WO2020229152A1 - Identification of candidate signs indicative of an ntrk oncogenic fusion - Google Patents

Identification of candidate signs indicative of an ntrk oncogenic fusion Download PDF

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Publication number
WO2020229152A1
WO2020229152A1 PCT/EP2020/061665 EP2020061665W WO2020229152A1 WO 2020229152 A1 WO2020229152 A1 WO 2020229152A1 EP 2020061665 W EP2020061665 W EP 2020061665W WO 2020229152 A1 WO2020229152 A1 WO 2020229152A1
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WIPO (PCT)
Prior art keywords
ntrk
image
patient
cancer
probability value
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Ceased
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PCT/EP2020/061665
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English (en)
French (fr)
Inventor
Arndt Schmitz
Eren Metin ELCI
Faidra STAVROPOULOU
Mikhail KACHALA
Antii KARLSSON
Mikko Tukiainen
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Bayer Consumer Care AG
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Bayer Consumer Care AG
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Priority to CN202080033711.2A priority Critical patent/CN113785365A/zh
Priority to JP2021566992A priority patent/JP7518097B2/ja
Priority to US17/595,191 priority patent/US12217851B2/en
Priority to CA3139352A priority patent/CA3139352A1/en
Priority to EP20720468.6A priority patent/EP3966830A1/en
Publication of WO2020229152A1 publication Critical patent/WO2020229152A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • 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
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/096—Transfer learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/698—Matching; Classification
    • 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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • 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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • 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
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing

Definitions

  • the present invention serves to determine a probability value from patient data associated with a subject patient by means of a prediction model, the probability value indicating the probability of the subject patient suffering from cancer caused by a mutation of a neurotrophic receptor tyrosine kinase (NTRK) gene.
  • NTRK neurotrophic receptor tyrosine kinase
  • Each network node represents a simple calculation of the weighted sum of inputs from prior nodes and a non-linear output function. The combined calculation of the network nodes relates the inputs to the output(s).
  • the prediction model generates a probability value indicating the probability of a patient suffering from cancer caused by an NTRK oncogenic fusion.
  • the probability value can be outputted to a user and/or stored in a database.
  • the probability value can be a real number in the range from 0 to 1, whereas a probability value of 0 means that it is impossible that the cancer is caused by an NTRK oncogenic fusion, and a probability value of 1 means that there is no doubt that the cancer is caused by an NTRK oncogenic fusion.
  • the probability value can also be expressed by a percentage.
  • the Trk inhibitor is Larotrectinib.
  • processor/s, display and input means may be used to process, display e.g. on a computer screen or other computer output device, store, and accept information such as information used by or generated by any of the methods and system shown and described herein; the above processor/s, display and input means including computer programs, in accordance with some or all of the embodiments of the present invention.
  • the steps are:
  • the convolutional layers are sparsely connected, which differs from traditional neural network configuration found in the fully connected layers (108).
  • Traditional neural network layers are fully connected, such that every output unit interacts with every input unit.
  • the convolutional layers are sparsely connected because the output of the convolution of a field is input (instead of the respective state value of each of the nodes in the field) to the nodes of the subsequent layer, as illustrated.
  • the kernels associated with the convolutional layers perform convolution operations, the output of which is sent to the next layer.
  • the dimensionality reduction performed within the convolutional layers is one aspect that enables the CNN to process large images.
  • Fig. 8 illustrates an exemplary training and deployment of a neural network. Once a given network has been structured for a task the neural network is trained using a training dataset (1102).
  • Fig. 10 (c) illustrates a digital image of an adjacent FFPE tumor tissue section stained with an antibody for indicating presence of TRK proteins using immunohistochemistry. Note that the digital image corresponds very well to the predictions made by the prediction model as shown in Fig. 10 (b), even though the molecular assay was conducted on an adjacent tissue section.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • General Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Radiology & Medical Imaging (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Quality & Reliability (AREA)
  • Multimedia (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Apparatus Associated With Microorganisms And Enzymes (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
PCT/EP2020/061665 2019-05-10 2020-04-28 Identification of candidate signs indicative of an ntrk oncogenic fusion Ceased WO2020229152A1 (en)

