Stephen C Schmechel

US Patent:

20030175761, Sep 18, 2003

Filed:

Dec 6, 2002

Appl. No.:

10/313669

Inventors:

Daniel Sabath - Seattle WA, US
Stephen Schmechel - Seattle WA, US
Robert LeVasseur - Seattle WA, US
Kathleen Yang - Woodinville WA, US
Karen Koehler - Brier WA, US

International Classification:

C12Q001/68

US Classification:

435/006000

Abstract:

Provided are genes whose expression patterns allow differentiation between benign lymph node tissue, follicular lymphoma tissue, mantle cell lymphoma tissue and small lymphocytic lymphoma tissue. These genes are useful as diagnostic markers for lymphoma. The protein products of these genes are useful in diagnostic and therapeutic applications, including monoclonal antibodies, lymphoma-specific chemotherapeutic agents, and gene therapies.

US Patent:

20200250817, Aug 6, 2020

Filed:

Jan 8, 2020

Appl. No.:

16/736914

Inventors:

- Minneapolis MN, US
Gregory John Metzger - Lake Elmo MN, US
Joseph S. Koopmeiners - Edina MN, US
Jonathan Henriksen - Seattle WA, US
Stephen C. Schmechel - Longboat Key FL, US

International Classification:

G06T 7/00
G16H 50/20
G16H 30/20
G01N 33/574

Abstract:

Automated, machine learning-based systems are described for the analysis and annotation (i.e., detection or delineation) of prostate cancer (PCa) on histologically-stained pathology slides of prostatectomy specimens. A technical framework is described for automating the annotation of predicted PCa that is based on, for example, automated spatial alignment and colorimetric analysis of both H&E and IHC whole-slide images (WSIs). The WSIs may, as one example, be stained with a particular triple-antibody cocktail against high-molecular weight cytokeratin (HMWCK), p63, and a-methylacyl CoA racemase (AMACR).

US Patent:

20160292855, Oct 6, 2016

Filed:

Apr 1, 2016

Appl. No.:

15/089273

Inventors:

- Minneapolis MN, US
Stephen C. Schmechel - Seattle WA, US
Chaitanya Kalavagunta - Crofton MD, US
Joseph S. Koopmeiners - St. Paul MN, US
Christopher A. Warlick - Lake Elmo MN, US

International Classification:

G06T 7/00
A61B 5/00
A61B 5/055

Abstract:

A user-independent, quantitative, multiparametric MRI model is developed and validated on co-registered correlative histopathology, yielding improved performance for cancer detection over single parameter estimators. A computing device may be configured to receive a first parametric map that maps imaged tissue of a patient using values of a first parameter, and a second parametric map that maps the imaged tissue using values of a second parameter, wherein the parametric maps are generated from medical imaging data for the imaged tissue. The computing device may be further configured to apply a multiparametric model to the maps to generate at least one Composite Biomarker Score for the tissue, the model being a function of the first parameter and the second parameter, The function may be determined based on co-registered histopathology data. The computing device may be further configured to generate an indication of whether the tissue includes predicted cancer, and output the indication.