WO2023118586A1 - Blast cell classification - Google Patents

Blast cell classification Download PDF

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Publication number
WO2023118586A1
WO2023118586A1 PCT/EP2022/087779 EP2022087779W WO2023118586A1 WO 2023118586 A1 WO2023118586 A1 WO 2023118586A1 EP 2022087779 W EP2022087779 W EP 2022087779W WO 2023118586 A1 WO2023118586 A1 WO 2023118586A1
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WO
WIPO (PCT)
Prior art keywords
blast
blast cell
cell
value
lymphoid
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/EP2022/087779
Other languages
English (en)
French (fr)
Inventor
Nils BRUENGGEL
Patrick Conway
Simon John Davidson
Emilie DEJEAN
Jacob GILDENBLAT
Chen Sagiv
Pascal Vallotton
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
F Hoffmann La Roche AG
Roche Diagnostics GmbH
Roche Diagnostics Operations Inc
Original Assignee
F Hoffmann La Roche AG
Roche Diagnostics GmbH
Roche Diagnostics Operations Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by F Hoffmann La Roche AG, Roche Diagnostics GmbH, Roche Diagnostics Operations Inc filed Critical F Hoffmann La Roche AG
Priority to JP2024538153A priority Critical patent/JP2025501599A/ja
Priority to CN202280085313.4A priority patent/CN118591826A/zh
Priority to US18/723,851 priority patent/US20250046455A1/en
Priority to EP22839373.2A priority patent/EP4453894B1/en
Publication of WO2023118586A1 publication Critical patent/WO2023118586A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images

Definitions

  • a first aspect of the present invention provides a computer-implemented method of differentiating between lymphoid blast cells and myeloid blast cells , the computer-implemented method comprising : receiving a digital image containing one or more blast cells ; applying a parametric model classifier to one or more portions of the digital image each containing a respective blast cell , the parametric model classifier configured to generate an output indicative of whether each blast cell is a lymphoid blast cell or a myeloid blast cell .
  • the sample is stained .
  • the digital image may be an image of a stained sample on a slide .
  • Stains which may be used include : Romanowsky staining , Giemsa staining , Jenner staining, Wright staining , Field staining, May-Grunwald staining , and Leishman staining . It will be appreciated that other kinds of stains may also be used .
  • the output of the parametric classifier is configured to be inconclusive or uncertain.
  • ResNets are advantageous because s kipping effectively simplifies the network, using fewer layers in the initial training stages , thereby speeding learning by reducing the impact of vanishing gradients , as there are fewer layers to propagate through .
  • the network then gradually restores the s kipped layers as it learns the feature space . Towards the end of training, when all layers are expanded, it stays closer to the manifold and thus learns faster .
  • suitable ResNets include : ResNet34 , ResNet50 and ResNetl OO, in which the number represents the number of layers present in the residual neural network .
  • Other types of convolutional neural networks such as Inception 4 , VGG 5 , and Ef f icientNet 6 may also be used in implementations of the invention .
  • CD marker screenings are available , each targeting a particular biomarker .
  • a clinician may identify a CD marker to use for screening based on the output of the parametric model classifier .
  • the computer-implemented method may further comprise selecting, from a plurality of available CD markers , one or more CD markers , based on the output of the parametric model classifier . In this way, a CD marker to be used for subsequent testing can be identified automatically based on the output of the parametric model .
  • a second aspect of the present invention therefore provides a computer- implemented method of generating a parametric model classifier configured to differentiate between lymphoid blast cells and myeloid blast cells in a digital image , the computer- implemented method comprising : receiving a plurality of pairs of labelled training data, each pair of labelled training data including : input data comprising a digital image of a blast cell from a patient who has been diagnosed with either acute myeloid leukaemia or acute lymphoid leukaemia ; and output data comprising an indication of whether the patient has acute myeloid leukaemia or acute lymphoid leukaemia ; and training a parametric model classifier using the training data .
  • the deep neural network classifier of the eleventh aspect of the invention is generated using a computer-implemented method comprising : receiving a plurality of pairs of labelled training data , each pair of labelled training data including : input data comprising a digital image of a blast cell from a patient who has been diagnosed with either acute myeloid leukaemia or acute lymphoid leukaemia; output data comprising an indication of whether the patient has acute myeloid leukaemia or acute lymphoid leukaemia , the output comprising a numerical value x in the taking the value 0 or 1 , wherein : either if the value x is equal to 1 , the blast cell is a lymphoid blast cell , and if the value x is equal to 0 , the blast cell is a myeloid blast cell
  • a twelfth aspect of the invention provides a clinical decision support system, comprising : a computing device having a processor, the processor configured to generate a provisional diagnosis of acute myeloid leukaemia or acute lymphoid leukaemia by performing the computer-implemented method of the eleventh aspect of the invention and wherein the processor is further configured to : based on the patient level score calculated based on the output of the deep neural network classifier , determine whether a patient whose blast cells are shown in the digital image is suffering from acute myeloid leukaemia, acute lymphoid leukaemia, or neither ; and generate , based on a result of the determination, instructions configured to cause a display device of a computing system to display the result of the determination .
  • the output of the blast cell classification module 412 is preferably a number from 0 to 1 , the number representing a probability or a confidence level that the blast cell in question is either a myeloid blast cell or a lymphoid blast cell .
  • the model may return two probabilities , one that the blast cell is a lymphoid blast cell , and one that the blast cell is a myeloid blast cell . These probabilities should add to 1 ( or 100% , or equivalent ) . Then, once a plurality of probabilities have been calculated using the parametric model 416 , by the blast cell classification module 412 , various different steps may be taken . In Fig .
  • the method may further include a step of determining an appropriate CD marker for a subsequent screening step based on e . g . the patient level score .