Priority Applications (5)

Application Number Priority Date Filing Date Title
CN202080033711.2A CN113785365A (zh) 2019-05-10 2020-04-28 指示ntrk致癌融合的候选标志的鉴定
JP2021566992A JP7518097B2 (ja) 2019-05-10 2020-04-28 Ｎｔｒｋの発癌性融合を示す候補となる徴候の識別
US17/595,191 US12217851B2 (en) 2019-05-10 2020-04-28 Identification of candidate signs indicative of an NTRK oncogenic fusion
CA3139352A CA3139352A1 (en) 2019-05-10 2020-04-28 Identification of candidate signs indicative of an ntrk oncogenic fusion
EP20720468.6A EP3966830A1 (en) 2019-05-10 2020-04-28 Identification of candidate signs indicative of an ntrk oncogenic fusion

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EP19173832 2019-05-10
EP19173832.7 2019-05-10

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EP (1) EP3966830A1 (https=)
JP (1) JP7518097B2 (https=)
CN (1) CN113785365A (https=)
CA (1) CA3139352A1 (https=)
WO (1) WO2020229152A1 (https=)

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WO2023208663A1 (en) 2022-04-26 2023-11-02 Bayer Aktiengesellschaft Multiple-instance learning based on regional embeddings
WO2023213623A1 (en) 2022-05-03 2023-11-09 Bayer Aktiengesellschaft Dynamic sampling strategy for multiple-instance learning
US20230420116A1 (en) * 2021-03-09 2023-12-28 PAIGE.AI, Inc. Systems and methods for artificial intelligence powered molecular workflow verifying slide and block quality for testing
EP4471710A1 (de) 2023-05-30 2024-12-04 Bayer AG Erkennen von artefakten in synthetischen medizinischen aufnahmen
EP4475070A1 (de) 2023-06-05 2024-12-11 Bayer AG Erkennen von artefakten in synthetischen medizinischen aufnahmen
EP4492324A1 (de) 2023-07-12 2025-01-15 Bayer AG Erkennen von artefakten in synthetischen medizinischen aufnahmen
EP4498324A1 (de) 2023-07-25 2025-01-29 Bayer AG Erkennen von artefakten in synthetischen bildern
EP4560648A1 (en) 2023-11-22 2025-05-28 Bayer AG Generating synthetic training data
EP4567715A1 (en) 2023-12-06 2025-06-11 Bayer Aktiengesellschaft Generating synthetic representations
WO2025119803A1 (en) 2023-12-06 2025-06-12 Bayer Aktiengesellschaft Generating synthetic medical representations
EP4571650A1 (en) 2023-12-12 2025-06-18 Bayer AG Generating synthetic images
EP4575997A1 (en) 2023-12-18 2025-06-25 Bayer Aktiengesellschaft Generating synthetic images
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US12183451B2 (en) * 2021-03-09 2024-12-31 PAIGE.AI, Inc. Systems and methods for artificial intelligence powered molecular workflow verifying slide and block quality for testing
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WO2023208663A1 (en) 2022-04-26 2023-11-02 Bayer Aktiengesellschaft Multiple-instance learning based on regional embeddings
WO2023213623A1 (en) 2022-05-03 2023-11-09 Bayer Aktiengesellschaft Dynamic sampling strategy for multiple-instance learning
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EP4560648A1 (en) 2023-11-22 2025-05-28 Bayer AG Generating synthetic training data
EP4567715A1 (en) 2023-12-06 2025-06-11 Bayer Aktiengesellschaft Generating synthetic representations
WO2025119803A1 (en) 2023-12-06 2025-06-12 Bayer Aktiengesellschaft Generating synthetic medical representations
EP4571650A1 (en) 2023-12-12 2025-06-18 Bayer AG Generating synthetic images
EP4575997A1 (en) 2023-12-18 2025-06-25 Bayer Aktiengesellschaft Generating synthetic images
WO2025190826A1 (en) 2024-03-15 2025-09-18 Bayer Aktiengesellschaft Generation of a synthetic medical image

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