Hunting for Elusive Targets, Markers, Signals & Pathways

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Detection, Diagnosis, Prognosis, Treatment, Measurement, Monitoring & Reporting of Chronic Disease

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Hunting for Elusive Targets, Markers, Signals & Pathways – Jim Smurro [MassBio 2013]

“Hunting for Elusive Targets, Markers, Signals & Pathways with Photons, Quantims, Voxels & Vismemes”Early Detection, Diagnosis, Prognosis, Treatment, Measurement and Monitoring of Chronic Diseasewith Clinical Cognitive Vismemes over Visual Neural Networks for Personalized Precision Medicine

Jim Smurro discusses clinical applications of Visual Neural Networking [VNN] connectivity tools and collaboration technology to support multisensory communications, collaboration, teamwork and training for 'faster, smarter, better’ development of drugs, devices and care delivery: ’faster’ discovery in life sciences, 'smarter’ development of drugs and devices, and 'better’ coordinated delivery of patientcentric value care.

VNN technology enables visual integration of multimodal, multiscalar imagewaveforms, signals and spectra for more precise and predictivedetection, diagnosis, prognosis, treatment and monitoring of chronic disease. VNN also supports more rapid learning with knowledge retention, and 'faster, smarter, better' teams with the creation and exchange of 'clinical cognitive vismemes' semantically searchable, shareable packetsof clinical cognition medical imagewaveforms, signals and spectra enriched and encapsulated with multisensory annotation and instruction.

Visual systems integration at the nexus of cognition facilitates fusion, analysis and correlation of both phenotypic and genomic information through integrative visual bioinformatics. Multidisciplinary teams of domain knowledge experts utilize visual bioinformatic tools that combineanatomic, cellular and molecular imaging with metabolomic, genomic and proteomic data streams. Their specialist expertise can be further enhanced by tools for computerassisted detection [CADe] and augmented pattern recognition, such as Spatially Invariant Vector Quantization[SIVQ], and by adaptive mindmachine learning systems powered with NLP semantic search. Annotated imaging markup tools [AIM] readily permit capture of ontologybased imaging notations e.g., phenotype information with bothquantitative (texture, shape, boundary) and semantic features in standard formats for annotated imagewaveform capture and metadata exchange. Shared semantic tags permit indexed search and correlation analysis with natural language processing with common data elements [CDE’s] in shared taxonomies, ontologies and vocabularies.

Multidisciplinary teams of domain knowledge experts are able to collaborate and share their expertise – in real time or asynchronously – over interconnected networks that facilitate the exchange of annotated imagewaveforms enriched with quantitative imaging metadata. Multimodal, multiscalar image fusion at the point of shared cognition i.e., integrating anatomic cellular and molecular imaging with metabolomic,genomic, and proteomic datavisualized markers, enhances the possibilities for earlier detection, diagnosis, treatment and monitoring of disease.

The challenge of early detection can be seen as a problem of recognizing clinical signals transmitted by biologic symptom clusters over noisy channels using molecular information theory [adapted from Shannon et al] along with techniques of Bayesian inference with probabilistic ranking of candidate solutions [as demonstrated by IBM Watson’s recent triumph over human champions in the quiz show Jeopardy!] Population growth, extended longevity and escalating costs of care [including indirect costs of lost productivity] will fuel increased demandsfor earlier detection, diagnosis, prognosis, treatment, measurement and monitoring of chronic disease, and inevitably push the search for clinical 'signal intelligence’ and 'augmented pattern recognition beyond traditional boundaries of genomic and chemistrycentric approaches.

Nanobiotechnology techniques, including the capture and recording of quantitative measures of physical properties of cells [e.g., elasticity, adhesion, electric potential, etc.] may better our understanding of the physics of cellular activity. Those investigatory efforts, assisted bybioinformatics tools for integrative systems biology, including visual neural networking with clinical cognitive vismemes, could be seen as “Hunting for Elusive Targets, Markers, Signals & Pathways with Photons, Quantims, Voxels & Vismemes”


“Hunting for Elusive Targets, Markers, Signals & Pathways with Photons, Quantims, Voxels & Vismemes”Early Detection, Diagnosis, Prognosis, Treatment, Measurement and Monitoring with Multi-modal, Multi-scalar Clinical Cognitive Vismemes