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Pathology (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Radiology & Medical Imaging (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Image Analysis (AREA)
PCT/EP2022/087779 2021-12-24 2022-12-23 Blast cell classification Ceased WO2023118586A1 (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
JP2024538153A JP2025501599A (ja) 2021-12-24 2022-12-23 芽球細胞分類
CN202280085313.4A CN118591826A (zh) 2021-12-24 2022-12-23 母细胞分类
US18/723,851 US20250046455A1 (en) 2021-12-24 2022-12-23 Blast cell classification
EP22839373.2A EP4453894B1 (en) 2021-12-24 2022-12-23 Blast cell classification

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP21217712 2021-12-24
EP21217712.5 2021-12-24

Publications (1)

Publication Number Publication Date
WO2023118586A1 true WO2023118586A1 (en) 2023-06-29

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PCT/EP2022/087779 Ceased WO2023118586A1 (en) 2021-12-24 2022-12-23 Blast cell classification

Country Status (5)

Country Link
US (1) US20250046455A1 (https=)
EP (1) EP4453894B1 (https=)
JP (1) JP2025501599A (https=)
CN (1) CN118591826A (https=)
WO (1) WO2023118586A1 (https=)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2025131829A1 (en) 2023-12-18 2025-06-26 F. Hoffmann-La Roche Ag Method and system for applying a liquid sample onto a substrate for image analysis

Citations (1)

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Publication number Priority date Publication date Assignee Title
US20090269799A1 (en) 2008-04-25 2009-10-29 Constitutional Medical Investors, Inc. Method of determining a complete blood count and a white blood cell differential count

Patent Citations (5)

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US20090269799A1 (en) 2008-04-25 2009-10-29 Constitutional Medical Investors, Inc. Method of determining a complete blood count and a white blood cell differential count
US20100284602A1 (en) 2008-04-25 2010-11-11 Constitution Medical Investors, Inc. Method for determining a complete blood count on a white blood cell differential count
US20110014645A1 (en) 2008-04-25 2011-01-20 Constitution Medical Investors, Inc. Method for determining a complete blood count on a white blood cell differential count
WO2012030313A1 (en) 2008-04-25 2012-03-08 James Winkelman Method of determining a complete blood count and a white blood cell differential count
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2025131829A1 (en) 2023-12-18 2025-06-26 F. Hoffmann-La Roche Ag Method and system for applying a liquid sample onto a substrate for image analysis

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Publication number Publication date
US20250046455A1 (en) 2025-02-06
EP4453894A1 (en) 2024-10-30
CN118591826A (zh) 2024-09-03
EP4453894B1 (en) 2026-04-01
JP2025501599A (ja) 2025-01-22

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