公开(公告)号：US20250054630A1

公开(公告)日：2025-02-13

申请号：US18929474

申请日：2024-10-28

Applicant: Genentech, Inc.

Inventor： Cleopatra KOZLOWSKI ,  Daniel RUDERMAN

IPC: G16H50/20 ,  G16B15/00 ,  G16H10/60

Abstract: A method for image-based hepatocellular carcinoma (HCC) molecular subtype classification may include determining, within an image depicting a plurality of cells of a biological sample, a plurality of tiles with each tile depicting a portion of the plurality of cells comprising the sample. A machine learning model may be applied to determine a molecular subtype for the portion of the plurality of cells depicted in each tile. Moreover, an overall molecular subtype for the plurality of cells depicted in the image of the biological sample may be determined based on the molecular subtype of the portion of the plurality of cells depicted in each tile of the plurality of tiles. For example, another machine learning model may be applied to determine the overall molecular subtype of the plurality of cells depicted in the image of the biological sample. Related systems and computer program products are also provided.

公开(公告)号：US20240087122A1

公开(公告)日：2024-03-14

申请号：US18516406

申请日：2023-11-21

Applicant: Genentech, Inc.

Inventor： Jennifer Margaret GILTNANE ,  Sertan KAYA ,  Cleopatra KOZLOWSKI ,  Geoffrey Fredrick SCHAU

IPC: G06T7/00 ,  G06V10/25 ,  G06V10/26 ,  G06V10/762 ,  G06V10/774 ,  G06V10/82 ,  G06V20/69 ,  G16B15/00

CPC classification number: G06T7/0012 ,  G06V10/25 ,  G06V10/26 ,  G06V10/762 ,  G06V10/774 ,  G06V10/82 ,  G06V20/695 ,  G06V20/698 ,  G16B15/00 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06V2201/03

Abstract: In one embodiment, a method includes accessing a digital pathology image that depicts a tissue sample from a subject under a treatment, detecting tertiary lymphoid structures depicted within the digital pathology image of the tissue sample based on a machine-learning model, determining descriptive information associated with the detected tertiary lymphoid structures for the detected tertiary lymphoid structures, wherein the descriptive information comprises at least a maturation state associated with each of the detected tertiary lymphoid structures, and determining an outcome of the subject in response to the treatment based on the detected tertiary lymphoid structures and the descriptive information.

公开(公告)号：US20230162515A1

公开(公告)日：2023-05-25

申请号：US18094850

申请日：2023-01-09

Applicant: Genentech, Inc.

Inventor： Cleopatra KOZLOWSKI ,  Reheman BAIKEJIANG

IPC: G06V20/69 ,  G06V10/774 ,  G06V10/77 ,  G16B20/00 ,  G16B40/20

CPC classification number: G06V20/695 ,  G06V20/698 ,  G06V10/774 ,  G06V10/7715 ,  G16B20/00 ,  G16B40/20

Abstract: In one embodiment, a method includes, receiving a digital pathology image of a tissue sample and subdividing the digital pathology image into a plural in of patches. For each patch of the plurality of patches, the method includes identify an image feature detected in the patch and generating one or more labels corresponding to the image feature identified in the patch using a machine-learning model. The method includes determining, based on the generated labels, a heterogeneity metric for the tissue sample. The method includes generating an assessment of the tissue sample based on the heterogeneity metric.

公开(公告)号：US20210098082A1

公开(公告)日：2021-04-01

申请号：US17033161

申请日：2020-09-25

Applicant: GENENTECH, INC.

Inventor： Akshata Ramrao UDYAVAR ,  Yulei WANG ,  Cleopatra KOZLOWSKI

IPC: G16B40/00 ,  A61P35/00 ,  C07K16/22 ,  C07K16/28 ,  A61K38/21

Abstract: A machine-learning model (e.g., a clustering model) may be used to predict a phenotype of a tumor based on expression levels of a set of genes. The set of genes may have been identified using a same or different machine-learning model. The phenotype may include an immune-excluded, immune-desert or an inflamed/infiltrated phenotype. A treatment strategy and/or treatment recommendation may be identified based on the predicted phenotype.