“Quantim” = quantitative imaging metadata; imaging phenotype description = quantitative information (texture, shape, boundary), + semantic features for indexed NLP search]

“Vismeme” = visual meme; annotated medical images, enriched and enhanced with human cognition, accompanied by curated metadata + payloads of ‘quantim’ information from one or more imaging modalities, visual bioinformatics networks Notes: this presentation

1. builds upon ideas previously presented during demonstration of prototype VNN technology at the 2010 mHealth Summit in Washington, DC "Bending the Cost Curve with Disruptive Technology: Collaborative Image Sharing for Value CareGiving Teams" [sandwiched in between keynote remarks from Bill Gates, Aneesh Chopra, Francis Collins and Ted Turner]

2. expands upon a presentation with radiology colleagues at SIIM 2011 [the Society for Imaging Informatics in Medicine] in Washington, DC

3. incorporates followon discussions with the Watson DeepQA commercialization team at the IBM Healthcare Innovation Symposium: “Watson” technology and its application to the Collaboratory Model for healthcare IBM Innovation Center – Waltham, MA June 2011

4. extends ideas developed for presentation to NCI’s [NIH] Cancer Biomarkers Research Group at the Scientific Workshop for the Early Detection Research Network [EDRN] in September 2011

“Visual Neural Networking with Mobile Medical Imaging: Disruptive Tools & Technologies for CareGiving Teams” Authors: Krishna Surapaneni, MD, MPh, New York Presbyterian Hospital/Columbia University Medical Center; Jim Smurro, MS, MBA; Eliot L. Siegel, MD, FSIIM – [now helping to train IBM Watson for Healthcare DeepQA] Clinical collaboration in the cloud: visual neural networking with CADe disruptive tools & technology for diagnostic imaging integration in the era of personalized precision medicine” Jim Smurro, S.M, M.B.A., Harvard University

Eliot Siegel, M.D., pictured with IBM's Watson at the University of Maryland, believes the

supercomputer may soon become a routine tool for diagnostic radiologists in addition to PACS, advanced visualization and speech recognition.


$$11 $$110000

The Value of Earlier Cancer Detection

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7th Early Detection Research Network (EDRN) Scientific Workshop

23rd EDRN Steering Committee Meeting September 13-16, 2011

Hyatt Dulles Herndon, Virgina

Highlights of Poster Session 1

Tuesday, September 13, 2011

2:00 – 3:00 p.m. Luray Ballroom

Moderators: o Brian Haab, Ph.D., Van Andel Research Institute o Lynn Sorbara, Ph.D., National Cancer Institute

Posters to be Highlighted 2:00 – 2:10 p.m. Hormone content of nipple aspiration fluid (NAF) is related to

polymorphisms in steroid metabolism genes (Board #39) Seema Khan, M.D., Northwestern University

2:10 – 2:20 p.m. Hydrazide coated chip for capture and on target analysis of

glycans (Board #58) S. Jake Yang, Ph.D., Johns Hopkins Medical Institute

2:20 – 2:30 p.m. Micro-RNA-seq expression profiles in lung adenocarcinoma

tissues versus adjacent lung tissues (Board #45) Simon Spivack, M.D., M.P.H., Albert Einstein College of Medicine

2:30 -2:40 p.m. A high resolution capillary LC fractionation strategy with smart

selection of target peptide fractions for picogram/milliliter level protein quantification in human plasma/serum using SRM (Board #74) Tujin Shi, Ph.D., Pacific Northwest National Laboratory

2:40 – 2:50 p.m. Clinical collaboration in the cloud: visual neural networking

with CADe disruptive technology for diagnostic imaging integration in the era of personalized precision medicine(Board #88) Jim Smurro, S.M, M.B.A., Harvard University

2:50 – 3:00 p.m. Q/A

Visual Neural Networking with CADe - Disruptive Tools & Technology for Diagnostic Imaging Integration in the Era of Personalized Precision Medicine

Hunting for Elusive Targets, Markers, Signals & Pathways

Healthcare

Transcript of Hunting for Elusive Targets, Markers, Signals & Pathways