公开(公告)号：US20240346804A1

公开(公告)日：2024-10-17

申请号：US18612987

申请日：2024-03-21

Applicant: Genentech, Inc.

Inventor： Jeffrey Ryan EASTHAM ,  Hartmut KOEPPEN ,  Xiao LI ,  Darya Yuryevna ORLOVA

IPC: G06V10/764 ,  G06T7/00 ,  G06V10/25

CPC classification number: G06V10/764 ,  G06T7/0012 ,  G06V10/25 ,  G06T2207/20081 ,  G06T2207/30096

Abstract: Described herein are systems, methods, and programming describing various pipelines for determining an immunophenotype of a tumor depicted by a digital pathology image based on immune cell density in the tumor epithelium and/or the tumor stroma and/or spatial information across all or part of the image. One or more machine learning models may be implemented by some or all of the pipelines. The pipelines may include a first pipeline using density thresholds for immunophenotyping, a second pipeline using immune cell density in tumor epithelium and tumor stroma for immunophenotyping, a third pipeline using spatial information of the digital pathology image for immunophenotyping, and a fourth pipeline that combines aspects of the second and third pipelines for immunophenotyping.

公开(公告)号：US20240104948A1

公开(公告)日：2024-03-28

申请号：US18516417

申请日：2023-11-21

Applicant: Genentech, Inc.

Inventor： Jeffrey Ryan EASTHAM ,  Hartmut Koeppen ,  Xiao Li ,  Darya Yuryevna Orlova

IPC: G06V20/69 ,  G06T7/00 ,  G06T7/11 ,  G06V10/25 ,  G06V10/26 ,  G06V10/77 ,  G06V10/82

CPC classification number: G06V20/698 ,  G06T7/0012 ,  G06T7/11 ,  G06V10/25 ,  G06V10/267 ,  G06V10/77 ,  G06V10/82 ,  G06V20/695 ,  G06T2207/10056 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Systems and methods relate to processing digital pathology images. More specifically, techniques include accessing a digital pathology image that depicts a section of a biological sample, wherein the digital pathology image comprises regions displaying reactivity to a plurality of stains. For each of a plurality of tiles of the digital pathology image, a local-density measurement is calculated for each of a plurality of biological object types. One or more spatial-distribution metrics may be generated for the biological object types based at least in part on the calculated local-density measurements. A tumor immunophenotype may then be generated for the digital pathology image based at least in part on the local-density measurements or the one or more spatial-distribution metrics.