA) Guideline

Dror Nir, PhD

11.2.4        Today’s fundamental challenge in Prostate cancer screening

Dror Nir, PhD

11.2.5       State of the art in oncologic imaging of Prostate

Dror Nir, PhD

11.2.6        From AUA 2013: “HistoScanning”- aided template biopsies for patients with previous negative TRUS biopsies

Dror Nir, PhD

11.2.7     On the road to improve prostate biopsy

Dror Nir, PhD

11.2.8       Ultrasound imaging as an instrument for measuring tissue elasticity: “Shear-wave Elastography” VS. “Strain-Imaging”

Dror Nir, PhD

11.2.9       What could transform an underdog into a winner?

Dror Nir, PhD

11.2.10        Ultrasound-based Screening for Ovarian Cancer

Dror Nir, PhD

11.2.11        Imaging Guided Cancer-Therapy – a Discipline in Need of Guidance

Dror Nir, PhD

11.3   MRI & PET/MRI

11.3.1     Introducing smart-imaging into radiologists’ daily practice

Dror Nir, PhD

11.3.2     Imaging: seeing or imagining? (Part 2)

[Part 1 is included in the ultrasound section above]

Dror Nir, PhD

11.3.3    Imaging-guided biopsies: Is there a preferred strategy to choose?

Dror Nir, PhD

11.3.4     New clinical results support Imaging-guidance for targeted prostate biopsy

Dror Nir, PhD

11.3.5      Whole-body imaging as cancer screening tool; answering an unmet clinical need?

Dror Nir, PhD

11.3.6        State of the art in oncologic imaging of Lymphoma

Dror Nir, PhD

11.3.7      A corner in the medical imaging’s ECO system

Dror Nir, PhD

11.4  CT, Mammography & PET/CT 

11.4.1      Causes and imaging features of false positives and false negatives on 18F-PET/CT in oncologic imaging

Dror Nir, PhD

11.4.2     Minimally invasive image-guided therapy for inoperable hepatocellular carcinoma

Dror Nir, PhD

11.4.3        Improving Mammography-based imaging for better treatment planning

Dror Nir, PhD

11.4.4       Closing the Mammography gap

Dror Nir, PhD

11.4.5       State of the art in oncologic imaging of lungs

Dror Nir, PhD

11.4.6       Ovarian Cancer and fluorescence-guided surgery: A report

Tilda Barliya, PhD

11.5  Optical Coherent Tomography (OCT)

11.5.1       Optical Coherent Tomography – emerging technology in cancer patient management

Dror Nir, PhD

11.5.2     New Imaging device bears a promise for better quality control of breast-cancer lumpectomies – considering the cost impact

Dror Nir, PhD

11.5.3        Virtual Biopsy – is it possible?

Dror Nir, PhD

11.5.4      New development in measuring mechanical properties of tissue

Dror Nir, PhD

Chapter 12. Nanotechnology Imparts New Advances in Cancer Treatment,  Detection, and Imaging  

12.1     DNA Nanotechnology

Tilda Barliya, PhD

12.2     Nanotechnology, personalized medicine and DNA sequencing

Tilda Barliya, PhD       

12.3     Nanotech Therapy for Breast Cancer

Tilda Barliya, PhD

12.4     Prostate Cancer and Nanotecnology

Tilda Barliya, PhD

12.5     Nanotechnology: Detecting and Treating metastatic cancer in the lymph node

Tilda Barliya, PhD

12.6     Nanotechnology Tackles Brain Cancer

Tilda Barliya, PhD

12.7     Lung Cancer (NSCLC), drug administration and nanotechnology

Tilda Barliya, PhD

Volume Epilogue by Larry H. Bernstein, MD, FACP

Epilogue: Envisioning New Insights in Cancer Translational Biology

Larry H. Berstein, MD, FACP

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MGH & BWH Researchers: Brains of cognitively normal older individuals carrying the APOE4 gene variant – association between lower weight and more extensive deposits of the Alzheimer’s-associated protein beta-amyloid

Reporter: Aviva Lev-Ari, PhD, RN

While the concept of a preclinical version of Alzheimer’s disease is theoretical and not yet being used to guide clinical diagnosis or treatment, the current hypothesis involves three stages. Individuals at stage 1 are cognitively normal but have elevated amyloid deposits; stage 2 adds evidence of neurodegeneration, such as elevated tau deposits or characteristic loss of certain brain tissues, with no cognitive symptoms; and stage 3 adds cognitive changes that, while still in a normal range, indicate a decline for that individual. The current study is part of the MGH-based Harvard Aging Brain Study (HABS), designed to identify markers that predict who is likely to develop Alzheimer’s disease and how soon symptoms are likely to develop.

This investigation explored the relationship between body mass index (BMI) and beta amyloid levels in the brains of the first 280 participants to enroll in HABS, who were ages 62 to 90, cognitively normal and in good general health. Participants’ initial enrollment data included medical histories; physical exams; testing for the presence of APOE4, the major genetic risk factor for late-onset Alzheimer’s; and PET imaging with Pittsburgh compound B (PiB), which can visualize amyloid plaques in the brain.

After adjusting for factors including age, sex, education and APOE4 status, researchers found that having a lower BMI was associated with greater retention of PiB, indicating more extensive amyloid deposits in the brain. The association was most pronounced in normal-weight participants, who were the group with the lowest BMI in the study. Analysis focused on APOE status revealed that the association between lower BMI and greater PiB retention was particularly significant for individuals with the APOE4 gene variant, which is associated with increased Alzheimer’s disease risk.

SOURCE

MGH News Release

Tuesday, August 2, 2016

Lower weight in late life may increase risk of Alzheimer’s Disease

http://www.massgeneral.org/News/pressrelease.aspx?id=1970

 

Other related articles published in this Open Access Online Scientific Journal include the following:

 

Alzheimer’s Disease: Novel Therapeutical Approaches — Articles of Note @PharmaceuticalIntelligence.com

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/04/05/alzheimers-disease-novel-therapeutical-approaches-articles-of-note-pharmaceuticalintelligence-com/

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