Predicting total nucleic acid yield and dissection boundaries for histology slides

Patent metadata

Field	Value
Publication number	US-12165236-B2
Application number	US-202217829228-A
Country	US
Kind code	B2
Filing date	May 31, 2022
Priority date	May 14, 2018
Publication date	Dec 10, 2024
Grant date	Dec 10, 2024

Abstract

A method for qualifying a specimen prepared on one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and providing associated unstained slides for subsequent nucleic acid analysis is provided.

First claim

What is claimed:

1. A computer-implemented method for predicting an expected yield of nucleic acid from tumor cells within a dissection boundary on a histology slide, the method comprising:
receiving a digital image of the histology slide at an image-based nucleic acid yield prediction system having one or more processors;
identifying, using the one or more processors, tumor cells of the histology slide from the digital image using a trained cell segmentation model, wherein the trained cell segmentation model is a pixel-resolution three-dimensional classification model trained to classify a cell interior, a cell border and a cell interior, and defining a dissection boundary of the histology slide and corresponding to the identified tumor cells; and
predicting, using the one or more processors, the expected yield of nucleic acid for the tumor cells within the dissection boundary by providing the digital image to a machine learning model trained on images of a plurality of histology slides having labeled dissection boundaries and labeled total nucleic yield, the plurality of histology slides associated with imaging features.

2. The method of claim 1 , wherein the imaging features comprises tumor shape features, cell shape features, and/or cell texture features.

3. The method of claim 1 , wherein the imaging features comprises tumor shape features in the form of tumor area, tumor perimeter, tumor circularity, tumor density, and/or number of tumors.

4. The method of claim 1 , wherein the imaging features comprises cell shape features in the form of cell area, cell perimeter, cell circularity, and/or cell density.

5. The method of claim 1 , wherein the imaging features comprises cell texture features in the form of RGB texture patterns, grayscale texture patterns, gradient and/or features.

6. The method of claim 1 , further comprising:
accepting an associated unstained slide of the histology slide for next-generation sequencing when the predicted expected yield of nucleic acid exceeds a minimum threshold.

7. The method of claim 6 , wherein the minimum threshold is 50 ng.

8. The method of claim 1 , wherein when the predicted expected yield of nucleic acid fails to satisfy a target total nucleic acid yield:
identifying a number of associated unstained slides that satisfies the target total nucleic acid yield; and
accepting the number of associated unstained slides for next-generation sequencing.

9. The method of claim 8 , wherein the target total nucleic acid yield is selected from a range between and including 50 ng-2000 ng.

10. The method of claim 8 , wherein the associated unstained slides are flagged for scrapping.

11. The method of claim 8 , wherein the associated unstained slides comprise tissue from the same formalin-fixed paraffin embedded specimen.

12. The method of claim 8 , further comprising superimposing, using a viewer, the dissection boundary mask over the digital image of the associated unstained slides to visually indicate to a user which tumor cells to scrape.

13. The method of claim 8 , further comprising:
generating a tumor area mask that defines the dissection boundary by providing the digital image of the histology slide to a model trained on a plurality of histology slide having dissection labels.

14. The method of claim 13 , wherein a viewer superimposes the tumor area mask over the digital image to visually indicate to a user which tumor cells to scrape.

15. The method of claim 1 , wherein predicting the expected yield of nucleic acid for the tumor cells within the dissection boundary further comprises:
counting the number of tumor cells identified within the dissection boundary and multiplying the count by a tumor cell average nucleic acid yield and adjusting to account for the identified imaging features.

16. The method of claim 1 , wherein predicting the expected yield of nucleic acid for the tumor cells within the dissection boundary further comprises:
calculating the surface area of the dissection boundary and multiplying the surface area by a dissection boundary average nucleic acid yield and adjusting to account for the identified imaging features.

17. The method of claim 1 , wherein identifying tumor cells of the histology slide from the digital image using the trained cell segmentation model comprises:
applying, using the one or more processors, a plurality of tile images formed from the digital image to the trained cell segmentation model and, for each tile, assigning a cell classification to one or more pixels within the tile image.

18. The method of claim 17 , wherein assigning the cell classification to one or more pixels within the tile image comprises:
identifying, using the one or more processors, the one or more pixels as a cell interior, a cell border, or a cell exterior and classifying the one or more pixels as the cell interior, the cell border, or the cell exterior.

19. A computing device configured to predict an expected yield of nucleic acid from tumor cells within a dissection boundary on a histology slide, the computing device comprising:
one or more memories; and
one or more processors configured to,
receive a digital image of the histology slide at an image-based nucleic acid yield prediction system having one or more processors;
identify, using the one or more processors, tumor cells of the histology slide from the digital image using a trained cell segmentation model, wherein the trained cell segmentation model is a pixel-resolution three-dimensional classification model trained to classify a cell interior, a cell border and a cell interior, and defining a dissection boundary of the histology slide and corresponding to the identified tumor cells; and
predict the expected yield of nucleic acid for the tumor cells within the dissection boundary by providing the digital image to a machine learning model trained on images of a plurality of histology slides having labeled dissection boundaries and labeled total nucleic yield, the plurality of histology slides associated with imaging features.

20. A computer system for predicting an expected yield of nucleic acid from tumor cells within a dissection boundary on a histology slide, the computer system comprising one or more processors configured to:
receive a digital image of the histology slide at an image-based nucleic acid yield prediction system having one or more processors;
identify, using the one or more processors, tumor cells of the histology slide from the digital image using a trained cell segmentation model, wherein the trained cell segmentation model is a pixel-resolution three-dimensional classification model trained to classify a cell interior, a cell border and a cell interior, and define a dissection boundary of the histology slide and corresponding to the identified tumor cells; and
predict the expected yield of nucleic acid for the tumor cells within the dissection boundary by providing the digital image to a machine learning model trained on images of a plurality of histology slides having labeled dissection boundaries and labeled total nucleic yield, the plurality of histology slides associated with imaging features.

Assignees

• Tempus Ai Inc

Inventors

• Yip Stephen
• Ho Irvin
• Sha Lingdao
• Osinski Boleslaw
• Khan Aly Azeem
• Kruger Andrew J
• Carlson Michael
• Greenwald Abel
• Willis Caleb
• Westley Andrew
• Jones Ryan
• Mahon Brett

Classifications

• G06T11/26

  Drawing of charts or graphs · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06V20/698

  Matching; Classification · CPC title